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Sample records for binding pose prediction

  1. Using physics-based pose predictions and free energy perturbation calculations to predict binding poses and relative binding affinities for FXR ligands in the D3R Grand Challenge 2

    Science.gov (United States)

    Athanasiou, Christina; Vasilakaki, Sofia; Dellis, Dimitris; Cournia, Zoe

    2018-01-01

    Computer-aided drug design has become an integral part of drug discovery and development in the pharmaceutical and biotechnology industry, and is nowadays extensively used in the lead identification and lead optimization phases. The drug design data resource (D3R) organizes challenges against blinded experimental data to prospectively test computational methodologies as an opportunity for improved methods and algorithms to emerge. We participated in Grand Challenge 2 to predict the crystallographic poses of 36 Farnesoid X Receptor (FXR)-bound ligands and the relative binding affinities for two designated subsets of 18 and 15 FXR-bound ligands. Here, we present our methodology for pose and affinity predictions and its evaluation after the release of the experimental data. For predicting the crystallographic poses, we used docking and physics-based pose prediction methods guided by the binding poses of native ligands. For FXR ligands with known chemotypes in the PDB, we accurately predicted their binding modes, while for those with unknown chemotypes the predictions were more challenging. Our group ranked #1st (based on the median RMSD) out of 46 groups, which submitted complete entries for the binding pose prediction challenge. For the relative binding affinity prediction challenge, we performed free energy perturbation (FEP) calculations coupled with molecular dynamics (MD) simulations. FEP/MD calculations displayed a high success rate in identifying compounds with better or worse binding affinity than the reference (parent) compound. Our studies suggest that when ligands with chemical precedent are available in the literature, binding pose predictions using docking and physics-based methods are reliable; however, predictions are challenging for ligands with completely unknown chemotypes. We also show that FEP/MD calculations hold predictive value and can nowadays be used in a high throughput mode in a lead optimization project provided that crystal structures of

  2. Using physics-based pose predictions and free energy perturbation calculations to predict binding poses and relative binding affinities for FXR ligands in the D3R Grand Challenge 2.

    Science.gov (United States)

    Athanasiou, Christina; Vasilakaki, Sofia; Dellis, Dimitris; Cournia, Zoe

    2018-01-01

    Computer-aided drug design has become an integral part of drug discovery and development in the pharmaceutical and biotechnology industry, and is nowadays extensively used in the lead identification and lead optimization phases. The drug design data resource (D3R) organizes challenges against blinded experimental data to prospectively test computational methodologies as an opportunity for improved methods and algorithms to emerge. We participated in Grand Challenge 2 to predict the crystallographic poses of 36 Farnesoid X Receptor (FXR)-bound ligands and the relative binding affinities for two designated subsets of 18 and 15 FXR-bound ligands. Here, we present our methodology for pose and affinity predictions and its evaluation after the release of the experimental data. For predicting the crystallographic poses, we used docking and physics-based pose prediction methods guided by the binding poses of native ligands. For FXR ligands with known chemotypes in the PDB, we accurately predicted their binding modes, while for those with unknown chemotypes the predictions were more challenging. Our group ranked #1st (based on the median RMSD) out of 46 groups, which submitted complete entries for the binding pose prediction challenge. For the relative binding affinity prediction challenge, we performed free energy perturbation (FEP) calculations coupled with molecular dynamics (MD) simulations. FEP/MD calculations displayed a high success rate in identifying compounds with better or worse binding affinity than the reference (parent) compound. Our studies suggest that when ligands with chemical precedent are available in the literature, binding pose predictions using docking and physics-based methods are reliable; however, predictions are challenging for ligands with completely unknown chemotypes. We also show that FEP/MD calculations hold predictive value and can nowadays be used in a high throughput mode in a lead optimization project provided that crystal structures of

  3. Binding pose and affinity prediction in the 2016 D3R Grand Challenge 2 using the Wilma-SIE method

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    Hogues, Hervé; Sulea, Traian; Gaudreault, Francis; Corbeil, Christopher R.; Purisima, Enrico O.

    2018-01-01

    The Farnesoid X receptor (FXR) exhibits significant backbone movement in response to the binding of various ligands and can be a challenge for pose prediction algorithms. As part of the D3R Grand Challenge 2, we tested Wilma-SIE, a rigid-protein docking method, on a set of 36 FXR ligands for which the crystal structures had originally been blinded. These ligands covered several classes of compounds. To overcome the rigid protein limitations of the method, we used an ensemble of publicly available structures for FXR from the PDB. The use of the ensemble allowed Wilma-SIE to predict poses with average and median RMSDs of 2.3 and 1.4 Å, respectively. It was quite clear, however, that had we used a single structure for the receptor the success rate would have been much lower. The most successful predictions were obtained on chemical classes for which one or more crystal structures of the receptor bound to a molecule of the same class was available. In the absence of a crystal structure for the class, observing a consensus binding mode for the ligands of the class using one or more receptor structures of other classes seemed to be indicative of a reasonable pose prediction. Affinity prediction proved to be more challenging with generally poor correlation with experimental IC50s (Kendall tau 0.3). Even when the 36 crystal structures were used the accuracy of the predicted affinities was not appreciably improved. A possible cause of difficulty is the internal energy strain arising from conformational differences in the receptor across complexes, which may need to be properly estimated and incorporated into the SIE scoring function.

  4. D3R Grand Challenge 2: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies

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    Gaieb, Zied; Liu, Shuai; Gathiaka, Symon; Chiu, Michael; Yang, Huanwang; Shao, Chenghua; Feher, Victoria A.; Walters, W. Patrick; Kuhn, Bernd; Rudolph, Markus G.; Burley, Stephen K.; Gilson, Michael K.; Amaro, Rommie E.

    2018-01-01

    The Drug Design Data Resource (D3R) ran Grand Challenge 2 (GC2) from September 2016 through February 2017. This challenge was based on a dataset of structures and affinities for the nuclear receptor farnesoid X receptor (FXR), contributed by F. Hoffmann-La Roche. The dataset contained 102 IC50 values, spanning six orders of magnitude, and 36 high-resolution co-crystal structures with representatives of four major ligand classes. Strong global participation was evident, with 49 participants submitting 262 prediction submission packages in total. Procedurally, GC2 mimicked Grand Challenge 2015 (GC2015), with a Stage 1 subchallenge testing ligand pose prediction methods and ranking and scoring methods, and a Stage 2 subchallenge testing only ligand ranking and scoring methods after the release of all blinded co-crystal structures. Two smaller curated sets of 18 and 15 ligands were developed to test alchemical free energy methods. This overview summarizes all aspects of GC2, including the dataset details, challenge procedures, and participant results. We also consider implications for progress in the field, while highlighting methodological areas that merit continued development. Similar to GC2015, the outcome of GC2 underscores the pressing need for methods development in pose prediction, particularly for ligand scaffolds not currently represented in the Protein Data Bank (http://www.pdb.org), and in affinity ranking and scoring of bound ligands.

  5. An NMR-based scoring function improves the accuracy of binding pose predictions by docking by two orders of magnitude

    Energy Technology Data Exchange (ETDEWEB)

    Orts, Julien [EMBL, Structure and Computational Biology Unit (Germany); Bartoschek, Stefan [Industriepark Hoechst, Sanofi-Aventis Deutschland GmbH, R and D LGCR/Parallel Synthesis and Natural Products (Germany); Griesinger, Christian [Max Planck Institute for Biophysical Chemistry (Germany); Monecke, Peter [Industriepark Hoechst, Sanofi-Aventis Deutschland GmbH, R and D LGCR/Structure, Design and Informatics (Germany); Carlomagno, Teresa, E-mail: teresa.carlomagno@embl.de [EMBL, Structure and Computational Biology Unit (Germany)

    2012-01-15

    Low-affinity ligands can be efficiently optimized into high-affinity drug leads by structure based drug design when atomic-resolution structural information on the protein/ligand complexes is available. In this work we show that the use of a few, easily obtainable, experimental restraints improves the accuracy of the docking experiments by two orders of magnitude. The experimental data are measured in nuclear magnetic resonance spectra and consist of protein-mediated NOEs between two competitively binding ligands. The methodology can be widely applied as the data are readily obtained for low-affinity ligands in the presence of non-labelled receptor at low concentration. The experimental inter-ligand NOEs are efficiently used to filter and rank complex model structures that have been pre-selected by docking protocols. This approach dramatically reduces the degeneracy and inaccuracy of the chosen model in docking experiments, is robust with respect to inaccuracy of the structural model used to represent the free receptor and is suitable for high-throughput docking campaigns.

  6. Prospective evaluation of shape similarity based pose prediction method in D3R Grand Challenge 2015.

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    Kumar, Ashutosh; Zhang, Kam Y J

    2016-09-01

    Evaluation of ligand three-dimensional (3D) shape similarity is one of the commonly used approaches to identify ligands similar to one or more known active compounds from a library of small molecules. Apart from using ligand shape similarity as a virtual screening tool, its role in pose prediction and pose scoring has also been reported. We have recently developed a method that utilizes ligand 3D shape similarity with known crystallographic ligands to predict binding poses of query ligands. Here, we report the prospective evaluation of our pose prediction method through the participation in drug design data resource (D3R) Grand Challenge 2015. Our pose prediction method was used to predict binding poses of heat shock protein 90 (HSP90) and mitogen activated protein kinase kinase kinase kinase (MAP4K4) ligands and it was able to predict the pose within 2 Å root mean square deviation (RMSD) either as the top pose or among the best of five poses in a majority of cases. Specifically for HSP90 protein, a median RMSD of 0.73 and 0.68 Å was obtained for the top and the best of five predictions respectively. For MAP4K4 target, although the median RMSD for our top prediction was only 2.87 Å but the median RMSD of 1.67 Å for the best of five predictions was well within the limit for successful prediction. Furthermore, the performance of our pose prediction method for HSP90 and MAP4K4 ligands was always among the top five groups. Particularly, for MAP4K4 protein our pose prediction method was ranked number one both in terms of mean and median RMSD when the best of five predictions were considered. Overall, our D3R Grand Challenge 2015 results demonstrated that ligand 3D shape similarity with the crystal ligand is sufficient to predict binding poses of new ligands with acceptable accuracy.

  7. How To Deal with Multiple Binding Poses in Alchemical Relative Protein–Ligand Binding Free Energy Calculations

    Science.gov (United States)

    2016-01-01

    Recent advances in improved force fields and sampling methods have made it possible for the accurate calculation of protein–ligand binding free energies. Alchemical free energy perturbation (FEP) using an explicit solvent model is one of the most rigorous methods to calculate relative binding free energies. However, for cases where there are high energy barriers separating the relevant conformations that are important for ligand binding, the calculated free energy may depend on the initial conformation used in the simulation due to the lack of complete sampling of all the important regions in phase space. This is particularly true for ligands with multiple possible binding modes separated by high energy barriers, making it difficult to sample all relevant binding modes even with modern enhanced sampling methods. In this paper, we apply a previously developed method that provides a corrected binding free energy for ligands with multiple binding modes by combining the free energy results from multiple alchemical FEP calculations starting from all enumerated poses, and the results are compared with Glide docking and MM-GBSA calculations. From these calculations, the dominant ligand binding mode can also be predicted. We apply this method to a series of ligands that bind to c-Jun N-terminal kinase-1 (JNK1) and obtain improved free energy results. The dominant ligand binding modes predicted by this method agree with the available crystallography, while both Glide docking and MM-GBSA calculations incorrectly predict the binding modes for some ligands. The method also helps separate the force field error from the ligand sampling error, such that deviations in the predicted binding free energy from the experimental values likely indicate possible inaccuracies in the force field. An error in the force field for a subset of the ligands studied was identified using this method, and improved free energy results were obtained by correcting the partial charges assigned to the

  8. How to deal with multiple binding poses in alchemical relative protein-ligand binding free energy calculations.

    Science.gov (United States)

    Kaus, Joseph W; Harder, Edward; Lin, Teng; Abel, Robert; McCammon, J Andrew; Wang, Lingle

    2015-06-09

    Recent advances in improved force fields and sampling methods have made it possible for the accurate calculation of protein–ligand binding free energies. Alchemical free energy perturbation (FEP) using an explicit solvent model is one of the most rigorous methods to calculate relative binding free energies. However, for cases where there are high energy barriers separating the relevant conformations that are important for ligand binding, the calculated free energy may depend on the initial conformation used in the simulation due to the lack of complete sampling of all the important regions in phase space. This is particularly true for ligands with multiple possible binding modes separated by high energy barriers, making it difficult to sample all relevant binding modes even with modern enhanced sampling methods. In this paper, we apply a previously developed method that provides a corrected binding free energy for ligands with multiple binding modes by combining the free energy results from multiple alchemical FEP calculations starting from all enumerated poses, and the results are compared with Glide docking and MM-GBSA calculations. From these calculations, the dominant ligand binding mode can also be predicted. We apply this method to a series of ligands that bind to c-Jun N-terminal kinase-1 (JNK1) and obtain improved free energy results. The dominant ligand binding modes predicted by this method agree with the available crystallography, while both Glide docking and MM-GBSA calculations incorrectly predict the binding modes for some ligands. The method also helps separate the force field error from the ligand sampling error, such that deviations in the predicted binding free energy from the experimental values likely indicate possible inaccuracies in the force field. An error in the force field for a subset of the ligands studied was identified using this method, and improved free energy results were obtained by correcting the partial charges assigned to the

  9. Combining in silico and in cerebro approaches for virtual screening and pose prediction in SAMPL4.

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    Voet, Arnout R D; Kumar, Ashutosh; Berenger, Francois; Zhang, Kam Y J

    2014-04-01

    The SAMPL challenges provide an ideal opportunity for unbiased evaluation and comparison of different approaches used in computational drug design. During the fourth round of this SAMPL challenge, we participated in the virtual screening and binding pose prediction on inhibitors targeting the HIV-1 integrase enzyme. For virtual screening, we used well known and widely used in silico methods combined with personal in cerebro insights and experience. Regular docking only performed slightly better than random selection, but the performance was significantly improved upon incorporation of additional filters based on pharmacophore queries and electrostatic similarities. The best performance was achieved when logical selection was added. For the pose prediction, we utilized a similar consensus approach that amalgamated the results of the Glide-XP docking with structural knowledge and rescoring. The pose prediction results revealed that docking displayed reasonable performance in predicting the binding poses. However, prediction performance can be improved utilizing scientific experience and rescoring approaches. In both the virtual screening and pose prediction challenges, the top performance was achieved by our approaches. Here we describe the methods and strategies used in our approaches and discuss the rationale of their performances.

  10. Performance of multiple docking and refinement methods in the pose prediction D3R prospective Grand Challenge 2016

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    Fradera, Xavier; Verras, Andreas; Hu, Yuan; Wang, Deping; Wang, Hongwu; Fells, James I.; Armacost, Kira A.; Crespo, Alejandro; Sherborne, Brad; Wang, Huijun; Peng, Zhengwei; Gao, Ying-Duo

    2018-01-01

    We describe the performance of multiple pose prediction methods for the D3R 2016 Grand Challenge. The pose prediction challenge includes 36 ligands, which represent 4 chemotypes and some miscellaneous structures against the FXR ligand binding domain. In this study we use a mix of fully automated methods as well as human-guided methods with considerations of both the challenge data and publicly available data. The methods include ensemble docking, colony entropy pose prediction, target selection by molecular similarity, molecular dynamics guided pose refinement, and pose selection by visual inspection. We evaluated the success of our predictions by method, chemotype, and relevance of publicly available data. For the overall data set, ensemble docking, visual inspection, and molecular dynamics guided pose prediction performed the best with overall mean RMSDs of 2.4, 2.2, and 2.2 Å respectively. For several individual challenge molecules, the best performing method is evaluated in light of that particular ligand. We also describe the protein, ligand, and public information data preparations that are typical of our binding mode prediction workflow.

  11. Locating binding poses in protein-ligand systems using reconnaissance metadynamics

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    Söderhjelm, Pär; Tribello, Gareth A.; Parrinello, Michele

    2012-01-01

    A molecular dynamics-based protocol is proposed for finding and scoring protein-ligand binding poses. This protocol uses the recently developed reconnaissance metadynamics method, which employs a self-learning algorithm to construct a bias that pushes the system away from the kinetic traps where it would otherwise remain. The exploration of phase space with this algorithm is shown to be roughly six to eight times faster than unbiased molecular dynamics and is only limited by the time taken to diffuse about the surface of the protein. We apply this method to the well-studied trypsin–benzamidine system and show that we are able to refind all the poses obtained from a reference EADock blind docking calculation. These poses can be scored based on the length of time the system remains trapped in the pose. Alternatively, one can perform dimensionality reduction on the output trajectory and obtain a map of phase space that can be used in more expensive free-energy calculations. PMID:22440749

  12. Prediction of ICP Pose Uncertainties Using Monte Carlo Simulation with Synthetic Depth Images

    DEFF Research Database (Denmark)

    Iversen, Thorbjørn Mosekjær; Buch, Anders Glent; Kraft, Dirk

    2017-01-01

    on the generation of synthetic depth images in a Monte Carlo simulation. In this paper we demonstrate our method for depth sensors which rely on Kinect v1 like technology. We evaluate our method using real depth sensor recordings from the publicly available BigBird dataset. The evaluation shows that the uncertainty......In robotics, vision sensors are used to estimate the poses of objects in the environment. However, it is a fundamental problem that the estimated poses are not always accurate enough for a given robotic task. Proper sensor placement can mitigate this problem. We present a method which can predict...... the pose uncertainties in the Iterative Closest Point (ICP) algorithm, which is often used as the last critical pose refinement step in a pose estimation system. With our method we thus provide a crucial tool needed for the optimization of a robust pose estimation system. Our method relies...

  13. Predicting the binding modes and sites of metabolism of xenobiotics.

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    Mukherjee, Goutam; Lal Gupta, Pancham; Jayaram, B

    2015-07-01

    Metabolism studies are an essential integral part of ADMET profiling of drug candidates to evaluate their safety and efficacy. Cytochrome P-450 (CYP) metabolizes a wide variety of xenobiotics/drugs. The binding modes of these compounds with CYP and their intrinsic reactivities decide the metabolic products. We report here a novel computational protocol, which comprises docking of ligands to heme-containing CYPs and prediction of binding energies through a newly developed scoring function, followed by analyses of the docked structures and molecular orbitals of the ligand molecules, for predicting the sites of metabolism (SOM) of ligands. The calculated binding free energies of 121 heme-containing protein-ligand docked complexes yielded a correlation coefficient of 0.84 against experiment. Molecular orbital analyses of the resultant top three unique poses of the docked complexes provided a success rate of 87% in identifying the experimentally known sites of metabolism of the xenobiotics. The SOM prediction methodology is freely accessible at .

  14. A cross docking pipeline for improving pose prediction and virtual screening performance

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    Kumar, Ashutosh; Zhang, Kam Y. J.

    2018-01-01

    Pose prediction and virtual screening performance of a molecular docking method depend on the choice of protein structures used for docking. Multiple structures for a target protein are often used to take into account the receptor flexibility and problems associated with a single receptor structure. However, the use of multiple receptor structures is computationally expensive when docking a large library of small molecules. Here, we propose a new cross-docking pipeline suitable to dock a large library of molecules while taking advantage of multiple target protein structures. Our method involves the selection of a suitable receptor for each ligand in a screening library utilizing ligand 3D shape similarity with crystallographic ligands. We have prospectively evaluated our method in D3R Grand Challenge 2 and demonstrated that our cross-docking pipeline can achieve similar or better performance than using either single or multiple-receptor structures. Moreover, our method displayed not only decent pose prediction performance but also better virtual screening performance over several other methods.

  15. Retrospective Validation of a Structure-Based Virtual Screening Protocol to Identify Ligands for Estrogen Receptor Alpha and Its Application to Identify the Alpha-Mangostin Binding Pose

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    Agustina Setiawati

    2014-07-01

    Full Text Available The publicly available enhanced data of ligands and decoys for estrogen receptor alpha (ERα which were recently published has made the retrospective validation of a structure-based virtual screening (SBVS protocol to identify ligands for ERα possible. In this article, we present the retrospective validation of an SBVS protocol using PLANTS molecular docking software version 1.2 (PLANTS1.2 as the backbone software. The protocol shows better enrichment factor at 1% false positives (EF1% value and the Area Under Curve (AUC value of the Receiver Operator Characteristic (ROC compared to the original published protocol. Moreover, in all 1000 iterative attempts the protocol could reproduce the co-crystal pose of 4-hydroxitamoxifen in ERα binding pocket. It shows that the protocol is not only able to identify potent ligands for ERα but also able to be employed in examining binding pose of known ligand. Thence, the protocol was successfully employed to examine the binding poses of α-mangostin, an ERα ligand found in the Garcinia mangostana, L. pericarp.

  16. Predicting DNA-binding proteins and binding residues by complex structure prediction and application to human proteome.

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    Huiying Zhao

    Full Text Available As more and more protein sequences are uncovered from increasingly inexpensive sequencing techniques, an urgent task is to find their functions. This work presents a highly reliable computational technique for predicting DNA-binding function at the level of protein-DNA complex structures, rather than low-resolution two-state prediction of DNA-binding as most existing techniques do. The method first predicts protein-DNA complex structure by utilizing the template-based structure prediction technique HHblits, followed by binding affinity prediction based on a knowledge-based energy function (Distance-scaled finite ideal-gas reference state for protein-DNA interactions. A leave-one-out cross validation of the method based on 179 DNA-binding and 3797 non-binding protein domains achieves a Matthews correlation coefficient (MCC of 0.77 with high precision (94% and high sensitivity (65%. We further found 51% sensitivity for 82 newly determined structures of DNA-binding proteins and 56% sensitivity for the human proteome. In addition, the method provides a reasonably accurate prediction of DNA-binding residues in proteins based on predicted DNA-binding complex structures. Its application to human proteome leads to more than 300 novel DNA-binding proteins; some of these predicted structures were validated by known structures of homologous proteins in APO forms. The method [SPOT-Seq (DNA] is available as an on-line server at http://sparks-lab.org.

  17. CaMELS: In silico prediction of calmodulin binding proteins and their binding sites.

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    Abbasi, Wajid Arshad; Asif, Amina; Andleeb, Saiqa; Minhas, Fayyaz Ul Amir Afsar

    2017-09-01

    Due to Ca 2+ -dependent binding and the sequence diversity of Calmodulin (CaM) binding proteins, identifying CaM interactions and binding sites in the wet-lab is tedious and costly. Therefore, computational methods for this purpose are crucial to the design of such wet-lab experiments. We present an algorithm suite called CaMELS (CalModulin intEraction Learning System) for predicting proteins that interact with CaM as well as their binding sites using sequence information alone. CaMELS offers state of the art accuracy for both CaM interaction and binding site prediction and can aid biologists in studying CaM binding proteins. For CaM interaction prediction, CaMELS uses protein sequence features coupled with a large-margin classifier. CaMELS models the binding site prediction problem using multiple instance machine learning with a custom optimization algorithm which allows more effective learning over imprecisely annotated CaM-binding sites during training. CaMELS has been extensively benchmarked using a variety of data sets, mutagenic studies, proteome-wide Gene Ontology enrichment analyses and protein structures. Our experiments indicate that CaMELS outperforms simple motif-based search and other existing methods for interaction and binding site prediction. We have also found that the whole sequence of a protein, rather than just its binding site, is important for predicting its interaction with CaM. Using the machine learning model in CaMELS, we have identified important features of protein sequences for CaM interaction prediction as well as characteristic amino acid sub-sequences and their relative position for identifying CaM binding sites. Python code for training and evaluating CaMELS together with a webserver implementation is available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#camels. © 2017 Wiley Periodicals, Inc.

  18. Binding site graphs: a new graph theoretical framework for prediction of transcription factor binding sites.

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    Timothy E Reddy

    2007-05-01

    Full Text Available Computational prediction of nucleotide binding specificity for transcription factors remains a fundamental and largely unsolved problem. Determination of binding positions is a prerequisite for research in gene regulation, a major mechanism controlling phenotypic diversity. Furthermore, an accurate determination of binding specificities from high-throughput data sources is necessary to realize the full potential of systems biology. Unfortunately, recently performed independent evaluation showed that more than half the predictions from most widely used algorithms are false. We introduce a graph-theoretical framework to describe local sequence similarity as the pair-wise distances between nucleotides in promoter sequences, and hypothesize that densely connected subgraphs are indicative of transcription factor binding sites. Using a well-established sampling algorithm coupled with simple clustering and scoring schemes, we identify sets of closely related nucleotides and test those for known TF binding activity. Using an independent benchmark, we find our algorithm predicts yeast binding motifs considerably better than currently available techniques and without manual curation. Importantly, we reduce the number of false positive predictions in yeast to less than 30%. We also develop a framework to evaluate the statistical significance of our motif predictions. We show that our approach is robust to the choice of input promoters, and thus can be used in the context of predicting binding positions from noisy experimental data. We apply our method to identify binding sites using data from genome scale ChIP-chip experiments. Results from these experiments are publicly available at http://cagt10.bu.edu/BSG. The graphical framework developed here may be useful when combining predictions from numerous computational and experimental measures. Finally, we discuss how our algorithm can be used to improve the sensitivity of computational predictions of

  19. Accurate prediction of peptide binding sites on protein surfaces.

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    Evangelia Petsalaki

    2009-03-01

    Full Text Available Many important protein-protein interactions are mediated by the binding of a short peptide stretch in one protein to a large globular segment in another. Recent efforts have provided hundreds of examples of new peptides binding to proteins for which a three-dimensional structure is available (either known experimentally or readily modeled but where no structure of the protein-peptide complex is known. To address this gap, we present an approach that can accurately predict peptide binding sites on protein surfaces. For peptides known to bind a particular protein, the method predicts binding sites with great accuracy, and the specificity of the approach means that it can also be used to predict whether or not a putative or predicted peptide partner will bind. We used known protein-peptide complexes to derive preferences, in the form of spatial position specific scoring matrices, which describe the binding-site environment in globular proteins for each type of amino acid in bound peptides. We then scan the surface of a putative binding protein for sites for each of the amino acids present in a peptide partner and search for combinations of high-scoring amino acid sites that satisfy constraints deduced from the peptide sequence. The method performed well in a benchmark and largely agreed with experimental data mapping binding sites for several recently discovered interactions mediated by peptides, including RG-rich proteins with SMN domains, Epstein-Barr virus LMP1 with TRADD domains, DBC1 with Sir2, and the Ago hook with Argonaute PIWI domain. The method, and associated statistics, is an excellent tool for predicting and studying binding sites for newly discovered peptides mediating critical events in biology.

  20. Transcription factor binding sites prediction based on modified nucleosomes.

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    Mohammad Talebzadeh

    Full Text Available In computational methods, position weight matrices (PWMs are commonly applied for transcription factor binding site (TFBS prediction. Although these matrices are more accurate than simple consensus sequences to predict actual binding sites, they usually produce a large number of false positive (FP predictions and so are impoverished sources of information. Several studies have employed additional sources of information such as sequence conservation or the vicinity to transcription start sites to distinguish true binding regions from random ones. Recently, the spatial distribution of modified nucleosomes has been shown to be associated with different promoter architectures. These aligned patterns can facilitate DNA accessibility for transcription factors. We hypothesize that using data from these aligned and periodic patterns can improve the performance of binding region prediction. In this study, we propose two effective features, "modified nucleosomes neighboring" and "modified nucleosomes occupancy", to decrease FP in binding site discovery. Based on these features, we designed a logistic regression classifier which estimates the probability of a region as a TFBS. Our model learned each feature based on Sp1 binding sites on Chromosome 1 and was tested on the other chromosomes in human CD4+T cells. In this work, we investigated 21 histone modifications and found that only 8 out of 21 marks are strongly correlated with transcription factor binding regions. To prove that these features are not specific to Sp1, we combined the logistic regression classifier with the PWM, and created a new model to search TFBSs on the genome. We tested the model using transcription factors MAZ, PU.1 and ELF1 and compared the results to those using only the PWM. The results show that our model can predict Transcription factor binding regions more successfully. The relative simplicity of the model and capability of integrating other features make it a superior method

  1. Peptide binding predictions for HLA DR, DP and DQ molecules

    DEFF Research Database (Denmark)

    Wang, P.; Sidney, J.; Kim, Y.

    2010-01-01

    BACKGROUND: MHC class II binding predictions are widely used to identify epitope candidates in infectious agents, allergens, cancer and autoantigens. The vast majority of prediction algorithms for human MHC class II to date have targeted HLA molecules encoded in the DR locus. This reflects a sign...

  2. Predicting nucleic acid binding interfaces from structural models of proteins.

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    Dror, Iris; Shazman, Shula; Mukherjee, Srayanta; Zhang, Yang; Glaser, Fabian; Mandel-Gutfreund, Yael

    2012-02-01

    The function of DNA- and RNA-binding proteins can be inferred from the characterization and accurate prediction of their binding interfaces. However, the main pitfall of various structure-based methods for predicting nucleic acid binding function is that they are all limited to a relatively small number of proteins for which high-resolution three-dimensional structures are available. In this study, we developed a pipeline for extracting functional electrostatic patches from surfaces of protein structural models, obtained using the I-TASSER protein structure predictor. The largest positive patches are extracted from the protein surface using the patchfinder algorithm. We show that functional electrostatic patches extracted from an ensemble of structural models highly overlap the patches extracted from high-resolution structures. Furthermore, by testing our pipeline on a set of 55 known nucleic acid binding proteins for which I-TASSER produces high-quality models, we show that the method accurately identifies the nucleic acids binding interface on structural models of proteins. Employing a combined patch approach we show that patches extracted from an ensemble of models better predicts the real nucleic acid binding interfaces compared with patches extracted from independent models. Overall, these results suggest that combining information from a collection of low-resolution structural models could be a valuable approach for functional annotation. We suggest that our method will be further applicable for predicting other functional surfaces of proteins with unknown structure. Copyright © 2011 Wiley Periodicals, Inc.

  3. Predicting binding within disordered protein regions to structurally characterised peptide-binding domains.

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    Waqasuddin Khan

    Full Text Available Disordered regions of proteins often bind to structured domains, mediating interactions within and between proteins. However, it is difficult to identify a priori the short disordered regions involved in binding. We set out to determine if docking such peptide regions to peptide binding domains would assist in these predictions.We assembled a redundancy reduced dataset of SLiM (Short Linear Motif containing proteins from the ELM database. We selected 84 sequences which had an associated PDB structures showing the SLiM bound to a protein receptor, where the SLiM was found within a 50 residue region of the protein sequence which was predicted to be disordered. First, we investigated the Vina docking scores of overlapping tripeptides from the 50 residue SLiM containing disordered regions of the protein sequence to the corresponding PDB domain. We found only weak discrimination of docking scores between peptides involved in binding and adjacent non-binding peptides in this context (AUC 0.58.Next, we trained a bidirectional recurrent neural network (BRNN using as input the protein sequence, predicted secondary structure, Vina docking score and predicted disorder score. The results were very promising (AUC 0.72 showing that multiple sources of information can be combined to produce results which are clearly superior to any single source.We conclude that the Vina docking score alone has only modest power to define the location of a peptide within a larger protein region known to contain it. However, combining this information with other knowledge (using machine learning methods clearly improves the identification of peptide binding regions within a protein sequence. This approach combining docking with machine learning is primarily a predictor of binding to peptide-binding sites, and is not intended as a predictor of specificity of binding to particular receptors.

  4. Prediction of GPCR-Ligand Binding Using Machine Learning Algorithms

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    Sangmin Seo

    2018-01-01

    Full Text Available We propose a novel method that predicts binding of G-protein coupled receptors (GPCRs and ligands. The proposed method uses hub and cycle structures of ligands and amino acid motif sequences of GPCRs, rather than the 3D structure of a receptor or similarity of receptors or ligands. The experimental results show that these new features can be effective in predicting GPCR-ligand binding (average area under the curve [AUC] of 0.944, because they are thought to include hidden properties of good ligand-receptor binding. Using the proposed method, we were able to identify novel ligand-GPCR bindings, some of which are supported by several studies.

  5. A systems biology approach to transcription factor binding site prediction.

    Directory of Open Access Journals (Sweden)

    Xiang Zhou

    2010-03-01

    Full Text Available The elucidation of mammalian transcriptional regulatory networks holds great promise for both basic and translational research and remains one the greatest challenges to systems biology. Recent reverse engineering methods deduce regulatory interactions from large-scale mRNA expression profiles and cross-species conserved regulatory regions in DNA. Technical challenges faced by these methods include distinguishing between direct and indirect interactions, associating transcription regulators with predicted transcription factor binding sites (TFBSs, identifying non-linearly conserved binding sites across species, and providing realistic accuracy estimates.We address these challenges by closely integrating proven methods for regulatory network reverse engineering from mRNA expression data, linearly and non-linearly conserved regulatory region discovery, and TFBS evaluation and discovery. Using an extensive test set of high-likelihood interactions, which we collected in order to provide realistic prediction-accuracy estimates, we show that a careful integration of these methods leads to significant improvements in prediction accuracy. To verify our methods, we biochemically validated TFBS predictions made for both transcription factors (TFs and co-factors; we validated binding site predictions made using a known E2F1 DNA-binding motif on E2F1 predicted promoter targets, known E2F1 and JUND motifs on JUND predicted promoter targets, and a de novo discovered motif for BCL6 on BCL6 predicted promoter targets. Finally, to demonstrate accuracy of prediction using an external dataset, we showed that sites matching predicted motifs for ZNF263 are significantly enriched in recent ZNF263 ChIP-seq data.Using an integrative framework, we were able to address technical challenges faced by state of the art network reverse engineering methods, leading to significant improvement in direct-interaction detection and TFBS-discovery accuracy. We estimated the accuracy

  6. An investigation into multi-dimensional prediction models to estimate the pose error of a quadcopter in a CSP plant setting

    Science.gov (United States)

    Lock, Jacobus C.; Smit, Willie J.; Treurnicht, Johann

    2016-05-01

    The Solar Thermal Energy Research Group (STERG) is investigating ways to make heliostats cheaper to reduce the total cost of a concentrating solar power (CSP) plant. One avenue of research is to use unmanned aerial vehicles (UAVs) to automate and assist with the heliostat calibration process. To do this, the pose estimation error of each UAV must be determined and integrated into a calibration procedure. A computer vision (CV) system is used to measure the pose of a quadcopter UAV. However, this CV system contains considerable measurement errors. Since this is a high-dimensional problem, a sophisticated prediction model must be used to estimate the measurement error of the CV system for any given pose measurement vector. This paper attempts to train and validate such a model with the aim of using it to determine the pose error of a quadcopter in a CSP plant setting.

  7. Predicting accurate absolute binding energies in aqueous solution

    DEFF Research Database (Denmark)

    Jensen, Jan Halborg

    2015-01-01

    Recent predictions of absolute binding free energies of host-guest complexes in aqueous solution using electronic structure theory have been encouraging for some systems, while other systems remain problematic. In this paper I summarize some of the many factors that could easily contribute 1-3 kcal...

  8. Prediction of DNA-binding specificity in zinc finger proteins

    Indian Academy of Sciences (India)

    2012-06-25

    Jun 25, 2012 ... respective DNA can help to generate engineered zinc fingers for therapeutic purposes involving genome targeting. Exploring the structure–function relationships of the existing zinc finger–DNA complexes can aid in predicting the probable zinc .... tered to a defined family based on binding data. How-.

  9. Assessing GPCR homology models constructed from templates of various transmembrane sequence identities: Binding mode prediction and docking enrichment.

    Science.gov (United States)

    Loo, Jason S E; Emtage, Abigail L; Ng, Kar Weng; Yong, Alene S J; Doughty, Stephen W

    2018-03-01

    GPCR crystal structures have become more readily accessible in recent years. However, homology models of GPCRs continue to play an important role as many GPCR structures remain unsolved. The new crystal structures now available provide not only additional templates for homology modelling but also the opportunity to assess the performance of homology models against their respective crystal structures and gain insight into the performance of such models. In this study we have constructed homology models from templates of various transmembrane sequence identities for eight GPCR targets to better understand the relationship between transmembrane sequence identity and model quality. Model quality was assessed relative to the crystal structure in terms of structural accuracy as well as performance in two typical structure-based drug design applications: ligand binding pose prediction and docking enrichment in virtual screening. Crystal structures significantly outperformed homology models in both assessments. Accurate ligand binding pose prediction was possible but difficult to achieve using homology models, even with the use of induced fit docking. In virtual screening using homology models still conferred significant enrichment compared to random selection, with a clear benefit also observed in using models optimized through induced fit docking. Our results indicate that while homology models that are reasonably accurate structurally can be constructed, without significant refinement homology models will be outperformed by crystal structures in ligand binding pose prediction and docking enrichment regardless of the template used, primarily due to the extremely high level of structural accuracy needed for such applications. Copyright © 2017 Elsevier Inc. All rights reserved.

  10. The next generation of transcription factor binding site prediction.

    Directory of Open Access Journals (Sweden)

    Anthony Mathelier

    Full Text Available Finding where transcription factors (TFs bind to the DNA is of key importance to decipher gene regulation at a transcriptional level. Classically, computational prediction of TF binding sites (TFBSs is based on basic position weight matrices (PWMs which quantitatively score binding motifs based on the observed nucleotide patterns in a set of TFBSs for the corresponding TF. Such models make the strong assumption that each nucleotide participates independently in the corresponding DNA-protein interaction and do not account for flexible length motifs. We introduce transcription factor flexible models (TFFMs to represent TF binding properties. Based on hidden Markov models, TFFMs are flexible, and can model both position interdependence within TFBSs and variable length motifs within a single dedicated framework. The availability of thousands of experimentally validated DNA-TF interaction sequences from ChIP-seq allows for the generation of models that perform as well as PWMs for stereotypical TFs and can improve performance for TFs with flexible binding characteristics. We present a new graphical representation of the motifs that convey properties of position interdependence. TFFMs have been assessed on ChIP-seq data sets coming from the ENCODE project, revealing that they can perform better than both PWMs and the dinucleotide weight matrix extension in discriminating ChIP-seq from background sequences. Under the assumption that ChIP-seq signal values are correlated with the affinity of the TF-DNA binding, we find that TFFM scores correlate with ChIP-seq peak signals. Moreover, using available TF-DNA affinity measurements for the Max TF, we demonstrate that TFFMs constructed from ChIP-seq data correlate with published experimentally measured DNA-binding affinities. Finally, TFFMs allow for the straightforward computation of an integrated TF occupancy score across a sequence. These results demonstrate the capacity of TFFMs to accurately model DNA

  11. HLA class I binding prediction via convolutional neural networks.

    Science.gov (United States)

    Vang, Yeeleng S; Xie, Xiaohui

    2017-09-01

    Many biological processes are governed by protein-ligand interactions. One such example is the recognition of self and non-self cells by the immune system. This immune response process is regulated by the major histocompatibility complex (MHC) protein which is encoded by the human leukocyte antigen (HLA) complex. Understanding the binding potential between MHC and peptides can lead to the design of more potent, peptide-based vaccines and immunotherapies for infectious autoimmune diseases. We apply machine learning techniques from the natural language processing (NLP) domain to address the task of MHC-peptide binding prediction. More specifically, we introduce a new distributed representation of amino acids, name HLA-Vec, that can be used for a variety of downstream proteomic machine learning tasks. We then propose a deep convolutional neural network architecture, name HLA-CNN, for the task of HLA class I-peptide binding prediction. Experimental results show combining the new distributed representation with our HLA-CNN architecture achieves state-of-the-art results in the majority of the latest two Immune Epitope Database (IEDB) weekly automated benchmark datasets. We further apply our model to predict binding on the human genome and identify 15 genes with potential for self binding. Codes to generate the HLA-Vec and HLA-CNN are publicly available at: https://github.com/uci-cbcl/HLA-bind . xhx@ics.uci.edu. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  12. Prediction of chloride ingress and binding in cement paste

    DEFF Research Database (Denmark)

    Geiker, Mette Rica; Nielsen, Erik Pram; Herforth, Duncan

    2007-01-01

    This paper summarizes recent work on an analytical model for predicting the ingress rate of chlorides in cement-based materials. An integral part of this is a thermodynamic model for predicting the phase equilibria in hydrated Portland cement. The model’s ability to predict chloride binding...... in Portland cement pastes at any content of chloride, alkalis, sulfates and carbonate was verified experimentally and found to be equally valid when applied to other data in the literature. The thermodynamic model for predicting the phase equilibria in hydrated Portland cement was introduced into an existing...... Finite Difference Model for the ingress of chlorides into concrete which takes into account its multi-component nature. The “composite theory” was then used to predict the diffusivity of each ion based on the phase assemblage present in the hydrated Portland cement paste. Agreement was found between...

  13. Scoring functions for transcription factor binding site prediction

    Directory of Open Access Journals (Sweden)

    Friberg Markus

    2005-04-01

    Full Text Available Abstract Background Transcription factor binding site (TFBS prediction is a difficult problem, which requires a good scoring function to discriminate between real binding sites and background noise. Many scoring functions have been proposed in the literature, but it is difficult to assess their relative performance, because they are implemented in different software tools using different search methods and different TFBS representations. Results Here we compare how several scoring functions perform on both real and semi-simulated data sets in a common test environment. We have also developed two new scoring functions and included them in the comparison. The data sets are from the yeast (S. cerevisiae genome. Our new scoring function LLBG (least likely under the background model performs best in this study. It achieves the best average rank for the correct motifs. Scoring functions based on positional bias performed quite poorly in this study. Conclusion LLBG may provide an interesting alternative to current scoring functions for TFBS prediction.

  14. Imputation for transcription factor binding predictions based on deep learning.

    Directory of Open Access Journals (Sweden)

    Qian Qin

    2017-02-01

    Full Text Available Understanding the cell-specific binding patterns of transcription factors (TFs is fundamental to studying gene regulatory networks in biological systems, for which ChIP-seq not only provides valuable data but is also considered as the gold standard. Despite tremendous efforts from the scientific community to conduct TF ChIP-seq experiments, the available data represent only a limited percentage of ChIP-seq experiments, considering all possible combinations of TFs and cell lines. In this study, we demonstrate a method for accurately predicting cell-specific TF binding for TF-cell line combinations based on only a small fraction (4% of the combinations using available ChIP-seq data. The proposed model, termed TFImpute, is based on a deep neural network with a multi-task learning setting to borrow information across transcription factors and cell lines. Compared with existing methods, TFImpute achieves comparable accuracy on TF-cell line combinations with ChIP-seq data; moreover, TFImpute achieves better accuracy on TF-cell line combinations without ChIP-seq data. This approach can predict cell line specific enhancer activities in K562 and HepG2 cell lines, as measured by massively parallel reporter assays, and predicts the impact of SNPs on TF binding.

  15. PREDICTED STRUCTURE AND BINDING MOTIFS OF COLLAGEN α1(XI).

    Science.gov (United States)

    McDougal, Owen M; Warner, Lisa R; Mallory, Chris; Oxford, Julia Thom

    2011-12-01

    The amino propeptide of collagen α1(XI) (NPP) has been shown to bind glycosaminoglycans and to form a dimer. While these are independent biochemical events, it is likely that dimerization facilitates the interaction with glycosaminoglycans or alternatively, that glycosaminoglycan interaction facilitates the formation of an NPP:NPP dimer. The computer program MODELLER was used to generate a homology model of the collagen α1(XI) NPP monomer using the crystal structure of the closely related noncollagenous-4 (NC4) domain of collagen α1(IX) (PDB:2UUR) as the template. Additionally, a dimer model of collagen α1(XI) NPP domain was created based upon the thrombospondin dimer template (PDB:1Z78). The structure of the dimer created in MODELLER was validated by comparison to a dimer model generated by docking two monomers of PDB:2UUR using ClusPro. Calculations of relative binding energy for the interaction between each collagen α1(XI) NPP model and glycosaminoglycans as ligands was performed using AutoDock4. Computational results support a higher affinity between heparan sulfate and a dimer compared to a monomer. These findings are supported by affinity chromatography experiments in which distinct monomer and dimer peaks were observed. Sequential point mutation studies of the putative binding site (147-KKKITK-152) indicated the importance of the basic lysine residue for binding to heparan sulfate. Two orders of magnitude change in binding affinity was predicted when comparing wild type to the mutation K152A. Experimental data supports the predicted change in affinity.

  16. DNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues.

    Science.gov (United States)

    Ma, Xin; Guo, Jing; Sun, Xiao

    2016-01-01

    DNA-binding proteins are fundamentally important in cellular processes. Several computational-based methods have been developed to improve the prediction of DNA-binding proteins in previous years. However, insufficient work has been done on the prediction of DNA-binding proteins from protein sequence information. In this paper, a novel predictor, DNABP (DNA-binding proteins), was designed to predict DNA-binding proteins using the random forest (RF) classifier with a hybrid feature. The hybrid feature contains two types of novel sequence features, which reflect information about the conservation of physicochemical properties of the amino acids, and the binding propensity of DNA-binding residues and non-binding propensities of non-binding residues. The comparisons with each feature demonstrated that these two novel features contributed most to the improvement in predictive ability. Furthermore, to improve the prediction performance of the DNABP model, feature selection using the minimum redundancy maximum relevance (mRMR) method combined with incremental feature selection (IFS) was carried out during the model construction. The results showed that the DNABP model could achieve 86.90% accuracy, 83.76% sensitivity, 90.03% specificity and a Matthews correlation coefficient of 0.727. High prediction accuracy and performance comparisons with previous research suggested that DNABP could be a useful approach to identify DNA-binding proteins from sequence information. The DNABP web server system is freely available at http://www.cbi.seu.edu.cn/DNABP/.

  17. Prediction of Nucleotide Binding Peptides Using Star Graph Topological Indices.

    Science.gov (United States)

    Liu, Yong; Munteanu, Cristian R; Fernández Blanco, Enrique; Tan, Zhiliang; Santos Del Riego, Antonino; Pazos, Alejandro

    2015-11-01

    The nucleotide binding proteins are involved in many important cellular processes, such as transmission of genetic information or energy transfer and storage. Therefore, the screening of new peptides for this biological function is an important research topic. The current study proposes a mixed methodology to obtain the first classification model that is able to predict new nucleotide binding peptides, using only the amino acid sequence. Thus, the methodology uses a Star graph molecular descriptor of the peptide sequences and the Machine Learning technique for the best classifier. The best model represents a Random Forest classifier based on two features of the embedded and non-embedded graphs. The performance of the model is excellent, considering similar models in the field, with an Area Under the Receiver Operating Characteristic Curve (AUROC) value of 0.938 and true positive rate (TPR) of 0.886 (test subset). The prediction of new nucleotide binding peptides with this model could be useful for drug target studies in drug development. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  18. Predicting "Hot" and "Warm" Spots for Fragment Binding.

    Science.gov (United States)

    Rathi, Prakash Chandra; Ludlow, R Frederick; Hall, Richard J; Murray, Christopher W; Mortenson, Paul N; Verdonk, Marcel L

    2017-05-11

    Computational fragment mapping methods aim to predict hotspots on protein surfaces where small fragments will bind. Such methods are popular for druggability assessment as well as structure-based design. However, to date researchers developing or using such tools have had no clear way of assessing the performance of these methods. Here, we introduce the first diverse, high quality validation set for computational fragment mapping. The set contains 52 diverse examples of fragment binding "hot" and "warm" spots from the Protein Data Bank (PDB). Additionally, we describe PLImap, a novel protocol for fragment mapping based on the Protein-Ligand Informatics force field (PLIff). We evaluate PLImap against the new fragment mapping test set, and compare its performance to that of simple shape-based algorithms and fragment docking using GOLD. PLImap is made publicly available from https://bitbucket.org/AstexUK/pli .

  19. Total Binding Affinity Profiles of Regulatory Regions Predict Transcription Factor Binding and Gene Expression in Human Cells.

    Directory of Open Access Journals (Sweden)

    Elena Grassi

    Full Text Available Transcription factors regulate gene expression by binding regulatory DNA. Understanding the rules governing such binding is an essential step in describing the network of regulatory interactions, and its pathological alterations. We show that describing regulatory regions in terms of their profile of total binding affinities for transcription factors leads to increased predictive power compared to methods based on the identification of discrete binding sites. This applies both to the prediction of transcription factor binding as revealed by ChIP-seq experiments and to the prediction of gene expression through RNA-seq. Further significant improvements in predictive power are obtained when regulatory regions are defined based on chromatin states inferred from histone modification data.

  20. Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification

    DEFF Research Database (Denmark)

    Andreatta, Massimo; Karosiene, Edita; Rasmussen, Michael

    2015-01-01

    A key event in the generation of a cellular response against malicious organisms through the endocytic pathway is binding of peptidic antigens by major histocompatibility complex class II (MHC class II) molecules. The bound peptide is then presented on the cell surface where it can be recognized...... by T helper lymphocytes. NetMHCIIpan is a state-of-the-art method for the quantitative prediction of peptide binding to any human or mouse MHC class II molecule of known sequence. In this paper, we describe an updated version of the method with improved peptide binding register identification. Binding...... register prediction is concerned with determining the minimal core region of nine residues directly in contact with the MHC binding cleft, a crucial piece of information both for the identification and design of CD4+ T cell antigens. When applied to a set of 51 crystal structures of peptide-MHC complexes...

  1. Mechanisms of Intentional Binding and Sensory Attenuation: The Role of Temporal Prediction, Temporal Control, Identity Prediction, and Motor Prediction

    Science.gov (United States)

    Hughes, Gethin; Desantis, Andrea; Waszak, Florian

    2013-01-01

    Sensory processing of action effects has been shown to differ from that of externally triggered stimuli, with respect both to the perceived timing of their occurrence (intentional binding) and to their intensity (sensory attenuation). These phenomena are normally attributed to forward action models, such that when action prediction is consistent…

  2. Development of estrogen receptor beta binding prediction model using large sets of chemicals.

    Science.gov (United States)

    Sakkiah, Sugunadevi; Selvaraj, Chandrabose; Gong, Ping; Zhang, Chaoyang; Tong, Weida; Hong, Huixiao

    2017-11-03

    We developed an ER β binding prediction model to facilitate identification of chemicals specifically bind ER β or ER α together with our previously developed ER α binding model. Decision Forest was used to train ER β binding prediction model based on a large set of compounds obtained from EADB. Model performance was estimated through 1000 iterations of 5-fold cross validations. Prediction confidence was analyzed using predictions from the cross validations. Informative chemical features for ER β binding were identified through analysis of the frequency data of chemical descriptors used in the models in the 5-fold cross validations. 1000 permutations were conducted to assess the chance correlation. The average accuracy of 5-fold cross validations was 93.14% with a standard deviation of 0.64%. Prediction confidence analysis indicated that the higher the prediction confidence the more accurate the predictions. Permutation testing results revealed that the prediction model is unlikely generated by chance. Eighteen informative descriptors were identified to be important to ER β binding prediction. Application of the prediction model to the data from ToxCast project yielded very high sensitivity of 90-92%. Our results demonstrated ER β binding of chemicals could be accurately predicted using the developed model. Coupling with our previously developed ER α prediction model, this model could be expected to facilitate drug development through identification of chemicals that specifically bind ER β or ER α .

  3. Large scale free energy calculations for blind predictions of protein-ligand binding: the D3R Grand Challenge 2015.

    Science.gov (United States)

    Deng, Nanjie; Flynn, William F; Xia, Junchao; Vijayan, R S K; Zhang, Baofeng; He, Peng; Mentes, Ahmet; Gallicchio, Emilio; Levy, Ronald M

    2016-09-01

    We describe binding free energy calculations in the D3R Grand Challenge 2015 for blind prediction of the binding affinities of 180 ligands to Hsp90. The present D3R challenge was built around experimental datasets involving Heat shock protein (Hsp) 90, an ATP-dependent molecular chaperone which is an important anticancer drug target. The Hsp90 ATP binding site is known to be a challenging target for accurate calculations of ligand binding affinities because of the ligand-dependent conformational changes in the binding site, the presence of ordered waters and the broad chemical diversity of ligands that can bind at this site. Our primary focus here is to distinguish binders from nonbinders. Large scale absolute binding free energy calculations that cover over 3000 protein-ligand complexes were performed using the BEDAM method starting from docked structures generated by Glide docking. Although the ligand dataset in this study resembles an intermediate to late stage lead optimization project while the BEDAM method is mainly developed for early stage virtual screening of hit molecules, the BEDAM binding free energy scoring has resulted in a moderate enrichment of ligand screening against this challenging drug target. Results show that, using a statistical mechanics based free energy method like BEDAM starting from docked poses offers better enrichment than classical docking scoring functions and rescoring methods like Prime MM-GBSA for the Hsp90 data set in this blind challenge. Importantly, among the three methods tested here, only the mean value of the BEDAM binding free energy scores is able to separate the large group of binders from the small group of nonbinders with a gap of 2.4 kcal/mol. None of the three methods that we have tested provided accurate ranking of the affinities of the 147 active compounds. We discuss the possible sources of errors in the binding free energy calculations. The study suggests that BEDAM can be used strategically to discriminate

  4. HemeBIND: a novel method for heme binding residue prediction by combining structural and sequence information

    Directory of Open Access Journals (Sweden)

    Hu Jianjun

    2011-05-01

    Full Text Available Abstract Background Accurate prediction of binding residues involved in the interactions between proteins and small ligands is one of the major challenges in structural bioinformatics. Heme is an essential and commonly used ligand that plays critical roles in electron transfer, catalysis, signal transduction and gene expression. Although much effort has been devoted to the development of various generic algorithms for ligand binding site prediction over the last decade, no algorithm has been specifically designed to complement experimental techniques for identification of heme binding residues. Consequently, an urgent need is to develop a computational method for recognizing these important residues. Results Here we introduced an efficient algorithm HemeBIND for predicting heme binding residues by integrating structural and sequence information. We systematically investigated the characteristics of binding interfaces based on a non-redundant dataset of heme-protein complexes. It was found that several sequence and structural attributes such as evolutionary conservation, solvent accessibility, depth and protrusion clearly illustrate the differences between heme binding and non-binding residues. These features can then be separately used or combined to build the structure-based classifiers using support vector machine (SVM. The results showed that the information contained in these features is largely complementary and their combination achieved the best performance. To further improve the performance, an attempt has been made to develop a post-processing procedure to reduce the number of false positives. In addition, we built a sequence-based classifier based on SVM and sequence profile as an alternative when only sequence information can be used. Finally, we employed a voting method to combine the outputs of structure-based and sequence-based classifiers, which demonstrated remarkably better performance than the individual classifier alone

  5. Machine learning competition in immunology – Prediction of HLA class I binding peptides

    DEFF Research Database (Denmark)

    Zhang, Guang Lan; Ansari, Hifzur Rahman; Bradley, Phil

    2011-01-01

    of peptide binding, therefore, determines the accuracy of the overall method. Computational predictions of peptide binding to HLA, both class I and class II, use a variety of algorithms ranging from binding motifs to advanced machine learning techniques ( [Brusic et al., 2004] and [Lafuente and Reche, 2009...

  6. Prediction of DNA-binding specificity in zinc finger proteins

    Indian Academy of Sciences (India)

    2012-06-25

    Jun 25, 2012 ... Support Vector Machine (SVM) is a state-of-the-art classifica- tion technique. Using canonical binding model, the C2H2 zinc finger protein–DNA interaction interface is modelled by the pairwise amino acid–base interactions. Using a classification framework, known examples of non-binding ZF–DNA pairs.

  7. A 3D-QSAR-driven approach to binding mode and affinity prediction

    DEFF Research Database (Denmark)

    Tosco, Paolo; Balle, Thomas

    2012-01-01

    A method for predicting the binding mode of a series of ligands is proposed. The procedure relies on three-dimensional quantitative structure-activity relationships (3D-QSAR) and does not require structural knowledge of the binding site. Candidate alignments are automatically built and ranked...... according to a consensus scoring function. 3D-QSAR analysis based on the selected binding mode enables affinity prediction of new drug candidates having less than 10 rotatable bonds....

  8. Prediction of Drug-Plasma Protein Binding Using Artificial Intelligence Based Algorithms.

    Science.gov (United States)

    Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

    2017-12-18

    Plasma protein binding (PPB) has vital importance in the characterization of drug distribution in the systemic circulation. Unfavorable PPB can pose a negative effect on clinical development of promising drug candidates. The drug distribution properties should be considered at the initial phases of the drug design and development. Therefore, PPB prediction models are receiving an increased attention. In the current study, we present a systematic approach using Support vector machine, Artificial neural network, k- nearest neighbor, Probabilistic neural network, Partial least square and Linear discriminant analysis to relate various in vitro and in silico molecular descriptors to a diverse dataset of 736 drugs/drug-like compounds. The overall accuracy of Support vector machine with Radial basis function kernel came out to be comparatively better than the rest of applied algorithms. The training set accuracy, validation set accuracy, precision, sensitivity, specificity and F1 score for the Suprort vector machine was found to be 89.73%, 89.97%, 92.56%, 87.26%, 91.97% and 0.898 respectively. This model can potentially be useful in screening of relevant drug candidates at the preliminary stages of drug design and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  9. Ebishushani: people poses places

    NARCIS (Netherlands)

    Andrea Stultiens

    2014-01-01

    Ebifananyi II – People Poses Places Andrea Stultiens People Poses Places is the second part of Ebifananyi, a book series that visualises historical Ugandan photo collections. In People Poses Places we delve into the archive of the photographer Musa Katuramu. In the mid 1930s, teacher and carpenter

  10. Cross-modality deep learning-based prediction of TAP binding and naturally processed peptide.

    Science.gov (United States)

    Besser, Hanan; Louzoun, Yoram

    2018-02-28

    Epitopes presented on MHC class I molecules pass multiple processing stages before their presentation on MHC molecules, the main ones being proteasomal cleavage and TAP binding. Transporter associated with antigen processing (TAP) binding is a necessary stage for most, but not all, MHC-I-binding peptides. The molecular determinants of TAP-binding peptides can be experimentally estimated from binding experiments and from the properties of peptides inducing a CD8 T cell response. We here propose novel optimization formalisms to combine binding and activation experimental results to produce a classifier for TAP binding using dual-output kernel and deep learning approaches. The application of these algorithms to the human and murine TAP binding leads to predictors that are much more precise than current state of the art methods. Moreover, the computed score is highly correlated with the observed binding energy. The new predictors show that TAP binding may be much more selective than previously assumed in humans and mice and sensitive to the properties of most positions of the peptides. Beyond the improved precision for TAP binding, we propose that the same approach holds in most molecular binding problems, where functional and binding measures are simultaneously available, and can be used to significantly improve the precision of binding prediction algorithms in general and immune system molecules specifically.

  11. A sequence-based dynamic ensemble learning system for protein ligand-binding site prediction

    KAUST Repository

    Chen, Peng

    2015-12-03

    Background: Proteins have the fundamental ability to selectively bind to other molecules and perform specific functions through such interactions, such as protein-ligand binding. Accurate prediction of protein residues that physically bind to ligands is important for drug design and protein docking studies. Most of the successful protein-ligand binding predictions were based on known structures. However, structural information is not largely available in practice due to the huge gap between the number of known protein sequences and that of experimentally solved structures

  12. A computational docking study for prediction of binding mode of diospyrin and derivatives: Inhibitors of human and leishmanial DNA topoisomerase-I.

    Science.gov (United States)

    Chhabra, Sandeep; Sharma, Pooja; Ghoshal, Nanda

    2007-08-15

    A computational approach was utilized to study the relative binding modes of diospyrin (bisnaphthoquinonoid) with the crystal structure of human DNA-TopoI and the recently reported Leishmania donavani DNA-TopoI. Additionally, the binding site interactions of amino derivatives of diospyrin with human TopoI were studied extensively. Based on the docking results, binding modes of diospyrin with the human and leishmanial TopoI catalytic core were predicted. The parallel use of two efficient and predictive docking programs, GOLD and Ligandfit, allowed mutual validation of the predicted binding poses. A reasonably good correlation coefficient between the calculated docking scores and the experimentally determined cytotoxicity helped in validating the docking method. Furthermore, a structure-based pharmacophore model was developed for L. donavani DNA-TopoI inhibition which helped in elucidating the topological and spatial requirements of the ligand-receptor interactions. This study provides an understanding of the structural basis of ligand binding to the topoisomerase receptor, which may be used for the structure-based design of potent and novel ligands for anticancer and antileishmanial therapy. To our knowledge, this is the first report of a binding mode exploration study for diospyrin and its derivatives as inhibitors of the leishmanial and human TopoI enzymes.

  13. MHC class I epitope binding prediction trained on small data sets

    DEFF Research Database (Denmark)

    Lundegaard, Claus; Nielsen, Morten; Lamberth, K.

    2004-01-01

    The identification of potential T-cell epitopes is important for development of new human or vetenary vaccines, both considering single protein/subunit vaccines, and for epitope/peptide vaccines as such. The highly diverse MHC class I alleles bind very different peptides, and accurate binding...... for predicting peptides binding to specific MHC class I alleles. The method combines advanced automatic scoring matrix generation with empirical position specific differential anchor weighting. The method leads to predictions with a comparable or higher accuracy than other established prediction servers, even...

  14. Prediction of the binding affinities of peptides to class II MHC using a regularized thermodynamic model

    Directory of Open Access Journals (Sweden)

    Mittelmann Hans D

    2010-01-01

    Full Text Available Abstract Background The binding of peptide fragments of extracellular peptides to class II MHC is a crucial event in the adaptive immune response. Each MHC allotype generally binds a distinct subset of peptides and the enormous number of possible peptide epitopes prevents their complete experimental characterization. Computational methods can utilize the limited experimental data to predict the binding affinities of peptides to class II MHC. Results We have developed the Regularized Thermodynamic Average, or RTA, method for predicting the affinities of peptides binding to class II MHC. RTA accounts for all possible peptide binding conformations using a thermodynamic average and includes a parameter constraint for regularization to improve accuracy on novel data. RTA was shown to achieve higher accuracy, as measured by AUC, than SMM-align on the same data for all 17 MHC allotypes examined. RTA also gave the highest accuracy on all but three allotypes when compared with results from 9 different prediction methods applied to the same data. In addition, the method correctly predicted the peptide binding register of 17 out of 18 peptide-MHC complexes. Finally, we found that suboptimal peptide binding registers, which are often ignored in other prediction methods, made significant contributions of at least 50% of the total binding energy for approximately 20% of the peptides. Conclusions The RTA method accurately predicts peptide binding affinities to class II MHC and accounts for multiple peptide binding registers while reducing overfitting through regularization. The method has potential applications in vaccine design and in understanding autoimmune disorders. A web server implementing the RTA prediction method is available at http://bordnerlab.org/RTA/.

  15. Binding Mode Prediction of Evodiamine within Vanilloid Receptor TRPV1

    Directory of Open Access Journals (Sweden)

    Huaping Liang

    2012-07-01

    Full Text Available Accurate assessment of the potential binding mode of drugs is crucial to computer-aided drug design paradigms. It has been reported that evodiamine acts as an agonist of the vanilloid receptor Transient receptor potential vanilloid-1 (TRPV1. However, the precise interaction between evodiamine and TRPV1 was still not fully understood. In this perspective, the homology models of TRPV1 were generated using the crystal structure of the voltage-dependent shaker family K+ channel as a template. We then performed docking and molecular dynamics simulation to gain a better understanding of the probable binding modes of evodiamine within the TRPV1 binding pocket. There are no significant interspecies differences in evodiamine binding in rat, human and rabbit TRPV1 models. Pharmacophore modeling further provided confidence for the validity of the docking studies. This study is the first to shed light on the structural determinants required for the interaction between TRPV1 and evodiamine, and gives new suggestions for the rational design of novel TRPV1 ligands.

  16. An overview of the prediction of protein DNA-binding sites.

    Science.gov (United States)

    Si, Jingna; Zhao, Rui; Wu, Rongling

    2015-03-06

    Interactions between proteins and DNA play an important role in many essential biological processes such as DNA replication, transcription, splicing, and repair. The identification of amino acid residues involved in DNA-binding sites is critical for understanding the mechanism of these biological activities. In the last decade, numerous computational approaches have been developed to predict protein DNA-binding sites based on protein sequence and/or structural information, which play an important role in complementing experimental strategies. At this time, approaches can be divided into three categories: sequence-based DNA-binding site prediction, structure-based DNA-binding site prediction, and homology modeling and threading. In this article, we review existing research on computational methods to predict protein DNA-binding sites, which includes data sets, various residue sequence/structural features, machine learning methods for comparison and selection, evaluation methods, performance comparison of different tools, and future directions in protein DNA-binding site prediction. In particular, we detail the meta-analysis of protein DNA-binding sites. We also propose specific implications that are likely to result in novel prediction methods, increased performance, or practical applications.

  17. An Overview of the Prediction of Protein DNA-Binding Sites

    Directory of Open Access Journals (Sweden)

    Jingna Si

    2015-03-01

    Full Text Available Interactions between proteins and DNA play an important role in many essential biological processes such as DNA replication, transcription, splicing, and repair. The identification of amino acid residues involved in DNA-binding sites is critical for understanding the mechanism of these biological activities. In the last decade, numerous computational approaches have been developed to predict protein DNA-binding sites based on protein sequence and/or structural information, which play an important role in complementing experimental strategies. At this time, approaches can be divided into three categories: sequence-based DNA-binding site prediction, structure-based DNA-binding site prediction, and homology modeling and threading. In this article, we review existing research on computational methods to predict protein DNA-binding sites, which includes data sets, various residue sequence/structural features, machine learning methods for comparison and selection, evaluation methods, performance comparison of different tools, and future directions in protein DNA-binding site prediction. In particular, we detail the meta-analysis of protein DNA-binding sites. We also propose specific implications that are likely to result in novel prediction methods, increased performance, or practical applications.

  18. Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction.

    Science.gov (United States)

    Schmidt, Florian; Gasparoni, Nina; Gasparoni, Gilles; Gianmoena, Kathrin; Cadenas, Cristina; Polansky, Julia K; Ebert, Peter; Nordström, Karl; Barann, Matthias; Sinha, Anupam; Fröhler, Sebastian; Xiong, Jieyi; Dehghani Amirabad, Azim; Behjati Ardakani, Fatemeh; Hutter, Barbara; Zipprich, Gideon; Felder, Bärbel; Eils, Jürgen; Brors, Benedikt; Chen, Wei; Hengstler, Jan G; Hamann, Alf; Lengauer, Thomas; Rosenstiel, Philip; Walter, Jörn; Schulz, Marcel H

    2017-01-09

    The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  19. Enzyme binding selectivity prediction: alpha-thrombin vs trypsin inhibition.

    Science.gov (United States)

    Mlinsek, G; Novic, M; Kotnik, M; Solmajer, T

    2004-01-01

    In the present work we explore the possibility of an in-depth computational analysis of available experimental X-ray structures in the specific case of a series of alpha-thrombin and trypsin complexes with their respective inhibitors for the development of a novel scoring function based on molecular electrostatic potential computed at the contact surface in the enzyme-inhibitor molecular complex. We subsequently employ the chemometrical approach to determine which are the interactions in the large volume of data that determine the resulting experimental binding constant between ligand and receptor. The results of the model evaluated with molecules in the independent validation set show that a reasonable average error of 1.30 log units of the difference between experimental and calculated binding constants was achieved in the system thrombin-trypsin, which is comparable with those of methods from the literature. Furthermore, by a careful preparation of the Kohonen top layer in the artificial neural network approach that is normally perceived as a "black box device", we have been able to follow the implications of the structure of the inhibitor-enzyme complex for the inhibitor's binding constant. The method appears to be suitable for evaluation of selectivity in structurally similar enzymatic systems, which is currently an important problem in drug design. Copyright 2004 American Chemical Society

  20. Genome-wide prediction, display and refinement of binding sites with information theory-based models

    Directory of Open Access Journals (Sweden)

    Leeder J Steven

    2003-09-01

    Full Text Available Abstract Background We present Delila-genome, a software system for identification, visualization and analysis of protein binding sites in complete genome sequences. Binding sites are predicted by scanning genomic sequences with information theory-based (or user-defined weight matrices. Matrices are refined by adding experimentally-defined binding sites to published binding sites. Delila-Genome was used to examine the accuracy of individual information contents of binding sites detected with refined matrices as a measure of the strengths of the corresponding protein-nucleic acid interactions. The software can then be used to predict novel sites by rescanning the genome with the refined matrices. Results Parameters for genome scans are entered using a Java-based GUI interface and backend scripts in Perl. Multi-processor CPU load-sharing minimized the average response time for scans of different chromosomes. Scans of human genome assemblies required 4–6 hours for transcription factor binding sites and 10–19 hours for splice sites, respectively, on 24- and 3-node Mosix and Beowulf clusters. Individual binding sites are displayed either as high-resolution sequence walkers or in low-resolution custom tracks in the UCSC genome browser. For large datasets, we applied a data reduction strategy that limited displays of binding sites exceeding a threshold information content to specific chromosomal regions within or adjacent to genes. An HTML document is produced listing binding sites ranked by binding site strength or chromosomal location hyperlinked to the UCSC custom track, other annotation databases and binding site sequences. Post-genome scan tools parse binding site annotations of selected chromosome intervals and compare the results of genome scans using different weight matrices. Comparisons of multiple genome scans can display binding sites that are unique to each scan and identify sites with significantly altered binding strengths

  1. Strength in numbers: achieving greater accuracy in MHC-I binding prediction by combining the results from multiple prediction tools

    Science.gov (United States)

    Trost, Brett; Bickis, Mik; Kusalik, Anthony

    2007-01-01

    Background Peptides derived from endogenous antigens can bind to MHC class I molecules. Those which bind with high affinity can invoke a CD8+ immune response, resulting in the destruction of infected cells. Much work in immunoinformatics has involved the algorithmic prediction of peptide binding affinity to various MHC-I alleles. A number of tools for MHC-I binding prediction have been developed, many of which are available on the web. Results We hypothesize that peptides predicted by a number of tools are more likely to bind than those predicted by just one tool, and that the likelihood of a particular peptide being a binder is related to the number of tools that predict it, as well as the accuracy of those tools. To this end, we have built and tested a heuristic-based method of making MHC-binding predictions by combining the results from multiple tools. The predictive performance of each individual tool is first ascertained. These performance data are used to derive weights such that the predictions of tools with better accuracy are given greater credence. The combined tool was evaluated using ten-fold cross-validation and was found to signicantly outperform the individual tools when a high specificity threshold is used. It performs comparably well to the best-performing individual tools at lower specificity thresholds. Finally, it also outperforms the combination of the tools resulting from linear discriminant analysis. Conclusion A heuristic-based method of combining the results of the individual tools better facilitates the scanning of large proteomes for potential epitopes, yielding more actual high-affinity binders while reporting very few false positives. PMID:17381846

  2. Assessing the model transferability for prediction of transcription factor binding sites based on chromatin accessibility.

    Science.gov (United States)

    Liu, Sheng; Zibetti, Cristina; Wan, Jun; Wang, Guohua; Blackshaw, Seth; Qian, Jiang

    2017-07-27

    Computational prediction of transcription factor (TF) binding sites in different cell types is challenging. Recent technology development allows us to determine the genome-wide chromatin accessibility in various cellular and developmental contexts. The chromatin accessibility profiles provide useful information in prediction of TF binding events in various physiological conditions. Furthermore, ChIP-Seq analysis was used to determine genome-wide binding sites for a range of different TFs in multiple cell types. Integration of these two types of genomic information can improve the prediction of TF binding events. We assessed to what extent a model built upon on other TFs and/or other cell types could be used to predict the binding sites of TFs of interest. A random forest model was built using a set of cell type-independent features such as specific sequences recognized by the TFs and evolutionary conservation, as well as cell type-specific features derived from chromatin accessibility data. Our analysis suggested that the models learned from other TFs and/or cell lines performed almost as well as the model learned from the target TF in the cell type of interest. Interestingly, models based on multiple TFs performed better than single-TF models. Finally, we proposed a universal model, BPAC, which was generated using ChIP-Seq data from multiple TFs in various cell types. Integrating chromatin accessibility information with sequence information improves prediction of TF binding.The prediction of TF binding is transferable across TFs and/or cell lines suggesting there are a set of universal "rules". A computational tool was developed to predict TF binding sites based on the universal "rules".

  3. Poor correspondence between predicted and experimental binding of peptides to class I MHC molecules

    DEFF Research Database (Denmark)

    Andersen, Mads Hald; Tan, L.; Søndergaard, Ib

    2000-01-01

    Naturally processed peptides presented by class I major histocompatibility complex (MHC) molecules display a characteristic allele specific motif of two or more essential amino acid side chains, the so-called peptide anchor residues, in the context of an 8-10 amino acid long peptide. Knowledge...... of the peptide binding motif of individual class I MHC molecules permits the selection of potential peptide antigens from proteins of infectious organisms that could induce protective T-cell-mediated immunity. Several methods have been developed for the prediction of potential class I MHC binding peptides. One...... of peptide binding motifs for individual class I MHC molecules. The actual binding was compared with the results obtained when analyzing the same peptides by two well-known, publicly available computer algorithms. We conclude that there is no strong correlation between actual and predicted binding when using...

  4. Using TESS to predict transcription factor binding sites in DNA sequence.

    Science.gov (United States)

    Schug, Jonathan

    2008-03-01

    This unit describes how to use the Transcription Element Search System (TESS). This Web site predicts transcription factor binding sites (TFBS) in DNA sequence using two different kinds of models of sites, strings and positional weight matrices. The binding of transcription factors to DNA is a major part of the control of gene expression. Transcription factors exhibit sequence-specific binding; they form stronger bonds to some DNA sequences than to others. Identification of a good binding site in the promoter for a gene suggests the possibility that the corresponding factor may play a role in the regulation of that gene. However, the sequences transcription factors recognize are typically short and allow for some amount of mismatch. Because of this, binding sites for a factor can typically be found at random every few hundred to a thousand base pairs. TESS has features to help sort through and evaluate the significance of predicted sites.

  5. CYP 2D6 Binding Affinity Predictions Using Multiple Ligand and Protein Conformations

    Directory of Open Access Journals (Sweden)

    Lovorka Perić-Hassler

    2013-12-01

    Full Text Available Because of the large flexibility and malleability of Cytochrome P450 enzymes (CYPs, in silico prediction of CYP binding affinities to drugs and other xenobiotic compounds is a true challenge. In the current work, we use an iterative linear interaction energy (LIE approach to compute CYP binding affinities from molecular dynamics (MD simulation. In order to improve sampling of conformational space, we combine results from simulations starting with different relevant protein-ligand geometries. For calculated binding free energies of a set of thiourea compounds binding to the flexible CYP 2D6 isoform, improved correlation with experiment was obtained by combining results of MD runs starting from distinct protein conformations and ligand-binding orientations. This accuracy was obtained from relatively short MD simulations, which makes our approach computationally attractive for automated calculations of ligand-binding affinities to flexible proteins such as CYPs.

  6. Prediction of HLA-A2 binding peptides using Bayesian network.

    Science.gov (United States)

    Astakhov, Vadim; Cherkasov, Artem

    2005-10-11

    Prediction of peptides binding to HLA (human leukocyte antigen) finds application in peptide vaccine design. A number of statistical and structural models have been developed in recent years for HLA binding peptide prediction. However, a Bayesian Network (BNT) model is not available. In this study we describe a BNT model for HLA-A2 binding peptide prediction. It has been demonstrated that the BNT model allows up to 99 % accurate identification of the HLA-A2 binding peptides and provides similar prediction accuracy compared to HMM (Hidden Markov Model) and ANN (Artificial Neural Network). At the same time, it has been shown that the BNT has that advantage that it allows more accurate performance for smaller sets of empirical data compared to the HMM and the ANN methods. When the size of the training set has been reduced to 40% from the original data, the identification of the HLA-A2 binding peptides by the BNT, ANN and HMM methods produced ARoc (area under receiver operating characteristic) values 0.88, 0.85, 0.85 respectively. The results of the work demonstrate certain advantages of using the Bayesian Networks in predicting the HLA binding peptides using smaller datasets.

  7. Prediction of small molecule binding property of protein domains with Bayesian classifiers based on Markov chains.

    Science.gov (United States)

    Bulashevska, Alla; Stein, Martin; Jackson, David; Eils, Roland

    2009-12-01

    Accurate computational methods that can help to predict biological function of a protein from its sequence are of great interest to research biologists and pharmaceutical companies. One approach to assume the function of proteins is to predict the interactions between proteins and other molecules. In this work, we propose a machine learning method that uses a primary sequence of a domain to predict its propensity for interaction with small molecules. By curating the Pfam database with respect to the small molecule binding ability of its component domains, we have constructed a dataset of small molecule binding and non-binding domains. This dataset was then used as training set to learn a Bayesian classifier, which should distinguish members of each class. The domain sequences of both classes are modelled with Markov chains. In a Jack-knife test, our classification procedure achieved the predictive accuracies of 77.2% and 66.7% for binding and non-binding classes respectively. We demonstrate the applicability of our classifier by using it to identify previously unknown small molecule binding domains. Our predictions are available as supplementary material and can provide very useful information to drug discovery specialists. Given the ubiquitous and essential role small molecules play in biological processes, our method is important for identifying pharmaceutically relevant components of complete proteomes. The software is available from the author upon request.

  8. Prediction of peptide binding to a major histocompatibility complex class I molecule based on docking simulation

    Science.gov (United States)

    Ishikawa, Takeshi

    2016-10-01

    Binding between major histocompatibility complex (MHC) class I molecules and immunogenic epitopes is one of the most important processes for cell-mediated immunity. Consequently, computational prediction of amino acid sequences of MHC class I binding peptides from a given sequence may lead to important biomedical advances. In this study, an efficient structure-based method for predicting peptide binding to MHC class I molecules was developed, in which the binding free energy of the peptide was evaluated by two individual docking simulations. An original penalty function and restriction of degrees of freedom were determined by analysis of 361 published X-ray structures of the complex and were then introduced into the docking simulations. To validate the method, calculations using a 50-amino acid sequence as a prediction target were performed. In 27 calculations, the binding free energy of the known peptide was within the top 5 of 166 peptides generated from the 50-amino acid sequence. Finally, demonstrative calculations using a whole sequence of a protein as a prediction target were performed. These data clearly demonstrate high potential of this method for predicting peptide binding to MHC class I molecules.

  9. Predicting peptides binding to MHC class II molecules using multi-objective evolutionary algorithms

    Directory of Open Access Journals (Sweden)

    Feng Lin

    2007-11-01

    Full Text Available Abstract Background Peptides binding to Major Histocompatibility Complex (MHC class II molecules are crucial for initiation and regulation of immune responses. Predicting peptides that bind to a specific MHC molecule plays an important role in determining potential candidates for vaccines. The binding groove in class II MHC is open at both ends, allowing peptides longer than 9-mer to bind. Finding the consensus motif facilitating the binding of peptides to a MHC class II molecule is difficult because of different lengths of binding peptides and varying location of 9-mer binding core. The level of difficulty increases when the molecule is promiscuous and binds to a large number of low affinity peptides. In this paper, we propose two approaches using multi-objective evolutionary algorithms (MOEA for predicting peptides binding to MHC class II molecules. One uses the information from both binders and non-binders for self-discovery of motifs. The other, in addition, uses information from experimentally determined motifs for guided-discovery of motifs. Results The proposed methods are intended for finding peptides binding to MHC class II I-Ag7 molecule – a promiscuous binder to a large number of low affinity peptides. Cross-validation results across experiments on two motifs derived for I-Ag7 datasets demonstrate better generalization abilities and accuracies of the present method over earlier approaches. Further, the proposed method was validated and compared on two publicly available benchmark datasets: (1 an ensemble of qualitative HLA-DRB1*0401 peptide data obtained from five different sources, and (2 quantitative peptide data obtained for sixteen different alleles comprising of three mouse alleles and thirteen HLA alleles. The proposed method outperformed earlier methods on most datasets, indicating that it is well suited for finding peptides binding to MHC class II molecules. Conclusion We present two MOEA-based algorithms for finding motifs

  10. Combined Approach of Patch-Surfer and PL-PatchSurfer for Protein-Ligand Binding Prediction in CSAR 2013 and 2014.

    Science.gov (United States)

    Zhu, Xiaolei; Shin, Woong-Hee; Kim, Hyungrae; Kihara, Daisuke

    2016-06-27

    The Community Structure-Activity Resource (CSAR) benchmark exercise provides a unique opportunity for researchers to objectively evaluate the performance of protein-ligand docking methods. Patch-Surfer and PL-PatchSurfer, molecular surface-based methods for predicting binding ligands of proteins developed in our group, were tested on both CSAR 2013 and 2014 benchmark exercises in combination with an empirical scoring function-based method, AutoDock, while we only participated in CSAR 2013 using Patch-Surfer. The prediction results for Phase 1 task in CSAR 2013 showed that Patch-Surfer was able to rank all the four designed binding proteins within top ranks, outperforming AutoDock Vina. In Phase 2 of 2013, PL-PatchSurfer correctly selected the correct ligand pose for two target proteins. PL-PatchSurfer performed reasonably well in ranking ligands according to their binding affinity and in selecting near-native ligand poses in 2013 Phase 3 and 2014 Phase 1, respectively, although AutoDock Vina showed better performance. Lastly, in the 2014 Phase 2 exercise, the PL-PatchSurfer scores computed for ligands to target protein pairs correlated well with their pIC50 values, which was better or comparable to results by other participants. Overall, our methods showed fairly good performance in CSAR 2013 and 2014. Unique characteristics of the methods are discussed in comparison with AutoDock.

  11. PRODIGY: a web server for predicting the binding affinity of protein-protein complexes.

    Science.gov (United States)

    Xue, Li C; Rodrigues, João Pglm; Kastritis, Panagiotis L; Bonvin, Alexandre Mjj; Vangone, Anna

    2016-12-01

    Gaining insights into the structural determinants of protein-protein interactions holds the key for a deeper understanding of biological functions, diseases and development of therapeutics. An important aspect of this is the ability to accurately predict the binding strength for a given protein-protein complex. Here we present PROtein binDIng enerGY prediction (PRODIGY), a web server to predict the binding affinity of protein-protein complexes from their 3D structure. The PRODIGY server implements our simple but highly effective predictive model based on intermolecular contacts and properties derived from non-interface surface. PRODIGY is freely available at: http://milou.science.uu.nl/services/PRODIGY CONTACT: a.m.j.j.bonvin@uu.nl, a.vangone@uu.nl. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  12. Contribution of Sequence Motif, Chromatin State, and DNA Structure Features to Predictive Models of Transcription Factor Binding in Yeast.

    Science.gov (United States)

    Tsai, Zing Tsung-Yeh; Shiu, Shin-Han; Tsai, Huai-Kuang

    2015-08-01

    Transcription factor (TF) binding is determined by the presence of specific sequence motifs (SM) and chromatin accessibility, where the latter is influenced by both chromatin state (CS) and DNA structure (DS) properties. Although SM, CS, and DS have been used to predict TF binding sites, a predictive model that jointly considers CS and DS has not been developed to predict either TF-specific binding or general binding properties of TFs. Using budding yeast as model, we found that machine learning classifiers trained with either CS or DS features alone perform better in predicting TF-specific binding compared to SM-based classifiers. In addition, simultaneously considering CS and DS further improves the accuracy of the TF binding predictions, indicating the highly complementary nature of these two properties. The contributions of SM, CS, and DS features to binding site predictions differ greatly between TFs, allowing TF-specific predictions and potentially reflecting different TF binding mechanisms. In addition, a "TF-agnostic" predictive model based on three DNA "intrinsic properties" (in silico predicted nucleosome occupancy, major groove geometry, and dinucleotide free energy) that can be calculated from genomic sequences alone has performance that rivals the model incorporating experiment-derived data. This intrinsic property model allows prediction of binding regions not only across TFs, but also across DNA-binding domain families with distinct structural folds. Furthermore, these predicted binding regions can help identify TF binding sites that have a significant impact on target gene expression. Because the intrinsic property model allows prediction of binding regions across DNA-binding domain families, it is TF agnostic and likely describes general binding potential of TFs. Thus, our findings suggest that it is feasible to establish a TF agnostic model for identifying functional regulatory regions in potentially any sequenced genome.

  13. Contribution of Sequence Motif, Chromatin State, and DNA Structure Features to Predictive Models of Transcription Factor Binding in Yeast.

    Directory of Open Access Journals (Sweden)

    Zing Tsung-Yeh Tsai

    2015-08-01

    Full Text Available Transcription factor (TF binding is determined by the presence of specific sequence motifs (SM and chromatin accessibility, where the latter is influenced by both chromatin state (CS and DNA structure (DS properties. Although SM, CS, and DS have been used to predict TF binding sites, a predictive model that jointly considers CS and DS has not been developed to predict either TF-specific binding or general binding properties of TFs. Using budding yeast as model, we found that machine learning classifiers trained with either CS or DS features alone perform better in predicting TF-specific binding compared to SM-based classifiers. In addition, simultaneously considering CS and DS further improves the accuracy of the TF binding predictions, indicating the highly complementary nature of these two properties. The contributions of SM, CS, and DS features to binding site predictions differ greatly between TFs, allowing TF-specific predictions and potentially reflecting different TF binding mechanisms. In addition, a "TF-agnostic" predictive model based on three DNA "intrinsic properties" (in silico predicted nucleosome occupancy, major groove geometry, and dinucleotide free energy that can be calculated from genomic sequences alone has performance that rivals the model incorporating experiment-derived data. This intrinsic property model allows prediction of binding regions not only across TFs, but also across DNA-binding domain families with distinct structural folds. Furthermore, these predicted binding regions can help identify TF binding sites that have a significant impact on target gene expression. Because the intrinsic property model allows prediction of binding regions across DNA-binding domain families, it is TF agnostic and likely describes general binding potential of TFs. Thus, our findings suggest that it is feasible to establish a TF agnostic model for identifying functional regulatory regions in potentially any sequenced genome.

  14. Deep convolutional neural networks for pan-specific peptide-MHC class I binding prediction.

    Science.gov (United States)

    Han, Youngmahn; Kim, Dongsup

    2017-12-28

    Computational scanning of peptide candidates that bind to a specific major histocompatibility complex (MHC) can speed up the peptide-based vaccine development process and therefore various methods are being actively developed. Recently, machine-learning-based methods have generated successful results by training large amounts of experimental data. However, many machine learning-based methods are generally less sensitive in recognizing locally-clustered interactions, which can synergistically stabilize peptide binding. Deep convolutional neural network (DCNN) is a deep learning method inspired by visual recognition process of animal brain and it is known to be able to capture meaningful local patterns from 2D images. Once the peptide-MHC interactions can be encoded into image-like array(ILA) data, DCNN can be employed to build a predictive model for peptide-MHC binding prediction. In this study, we demonstrated that DCNN is able to not only reliably predict peptide-MHC binding, but also sensitively detect locally-clustered interactions. Nonapeptide-HLA-A and -B binding data were encoded into ILA data. A DCNN, as a pan-specific prediction model, was trained on the ILA data. The DCNN showed higher performance than other prediction tools for the latest benchmark datasets, which consist of 43 datasets for 15 HLA-A alleles and 25 datasets for 10 HLA-B alleles. In particular, the DCNN outperformed other tools for alleles belonging to the HLA-A3 supertype. The F1 scores of the DCNN were 0.86, 0.94, and 0.67 for HLA-A*31:01, HLA-A*03:01, and HLA-A*68:01 alleles, respectively, which were significantly higher than those of other tools. We found that the DCNN was able to recognize locally-clustered interactions that could synergistically stabilize peptide binding. We developed ConvMHC, a web server to provide user-friendly web interfaces for peptide-MHC class I binding predictions using the DCNN. ConvMHC web server can be accessible via http://jumong.kaist.ac.kr:8080/convmhc

  15. FunFOLDQA: a quality assessment tool for protein-ligand binding site residue predictions.

    Directory of Open Access Journals (Sweden)

    Daniel B Roche

    Full Text Available The estimation of prediction quality is important because without quality measures, it is difficult to determine the usefulness of a prediction. Currently, methods for ligand binding site residue predictions are assessed in the function prediction category of the biennial Critical Assessment of Techniques for Protein Structure Prediction (CASP experiment, utilizing the Matthews Correlation Coefficient (MCC and Binding-site Distance Test (BDT metrics. However, the assessment of ligand binding site predictions using such metrics requires the availability of solved structures with bound ligands. Thus, we have developed a ligand binding site quality assessment tool, FunFOLDQA, which utilizes protein feature analysis to predict ligand binding site quality prior to the experimental solution of the protein structures and their ligand interactions. The FunFOLDQA feature scores were combined using: simple linear combinations, multiple linear regression and a neural network. The neural network produced significantly better results for correlations to both the MCC and BDT scores, according to Kendall's τ, Spearman's ρ and Pearson's r correlation coefficients, when tested on both the CASP8 and CASP9 datasets. The neural network also produced the largest Area Under the Curve score (AUC when Receiver Operator Characteristic (ROC analysis was undertaken for the CASP8 dataset. Furthermore, the FunFOLDQA algorithm incorporating the neural network, is shown to add value to FunFOLD, when both methods are employed in combination. This results in a statistically significant improvement over all of the best server methods, the FunFOLD method (6.43%, and one of the top manual groups (FN293 tested on the CASP8 dataset. The FunFOLDQA method was also found to be competitive with the top server methods when tested on the CASP9 dataset. To the best of our knowledge, FunFOLDQA is the first attempt to develop a method that can be used to assess ligand binding site

  16. PatchSurfers: Two methods for local molecular property-based binding ligand prediction.

    Science.gov (United States)

    Shin, Woong-Hee; Bures, Mark Gregory; Kihara, Daisuke

    2016-01-15

    Protein function prediction is an active area of research in computational biology. Function prediction can help biologists make hypotheses for characterization of genes and help interpret biological assays, and thus is a productive area for collaboration between experimental and computational biologists. Among various function prediction methods, predicting binding ligand molecules for a target protein is an important class because ligand binding events for a protein are usually closely intertwined with the proteins' biological function, and also because predicted binding ligands can often be directly tested by biochemical assays. Binding ligand prediction methods can be classified into two types: those which are based on protein-protein (or pocket-pocket) comparison, and those that compare a target pocket directly to ligands. Recently, our group proposed two computational binding ligand prediction methods, Patch-Surfer, which is a pocket-pocket comparison method, and PL-PatchSurfer, which compares a pocket to ligand molecules. The two programs apply surface patch-based descriptions to calculate similarity or complementarity between molecules. A surface patch is characterized by physicochemical properties such as shape, hydrophobicity, and electrostatic potentials. These properties on the surface are represented using three-dimensional Zernike descriptors (3DZD), which are based on a series expansion of a 3 dimensional function. Utilizing 3DZD for describing the physicochemical properties has two main advantages: (1) rotational invariance and (2) fast comparison. Here, we introduce Patch-Surfer and PL-PatchSurfer with an emphasis on PL-PatchSurfer, which is more recently developed. Illustrative examples of PL-PatchSurfer performance on binding ligand prediction as well as virtual drug screening are also provided. Copyright © 2015 Elsevier Inc. All rights reserved.

  17. Improving peptide-MHC class I binding prediction for unbalanced datasets

    Directory of Open Access Journals (Sweden)

    Tomaras Georgia D

    2008-09-01

    Full Text Available Abstract Background Establishment of peptide binding to Major Histocompatibility Complex class I (MHCI is a crucial step in the development of subunit vaccines and prediction of such binding could greatly reduce costs and accelerate the experimental process of identifying immunogenic peptides. Many methods have been applied to the prediction of peptide-MHCI binding, with some achieving outstanding performance. Because of the experimental methods used to measure binding or affinity between peptides and MHCI molecules, however, available datasets are enriched for nonbinders, and thus highly unbalanced. Although there is no consensus on the ideal class distribution for training sets, extremely unbalanced datasets can be detrimental to the performance of prediction algorithms. Results We have developed a decision-theoretic framework to construct cost-sensitive trees to predict peptide-MHCI binding and have used them to 1 Assess the impact of the training data's class distribution on classifier accuracy, and 2 Compare resampling and cost-sensitive methods as approaches to compensate for training data imbalance. Our results confirm that highly unbalanced training sets can reduce the accuracy of classifier predictions and show that, in the peptide-MHCI binding context, resampling methods do not improve the classifier performance. In contrast, cost-sensitive methods significantly improve accuracy of decision trees. Finally, we propose the use of a training scheme that, when the training set is enriched for nonbinders, consistently improves the overall classifier accuracy compared to cost-insensitive classifiers and, in particular, increases the sensitivity of the classifiers. This method minimizes the expected classification cost for large datasets. Conclusion Our method consistently improves the performance of decision trees in predicting peptide-MHC class I binding by using cost-balancing techniques to compensate for the imbalance in the training

  18. SAAMBE: Webserver to Predict the Charge of Binding Free Energy Caused by Amino Acids Mutations.

    Science.gov (United States)

    Petukh, Marharyta; Dai, Luogeng; Alexov, Emil

    2016-04-12

    Predicting the effect of amino acid substitutions on protein-protein affinity (typically evaluated via the change of protein binding free energy) is important for both understanding the disease-causing mechanism of missense mutations and guiding protein engineering. In addition, researchers are also interested in understanding which energy components are mostly affected by the mutation and how the mutation affects the overall structure of the corresponding protein. Here we report a webserver, the Single Amino Acid Mutation based change in Binding free Energy (SAAMBE) webserver, which addresses the demand for tools for predicting the change of protein binding free energy. SAAMBE is an easy to use webserver, which only requires that a coordinate file be inputted and the user is provided with various, but easy to navigate, options. The user specifies the mutation position, wild type residue and type of mutation to be made. The server predicts the binding free energy change, the changes of the corresponding energy components and provides the energy minimized 3D structure of the wild type and mutant proteins for download. The SAAMBE protocol performance was tested by benchmarking the predictions against over 1300 experimentally determined changes of binding free energy and a Pearson correlation coefficient of 0.62 was obtained. How the predictions can be used for discriminating disease-causing from harmless mutations is discussed. The webserver can be accessed via http://compbio.clemson.edu/saambe_webserver/.

  19. A web server for analysis, comparison and prediction of protein ligand binding sites.

    Science.gov (United States)

    Singh, Harinder; Srivastava, Hemant Kumar; Raghava, Gajendra P S

    2016-03-25

    One of the major challenges in the field of system biology is to understand the interaction between a wide range of proteins and ligands. In the past, methods have been developed for predicting binding sites in a protein for a limited number of ligands. In order to address this problem, we developed a web server named 'LPIcom' to facilitate users in understanding protein-ligand interaction. Analysis, comparison and prediction modules are available in the "LPIcom' server to predict protein-ligand interacting residues for 824 ligands. Each ligand must have at least 30 protein binding sites in PDB. Analysis module of the server can identify residues preferred in interaction and binding motif for a given ligand; for example residues glycine, lysine and arginine are preferred in ATP binding sites. Comparison module of the server allows comparing protein-binding sites of multiple ligands to understand the similarity between ligands based on their binding site. This module indicates that ATP, ADP and GTP ligands are in the same cluster and thus their binding sites or interacting residues exhibit a high level of similarity. Propensity-based prediction module has been developed for predicting ligand-interacting residues in a protein for more than 800 ligands. In addition, a number of web-based tools have been integrated to facilitate users in creating web logo and two-sample between ligand interacting and non-interacting residues. In summary, this manuscript presents a web-server for analysis of ligand interacting residue. This server is available for public use from URL http://crdd.osdd.net/raghava/lpicom .

  20. NetMHCpan, a method for MHC class I binding prediction beyond humans

    DEFF Research Database (Denmark)

    Hoof, Ilka; Peters, B; Sidney, J

    2009-01-01

    immunologists in interpreting cellular immune responses in large out-bred populations is demonstrated. Further, we used NetMHCpan-2.0 to predict potential binding peptides for the pig MHC class I molecule SLA-1*0401. Ninety-three percent of the predicted peptides were demonstrated to bind stronger than 500 n...... MHC molecule. The potentially unique specificity of the majority of HLA alleles that have been identified to date remains uncharacterized. Likewise, only a limited number of chimpanzee and rhesus macaque MHC class I molecules have been characterized experimentally. Here, we present NetMHCpan-2.......0, a method that generates quantitative predictions of the affinity of any peptide-MHC class I interaction. NetMHCpan-2.0 has been trained on the hitherto largest set of quantitative MHC binding data available, covering HLA-A and HLA-B, as well as chimpanzee, rhesus macaque, gorilla, and mouse MHC class I...

  1. Cell-type specificity of ChIP-predicted transcription factor binding sites

    Directory of Open Access Journals (Sweden)

    Håndstad Tony

    2012-08-01

    Full Text Available Abstract Background Context-dependent transcription factor (TF binding is one reason for differences in gene expression patterns between different cellular states. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq identifies genome-wide TF binding sites for one particular context—the cells used in the experiment. But can such ChIP-seq data predict TF binding in other cellular contexts and is it possible to distinguish context-dependent from ubiquitous TF binding? Results We compared ChIP-seq data on TF binding for multiple TFs in two different cell types and found that on average only a third of ChIP-seq peak regions are common to both cell types. Expectedly, common peaks occur more frequently in certain genomic contexts, such as CpG-rich promoters, whereas chromatin differences characterize cell-type specific TF binding. We also find, however, that genotype differences between the cell types can explain differences in binding. Moreover, ChIP-seq signal intensity and peak clustering are the strongest predictors of common peaks. Compared with strong peaks located in regions containing peaks for multiple transcription factors, weak and isolated peaks are less common between the cell types and are less associated with data that indicate regulatory activity. Conclusions Together, the results suggest that experimental noise is prevalent among weak peaks, whereas strong and clustered peaks represent high-confidence binding events that often occur in other cellular contexts. Nevertheless, 30-40% of the strongest and most clustered peaks show context-dependent regulation. We show that by combining signal intensity with additional data—ranging from context independent information such as binding site conservation and position weight matrix scores to context dependent chromatin structure—we can predict whether a ChIP-seq peak is likely to be present in other cellular contexts.

  2. MHC2NNZ: A novel peptide binding prediction approach for HLA DQ molecules

    Science.gov (United States)

    Xie, Jiang; Zeng, Xu; Lu, Dongfang; Liu, Zhixiang; Wang, Jiao

    2017-07-01

    The major histocompatibility complex class II (MHC-II) molecule plays a crucial role in immunology. Computational prediction of MHC-II binding peptides can help researchers understand the mechanism of immune systems and design vaccines. Most of the prediction algorithms for MHC-II to date have made large efforts in human leukocyte antigen (HLA, the name of MHC in Human) molecules encoded in the DR locus. However, HLA DQ molecules are equally important and have only been made less progress because it is more difficult to handle them experimentally. In this study, we propose an artificial neural network-based approach called MHC2NNZ to predict peptides binding to HLA DQ molecules. Unlike previous artificial neural network-based methods, MHC2NNZ not only considers sequence similarity features but also captures the chemical and physical properties, and a novel method incorporating these properties is proposed to represent peptide flanking regions (PFR). Furthermore, MHC2NNZ improves the prediction accuracy by combining with amino acid preference at more specific positions of the peptides binding core. By evaluating on 3549 peptides binding to six most frequent HLA DQ molecules, MHC2NNZ is demonstrated to outperform other state-of-the-art MHC-II prediction methods.

  3. Residue propensities, discrimination and binding site prediction of adenine and guanine phosphates

    Directory of Open Access Journals (Sweden)

    Ahmad Zulfiqar

    2011-05-01

    Full Text Available Abstract Background Adenine and guanine phosphates are involved in a number of biological processes such as cell signaling, metabolism and enzymatic cofactor functions. Binding sites in proteins for these ligands are often detected by looking for a previously known motif by alignment based search. This is likely to miss those where a similar binding site has not been previously characterized and when the binding sites do not follow the rule described by predefined motif. Also, it is intriguing how proteins select between adenine and guanine derivative with high specificity. Results Residue preferences for AMP, GMP, ADP, GDP, ATP and GTP have been investigated in details with additional comparison with cyclic variants cAMP and cGMP. We also attempt to predict residues interacting with these nucleotides using information derived from local sequence and evolutionary profiles. Results indicate that subtle differences exist between single residue preferences for specific nucleotides and taking neighbor environment and evolutionary context into account, successful models of their binding site prediction can be developed. Conclusion In this work, we explore how single amino acid propensities for these nucleotides play a role in the affinity and specificity of this set of nucleotides. This is expected to be helpful in identifying novel binding sites for adenine and guanine phosphates, especially when a known binding motif is not detectable.

  4. Structural insights into Cydia pomonella pheromone binding protein 2 mediated prediction of potentially active semiochemicals

    Science.gov (United States)

    Tian, Zhen; Liu, Jiyuan; Zhang, Yalin

    2016-03-01

    Given the advantages of behavioral disruption application in pest control and the damage of Cydia pomonella, due progresses have not been made in searching active semiochemicals for codling moth. In this research, 31 candidate semiochemicals were ranked for their binding potential to Cydia pomonella pheromone binding protein 2 (CpomPBP2) by simulated docking, and this sorted result was confirmed by competitive binding assay. This high predicting accuracy of virtual screening led to the construction of a rapid and viable method for semiochemicals searching. By reference to binding mode analyses, hydrogen bond and hydrophobic interaction were suggested to be two key factors in determining ligand affinity, so is the length of molecule chain. So it is concluded that semiochemicals of appropriate chain length with hydroxyl group or carbonyl group at one head tended to be favored by CpomPBP2. Residues involved in binding with each ligand were pointed out as well, which were verified by computational alanine scanning mutagenesis. Progress made in the present study helps establish an efficient method for predicting potentially active compounds and prepares for the application of high-throughput virtual screening in searching semiochemicals by taking insights into binding mode analyses.

  5. HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features

    Directory of Open Access Journals (Sweden)

    Rianon Zaman

    2017-01-01

    Full Text Available DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature.

  6. A novel method for improved accuracy of transcription factor binding site prediction

    KAUST Repository

    Khamis, Abdullah M.

    2018-03-20

    Identifying transcription factor (TF) binding sites (TFBSs) is important in the computational inference of gene regulation. Widely used computational methods of TFBS prediction based on position weight matrices (PWMs) usually have high false positive rates. Moreover, computational studies of transcription regulation in eukaryotes frequently require numerous PWM models of TFBSs due to a large number of TFs involved. To overcome these problems we developed DRAF, a novel method for TFBS prediction that requires only 14 prediction models for 232 human TFs, while at the same time significantly improves prediction accuracy. DRAF models use more features than PWM models, as they combine information from TFBS sequences and physicochemical properties of TF DNA-binding domains into machine learning models. Evaluation of DRAF on 98 human ChIP-seq datasets shows on average 1.54-, 1.96- and 5.19-fold reduction of false positives at the same sensitivities compared to models from HOCOMOCO, TRANSFAC and DeepBind, respectively. This observation suggests that one can efficiently replace the PWM models for TFBS prediction by a small number of DRAF models that significantly improve prediction accuracy. The DRAF method is implemented in a web tool and in a stand-alone software freely available at http://cbrc.kaust.edu.sa/DRAF.

  7. Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms

    Science.gov (United States)

    Jian, Jhih-Wei; Elumalai, Pavadai; Pitti, Thejkiran; Wu, Chih Yuan; Tsai, Keng-Chang; Chang, Jeng-Yih; Peng, Hung-Pin; Yang, An-Suei

    2016-01-01

    Predicting ligand binding sites (LBSs) on protein structures, which are obtained either from experimental or computational methods, is a useful first step in functional annotation or structure-based drug design for the protein structures. In this work, the structure-based machine learning algorithm ISMBLab-LIG was developed to predict LBSs on protein surfaces with input attributes derived from the three-dimensional probability density maps of interacting atoms, which were reconstructed on the query protein surfaces and were relatively insensitive to local conformational variations of the tentative ligand binding sites. The prediction accuracy of the ISMBLab-LIG predictors is comparable to that of the best LBS predictors benchmarked on several well-established testing datasets. More importantly, the ISMBLab-LIG algorithm has substantial tolerance to the prediction uncertainties of computationally derived protein structure models. As such, the method is particularly useful for predicting LBSs not only on experimental protein structures without known LBS templates in the database but also on computationally predicted model protein structures with structural uncertainties in the tentative ligand binding sites. PMID:27513851

  8. Description and prediction of peptide-MHC binding: the 'human MHC project'

    DEFF Research Database (Denmark)

    Buus, S

    1999-01-01

    MHC molecules are crucially involved in controlling the specific immune system. They are highly polymorphic receptors sampling peptides from the cellular environment and presenting these peptides for scrutiny by immune cells. Recent advances in combinatorial peptide chemistry have improved...... the description and prediction of peptide-MHC binding. It is envisioned that a complete mapping of human immune reactivities will be possible....

  9. PRODIGY : a web server for predicting the binding affinity of protein-protein complexes

    NARCIS (Netherlands)

    Xue, Li; Garcia Lopes Maia Rodrigues, João; Kastritis, Panagiotis L; Bonvin, Alexandre Mjj; Vangone, Anna

    2016-01-01

    Gaining insights into the structural determinants of protein-protein interactions holds the key for a deeper understanding of biological functions, diseases and development of therapeutics. An important aspect of this is the ability to accurately predict the binding strength for a given

  10. Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy.

    Science.gov (United States)

    Zou, Quan; Wan, Shixiang; Ju, Ying; Tang, Jijun; Zeng, Xiangxiang

    2016-12-23

    It is necessary and essential to discovery protein function from the novel primary sequences. Wet lab experimental procedures are not only time-consuming, but also costly, so predicting protein structure and function reliably based only on amino acid sequence has significant value. TATA-binding protein (TBP) is a kind of DNA binding protein, which plays a key role in the transcription regulation. Our study proposed an automatic approach for identifying TATA-binding proteins efficiently, accurately, and conveniently. This method would guide for the special protein identification with computational intelligence strategies. Firstly, we proposed novel fingerprint features for TBP based on pseudo amino acid composition, physicochemical properties, and secondary structure. Secondly, hierarchical features dimensionality reduction strategies were employed to improve the performance furthermore. Currently, Pretata achieves 92.92% TATA-binding protein prediction accuracy, which is better than all other existing methods. The experiments demonstrate that our method could greatly improve the prediction accuracy and speed, thus allowing large-scale NGS data prediction to be practical. A web server is developed to facilitate the other researchers, which can be accessed at http://server.malab.cn/preTata/ .

  11. Major histocompatibility complex class I binding predictions as a tool in epitope discovery

    DEFF Research Database (Denmark)

    Lundegaard, Claus; Lund, Ole; Buus, Søren

    2010-01-01

    Over the last decade, in silico models of the major histocompatibility complex (MHC) class I pathway have developed significantly. Before, peptide binding could only be reliably modelled for a few major human or mouse histocompatibility molecules; now, high-accuracy predictions are available...

  12. Electrostatics, structure prediction, and the energy landscapes for protein folding and binding.

    Science.gov (United States)

    Tsai, Min-Yeh; Zheng, Weihua; Balamurugan, D; Schafer, Nicholas P; Kim, Bobby L; Cheung, Margaret S; Wolynes, Peter G

    2016-01-01

    While being long in range and therefore weakly specific, electrostatic interactions are able to modulate the stability and folding landscapes of some proteins. The relevance of electrostatic forces for steering the docking of proteins to each other is widely acknowledged, however, the role of electrostatics in establishing specifically funneled landscapes and their relevance for protein structure prediction are still not clear. By introducing Debye-Hückel potentials that mimic long-range electrostatic forces into the Associative memory, Water mediated, Structure, and Energy Model (AWSEM), a transferable protein model capable of predicting tertiary structures, we assess the effects of electrostatics on the landscapes of thirteen monomeric proteins and four dimers. For the monomers, we find that adding electrostatic interactions does not improve structure prediction. Simulations of ribosomal protein S6 show, however, that folding stability depends monotonically on electrostatic strength. The trend in predicted melting temperatures of the S6 variants agrees with experimental observations. Electrostatic effects can play a range of roles in binding. The binding of the protein complex KIX-pKID is largely assisted by electrostatic interactions, which provide direct charge-charge stabilization of the native state and contribute to the funneling of the binding landscape. In contrast, for several other proteins, including the DNA-binding protein FIS, electrostatics causes frustration in the DNA-binding region, which favors its binding with DNA but not with its protein partner. This study highlights the importance of long-range electrostatics in functional responses to problems where proteins interact with their charged partners, such as DNA, RNA, as well as membranes. © 2015 The Protein Society.

  13. Automatic generation of bioinformatics tools for predicting protein-ligand binding sites.

    Science.gov (United States)

    Komiyama, Yusuke; Banno, Masaki; Ueki, Kokoro; Saad, Gul; Shimizu, Kentaro

    2016-03-15

    Predictive tools that model protein-ligand binding on demand are needed to promote ligand research in an innovative drug-design environment. However, it takes considerable time and effort to develop predictive tools that can be applied to individual ligands. An automated production pipeline that can rapidly and efficiently develop user-friendly protein-ligand binding predictive tools would be useful. We developed a system for automatically generating protein-ligand binding predictions. Implementation of this system in a pipeline of Semantic Web technique-based web tools will allow users to specify a ligand and receive the tool within 0.5-1 day. We demonstrated high prediction accuracy for three machine learning algorithms and eight ligands. The source code and web application are freely available for download at http://utprot.net They are implemented in Python and supported on Linux. shimizu@bi.a.u-tokyo.ac.jp Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

  14. Prediction of MHC class II binding peptides based on an iterative learning model

    Science.gov (United States)

    Murugan, Naveen; Dai, Yang

    2005-01-01

    Background Prediction of the binding ability of antigen peptides to major histocompatibility complex (MHC) class II molecules is important in vaccine development. The variable length of each binding peptide complicates this prediction. Motivated by a text mining model designed for building a classifier from labeled and unlabeled examples, we have developed an iterative supervised learning model for the prediction of MHC class II binding peptides. Results A linear programming (LP) model was employed for the learning task at each iteration, since it is fast and can re-optimize the previous classifier when the training sets are altered. The performance of the new model has been evaluated with benchmark datasets. The outcome demonstrates that the model achieves an accuracy of prediction that is competitive compared to the advanced predictors (the Gibbs sampler and TEPITOPE). The average areas under the ROC curve obtained from one variant of our model are 0.753 and 0.715 for the original and homology reduced benchmark sets, respectively. The corresponding values are respectively 0.744 and 0.673 for the Gibbs sampler and 0.702 and 0.667 for TEPITOPE. Conclusion The iterative learning procedure appears to be effective in prediction of MHC class II binders. It offers an alternative approach to this important predictionproblem. PMID:16351712

  15. Automatic generation of bioinformatics tools for predicting protein–ligand binding sites

    Science.gov (United States)

    Banno, Masaki; Ueki, Kokoro; Saad, Gul; Shimizu, Kentaro

    2016-01-01

    Motivation: Predictive tools that model protein–ligand binding on demand are needed to promote ligand research in an innovative drug-design environment. However, it takes considerable time and effort to develop predictive tools that can be applied to individual ligands. An automated production pipeline that can rapidly and efficiently develop user-friendly protein–ligand binding predictive tools would be useful. Results: We developed a system for automatically generating protein–ligand binding predictions. Implementation of this system in a pipeline of Semantic Web technique-based web tools will allow users to specify a ligand and receive the tool within 0.5–1 day. We demonstrated high prediction accuracy for three machine learning algorithms and eight ligands. Availability and implementation: The source code and web application are freely available for download at http://utprot.net. They are implemented in Python and supported on Linux. Contact: shimizu@bi.a.u-tokyo.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26545824

  16. Limitations of Ab Initio Predictions of Peptide Binding to MHC Class II Molecules

    DEFF Research Database (Denmark)

    Zhang, Hao; Lund, Ole; Nielsen, Morten

    2010-01-01

    Successful predictions of peptide MHC binding typically require a large set of binding data for the specific MHC molecule that is examined. Structure based prediction methods promise to circumvent this requirement by evaluating the physical contacts a peptide can make with an MHC molecule based...... on the highly conserved 3D structure of peptide:MHC complexes. While several such methods have been described before, most are not publicly available and have not been independently tested for their performance. We here implemented and evaluated three prediction methods for MHC class II molecules: statistical...... potentials derived from the analysis of known protein structures; energetic evaluation of different peptide snapshots in a molecular dynamics simulation; and direct analysis of contacts made in known 3D structures of peptide:MHC complexes. These methods are ab initio in that they require structural data...

  17. Prediction of Carbohydrate-Binding Proteins from Sequences Using Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Seizi Someya

    2010-01-01

    Full Text Available Carbohydrate-binding proteins are proteins that can interact with sugar chains but do not modify them. They are involved in many physiological functions, and we have developed a method for predicting them from their amino acid sequences. Our method is based on support vector machines (SVMs. We first clarified the definition of carbohydrate-binding proteins and then constructed positive and negative datasets with which the SVMs were trained. By applying the leave-one-out test to these datasets, our method delivered 0.92 of the area under the receiver operating characteristic (ROC curve. We also examined two amino acid grouping methods that enable effective learning of sequence patterns and evaluated the performance of these methods. When we applied our method in combination with the homology-based prediction method to the annotated human genome database, H-invDB, we found that the true positive rate of prediction was improved.

  18. Decoding ChIP-seq with a double-binding signal refines binding peaks to single-nucleotides and predicts cooperative interaction

    Science.gov (United States)

    Gomes, Antonio L.C.; Abeel, Thomas; Peterson, Matthew; Azizi, Elham; Lyubetskaya, Anna; Carvalho, Luís

    2014-01-01

    The comprehension of protein and DNA binding in vivo is essential to understand gene regulation. Chromatin immunoprecipitation followed by sequencing (ChIP-seq) provides a global map of the regulatory binding network. Most ChIP-seq analysis tools focus on identifying binding regions from coverage enrichment. However, less work has been performed to infer the physical and regulatory details inside the enriched regions. This research extends a previous blind-deconvolution approach to develop a post-peak–calling algorithm that improves binding site resolution and predicts cooperative interactions. At the core of our new method is a physically motivated model that characterizes the binding signal as an extreme value distribution. This model suggests a mathematical framework to study physical properties of DNA shearing from the ChIP-seq coverage. The model explains the ChIP-seq coverage with two signals: The first considers DNA fragments with only a single binding event, whereas the second considers fragments with two binding events (a double-binding signal). The model incorporates motif discovery and is able to detect multiple sites in an enriched region with single-nucleotide resolution, high sensitivity, and high specificity. Our method improves peak caller sensitivity, from less than 45% up to 94%, at a false positive rate ChIP-seq analysis: the identification of cooperative interaction. Predictions of known cooperative binding sites show a 0.85 area under an ROC curve. PMID:25024162

  19. Predicting the relative binding affinity of mineralocorticoid receptor antagonists by density functional methods

    Science.gov (United States)

    Roos, Katarina; Hogner, Anders; Ogg, Derek; Packer, Martin J.; Hansson, Eva; Granberg, Kenneth L.; Evertsson, Emma; Nordqvist, Anneli

    2015-12-01

    In drug discovery, prediction of binding affinity ahead of synthesis to aid compound prioritization is still hampered by the low throughput of the more accurate methods and the lack of general pertinence of one method that fits all systems. Here we show the applicability of a method based on density functional theory using core fragments and a protein model with only the first shell residues surrounding the core, to predict relative binding affinity of a matched series of mineralocorticoid receptor (MR) antagonists. Antagonists of MR are used for treatment of chronic heart failure and hypertension. Marketed MR antagonists, spironolactone and eplerenone, are also believed to be highly efficacious in treatment of chronic kidney disease in diabetes patients, but is contra-indicated due to the increased risk for hyperkalemia. These findings and a significant unmet medical need among patients with chronic kidney disease continues to stimulate efforts in the discovery of new MR antagonist with maintained efficacy but low or no risk for hyperkalemia. Applied on a matched series of MR antagonists the quantum mechanical based method gave an R2 = 0.76 for the experimental lipophilic ligand efficiency versus relative predicted binding affinity calculated with the M06-2X functional in gas phase and an R2 = 0.64 for experimental binding affinity versus relative predicted binding affinity calculated with the M06-2X functional including an implicit solvation model. The quantum mechanical approach using core fragments was compared to free energy perturbation calculations using the full sized compound structures.

  20. MULTIPRED2: A computational system for large-scale identification of peptides predicted to bind to HLA supertypes and alleles

    DEFF Research Database (Denmark)

    Zhang, Guang Lan; DeLuca, David S.; Keskin, Derin B.

    2011-01-01

    MULTIPRED2 is a computational system for facile prediction of peptide binding to multiple alleles belonging to human leukocyte antigen (HLA) class I and class II DR molecules. It enables prediction of peptide binding to products of individual HLA alleles, combination of alleles, or HLA supertypes...

  1. Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach

    DEFF Research Database (Denmark)

    Buus, S.; Lauemoller, S.L.; Worning, Peder

    2003-01-01

    We have generated Artificial Neural Networks (ANN) capable of performing sensitive, quantitative predictions of peptide binding to the MHC class I molecule, HLA-A*0204. We have shown that such quantitative ANN are superior to conventional classification ANN, that have been trained to predict bind...... of an iterative feedback loop whereby advanced, computational bioinformatics optimize experimental strategy, and vice versa....

  2. Predicting sequence and structural specificities of RNA binding regions recognized by splicing factor SRSF1

    Directory of Open Access Journals (Sweden)

    Wang Xin

    2011-12-01

    secondary structure play complementary roles during binding site recognition by SRSF1. Conclusion In this study, we presented a computational model to predict the sequence consensus and optimal RNA secondary structure for protein-RNA binding regions. The successful implementation on SRSF1 CLIP-seq data demonstrates great potential to improve our understanding on the binding specificity of RNA binding proteins.

  3. SCMHBP: prediction and analysis of heme binding proteins using propensity scores of dipeptides.

    Science.gov (United States)

    Liou, Yi-Fan; Charoenkwan, Phasit; Srinivasulu, Yerukala; Vasylenko, Tamara; Lai, Shih-Chung; Lee, Hua-Chin; Chen, Yi-Hsiung; Huang, Hui-Ling; Ho, Shinn-Ying

    2014-01-01

    Heme binding proteins (HBPs) are metalloproteins that contain a heme ligand (an iron-porphyrin complex) as the prosthetic group. Several computational methods have been proposed to predict heme binding residues and thereby to understand the interactions between heme and its host proteins. However, few in silico methods for identifying HBPs have been proposed. This work proposes a scoring card method (SCM) based method (named SCMHBP) for predicting and analyzing HBPs from sequences. A balanced dataset of 747 HBPs (selected using a Gene Ontology term GO:0020037) and 747 non-HBPs (selected from 91,414 putative non-HBPs) with an identity of 25% was firstly established. Consequently, a set of scores that quantified the propensity of amino acids and dipeptides to be HBPs is estimated using SCM to maximize the predictive accuracy of SCMHBP. Finally, the informative physicochemical properties of 20 amino acids are identified by utilizing the estimated propensity scores to be used to categorize HBPs. The training and mean test accuracies of SCMHBP applied to three independent test datasets are 85.90% and 71.57%, respectively. SCMHBP performs well relative to comparison with such methods as support vector machine (SVM), decision tree J48, and Bayes classifiers. The putative non-HBPs with high sequence propensity scores are potential HBPs, which can be further validated by experimental confirmation. The propensity scores of individual amino acids and dipeptides are examined to elucidate the interactions between heme and its host proteins. The following characteristics of HBPs are derived from the propensity scores: 1) aromatic side chains are important to the effectiveness of specific HBP functions; 2) a hydrophobic environment is important in the interaction between heme and binding sites; and 3) the whole HBP has low flexibility whereas the heme binding residues are relatively flexible. SCMHBP yields knowledge that improves our understanding of HBPs rather than merely

  4. Quantitative Prediction of Multivalent Ligand-Receptor Binding Affinities for Influenza, Cholera, and Anthrax Inhibition.

    Science.gov (United States)

    Liese, Susanne; Netz, Roland R

    2018-03-05

    Multivalency achieves strong, yet reversible binding by the simultaneous formation of multiple weak bonds. It is a key interaction principle in biology and promising for the synthesis of high-affinity inhibitors of pathogens. We present a molecular model for the binding affinity of synthetic multivalent ligands onto multivalent receptors consisting of n receptor units arranged on a regular polygon. Ligands consist of a geometrically matching rigid polygonal core to which monovalent ligand units are attached via flexible linker polymers, closely mimicking existing experimental designs. The calculated binding affinities quantitatively agree with experimental studies for cholera toxin ( n = 5) and anthrax receptor ( n = 7) and allow to predict optimal core size and optimal linker length. Maximal binding affinity is achieved for a core that matches the receptor size and for linkers that have an equilibrium end-to-end distance that is slightly longer than the geometric separation between ligand core and receptor sites. Linkers that are longer than optimal are greatly preferable compared to shorter linkers. The angular steric restriction between ligand unit and linker polymer is shown to be a key parameter. We construct an enhancement diagram that quantifies the multivalent binding affinity compared to monovalent ligands. We conclude that multivalent ligands against influenza viral hemagglutinin ( n = 3), cholera toxin ( n = 5), and anthrax receptor ( n = 7) can outperform monovalent ligands only for a monovalent ligand affinity that exceeds a core-size dependent threshold value. Thus, multivalent drug design needs to balance core size, linker length, as well as monovalent ligand unit affinity.

  5. Quantitative online prediction of peptide binding to the major histocompatibility complex.

    Science.gov (United States)

    Hattotuwagama, Channa K; Guan, Pingping; Doytchinova, Irini A; Zygouri, Christianna; Flower, Darren R

    2004-01-01

    With its implications for vaccine discovery, the accurate prediction of T cell epitopes is one of the key aspirations of computational vaccinology. We have developed a robust multivariate statistical method, based on partial least squares, for the quantitative prediction of peptide binding to major histocompatibility complexes (MHC), the principal checkpoint on the antigen presentation pathway. As a service to the immunobiology community, we have made a Perl implementation of the method available via a World Wide Web server. We call this server MHCPred. Access to the server is freely available from the URL: http://www.jenner.ac.uk/MHCPred. We have exemplified our method with a model for peptides binding to the common human MHC molecule HLA-B*3501.

  6. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

    DEFF Research Database (Denmark)

    Nielsen, Morten; Lund, Ole

    2009-01-01

    this binding event. RESULTS: Here, we present a novel artificial neural network-based method, NN-align that allows for simultaneous identification of the MHC class II binding core and binding affinity. NN-align is trained using a novel training algorithm that allows for correction of bias in the training data...... due to redundant binding core representation. Incorporation of information about the residues flanking the peptide-binding core is shown to significantly improve the prediction accuracy. The method is evaluated on a large-scale benchmark consisting of six independent data sets covering 14 human MHC...... class II alleles, and is demonstrated to outperform other state-of-the-art MHC class II prediction methods. CONCLUSION: The NN-align method is competitive with the state-of-the-art MHC class II peptide binding prediction algorithms. The method is publicly available at http://www.cbs.dtu.dk/services/Net...

  7. Prediction of protein-protein binding site by using core interface residue and support vector machine

    Directory of Open Access Journals (Sweden)

    Sun Zhonghua

    2008-12-01

    Full Text Available Abstract Background The prediction of protein-protein binding site can provide structural annotation to the protein interaction data from proteomics studies. This is very important for the biological application of the protein interaction data that is increasing rapidly. Moreover, methods for predicting protein interaction sites can also provide crucial information for improving the speed and accuracy of protein docking methods. Results In this work, we describe a binding site prediction method by designing a new residue neighbour profile and by selecting only the core-interface residues for SVM training. The residue neighbour profile includes both the sequential and the spatial neighbour residues of an interface residue, which is a more complete description of the physical and chemical characteristics surrounding the interface residue. The concept of core interface is applied in selecting the interface residues for training the SVM models, which is shown to result in better discrimination between the core interface and other residues. The best SVM model trained was tested on a test set of 50 randomly selected proteins. The sensitivity, specificity, and MCC for the prediction of the core interface residues were 60.6%, 53.4%, and 0.243, respectively. Our prediction results on this test set were compared with other three binding site prediction methods and found to perform better. Furthermore, our method was tested on the 101 unbound proteins from the protein-protein interaction benchmark v2.0. The sensitivity, specificity, and MCC of this test were 57.5%, 32.5%, and 0.168, respectively. Conclusion By improving both the descriptions of the interface residues and their surrounding environment and the training strategy, better SVM models were obtained and shown to outperform previous methods. Our tests on the unbound protein structures suggest further improvement is possible.

  8. On the prediction of DNA-binding proteins only from primary sequences: A deep learning approach.

    Directory of Open Access Journals (Sweden)

    Yu-Hui Qu

    Full Text Available DNA-binding proteins play pivotal roles in alternative splicing, RNA editing, methylating and many other biological functions for both eukaryotic and prokaryotic proteomes. Predicting the functions of these proteins from primary amino acids sequences is becoming one of the major challenges in functional annotations of genomes. Traditional prediction methods often devote themselves to extracting physiochemical features from sequences but ignoring motif information and location information between motifs. Meanwhile, the small scale of data volumes and large noises in training data result in lower accuracy and reliability of predictions. In this paper, we propose a deep learning based method to identify DNA-binding proteins from primary sequences alone. It utilizes two stages of convolutional neutral network to detect the function domains of protein sequences, and the long short-term memory neural network to identify their long term dependencies, an binary cross entropy to evaluate the quality of the neural networks. When the proposed method is tested with a realistic DNA binding protein dataset, it achieves a prediction accuracy of 94.2% at the Matthew's correlation coefficient of 0.961. Compared with the LibSVM on the arabidopsis and yeast datasets via independent tests, the accuracy raises by 9% and 4% respectively. Comparative experiments using different feature extraction methods show that our model performs similar accuracy with the best of others, but its values of sensitivity, specificity and AUC increase by 27.83%, 1.31% and 16.21% respectively. Those results suggest that our method is a promising tool for identifying DNA-binding proteins.

  9. Rapid and accurate prediction and scoring of water molecules in protein binding sites.

    Directory of Open Access Journals (Sweden)

    Gregory A Ross

    Full Text Available Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules. WaterDock was validated using data from X-ray crystallography, neutron diffraction and molecular dynamics simulations and correctly predicted 97% of the water molecules in the test set. In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. When applied to WaterDock predictions in the Astex Diverse Set of protein ligand complexes, we could identify whether a water molecule was conserved or displaced to an accuracy of 75%. A second model predicted whether water molecules were displaced by polar groups or by non-polar groups to an accuracy of 80%. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity.

  10. Rapid and Accurate Prediction and Scoring of Water Molecules in Protein Binding Sites

    Science.gov (United States)

    Ross, Gregory A.; Morris, Garrett M.; Biggin, Philip C.

    2012-01-01

    Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules. WaterDock was validated using data from X-ray crystallography, neutron diffraction and molecular dynamics simulations and correctly predicted 97% of the water molecules in the test set. In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. When applied to WaterDock predictions in the Astex Diverse Set of protein ligand complexes, we could identify whether a water molecule was conserved or displaced to an accuracy of 75%. A second model predicted whether water molecules were displaced by polar groups or by non-polar groups to an accuracy of 80%. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity. PMID:22396746

  11. PiRaNhA: A server for the computational prediction of RNA-binding residues in protein sequences

    OpenAIRE

    Murakami, Yoichi; Spriggs, Ruth V; Nakamura, Haruki; Jones, Susan

    2010-01-01

    The PiRaNhA web server is a publicly available online resource that automatically predicts the location of RNA-binding residues (RBRs) in protein sequences. The goal of functional annotation of sequences in the field of RNA binding is to provide predictions of high accuracy that require only small numbers of targeted mutations for verification. The PiRaNhA server uses a support vector machine (SVM), with position-specific scoring matrices, residue interface propensity, predicted residue acces...

  12. The IntFOLD server: an integrated web resource for protein fold recognition, 3D model quality assessment, intrinsic disorder prediction, domain prediction and ligand binding site prediction.

    Science.gov (United States)

    Roche, Daniel B; Buenavista, Maria T; Tetchner, Stuart J; McGuffin, Liam J

    2011-07-01

    The IntFOLD server is a novel independent server that integrates several cutting edge methods for the prediction of structure and function from sequence. Our guiding principles behind the server development were as follows: (i) to provide a simple unified resource that makes our prediction software accessible to all and (ii) to produce integrated output for predictions that can be easily interpreted. The output for predictions is presented as a simple table that summarizes all results graphically via plots and annotated 3D models. The raw machine readable data files for each set of predictions are also provided for developers, which comply with the Critical Assessment of Methods for Protein Structure Prediction (CASP) data standards. The server comprises an integrated suite of five novel methods: nFOLD4, for tertiary structure prediction; ModFOLD 3.0, for model quality assessment; DISOclust 2.0, for disorder prediction; DomFOLD 2.0 for domain prediction; and FunFOLD 1.0, for ligand binding site prediction. Predictions from the IntFOLD server were found to be competitive in several categories in the recent CASP9 experiment. The IntFOLD server is available at the following web site: http://www.reading.ac.uk/bioinf/IntFOLD/.

  13. Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening.

    Science.gov (United States)

    Ain, Qurrat Ul; Aleksandrova, Antoniya; Roessler, Florian D; Ballester, Pedro J

    2015-01-01

    Docking tools to predict whether and how a small molecule binds to a target can be applied if a structural model of such target is available. The reliability of docking depends, however, on the accuracy of the adopted scoring function (SF). Despite intense research over the years, improving the accuracy of SFs for structure-based binding affinity prediction or virtual screening has proven to be a challenging task for any class of method. New SFs based on modern machine-learning regression models, which do not impose a predetermined functional form and thus are able to exploit effectively much larger amounts of experimental data, have recently been introduced. These machine-learning SFs have been shown to outperform a wide range of classical SFs at both binding affinity prediction and virtual screening. The emerging picture from these studies is that the classical approach of using linear regression with a small number of expert-selected structural features can be strongly improved by a machine-learning approach based on nonlinear regression allied with comprehensive data-driven feature selection. Furthermore, the performance of classical SFs does not grow with larger training datasets and hence this performance gap is expected to widen as more training data becomes available in the future. Other topics covered in this review include predicting the reliability of a SF on a particular target class, generating synthetic data to improve predictive performance and modeling guidelines for SF development. WIREs Comput Mol Sci 2015, 5:405-424. doi: 10.1002/wcms.1225 For further resources related to this article, please visit the WIREs website.

  14. Prediction of trypsin/molecular fragment binding affinities by free energy decomposition and empirical scores

    Science.gov (United States)

    Benson, Mark L.; Faver, John C.; Ucisik, Melek N.; Dashti, Danial S.; Zheng, Zheng; Merz, Kenneth M.

    2012-05-01

    Two families of binding affinity estimation methodologies are described which were utilized in the SAMPL3 trypsin/fragment binding affinity challenge. The first is a free energy decomposition scheme based on a thermodynamic cycle, which included separate contributions from enthalpy and entropy of binding as well as a solvent contribution. Enthalpic contributions were estimated with PM6-DH2 semiempirical quantum mechanical interaction energies, which were modified with a statistical error correction procedure. Entropic contributions were estimated with the rigid-rotor harmonic approximation, and solvent contributions to the free energy were estimated with several different methods. The second general methodology is the empirical score LISA, which contains several physics-based terms trained with the large PDBBind database of protein/ligand complexes. Here we also introduce LISA+, an updated version of LISA which, prior to scoring, classifies systems into one of four classes based on a ligand's hydrophobicity and molecular weight. Each version of the two methodologies (a total of 11 methods) was trained against a compiled set of known trypsin binders available in the Protein Data Bank to yield scaling parameters for linear regression models. Both raw and scaled scores were submitted to SAMPL3. Variants of LISA showed relatively low absolute errors but also low correlation with experiment, while the free energy decomposition methods had modest success when scaling factors were included. Nonetheless, re-scaled LISA yielded the best predictions in the challenge in terms of RMS error, and six of these models placed in the top ten best predictions by RMS error. This work highlights some of the difficulties of predicting binding affinities of small molecular fragments to protein receptors as well as the benefit of using training data.

  15. Predicting binding affinities of protein ligands from three-dimensional models: application to peptide binding to class I major histocompatibility proteins

    DEFF Research Database (Denmark)

    Rognan, D; Lauemoller, S L; Holm, A

    1999-01-01

    A simple and fast free energy scoring function (Fresno) has been developed to predict the binding free energy of peptides to class I major histocompatibility (MHC) proteins. It differs from existing scoring functions mainly by the explicit treatment of ligand desolvation and of unfavorable protein...... coordinates of the MHC-bound peptide have first been determined with an accuracy of about 1-1.5 A. Furthermore, it may be easily recalibrated for any protein-ligand complex.......) and of a series of 16 peptides to H-2K(k). Predictions were more accurate for HLA-A2-binding peptides as the training set had been built from experimentally determined structures. The average error in predicting the binding free energy of the test peptides was 3.1 kJ/mol. For the homology model-derived equation...

  16. G-LoSA for Prediction of Protein-Ligand Binding Sites and Structures.

    Science.gov (United States)

    Lee, Hui Sun; Im, Wonpil

    2017-01-01

    Recent advances in high-throughput structure determination and computational protein structure prediction have significantly enriched the universe of protein structure. However, there is still a large gap between the number of available protein structures and that of proteins with annotated function in high accuracy. Computational structure-based protein function prediction has emerged to reduce this knowledge gap. The identification of a ligand binding site and its structure is critical to the determination of a protein's molecular function. We present a computational methodology for predicting small molecule ligand binding site and ligand structure using G-LoSA, our protein local structure alignment and similarity measurement tool. All the computational procedures described here can be easily implemented using G-LoSA Toolkit, a package of standalone software programs and preprocessed PDB structure libraries. G-LoSA and G-LoSA Toolkit are freely available to academic users at http://compbio.lehigh.edu/GLoSA . We also illustrate a case study to show the potential of our template-based approach harnessing G-LoSA for protein function prediction.

  17. Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest.

    Science.gov (United States)

    Li, Hongjian; Leung, Kwong-Sak; Wong, Man-Hon; Ballester, Pedro J

    2015-06-12

    Docking scoring functions can be used to predict the strength of protein-ligand binding. It is widely believed that training a scoring function with low-quality data is detrimental for its predictive performance. Nevertheless, there is a surprising lack of systematic validation experiments in support of this hypothesis. In this study, we investigated to which extent training a scoring function with data containing low-quality structural and binding data is detrimental for predictive performance. We actually found that low-quality data is not only non-detrimental, but beneficial for the predictive performance of machine-learning scoring functions, though the improvement is less important than that coming from high-quality data. Furthermore, we observed that classical scoring functions are not able to effectively exploit data beyond an early threshold, regardless of its quality. This demonstrates that exploiting a larger data volume is more important for the performance of machine-learning scoring functions than restricting to a smaller set of higher data quality.

  18. Boneless Pose Editing and Animation

    DEFF Research Database (Denmark)

    Bærentzen, Jakob Andreas; Hansen, Kristian Evers; Erleben, Kenny

    2007-01-01

    In this paper, we propose a pose editing and animation method for triangulated surfaces based on a user controlled partitioning of the model into deformable parts and rigid parts which are denoted handles. In our pose editing system, the user can sculpt a set of poses simply by transforming...... the handles for each pose. Using Laplacian editing, the deformable parts are deformed to match the handles. In our animation system the user can constrain one or several handles in order to define a new pose. New poses are interpolated from the examples poses, by solving a small non-linear optimization...... problem in order to obtain the interpolation weights. While the system can be used simply for building poses, it is also an animation system. The user can specify a path for a given constraint and the model is animated correspondingly....

  19. Improved methods for predicting peptide binding affinity to MHC class II molecules.

    Science.gov (United States)

    Jensen, Kamilla Kjaergaard; Andreatta, Massimo; Marcatili, Paolo; Buus, Søren; Greenbaum, Jason A; Yan, Zhen; Sette, Alessandro; Peters, Bjoern; Nielsen, Morten

    2018-01-06

    Major histocompatibility complex class II (MHC-II) molecules are expressed on the surface of professional antigen-presenting cells where they display peptides to T helper cells, which orchestrate the onset and outcome of many host immune responses. Understanding which peptides will be presented by the MHC-II molecule is therefore important for understanding the activation of T helper cells and can be used to identify T-cell epitopes. We here present updated versions of two MHC-II-peptide binding affinity prediction methods, NetMHCII and NetMHCIIpan. These were constructed using an extended data set of quantitative MHC-peptide binding affinity data obtained from the Immune Epitope Database covering HLA-DR, HLA-DQ, HLA-DP and H-2 mouse molecules. We show that training with this extended data set improved the performance for peptide binding predictions for both methods. Both methods are publicly available at www.cbs.dtu.dk/services/NetMHCII-2.3 and www.cbs.dtu.dk/services/NetMHCIIpan-3.2. © 2018 John Wiley & Sons Ltd.

  20. SNBRFinder: A Sequence-Based Hybrid Algorithm for Enhanced Prediction of Nucleic Acid-Binding Residues.

    Directory of Open Access Journals (Sweden)

    Xiaoxia Yang

    Full Text Available Protein-nucleic acid interactions are central to various fundamental biological processes. Automated methods capable of reliably identifying DNA- and RNA-binding residues in protein sequence are assuming ever-increasing importance. The majority of current algorithms rely on feature-based prediction, but their accuracy remains to be further improved. Here we propose a sequence-based hybrid algorithm SNBRFinder (Sequence-based Nucleic acid-Binding Residue Finder by merging a feature predictor SNBRFinderF and a template predictor SNBRFinderT. SNBRFinderF was established using the support vector machine whose inputs include sequence profile and other complementary sequence descriptors, while SNBRFinderT was implemented with the sequence alignment algorithm based on profile hidden Markov models to capture the weakly homologous template of query sequence. Experimental results show that SNBRFinderF was clearly superior to the commonly used sequence profile-based predictor and SNBRFinderT can achieve comparable performance to the structure-based template methods. Leveraging the complementary relationship between these two predictors, SNBRFinder reasonably improved the performance of both DNA- and RNA-binding residue predictions. More importantly, the sequence-based hybrid prediction reached competitive performance relative to our previous structure-based counterpart. Our extensive and stringent comparisons show that SNBRFinder has obvious advantages over the existing sequence-based prediction algorithms. The value of our algorithm is highlighted by establishing an easy-to-use web server that is freely accessible at http://ibi.hzau.edu.cn/SNBRFinder.

  1. A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking.

    Science.gov (United States)

    Ballester, Pedro J; Mitchell, John B O

    2010-05-01

    Accurately predicting the binding affinities of large sets of diverse protein-ligand complexes is an extremely challenging task. The scoring functions that attempt such computational prediction are essential for analysing the outputs of molecular docking, which in turn is an important technique for drug discovery, chemical biology and structural biology. Each scoring function assumes a predetermined theory-inspired functional form for the relationship between the variables that characterize the complex, which also include parameters fitted to experimental or simulation data and its predicted binding affinity. The inherent problem of this rigid approach is that it leads to poor predictivity for those complexes that do not conform to the modelling assumptions. Moreover, resampling strategies, such as cross-validation or bootstrapping, are still not systematically used to guard against the overfitting of calibration data in parameter estimation for scoring functions. We propose a novel scoring function (RF-Score) that circumvents the need for problematic modelling assumptions via non-parametric machine learning. In particular, Random Forest was used to implicitly capture binding effects that are hard to model explicitly. RF-Score is compared with the state of the art on the demanding PDBbind benchmark. Results show that RF-Score is a very competitive scoring function. Importantly, RF-Score's performance was shown to improve dramatically with training set size and hence the future availability of more high-quality structural and interaction data is expected to lead to improved versions of RF-Score. pedro.ballester@ebi.ac.uk; jbom@st-andrews.ac.uk Supplementary data are available at Bioinformatics online.

  2. Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method

    DEFF Research Database (Denmark)

    Nielsen, Morten; Lundegaard, Claus; Lund, Ole

    2007-01-01

    BACKGROUND: Antigen presenting cells (APCs) sample the extra cellular space and present peptides from here to T helper cells, which can be activated if the peptides are of foreign origin. The peptides are presented on the surface of the cells in complex with major histocompatibility class II (MHC...... II) molecules. Identification of peptides that bind MHC II molecules is thus a key step in rational vaccine design and developing methods for accurate prediction of the peptide:MHC interactions play a central role in epitope discovery. The MHC class II binding groove is open at both ends making...... the correct alignment of a peptide in the binding groove a crucial part of identifying the core of an MHC class II binding motif. Here, we present a novel stabilization matrix alignment method, SMM-align, that allows for direct prediction of peptide:MHC binding affinities. The predictive performance...

  3. Prediction of Carbohydrate Binding Sites on Protein Surfaces with 3-Dimensional Probability Density Distributions of Interacting Atoms

    Science.gov (United States)

    Tsai, Keng-Chang; Jian, Jhih-Wei; Yang, Ei-Wen; Hsu, Po-Chiang; Peng, Hung-Pin; Chen, Ching-Tai; Chen, Jun-Bo; Chang, Jeng-Yih; Hsu, Wen-Lian; Yang, An-Suei

    2012-01-01

    Non-covalent protein-carbohydrate interactions mediate molecular targeting in many biological processes. Prediction of non-covalent carbohydrate binding sites on protein surfaces not only provides insights into the functions of the query proteins; information on key carbohydrate-binding residues could suggest site-directed mutagenesis experiments, design therapeutics targeting carbohydrate-binding proteins, and provide guidance in engineering protein-carbohydrate interactions. In this work, we show that non-covalent carbohydrate binding sites on protein surfaces can be predicted with relatively high accuracy when the query protein structures are known. The prediction capabilities were based on a novel encoding scheme of the three-dimensional probability density maps describing the distributions of 36 non-covalent interacting atom types around protein surfaces. One machine learning model was trained for each of the 30 protein atom types. The machine learning algorithms predicted tentative carbohydrate binding sites on query proteins by recognizing the characteristic interacting atom distribution patterns specific for carbohydrate binding sites from known protein structures. The prediction results for all protein atom types were integrated into surface patches as tentative carbohydrate binding sites based on normalized prediction confidence level. The prediction capabilities of the predictors were benchmarked by a 10-fold cross validation on 497 non-redundant proteins with known carbohydrate binding sites. The predictors were further tested on an independent test set with 108 proteins. The residue-based Matthews correlation coefficient (MCC) for the independent test was 0.45, with prediction precision and sensitivity (or recall) of 0.45 and 0.49 respectively. In addition, 111 unbound carbohydrate-binding protein structures for which the structures were determined in the absence of the carbohydrate ligands were predicted with the trained predictors. The overall

  4. Yoga Poses Increase Subjective Energy and State Self-Esteem in Comparison to 'Power Poses'.

    Science.gov (United States)

    Golec de Zavala, Agnieszka; Lantos, Dorottya; Bowden, Deborah

    2017-01-01

    Research on beneficial consequences of yoga focuses on the effects of yogic breathing and meditation. Less is known about the psychological effects of performing yoga postures. The present study investigated the effects of yoga poses on subjective sense of energy and self-esteem. The effects of yoga postures were compared to the effects of 'power poses,' which arguably increase the sense of power and self-confidence due to their association with interpersonal dominance (Carney et al., 2010). The study tested the novel prediction that yoga poses, which are not associated with interpersonal dominance but increase bodily energy, would increase the subjective feeling of energy and therefore increase self-esteem compared to 'high power' and 'low power' poses. A two factorial, between participants design was employed. Participants performed either two standing yoga poses with open front of the body ( n = 19), two standing yoga poses with covered front of the body ( n = 22), two expansive, high power poses ( n = 21), or two constrictive, low power poses ( n = 20) for 1-min each. The results showed that yoga poses in comparison to 'power poses' increased self-esteem. This effect was mediated by an increased subjective sense of energy and was observed when baseline trait self-esteem was controlled for. These results suggest that the effects of performing open, expansive body postures may be driven by processes other than the poses' association with interpersonal power and dominance. This study demonstrates that positive effects of yoga practice can occur after performing yoga poses for only 2 min.

  5. LIGSITEcsc: predicting ligand binding sites using the Connolly surface and degree of conservation

    Directory of Open Access Journals (Sweden)

    Schroeder Michael

    2006-09-01

    Full Text Available Abstract Background Identifying pockets on protein surfaces is of great importance for many structure-based drug design applications and protein-ligand docking algorithms. Over the last ten years, many geometric methods for the prediction of ligand-binding sites have been developed. Results We present LIGSITEcsc, an extension and implementation of the LIGSITE algorithm. LIGSITEcsc is based on the notion of surface-solvent-surface events and the degree of conservation of the involved surface residues. We compare our algorithm to four other approaches, LIGSITE, CAST, PASS, and SURFNET, and evaluate all on a dataset of 48 unbound/bound structures and 210 bound-structures. LIGSITEcsc performs slightly better than the other tools and achieves a success rate of 71% and 75%, respectively. Conclusion The use of the Connolly surface leads to slight improvements, the prediction re-ranking by conservation to significant improvements of the binding site predictions. A web server for LIGSITEcsc and its source code is available at scoppi.biotec.tu-dresden.de/pocket.

  6. The PickPocket method for predicting binding specificities for receptors based on receptor pocket similarities: application to MHC-peptide binding

    DEFF Research Database (Denmark)

    Zhang, H.; Lund, Ole; Nielsen, M.

    2009-01-01

    of the specificities of MHC molecules in this library weighted by the similarity of their pocket-residues to the query. This PickPocket method is demonstrated to accurately predict MHC-peptide binding for a broad range of MHC alleles, including human and non-human species. In contrast to neural network-based pan-specific......Motivation: Receptor-ligand interactions play an important role in controlling many biological systems. One prominent example is the binding of peptides to the major histocompatibility complex (MHC) molecules controlling the onset of cellular immune responses. Thousands of MHC allelic versions...... exist, making determination of the binding specificity for each variant experimentally infeasible. Here, we present a method that can extrapolate from variants with known binding specificity to those where no experimental data are available. Results: For each position in the peptide ligand, we extracted...

  7. Improved methods for predicting peptide binding affinity to MHC class II molecules

    DEFF Research Database (Denmark)

    Jensen, Kamilla Kjærgaard; Andreatta, Massimo; Marcatili, Paolo

    2018-01-01

    Major histocompatibility complex class II (MHC-II) molecules are expressed on the surface of professional antigen presenting cells where they display peptides to T helper cells, which orchestrate the onset and outcome of many host immune responses. Understanding which peptides will be presented...... by the MHC-II molecule is therefore important for understanding the activation of T helper cells and can be used to identify T-cell epitopes. We here present updated versions of two MHC class II peptide binding affinity prediction methods, NetMHCII and NetMHCIIpan. These were constructed using an extended...

  8. Improved pan-specific MHC class I peptide-binding predictions using a novel representation of the MHC-binding cleft environment

    DEFF Research Database (Denmark)

    Carrasco Pro, S.; Zimic, M.; Nielsen, Morten

    2014-01-01

    Major histocompatibility complex (MHC) molecules play a key role in cell-mediated immune responses presenting bounded peptides for recognition by the immune system cells. Several in silico methods have been developed to predict the binding affinity of a given peptide to a specific MHC molecule. O...

  9. Peptide binding specificity of major histocompatibility complex class I resolved into an array of apparently independent subspecificities: quantitation by peptide libraries and improved prediction of binding

    DEFF Research Database (Denmark)

    Stryhn, A; Pedersen, L O; Romme, T

    1996-01-01

    Considerable interest has focused on understanding how major histocompatibility complex (MHC) specificity is generated and characterizing the specificity of MHC molecules with the ultimate goal being to predict peptide binding. We have used a strategy where all possible peptides of a particular...

  10. Cost Function Network-based Design of Protein-Protein Interactions: predicting changes in binding affinity.

    Science.gov (United States)

    Viricel, Clément; de Givry, Simon; Schiex, Thomas; Barbe, Sophie

    2018-02-20

    Accurate and economic methods to predict change in protein binding free energy upon mutation are imperative to accelerate the design of proteins for a wide range of applications. Free energy is defined by enthalpic and entropic contributions. Following the recent progresses of Artificial Intelligence-based algorithms for guaranteed NP-hard energy optimization and partition function computation, it becomes possible to quickly compute minimum energy conformations and to reliably estimate the entropic contribution of side-chains in the change of free energy of large protein interfaces. Using guaranteed Cost Function Network algorithms, Rosetta energy functions and Dunbrack's rotamer library, we developed and assessed EasyE and JayZ, two methods for binding affinity estimation that ignore or include conformational entropic contributions on a large benchmark of binding affinity experimental measures. If both approaches outperform most established tools, we observe that side-chain conformational entropy brings little or no improvement on most systems but becomes crucial in some rare cases. as open-source Python/C ++ code at sourcesup.renater.fr/projects/easy-jayz. thomas.schiex@inra.fr and sophie.barbe@insa-toulouse.fr. Supplementary data are available at Bioinformatics online.

  11. Binding mode and free energy prediction of fisetin/β-cyclodextrin inclusion complexes

    Directory of Open Access Journals (Sweden)

    Bodee Nutho

    2014-11-01

    Full Text Available In the present study, our aim is to investigate the preferential binding mode and encapsulation of the flavonoid fisetin in the nano-pore of β-cyclodextrin (β-CD at the molecular level using various theoretical approaches: molecular docking, molecular dynamics (MD simulations and binding free energy calculations. The molecular docking suggested four possible fisetin orientations in the cavity through its chromone or phenyl ring with two different geometries of fisetin due to the rotatable bond between the two rings. From the multiple MD results, the phenyl ring of fisetin favours its inclusion into the β-CD cavity, whilst less binding or even unbinding preference was observed in the complexes where the larger chromone ring is located in the cavity. All MM- and QM-PBSA/GBSA free energy predictions supported the more stable fisetin/β-CD complex of the bound phenyl ring. Van der Waals interaction is the key force in forming the complexes. In addition, the quantum mechanics calculations with M06-2X/6-31G(d,p clearly showed that both solvation effect and BSSE correction cannot be neglected for the energy determination of the chosen system.

  12. Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach

    DEFF Research Database (Denmark)

    Buus, S; Lauemøller, S L; Worning, P

    2003-01-01

    We have generated Artificial Neural Networks (ANN) capable of performing sensitive, quantitative predictions of peptide binding to the MHC class I molecule, HLA-A*0204. We have shown that such quantitative ANN are superior to conventional classification ANN, that have been trained to predict...... binding vs non-binding peptides. Furthermore, quantitative ANN allowed a straightforward application of a 'Query by Committee' (QBC) principle whereby particularly information-rich peptides could be identified and subsequently tested experimentally. Iterative training based on QBC-selected peptides...

  13. Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan

    DEFF Research Database (Denmark)

    Nielsen, Morten; Lundegaard, Claus; Blicher, Thomas

    2008-01-01

    is derived from a large compilation of quantitative HLA-DR binding events covering 14 of the more than 500 known HLA-DR alleles. Taking both peptide and HLA sequence information into account, the method can generalize and predict peptide binding also for HLA-DR molecules where experimental data is absent...... class II molecules is therefore of pivotal importance for rational discovery of immune epitopes. HLA-DR is a prominent example of a human MHC class II. Here, we present a method, NetMHCIIpan, that allows for pan-specific predictions of peptide binding to any HLA-DR molecule of known sequence. The method...

  14. Ligand-induced conformational changes: Improved predictions of ligand binding conformations and affinities

    DEFF Research Database (Denmark)

    Frimurer, T.M.; Peters, Günther H.J.; Iversen, L.F.

    2003-01-01

    A computational docking strategy using multiple conformations of the target protein is discussed and evaluated. A series of low molecular weight, competitive, nonpeptide protein tyrosine phosphatase inhibitors are considered for which the x-ray crystallographic structures in complex with protein...... tyrosine phosphatase 1 B (PTP1B) are known. To obtain a quantitative measure of the impact of conformational changes induced by the inhibitors, these were docked to the active site region of various structures of PTP1B using the docking program FlexX. Firstly, the inhibitors were docked to a PTP1B crystal...... predicted binding energy and a correct docking mode. Thirdly, to improve the predictability of the docking procedure in the general case, where only a single target protein structure is known, we evaluate an approach which takes possible protein side-chain conformational changes into account. Here, side...

  15. Single-nucleotide mutation matrix: a new model for predicting the NF-κB DNA binding sites.

    Science.gov (United States)

    Du, Wenxin; Gao, Jing; Wang, Tingting; Wang, Jinke

    2014-01-01

    In this study, we established a single nucleotide mutation matrix (SNMM) model based on the relative binding affinities of NF-κB p50 homodimer to a wild-type binding site (GGGACTTTCC) and its all single-nucleotide mutants detected with the double-stranded DNA microarray. We evaluated this model by scoring different groups of 10-bp DNA sequences with this model and analyzing the correlations between the scores and the relative binding affinities detected with three wet experiments, including the electrophoresis mobility shift assay (EMSA), the protein-binding microarray (PBM) and the systematic evolution of ligands by exponential enrichment-sequencing (SELEX-Seq). The results revealed that the SNMM scores were strongly correlated with the detected binding affinities. We also scored the DNA sequences with other three models, including the principal coordinate (PC) model, the position weight matrix scoring algorithm (PWMSA) model and the Match model, and analyzed the correlations between the scores and the detected binding affinities. In comparison with these models, the SNMM model achieved reliable results. We finally determined 0.747 as the optimal threshold for predicting the NF-κB DNA-binding sites with the SNMM model. The SNMM model thus provides a new alternative model for scoring the relative binding affinities of NF-κB to the 10-bp DNA sequences and predicting the NF-κB DNA-binding sites.

  16. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

    Directory of Open Access Journals (Sweden)

    Lund Ole

    2009-09-01

    Full Text Available Abstract Background The major histocompatibility complex (MHC molecule plays a central role in controlling the adaptive immune response to infections. MHC class I molecules present peptides derived from intracellular proteins to cytotoxic T cells, whereas MHC class II molecules stimulate cellular and humoral immunity through presentation of extracellularly derived peptides to helper T cells. Identification of which peptides will bind a given MHC molecule is thus of great importance for the understanding of host-pathogen interactions, and large efforts have been placed in developing algorithms capable of predicting this binding event. Results Here, we present a novel artificial neural network-based method, NN-align that allows for simultaneous identification of the MHC class II binding core and binding affinity. NN-align is trained using a novel training algorithm that allows for correction of bias in the training data due to redundant binding core representation. Incorporation of information about the residues flanking the peptide-binding core is shown to significantly improve the prediction accuracy. The method is evaluated on a large-scale benchmark consisting of six independent data sets covering 14 human MHC class II alleles, and is demonstrated to outperform other state-of-the-art MHC class II prediction methods. Conclusion The NN-align method is competitive with the state-of-the-art MHC class II peptide binding prediction algorithms. The method is publicly available at http://www.cbs.dtu.dk/services/NetMHCII-2.0.

  17. Prediction and dissection of widely-varying association rate constants of actin-binding proteins.

    Science.gov (United States)

    Pang, Xiaodong; Zhou, Kenneth H; Qin, Sanbo; Zhou, Huan-Xiang

    2012-01-01

    Actin is an abundant protein that constitutes a main component of the eukaryotic cytoskeleton. Its polymerization and depolymerization are regulated by a variety of actin-binding proteins. Their functions range from nucleation of actin polymerization to sequestering G-actin in 1∶1 complexes. The kinetics of forming these complexes, with rate constants varying at least three orders of magnitude, is critical to the distinct regulatory functions. Previously we have developed a transient-complex theory for computing protein association mechanisms and association rate constants. The transient complex refers to an intermediate in which the two associating proteins have near-native separation and relative orientation but have yet to form short-range specific interactions of the native complex. The association rate constant is predicted as k(a) = k(a0) e(-ΔG(el*)/k(B)T), where k(a0) is the basal rate constant for reaching the transient complex by free diffusion, and the Boltzmann factor captures the bias of long-range electrostatic interactions. Here we applied the transient-complex theory to study the association kinetics of seven actin-binding proteins with G-actin. These proteins exhibit three classes of association mechanisms, due to their different molecular shapes and flexibility. The 1000-fold k(a) variations among them can mostly be attributed to disparate electrostatic contributions. The basal rate constants also showed variations, resulting from the different shapes and sizes of the interfaces formed by the seven actin-binding proteins with G-actin. This study demonstrates the various ways that actin-binding proteins use physical properties to tune their association mechanisms and rate constants to suit distinct regulatory functions.

  18. Prediction of Water Binding to Protein Hydration Sites with a Discrete, Semiexplicit Solvent Model.

    Science.gov (United States)

    Setny, Piotr

    2015-12-08

    Buried water molecules are ubiquitous in protein structures and are found at the interface of most protein-ligand complexes. Determining their distribution and thermodynamic effect is a challenging yet important task, of great of practical value for the modeling of biomolecular structures and their interactions. In this study, we present a novel method aimed at the prediction of buried water molecules in protein structures and estimation of their binding free energies. It is based on a semiexplicit, discrete solvation model, which we previously introduced in the context of small molecule hydration. The method is applicable to all macromolecular structures described by a standard all-atom force field, and predicts complete solvent distribution within a single run with modest computational cost. We demonstrate that it indicates positions of buried hydration sites, including those filled by more than one water molecule, and accurately differentiates them from sterically accessible to water but void regions. The obtained estimates of water binding free energies are in fair agreement with reference results determined with the double decoupling method.

  19. Comparison of Performance of Docking, LIE, Metadynamics and QSAR in Predicting Binding Affinity of Benzenesulfonamides.

    Science.gov (United States)

    Raškevičius, Vytautas; Kairys, Visvaldas

    2015-01-01

    The design of inhibitors specific for one relevant carbonic anhydrase isozyme is the major challenge in the new therapeutic agents development. Comparative computational chemical structure and biological activity relationship studies on a series of carbonic anhydrase II inhibitors, benzenesulfonamide derivatives, bearing pyrimidine moieties are reported in this paper using docking, Linear Interaction Energy (LIE), Metadynamics and Quantitative Structure Activity Relationship (QSAR) methods. The computed binding affinities were compared with the experimental data with the goal to explore strengths and weaknesses of various approaches applied to the investigated carbonic anhydrase/inhibitor system. From the tested methods initially only QSAR showed promising results (R2=0.83-0.89 between experimentally determined versus predicted pKd values.). Possible reasons for this performance were discussed. A modification of the LIE method was suggested which used an alternative LIE-like equation yielding significantly improved results (R2 between the experimentally determined versus the predicted ΔG(bind) improved from 0.24 to 0.50).

  20. Pose estimation with correspondences determination

    Science.gov (United States)

    Dong, Hang; Sun, Changku; Zhu, Ruizhe; Wang, Peng

    2018-01-01

    Pose estimation by monocular is finding the pose of the object by a single image of feature points on the object, which must meet the requirements of detecting all the feature points and matching them in the image. But it will be difficult to obtain the correct pose if part of the feature points are occluded when the object moving a large scale. We proposed a method for finding the pose on the condition that the correspondences between the object points and the image points are unknown. The method combines two algorithms: one algorithm is SoftAssign, which constructs a weight matrix of feature points and image points, and determines the correspondences by iteration loop processing; the other algorithm is OI(orthogonal iteration), which derives an iterative algorithm which directly computes orthogonal and globally convergent rotation matrices.We nest the two algorithms into one iteration loop.An appropriate pose will be chosen from a set of reference poses as the initial pose of object at the beginning of the loop, then we process the weight matrix to confirm the correspondences and calculate the optimal solution of rotation matrices alternately until the object space collinearity error is less than the threshold, each estimation will be closer to the truth pose than the last one through every iteration loop. Experimentally, the method proved to be efficient and have a high precision pose estimation of 3D object with large-scale motion.

  1. Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach

    DEFF Research Database (Denmark)

    Buus, S.; Lauemoller, S.L.; Worning, Peder

    2003-01-01

    We have generated Artificial Neural Networks (ANN) capable of performing sensitive, quantitative predictions of peptide binding to the MHC class I molecule, HLA-A*0204. We have shown that such quantitative ANN are superior to conventional classification ANN, that have been trained to predict...

  2. Improving binding mode and binding affinity predictions of docking by ligand-based search of protein conformations: evaluation in D3R grand challenge 2015

    Science.gov (United States)

    Xu, Xianjin; Yan, Chengfei; Zou, Xiaoqin

    2017-08-01

    The growing number of protein-ligand complex structures, particularly the structures of proteins co-bound with different ligands, in the Protein Data Bank helps us tackle two major challenges in molecular docking studies: the protein flexibility and the scoring function. Here, we introduced a systematic strategy by using the information embedded in the known protein-ligand complex structures to improve both binding mode and binding affinity predictions. Specifically, a ligand similarity calculation method was employed to search a receptor structure with a bound ligand sharing high similarity with the query ligand for the docking use. The strategy was applied to the two datasets (HSP90 and MAP4K4) in recent D3R Grand Challenge 2015. In addition, for the HSP90 dataset, a system-specific scoring function (ITScore2_hsp90) was generated by recalibrating our statistical potential-based scoring function (ITScore2) using the known protein-ligand complex structures and the statistical mechanics-based iterative method. For the HSP90 dataset, better performances were achieved for both binding mode and binding affinity predictions comparing with the original ITScore2 and with ensemble docking. For the MAP4K4 dataset, although there were only eight known protein-ligand complex structures, our docking strategy achieved a comparable performance with ensemble docking. Our method for receptor conformational selection and iterative method for the development of system-specific statistical potential-based scoring functions can be easily applied to other protein targets that have a number of protein-ligand complex structures available to improve predictions on binding.

  3. ProBiS-ligands: a web server for prediction of ligands by examination of protein binding sites.

    Science.gov (United States)

    Konc, Janez; Janežič, Dušanka

    2014-07-01

    The ProBiS-ligands web server predicts binding of ligands to a protein structure. Starting with a protein structure or binding site, ProBiS-ligands first identifies template proteins in the Protein Data Bank that share similar binding sites. Based on the superimpositions of the query protein and the similar binding sites found, the server then transposes the ligand structures from those sites to the query protein. Such ligand prediction supports many activities, e.g. drug repurposing. The ProBiS-ligands web server, an extension of the ProBiS web server, is open and free to all users at http://probis.cmm.ki.si/ligands. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

  4. Human action recognition based on estimated weak poses

    Science.gov (United States)

    Gong, Wenjuan; Gonzàlez, Jordi; Roca, Francesc Xavier

    2012-12-01

    We present a novel method for human action recognition (HAR) based on estimated poses from image sequences. We use 3D human pose data as additional information and propose a compact human pose representation, called a weak pose, in a low-dimensional space while still keeping the most discriminative information for a given pose. With predicted poses from image features, we map the problem from image feature space to pose space, where a Bag of Poses (BOP) model is learned for the final goal of HAR. The BOP model is a modified version of the classical bag of words pipeline by building the vocabulary based on the most representative weak poses for a given action. Compared with the standard k-means clustering, our vocabulary selection criteria is proven to be more efficient and robust against the inherent challenges of action recognition. Moreover, since for action recognition the ordering of the poses is discriminative, the BOP model incorporates temporal information: in essence, groups of consecutive poses are considered together when computing the vocabulary and assignment. We tested our method on two well-known datasets: HumanEva and IXMAS, to demonstrate that weak poses aid to improve action recognition accuracies. The proposed method is scene-independent and is comparable with the state-of-art method.

  5. Problem posing reflections and applications

    CERN Document Server

    Brown, Stephen I

    2014-01-01

    As a result of the editors' collaborative teaching at Harvard in the late 1960s, they produced a ground-breaking work -- The Art Of Problem Posing -- which related problem posing strategies to the already popular activity of problem solving. It took the concept of problem posing and created strategies for engaging in that activity as a central theme in mathematics education. Based in part upon that work and also upon a number of articles by its authors, other members of the mathematics education community began to apply and expand upon their ideas. This collection of thirty readings is a tes

  6. Large-scale binding ligand prediction by improved patch-based method Patch-Surfer2.0.

    Science.gov (United States)

    Zhu, Xiaolei; Xiong, Yi; Kihara, Daisuke

    2015-03-01

    Ligand binding is a key aspect of the function of many proteins. Thus, binding ligand prediction provides important insight in understanding the biological function of proteins. Binding ligand prediction is also useful for drug design and examining potential drug side effects. We present a computational method named Patch-Surfer2.0, which predicts binding ligands for a protein pocket. By representing and comparing pockets at the level of small local surface patches that characterize physicochemical properties of the local regions, the method can identify binding pockets of the same ligand even if they do not share globally similar shapes. Properties of local patches are represented by an efficient mathematical representation, 3D Zernike Descriptor. Patch-Surfer2.0 has significant technical improvements over our previous prototype, which includes a new feature that captures approximate patch position with a geodesic distance histogram. Moreover, we constructed a large comprehensive database of ligand binding pockets that will be searched against by a query. The benchmark shows better performance of Patch-Surfer2.0 over existing methods. http://kiharalab.org/patchsurfer2.0/ CONTACT: dkihara@purdue.edu Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  7. EL_PSSM-RT: DNA-binding residue prediction by integrating ensemble learning with PSSM Relation Transformation.

    Science.gov (United States)

    Zhou, Jiyun; Lu, Qin; Xu, Ruifeng; He, Yulan; Wang, Hongpeng

    2017-08-29

    Prediction of DNA-binding residue is important for understanding the protein-DNA recognition mechanism. Many computational methods have been proposed for the prediction, but most of them do not consider the relationships of evolutionary information between residues. In this paper, we first propose a novel residue encoding method, referred to as the Position Specific Score Matrix (PSSM) Relation Transformation (PSSM-RT), to encode residues by utilizing the relationships of evolutionary information between residues. PDNA-62 and PDNA-224 are used to evaluate PSSM-RT and two existing PSSM encoding methods by five-fold cross-validation. Performance evaluations indicate that PSSM-RT is more effective than previous methods. This validates the point that the relationship of evolutionary information between residues is indeed useful in DNA-binding residue prediction. An ensemble learning classifier (EL_PSSM-RT) is also proposed by combining ensemble learning model and PSSM-RT to better handle the imbalance between binding and non-binding residues in datasets. EL_PSSM-RT is evaluated by five-fold cross-validation using PDNA-62 and PDNA-224 as well as two independent datasets TS-72 and TS-61. Performance comparisons with existing predictors on the four datasets demonstrate that EL_PSSM-RT is the best-performing method among all the predicting methods with improvement between 0.02-0.07 for MCC, 4.18-21.47% for ST and 0.013-0.131 for AUC. Furthermore, we analyze the importance of the pair-relationships extracted by PSSM-RT and the results validates the usefulness of PSSM-RT for encoding DNA-binding residues. We propose a novel prediction method for the prediction of DNA-binding residue with the inclusion of relationship of evolutionary information and ensemble learning. Performance evaluation shows that the relationship of evolutionary information between residues is indeed useful in DNA-binding residue prediction and ensemble learning can be used to address the data imbalance

  8. In Silico Prediction of Chemicals Binding to Aromatase with Machine Learning Methods.

    Science.gov (United States)

    Du, Hanwen; Cai, Yingchun; Yang, Hongbin; Zhang, Hongxiao; Xue, Yuhan; Liu, Guixia; Tang, Yun; Li, Weihua

    2017-05-15

    Environmental chemicals may affect endocrine systems through multiple mechanisms, one of which is via effects on aromatase (also known as CYP19A1), an enzyme critical for maintaining the normal balance of estrogens and androgens in the body. Therefore, rapid and efficient identification of aromatase-related endocrine disrupting chemicals (EDCs) is important for toxicology and environment risk assessment. In this study, on the basis of the Tox21 10K compound library, in silico classification models for predicting aromatase binders/nonbinders were constructed by machine learning methods. To improve the prediction ability of the models, a combined classifier (CC) strategy that combines different independent machine learning methods was adopted. Performances of the models were measured by test and external validation sets containing 1336 and 216 chemicals, respectively. The best model was obtained with the MACCS (Molecular Access System) fingerprint and CC method, which exhibited an accuracy of 0.84 for the test set and 0.91 for the external validation set. Additionally, several representative substructures for characterizing aromatase binders, such as ketone, lactone, and nitrogen-containing derivatives, were identified using information gain and substructure frequency analysis. Our study provided a systematic assessment of chemicals binding to aromatase. The built models can be helpful to rapidly identify potential EDCs targeting aromatase.

  9. Usefulness of intestinal fatty acid-binding protein in predicting strangulated small bowel obstruction.

    Directory of Open Access Journals (Sweden)

    Hirotada Kittaka

    Full Text Available BACKGROUND: The level of intestinal fatty acid-binding protein (I-FABP is considered to be useful diagnostic markers of small bowel ischemia. The purpose of this retrospective study was to investigate whether the serum I-FABP level is a predictive marker of strangulation in patients with small bowel obstruction (SBO. METHODS: A total of 37 patients diagnosed with SBO were included in this study. The serum I-FABP levels were retrospectively compared between the patients with strangulation and those with simple obstruction, and cut-off values for the diagnosis of strangulation were calculated using a receiver operating characteristic curve. In addition, the sensitivity, specificity, positive predictive value (PPV and negative predictive value (NPV were calculated. RESULTS: Twenty-one patients were diagnosed with strangulated SBO. The serum I-FABP levels were significantly higher in the patients with strangulation compared with those observed in the patients with simple obstruction (18.5 vs. 1.6 ng/ml p<0.001. Using a cut-off value of 6.5 ng/ml, the sensitivity, specificity, PPV and NPV were 71.4%, 93.8%, 93.8% and 71.4%, respectively. An I-FABP level greater than 6.5 ng/ml was found to be the only independent significant factor for a higher likelihood of strangulated SBO (P =  0.02; odds ratio: 19.826; 95% confidence interval: 2.1560 - 488.300. CONCLUSIONS: The I-FABP level is a useful marker for discriminating between strangulated SBO and simple SBO in patients with SBO.

  10. A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals

    Directory of Open Access Journals (Sweden)

    Huixiao Hong

    2016-03-01

    Full Text Available Endocrine disruptors such as polychlorinated biphenyls (PCBs, diethylstilbestrol (DES and dichlorodiphenyltrichloroethane (DDT are agents that interfere with the endocrine system and cause adverse health effects. Huge public health concern about endocrine disruptors has arisen. One of the mechanisms of endocrine disruption is through binding of endocrine disruptors with the hormone receptors in the target cells. Entrance of endocrine disruptors into target cells is the precondition of endocrine disruption. The binding capability of a chemical with proteins in the blood affects its entrance into the target cells and, thus, is very informative for the assessment of potential endocrine disruption of chemicals. α-fetoprotein is one of the major serum proteins that binds to a variety of chemicals such as estrogens. To better facilitate assessment of endocrine disruption of environmental chemicals, we developed a model for α-fetoprotein binding activity prediction using the novel pattern recognition method (Decision Forest and the molecular descriptors calculated from two-dimensional structures by Mold2 software. The predictive capability of the model has been evaluated through internal validation using 125 training chemicals (average balanced accuracy of 69% and external validations using 22 chemicals (balanced accuracy of 71%. Prediction confidence analysis revealed the model performed much better at high prediction confidence. Our results indicate that the model is useful (when predictions are in high confidence in endocrine disruption risk assessment of environmental chemicals though improvement by increasing number of training chemicals is needed.

  11. Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2.

    Science.gov (United States)

    de Ávila, Maurício Boff; Xavier, Mariana Morrone; Pintro, Val Oliveira; de Azevedo, Walter Filgueira

    2017-12-09

    Here we report the development of a machine-learning model to predict binding affinity based on the crystallographic structures of protein-ligand complexes. We used an ensemble of crystallographic structures (resolution better than 1.5 Å resolution) for which half-maximal inhibitory concentration (IC 50 ) data is available. Polynomial scoring functions were built using as explanatory variables the energy terms present in the MolDock and PLANTS scoring functions. Prediction performance was tested and the supervised machine learning models showed improvement in the prediction power, when compared with PLANTS and MolDock scoring functions. In addition, the machine-learning model was applied to predict binding affinity of CDK2, which showed a better performance when compared with AutoDock4, AutoDock Vina, MolDock, and PLANTS scores. Copyright © 2017 Elsevier Inc. All rights reserved.

  12. Prediction of protein binding sites using physical and chemical descriptors and the support vector machine regression method

    International Nuclear Information System (INIS)

    Sun Zhong-Hua; Jiang Fan

    2010-01-01

    In this paper a new continuous variable called core-ratio is defined to describe the probability for a residue to be in a binding site, thereby replacing the previous binary description of the interface residue using 0 and 1. So we can use the support vector machine regression method to fit the core-ratio value and predict the protein binding sites. We also design a new group of physical and chemical descriptors to characterize the binding sites. The new descriptors are more effective, with an averaging procedure used. Our test shows that much better prediction results can be obtained by the support vector regression (SVR) method than by the support vector classification method. (rapid communication)

  13. Computational prediction of binding affinity for CYP1A2-ligand complexes using empirical free energy calculations

    DEFF Research Database (Denmark)

    Poongavanam, Vasanthanathan; Olsen, Lars; Jørgensen, Flemming Steen

    2010-01-01

    , and methods based on statistical mechanics. In the present investigation, we started from an LIE model to predict the binding free energy of structurally diverse compounds of cytochrome P450 1A2 ligands, one of the important human metabolizing isoforms of the cytochrome P450 family. The data set includes both...... substrates and inhibitors. It appears that the electrostatic contribution to the binding free energy becomes negligible in this particular protein and a simple empirical model was derived, based on a training set of eight compounds. The root mean square error for the training set was 3.7 kJ/mol. Subsequent......Predicting binding affinities for receptor-ligand complexes is still one of the challenging processes in computational structure-based ligand design. Many computational methods have been developed to achieve this goal, such as docking and scoring methods, the linear interaction energy (LIE) method...

  14. COMPARATIVE MODELLING AND LIGAND BINDING SITE PREDICTION OF A FAMILY 43 GLYCOSIDE HYDROLASE FROM Clostridium thermocellum

    Directory of Open Access Journals (Sweden)

    Shadab Ahmed

    2012-06-01

    Full Text Available The phylogenetic analysis of Clostridium thermocellum family 43 glycoside hydrolase (CtGH43 showed close evolutionary relation with carbohydrate binding family 6 proteins from C. cellulolyticum, C. papyrosolvens, C. cellulyticum, and A. cellulyticum. Comparative modeling of CtGH43 was performed based on crystal structures with PDB IDs 3C7F, 1YIF, 1YRZ, 2EXH and 1WL7. The structure having lowest MODELLER objective function was selected. The three-dimensional structure revealed typical 5-fold beta–propeller architecture. Energy minimization and validation of predicted model with VERIFY 3D indicated acceptability of the proposed atomic structure. The Ramachandran plot analysis by RAMPAGE confirmed that family 43 glycoside hydrolase (CtGH43 contains little or negligible segments of helices. It also showed that out of 301 residues, 267 (89.3% were in most favoured region, 23 (7.7% were in allowed region and 9 (3.0% were in outlier region. IUPred analysis of CtGH43 showed no disordered region. Active site analysis showed presence of two Asp and one Glu, assumed to form a catalytic triad. This study gives us information about three-dimensional structure and reaffirms the fact that it has the similar core 5-fold beta–propeller architecture and so probably has the same inverting mechanism of action with the formation of above mentioned catalytic triad for catalysis of polysaccharides.

  15. First principle prediction of shallow defect level binding energies and deep level nonradiative recombination rates

    Science.gov (United States)

    Wang, Linwang

    2014-03-01

    Accurate calculation of defect level energies in semiconductors and their carrier capturing rate is an important issue in ab initio prediction of semiconductor properties. In this talk, I will present our result work in ab initio shallow level calculation and deep level caused nonradiative recombination rate calculation. In the shallow acceptor level calculation, a large system up to 64,000 atoms needs to be used to properly describe the weakly bounded hole wave functions. The single particle Hamiltonian of that system is patched from bulk potential and central potential. Furthermore, GW calculation is used to correct the one site potential of the impurity atom. The resulting binding energy agrees excellently with the experiments within 10 meV. To calculate the nonradiative decay rate, the electron-phonon coupling constants in the defect system are calculated all at once using a new variational algorithm. Multiphonon process formalism is used to calculate the nonradiative decay rate. It is found that the transition is induced by the electron and the optical phonon coupling, but the energy conservation is mostly satisfied by the acoustic phonons. The new algorithm allows fast calculation of such nonradiative decay rate for any defect levels, as well as other multiphonon processes in nanostructures. This work was supported by the Director, Office of Science (SC), Basic Energy Science (BES)/Materials Science and Engineering Division (MSED) of the U.S. Department of Energy (DOE) under the contract No. DE-AC02-05CH11231.

  16. Prediction of mono- and di-nucleotide-specific DNA-binding sites in proteins using neural networks

    Directory of Open Access Journals (Sweden)

    Mizuguchi Kenji

    2009-05-01

    Full Text Available Abstract Background DNA recognition by proteins is one of the most important processes in living systems. Therefore, understanding the recognition process in general, and identifying mutual recognition sites in proteins and DNA in particular, carries great significance. The sequence and structural dependence of DNA-binding sites in proteins has led to the development of successful machine learning methods for their prediction. However, all existing machine learning methods predict DNA-binding sites, irrespective of their target sequence and hence, none of them is helpful in identifying specific protein-DNA contacts. In this work, we formulate the problem of predicting specific DNA-binding sites in terms of contacts between the residue environments of proteins and the identity of a mononucleotide or a dinucleotide step in DNA. The aim of this work is to take a protein sequence or structural features as inputs and predict for each amino acid residue if it binds to DNA at locations identified by one of the four possible mononucleotides or one of the 10 unique dinucleotide steps. Contact predictions are made at various levels of resolution viz. in terms of side chain, backbone and major or minor groove atoms of DNA. Results Significant differences in residue preferences for specific contacts are observed, which combined with other features, lead to promising levels of prediction. In general, PSSM-based predictions, supported by secondary structure and solvent accessibility, achieve a good predictability of ~70–80%, measured by the area under the curve (AUC of ROC graphs. The major and minor groove contact predictions stood out in terms of their poor predictability from sequences or PSSM, which was very strongly (>20 percentage points compensated by the addition of secondary structure and solvent accessibility information, revealing a predominant role of local protein structure in the major/minor groove DNA-recognition. Following a detailed

  17. Modernizing emergency alerts poses challenges

    OpenAIRE

    Center for Homeland Defense and Security

    2010-01-01

    Center for Homeland Defense and Security, OUT OF THE CLASSROOM Download the paper: Paper: IPAWS (Integrated Public Alert and Warning System)” Modernizing emergency alerts poses challenges Anthony Cox is interested in the next generation of emergency alert systems.Any television viewer...

  18. Predicting Binding Free Energy Change Caused by Point Mutations with Knowledge-Modified MM/PBSA Method.

    Directory of Open Access Journals (Sweden)

    Marharyta Petukh

    2015-07-01

    Full Text Available A new methodology termed Single Amino Acid Mutation based change in Binding free Energy (SAAMBE was developed to predict the changes of the binding free energy caused by mutations. The method utilizes 3D structures of the corresponding protein-protein complexes and takes advantage of both approaches: sequence- and structure-based methods. The method has two components: a MM/PBSA-based component, and an additional set of statistical terms delivered from statistical investigation of physico-chemical properties of protein complexes. While the approach is rigid body approach and does not explicitly consider plausible conformational changes caused by the binding, the effect of conformational changes, including changes away from binding interface, on electrostatics are mimicked with amino acid specific dielectric constants. This provides significant improvement of SAAMBE predictions as indicated by better match against experimentally determined binding free energy changes over 1300 mutations in 43 proteins. The final benchmarking resulted in a very good agreement with experimental data (correlation coefficient 0.624 while the algorithm being fast enough to allow for large-scale calculations (the average time is less than a minute per mutation.

  19. Predicting combinatorial binding of transcription factors to regulatory elements in the human genome by association rule mining

    Directory of Open Access Journals (Sweden)

    Iyer Vishwanath R

    2007-11-01

    Full Text Available Abstract Background Cis-acting transcriptional regulatory elements in mammalian genomes typically contain specific combinations of binding sites for various transcription factors. Although some cis-regulatory elements have been well studied, the combinations of transcription factors that regulate normal expression levels for the vast majority of the 20,000 genes in the human genome are unknown. We hypothesized that it should be possible to discover transcription factor combinations that regulate gene expression in concert by identifying over-represented combinations of sequence motifs that occur together in the genome. In order to detect combinations of transcription factor binding motifs, we developed a data mining approach based on the use of association rules, which are typically used in market basket analysis. We scored each segment of the genome for the presence or absence of each of 83 transcription factor binding motifs, then used association rule mining algorithms to mine this dataset, thus identifying frequently occurring pairs of distinct motifs within a segment. Results Support for most pairs of transcription factor binding motifs was highly correlated across different chromosomes although pair significance varied. Known true positive motif pairs showed higher association rule support, confidence, and significance than background. Our subsets of high-confidence, high-significance mined pairs of transcription factors showed enrichment for co-citation in PubMed abstracts relative to all pairs, and the predicted associations were often readily verifiable in the literature. Conclusion Functional elements in the genome where transcription factors bind to regulate expression in a combinatorial manner are more likely to be predicted by identifying statistically and biologically significant combinations of transcription factor binding motifs than by simply scanning the genome for the occurrence of binding sites for a single transcription

  20. In silico engineering and optimization of Transcription Activator-Like Effectors and their derivatives for improved DNA binding predictions.

    KAUST Repository

    Piatek, Marek J.

    2015-12-01

    Transcription Activator-Like Effectors (TALEs) can be used as adaptable DNAbinding modules to create site-specific chimeric nucleases or synthetic transcriptional regulators. The central repeat domain mediates specific DNA binding via hypervariable repeat di-residues (RVDs). This DNA-Binding Domain can be engineered to bind preferentially to any user-selected DNA sequence if engineered appropriately. Therefore, TALEs and their derivatives have become indispensable molecular tools in site-specific manipulation of genes and genomes. This thesis revolves around two problems: in silico design and improved binding site prediction of TALEs. In the first part, a study is shown where TALEs are successfully designed in silico and validated in laboratory to yield the anticipated effects on selected genes. Software is developed to accompany the process of designing and prediction of binding sites. I expanded the functionality of the software to be used as a more generic set of tools for the design, target and offtarget searching. Part two contributes a method and associated toolkit developed to allow users to design in silico optimized synthetic TALEs with user-defined specificities for various experimental purposes. This method is based on a mutual relationship of three consecutive tandem repeats in the DNA-binding domain. This approach revealed positional and compositional bias behind the binding of TALEs to DNA. In conclusion, I developed methods, approaches, and software to enhance the functionality of synthetic TALEs, which should improve understanding of TALEs biology and will further advance genome-engineering applications in various organisms and cell types.

  1. Predicted 3D structures of olfactory receptors with details of odorant binding to OR1G1

    Science.gov (United States)

    Kim, Soo-Kyung; Goddard, William A.

    2014-12-01

    Olfactory receptors (ORs) are responsible for mediating the sense of smell; they allow humans to recognize an enormous number of odors but the connection between binding and perception is not known. We predict the ensemble of low energy structures for the human OR1G1 (hOR1G1) and also for six other diverse ORs, using the G protein-coupled receptor Ensemble of Structures in Membrane BiLayer Environment complete sampling method that samples 13 trillion different rotations and tilts using four different templates to predict the 24 structures likely to be important in binding and activation. Our predicted most stable structures of hOR1G1 have a salt-bridge between the conserved D3.49 and K6.30 in the D(E)RY region, that we expect to be associated with an inactive form. The hOR1G1 structure also has specific interaction in transmembrane domains (TMD) 3-6 (E3.39 and H6.40), which is likely an important conformational feature for all hORs because of the 94 to 98 % conservation among all hOR sequences. Of the five ligands studied (nonanal, 9-decen-1-ol, 1-nonanol, camphor, and n-butanal), we find that the 4 expected to bind lead to similar binding energies with nonanol the strongest.

  2. Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study.

    Science.gov (United States)

    Li, Hongjian; Leung, Kwong-Sak; Wong, Man-Hon; Ballester, Pedro J

    2014-08-27

    State-of-the-art protein-ligand docking methods are generally limited by the traditionally low accuracy of their scoring functions, which are used to predict binding affinity and thus vital for discriminating between active and inactive compounds. Despite intensive research over the years, classical scoring functions have reached a plateau in their predictive performance. These assume a predetermined additive functional form for some sophisticated numerical features, and use standard multivariate linear regression (MLR) on experimental data to derive the coefficients. In this study we show that such a simple functional form is detrimental for the prediction performance of a scoring function, and replacing linear regression by machine learning techniques like random forest (RF) can improve prediction performance. We investigate the conditions of applying RF under various contexts and find that given sufficient training samples RF manages to comprehensively capture the non-linearity between structural features and measured binding affinities. Incorporating more structural features and training with more samples can both boost RF performance. In addition, we analyze the importance of structural features to binding affinity prediction using the RF variable importance tool. Lastly, we use Cyscore, a top performing empirical scoring function, as a baseline for comparison study. Machine-learning scoring functions are fundamentally different from classical scoring functions because the former circumvents the fixed functional form relating structural features with binding affinities. RF, but not MLR, can effectively exploit more structural features and more training samples, leading to higher prediction performance. The future availability of more X-ray crystal structures will further widen the performance gap between RF-based and MLR-based scoring functions. This further stresses the importance of substituting RF for MLR in scoring function development.

  3. Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan.

    Directory of Open Access Journals (Sweden)

    Morten Nielsen

    2008-07-01

    Full Text Available CD4 positive T helper cells control many aspects of specific immunity. These cells are specific for peptides derived from protein antigens and presented by molecules of the extremely polymorphic major histocompatibility complex (MHC class II system. The identification of peptides that bind to MHC class II molecules is therefore of pivotal importance for rational discovery of immune epitopes. HLA-DR is a prominent example of a human MHC class II. Here, we present a method, NetMHCIIpan, that allows for pan-specific predictions of peptide binding to any HLA-DR molecule of known sequence. The method is derived from a large compilation of quantitative HLA-DR binding events covering 14 of the more than 500 known HLA-DR alleles. Taking both peptide and HLA sequence information into account, the method can generalize and predict peptide binding also for HLA-DR molecules where experimental data is absent. Validation of the method includes identification of endogenously derived HLA class II ligands, cross-validation, leave-one-molecule-out, and binding motif identification for hitherto uncharacterized HLA-DR molecules. The validation shows that the method can successfully predict binding for HLA-DR molecules-even in the absence of specific data for the particular molecule in question. Moreover, when compared to TEPITOPE, currently the only other publicly available prediction method aiming at providing broad HLA-DR allelic coverage, NetMHCIIpan performs equivalently for alleles included in the training of TEPITOPE while outperforming TEPITOPE on novel alleles. We propose that the method can be used to identify those hitherto uncharacterized alleles, which should be addressed experimentally in future updates of the method to cover the polymorphism of HLA-DR most efficiently. We thus conclude that the presented method meets the challenge of keeping up with the MHC polymorphism discovery rate and that it can be used to sample the MHC "space," enabling a

  4. MHC class I epitope binding prediction trained on small data sets

    DEFF Research Database (Denmark)

    Lundegaard, Claus; Nielsen, Morten; Lamberth, K.

    2004-01-01

    The identification of potential T-cell epitopes is important for development of new human or vetenary vaccines, both considering single protein/subunit vaccines, and for epitope/peptide vaccines as such. The highly diverse MHC class I alleles bind very different peptides, and accurate binding pre...... in situations where only very limited data are available for training....

  5. PROCARB: A Database of Known and Modelled Carbohydrate-Binding Protein Structures with Sequence-Based Prediction Tools

    Directory of Open Access Journals (Sweden)

    Adeel Malik

    2010-01-01

    Full Text Available Understanding of the three-dimensional structures of proteins that interact with carbohydrates covalently (glycoproteins as well as noncovalently (protein-carbohydrate complexes is essential to many biological processes and plays a significant role in normal and disease-associated functions. It is important to have a central repository of knowledge available about these protein-carbohydrate complexes as well as preprocessed data of predicted structures. This can be significantly enhanced by tools de novo which can predict carbohydrate-binding sites for proteins in the absence of structure of experimentally known binding site. PROCARB is an open-access database comprising three independently working components, namely, (i Core PROCARB module, consisting of three-dimensional structures of protein-carbohydrate complexes taken from Protein Data Bank (PDB, (ii Homology Models module, consisting of manually developed three-dimensional models of N-linked and O-linked glycoproteins of unknown three-dimensional structure, and (iii CBS-Pred prediction module, consisting of web servers to predict carbohydrate-binding sites using single sequence or server-generated PSSM. Several precomputed structural and functional properties of complexes are also included in the database for quick analysis. In particular, information about function, secondary structure, solvent accessibility, hydrogen bonds and literature reference, and so forth, is included. In addition, each protein in the database is mapped to Uniprot, Pfam, PDB, and so forth.

  6. Is supergravity well-posed?

    International Nuclear Information System (INIS)

    Isenberg, J.; Bao, D.; Yasskin, P.B.

    1983-01-01

    One rather fundamental question concerning supergravity remains unresolved: Is supergravity a well-posed field theory? That is, does a set of certain (Cauchy) data specified on some initial spacelike surface determine a unique, causally propagating spacetime solution of the supergravity field equations (at least in some finite neighborhood of the initial surface)? In this paper, the authors give a very brief report on work directed towards answering this question. (Auth.)

  7. Refining Visually Detected Object poses

    DEFF Research Database (Denmark)

    Holm, Preben; Petersen, Henrik Gordon

    2010-01-01

    Automated industrial assembly today require that the 3D position and orientation (hereafter ''pose`) of the objects to be assembled are known precisely. Today this precision is mostly established by a dedicated mechanical object alignment system. However, such systems are often dedicated to the p......Automated industrial assembly today require that the 3D position and orientation (hereafter ''pose`) of the objects to be assembled are known precisely. Today this precision is mostly established by a dedicated mechanical object alignment system. However, such systems are often dedicated...... to the particular object and in order to handle the demand for flexibility, there is an increasing demand for avoiding such dedicated mechanical alignment systems. Rather, it would be desirable to automatically locate and grasp randomly placed objects from tables, conveyor belts or even bins with a high accuracy...... that enables direct assembly. Conventional vision systems and laser triangulation systems can locate randomly placed known objects (with 3D CAD models available) with some accuracy, but not necessarily a good enough accuracy. In this paper, we present a novel method for refining the pose accuracy of an object...

  8. SVM prediction of ligand-binding sites in bacterial lipoproteins employing shape and physio-chemical descriptors.

    Science.gov (United States)

    Kadam, Kiran; Prabhakar, Prashant; Jayaraman, V K

    2012-11-01

    Bacterial lipoproteins play critical roles in various physiological processes including the maintenance of pathogenicity and numbers of them are being considered as potential candidates for generating novel vaccines. In this work, we put forth an algorithm to identify and predict ligand-binding sites in bacterial lipoproteins. The method uses three types of pocket descriptors, namely fpocket descriptors, 3D Zernike descriptors and shell descriptors, and combines them with Support Vector Machine (SVM) method for the classification. The three types of descriptors represent shape-based properties of the pocket as well as its local physio-chemical features. All three types of descriptors, along with their hybrid combinations are evaluated with SVM and to improve classification performance, WEKA-InfoGain feature selection is applied. Results obtained in the study show that the classifier successfully differentiates between ligand-binding and non-binding pockets. For the combination of three types of descriptors, 10 fold cross-validation accuracy of 86.83% is obtained for training while the selected model achieved test Matthews Correlation Coefficient (MCC) of 0.534. Individually or in combination with new and existing methods, our model can be a very useful tool for the prediction of potential ligand-binding sites in bacterial lipoproteins.

  9. A Simple PB/LIE Free Energy Function Accurately Predicts the Peptide Binding Specificity of the Tiam1 PDZ Domain

    Directory of Open Access Journals (Sweden)

    Nicolas Panel

    2017-09-01

    Full Text Available PDZ domains generally bind short amino acid sequences at the C-terminus of target proteins, and short peptides can be used as inhibitors or model ligands. Here, we used experimental binding assays and molecular dynamics simulations to characterize 51 complexes involving the Tiam1 PDZ domain and to test the performance of a semi-empirical free energy function. The free energy function combined a Poisson-Boltzmann (PB continuum electrostatic term, a van der Waals interaction energy, and a surface area term. Each term was empirically weighted, giving a Linear Interaction Energy or “PB/LIE” free energy. The model yielded a mean unsigned deviation of 0.43 kcal/mol and a Pearson correlation of 0.64 between experimental and computed free energies, which was superior to a Null model that assumes all complexes have the same affinity. Analyses of the models support several experimental observations that indicate the orientation of the α2 helix is a critical determinant for peptide specificity. The models were also used to predict binding free energies for nine new variants, corresponding to point mutants of the Syndecan1 and Caspr4 peptides. The predictions did not reveal improved binding; however, they suggest that an unnatural amino acid could be used to increase protease resistance and peptide lifetimes in vivo. The overall performance of the model should allow its use in the design of new PDZ ligands in the future.

  10. A strategy for interaction site prediction between phospho-binding modules and their partners identified from proteomic data.

    Science.gov (United States)

    Aucher, Willy; Becker, Emmanuelle; Ma, Emilie; Miron, Simona; Martel, Arnaud; Ochsenbein, Françoise; Marsolier-Kergoat, Marie-Claude; Guerois, Raphaël

    2010-12-01

    Small and large scale proteomic technologies are providing a wealth of potential interactions between proteins bearing phospho-recognition modules and their substrates. Resulting interaction maps reveal such a dense network of interactions that the functional dissection and understanding of these networks often require to break specific interactions while keeping the rest intact. Here, we developed a computational strategy, called STRIP, to predict the precise interaction site involved in an interaction with a phospho-recognition module. The method was validated by a two-hybrid screen carried out using the ForkHead Associated (FHA)1 domain of Rad53, a key protein of Saccharomyces cerevisiae DNA checkpoint, as a bait. In this screen we detected 11 partners, including Cdc7 and Cdc45, essential components of the DNA replication machinery. FHA domains are phospho-threonine binding modules and the threonines involved in both interactions could be predicted using the STRIP strategy. The threonines T484 and T189 in Cdc7 and Cdc45, respectively, were mutated and loss of binding could be monitored experimentally with the full-length proteins. The method was further tested for the analysis of 63 known Rad53 binding partners and provided several key insights regarding the threonines likely involved in these interactions. The STRIP method relies on a combination of conservation, phosphorylation likelihood, and binding specificity criteria and can be accessed via a web interface at http://biodev.extra.cea.fr/strip/.

  11. A Strategy for Interaction Site Prediction between Phospho-binding Modules and their Partners Identified from Proteomic Data*

    Science.gov (United States)

    Aucher, Willy; Becker, Emmanuelle; Ma, Emilie; Miron, Simona; Martel, Arnaud; Ochsenbein, Françoise; Marsolier-Kergoat, Marie-Claude; Guerois, Raphaël

    2010-01-01

    Small and large scale proteomic technologies are providing a wealth of potential interactions between proteins bearing phospho-recognition modules and their substrates. Resulting interaction maps reveal such a dense network of interactions that the functional dissection and understanding of these networks often require to break specific interactions while keeping the rest intact. Here, we developed a computational strategy, called STRIP, to predict the precise interaction site involved in an interaction with a phospho-recognition module. The method was validated by a two-hybrid screen carried out using the ForkHead Associated (FHA)1 domain of Rad53, a key protein of Saccharomyces cerevisiae DNA checkpoint, as a bait. In this screen we detected 11 partners, including Cdc7 and Cdc45, essential components of the DNA replication machinery. FHA domains are phospho-threonine binding modules and the threonines involved in both interactions could be predicted using the STRIP strategy. The threonines T484 and T189 in Cdc7 and Cdc45, respectively, were mutated and loss of binding could be monitored experimentally with the full-length proteins. The method was further tested for the analysis of 63 known Rad53 binding partners and provided several key insights regarding the threonines likely involved in these interactions. The STRIP method relies on a combination of conservation, phosphorylation likelihood, and binding specificity criteria and can be accessed via a web interface at http://biodev.extra.cea.fr/strip/. PMID:20733106

  12. A Simple PB/LIE Free Energy Function Accurately Predicts the Peptide Binding Specificity of the Tiam1 PDZ Domain.

    Science.gov (United States)

    Panel, Nicolas; Sun, Young Joo; Fuentes, Ernesto J; Simonson, Thomas

    2017-01-01

    PDZ domains generally bind short amino acid sequences at the C-terminus of target proteins, and short peptides can be used as inhibitors or model ligands. Here, we used experimental binding assays and molecular dynamics simulations to characterize 51 complexes involving the Tiam1 PDZ domain and to test the performance of a semi-empirical free energy function. The free energy function combined a Poisson-Boltzmann (PB) continuum electrostatic term, a van der Waals interaction energy, and a surface area term. Each term was empirically weighted, giving a Linear Interaction Energy or "PB/LIE" free energy. The model yielded a mean unsigned deviation of 0.43 kcal/mol and a Pearson correlation of 0.64 between experimental and computed free energies, which was superior to a Null model that assumes all complexes have the same affinity. Analyses of the models support several experimental observations that indicate the orientation of the α 2 helix is a critical determinant for peptide specificity. The models were also used to predict binding free energies for nine new variants, corresponding to point mutants of the Syndecan1 and Caspr4 peptides. The predictions did not reveal improved binding; however, they suggest that an unnatural amino acid could be used to increase protease resistance and peptide lifetimes in vivo . The overall performance of the model should allow its use in the design of new PDZ ligands in the future.

  13. An integrative approach to CTL epitope prediction: A combined algorithm integrating MHC class I binding, TAP transport efficiency, and proteasomal cleavage predictions

    DEFF Research Database (Denmark)

    Larsen, Mette Voldby; Lundegaard, Claus; Lamberth, K

    2005-01-01

    Reverse immunogenetic approaches attempt to optimize the selection of candidate epitopes, and thus minimize the experimental effort needed to identify new epitopes. When predicting cytotoxic T cell epitopes, the main focus has been on the highly specific MHC class I binding event. Methods have al.......The method is available at http://www.cbs.dtu.dk/services/NetCTL. Supplementary material is available at http://www.cbs.dtu.dk/suppl/immunology/CTL.php....

  14. Cultural adaptations to the differential threats posed by hot versus cold climates.

    Science.gov (United States)

    Murray, Damian R

    2013-10-01

    Hot and cold climates have posed differential threats to human survival throughout history. Cold temperatures can pose direct threats to survival in themselves, whereas hot temperatures may pose threats indirectly through higher prevalence of infectious disease. These differential threats yield convergent predictions for the relationship between more demanding climates and freedom of expression, but divergent predictions for freedom from discrimination.

  15. Integrating in silico and in vitro analysis of peptide binding affinity to HLA-Cw*0102: a bioinformatic approach to the prediction of new epitopes.

    Directory of Open Access Journals (Sweden)

    Valerie A Walshe

    2009-11-01

    Full Text Available Predictive models of peptide-Major Histocompatibility Complex (MHC binding affinity are important components of modern computational immunovaccinology. Here, we describe the development and deployment of a reliable peptide-binding prediction method for a previously poorly-characterized human MHC class I allele, HLA-Cw*0102.Using an in-house, flow cytometry-based MHC stabilization assay we generated novel peptide binding data, from which we derived a precise two-dimensional quantitative structure-activity relationship (2D-QSAR binding model. This allowed us to explore the peptide specificity of HLA-Cw*0102 molecule in detail. We used this model to design peptides optimized for HLA-Cw*0102-binding. Experimental analysis showed these peptides to have high binding affinities for the HLA-Cw*0102 molecule. As a functional validation of our approach, we also predicted HLA-Cw*0102-binding peptides within the HIV-1 genome, identifying a set of potent binding peptides. The most affine of these binding peptides was subsequently determined to be an epitope recognized in a subset of HLA-Cw*0102-positive individuals chronically infected with HIV-1.A functionally-validated in silico-in vitro approach to the reliable and efficient prediction of peptide binding to a previously uncharacterized human MHC allele HLA-Cw*0102 was developed. This technique is generally applicable to all T cell epitope identification problems in immunology and vaccinology.

  16. Predictions of RNA-binding ability and aggregation propensity of proteins

    OpenAIRE

    Agostini, Federico, 1985-

    2014-01-01

    RNA-binding proteins (RBPs) control the fate of a multitude of coding and non-coding transcripts. Formation of ribonucleoprotein (RNP) complexes fine-tunes regulation of post-transcriptional events and influences gene expression. Recently, it has been observed that non-canonical proteins with RNA-binding ability are enriched in structurally disordered and low-complexity regions that are generally involved in functional and dysfunctional associations. Therefore, it is possible that interaction...

  17. Improved pan-specific prediction of MHC class I peptide binding using a novel receptor clustering data partitioning strategy

    DEFF Research Database (Denmark)

    Mattsson, Andreas Holm; Kringelum, Jens Vindahl; Garde, C.

    2016-01-01

    Pan-specific prediction of receptor-ligand interaction is conventionally done using machine-learning methods that integrates information about both receptor and ligand primary sequences. To achieve optimal performance using machine learning, dealing with overfitting and data redundancy is critical...... strategy with the aim of altering this and construct data sets optimal for training of pan-specific receptor-ligand predictions focusing on receptor similarity rather than ligand similarity. We show that this receptor clustering method consistently in benchmarks covering affinity predictions, MHC ligand....... Most often so-called ligand clustering methods have been used to deal with these issues in the context of pan-specific receptor-ligand predictions, and the MHC system the approach has proven highly effective for extrapolating information from a limited set of receptors with well characterized binding...

  18. A community resource benchmarking predictions of peptide binding to MHC-I molecules

    DEFF Research Database (Denmark)

    Peters, B; Bui, HH; Pletscher-Frankild, Sune

    2006-01-01

    on the internet. While differences in the data used to generate these predictions hamper direct comparisons, we do conclude that tools based on combinatorial peptide libraries perform remarkably well. The transparent prediction evaluation on this dataset provides tool developers with a benchmark for comparison...... practice of tool developers having to generate reference predictions themselves, which can lead to underestimating the performance of prediction methods they are not as familiar with as their own. The overall goal of this effort is to provide a transparent prediction evaluation allowing bioinformaticians...... to identify promising features of prediction methods and providing guidance to immunologists regarding the reliability of prediction tools....

  19. Seasonal difference in brain serotonin transporter binding predicts symptom severity in patients with seasonal affective disorder

    DEFF Research Database (Denmark)

    Mc Mahon, Brenda; Andersen, Sofie B.; Madsen, Martin K.

    2016-01-01

    from the first longitudinal investigation of seasonal serotonin transporter fluctuations in both patients with seasonal affective disorder and in healthy individuals. Eighty 11 C-DASB positron emission tomography scans were conducted to quantify cerebral serotonin transporter binding; 23 healthy...... controls with low seasonality scores and 17 patients diagnosed with seasonal affective disorder were scanned in both summer and winter to investigate differences in cerebral serotonin transporter binding across groups and across seasons. The two groups had similar cerebral serotonin transporter binding...... in the summer but in their symptomatic phase during winter, patients with seasonal affective disorder had higher serotonin transporter than the healthy control subjects (P = 0.01). Compared to the healthy controls, patients with seasonal affective disorder changed their serotonin transporter significantly less...

  20. Asymmetry of /sup 3/H- imipramine binding may predict psychiatric illness

    Energy Technology Data Exchange (ETDEWEB)

    Demeter, E.; Tekes, K.; Majorossy, K.; Palkovits, M.; Soos, M.; Magyar, K.; Somogyl, E.

    1989-01-01

    The B/sub max/ and Kd values for /sup 3/H-imipramine binding were measured in post-mortem human brains from drug-free selected psychiatric subject homicide victims and normal controls. The two groups were comparable in age and gender. The number of imipramine binding sites in the frontal cortices of psychiatric subjects had significantly higher B/sub max/ values in the left hemisphere than in the right hemisphere. Inversely, the number of imipramine binding sites in the frontal cortices of normal controls were significantly higher in the right brain than in the left brain. It was postulated that the inhibiting effect of central serotonin has weakened in psychiatric cases, therefore the changes of presynaptic serotonergic activity might be associated with psychiatric illness in the left hemisphere of human brain.

  1. Prediction of transcription factor bindings sites affected by SNPs located at the osteopontin promoter.

    Science.gov (United States)

    Briones-Orta, Marco Antonio; Avendaño-Vázquez, S Eréndira; Ivette Aparicio-Bautista, Diana; Coombes, Jason D; Weber, Georg F; Syn, Wing-Kin

    2017-10-01

    This data contains information related to the research article entitled "Osteopontin splice variants and polymorphisms in Cancer Progression and Prognosis" [1]. Here, we describe an in silico analysis of transcription factors that could have altered binding to their DNA target sequence as a result of SNPs in the osteopontin gene promoter. We concentrated on SNPs associated with cancer risk and development. The analysis was performed with PROMO v3.0.2 software which incorporates TRANSFACT v6.4 of. We also present a figure depicting the putative transcription factor binding according to genotype.

  2. Relative binding affinity prediction of farnesoid X receptor in the D3R Grand Challenge 2 using FEP+

    Science.gov (United States)

    Schindler, Christina; Rippmann, Friedrich; Kuhn, Daniel

    2018-01-01

    Physics-based free energy simulations have increasingly become an important tool for predicting binding affinity and the recent introduction of automated protocols has also paved the way towards a more widespread use in the pharmaceutical industry. The D3R 2016 Grand Challenge 2 provided an opportunity to blindly test the commercial free energy calculation protocol FEP+ and assess its performance relative to other affinity prediction methods. The present D3R free energy prediction challenge was built around two experimental data sets involving inhibitors of farnesoid X receptor (FXR) which is a promising anticancer drug target. The FXR binding site is predominantly hydrophobic with few conserved interaction motifs and strong induced fit effects making it a challenging target for molecular modeling and drug design. For both data sets, we achieved reasonable prediction accuracy (RMSD ≈ 1.4 kcal/mol, rank 3-4 according to RMSD out of 20 submissions) comparable to that of state-of-the-art methods in the field. Our D3R results boosted our confidence in the method and strengthen our desire to expand its applications in future in-house drug design projects.

  3. Confirmation of a predicted lack of IgE binding to Cry3Bb1 from genetically modified (GM) crops.

    Science.gov (United States)

    Nakajima, Osamu; Koyano, Satoru; Akiyama, Hiroshi; Sawada, Jun-Ichi; Teshima, Reiko

    2010-04-01

    Some GM crops including MON863 corn and stack varieties contain Cry3Bb1 protein. Cry3Bb1 is very important from the standpoint of assessing the safety of GM crops. In this study Cry3Bb1 was assessed from the standpoint of possible binding to IgE from allergy patients. First, an ELISA that was improved in our laboratory was used to test serum samples from 13 corn allergy patients in the United States with recombinant Cry3Bb1 expressed in Escherichia coli, and serum samples from 55 patients in Japan with various food allergies were also assayed. Two samples from the Japanese allergy patients were suspected of being positive, but Western blotting analysis with purified Cry3Bb1 indicated that the binding between IgE and Cry3Bb1 was nonspecific. Ultimately, no specific binding between IgE and recombinant Cry3Bb1 was detected. Next, all proteins extracted from MON863 corn and non-GM corn were probed with IgE antibodies in serum samples from the corn allergy patients by Western blotting, but the staining patterns of MON863 and non-GM corn were similar, meaning that unintended allergic reactions to MON863 are unlikely to occur. Our study provides additional information that confirms the predicted lack of IgE binding to Cry3Bb1 in people with existing food allergies. Copyright 2009 Elsevier Inc. All rights reserved.

  4. Sex hormone-binding globulin levels predict insulin sensitivity, disposition index, and cardiovascular risk during puberty

    DEFF Research Database (Denmark)

    Sørensen, Kaspar; Aksglaede, Lise; Munch-Andersen, Thor

    2009-01-01

    Early puberty is associated with increased risk of subsequent cardiovascular disease. Low sex hormone-binding globulin (SHBG) levels are a feature of early puberty and of conditions associated with increased cardiovascular risk. The aim of the present study was to evaluate SHBG as a predictor...

  5. Interplay between magnetism and energetics in Fe-Cr alloys from a predictive noncollinear magnetic tight-binding model

    DEFF Research Database (Denmark)

    Soulairol, R.; Barreteau, Cyrille; Fu, Chu-Chun

    2016-01-01

    Magnetism is a key driving force controlling several thermodynamic and kinetic properties of Fe-Cr systems. We present a tight-binding model for Fe-Cr, where magnetism is treated beyond the usual collinear approximation. A major advantage of this model consists in a rather simple fitting procedure....... In particular, no specific property of the binary system is explicitly required in the fitting database. The present model is proved to be accurate and highly transferable for electronic, magnetic, and energetic properties of a large variety of structural and chemical environments: surfaces, interfaces......, embedded clusters, and the whole compositional range of the binary alloy. The occurrence of noncollinear magnetic configurations caused by magnetic frustrations is successfully predicted. The present tight-binding approach can apply to other binary magnetic transition-metal alloys. It is expected...

  6. EMF signals and ion/ligand binding kinetics: prediction of bioeffective waveform parameters.

    Science.gov (United States)

    Pilla, A A; Muehsam, D J; Markov, M S; Sisken, B F

    1999-02-01

    The kinetics of an electromagnetic field (EMF) target pathway are used to estimate frequency windows for EMF bioeffects. Ion/ligand binding is characterized via first order kinetics from which a specific electrical impedance can be derived. The resistance/capacitance properties of the binding pathway impedance, determined by the kinetics of the rate-determining step, define the frequency range over which the target pathway is most sensitive to external EMF. Applied signals may thus be configured such that their spectral content closely matches that of the target, using evaluation of the signal to thermal noise ratio to optimize waveform parameters. Using the approach proposed in this study, a pulsed radio frequency (PRF) waveform, currently employed clinically for soft tissue repair, was returned by modulation of burst duration, producing significant bioeffects at substantially reduced signal amplitude. Application is made to Ca2+/Calmodulin-dependent myosin phosphorylation, for which the binding time constants may be estimated from reported kinetics, neurite outgrowth from embryonic chick dorsal root explants and bone repair in a fracture model. The results showed that the retuned signal produced increased phosphorylation rates, neurite outgrowth and biomechanical strength that were indistinguishable from those produced by the clinical signal, but with a tenfold reduction in peak signal amplitude, approximately 800-fold reduction in average amplitude and approximately 10(6)-fold reduction in average power.

  7. Prediction of binding affinity and efficacy of thyroid hormone receptor ligands using QSAR and structure-based modeling methods

    Energy Technology Data Exchange (ETDEWEB)

    Politi, Regina [Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, University of North Carolina, Chapel Hill, NC 27599 (United States); Department of Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, NC 27599 (United States); Rusyn, Ivan, E-mail: iir@unc.edu [Department of Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, NC 27599 (United States); Tropsha, Alexander, E-mail: alex_tropsha@unc.edu [Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, University of North Carolina, Chapel Hill, NC 27599 (United States)

    2014-10-01

    The thyroid hormone receptor (THR) is an important member of the nuclear receptor family that can be activated by endocrine disrupting chemicals (EDC). Quantitative Structure–Activity Relationship (QSAR) models have been developed to facilitate the prioritization of THR-mediated EDC for the experimental validation. The largest database of binding affinities available at the time of the study for ligand binding domain (LBD) of THRβ was assembled to generate both continuous and classification QSAR models with an external accuracy of R{sup 2} = 0.55 and CCR = 0.76, respectively. In addition, for the first time a QSAR model was developed to predict binding affinities of antagonists inhibiting the interaction of coactivators with the AF-2 domain of THRβ (R{sup 2} = 0.70). Furthermore, molecular docking studies were performed for a set of THRβ ligands (57 agonists and 15 antagonists of LBD, 210 antagonists of the AF-2 domain, supplemented by putative decoys/non-binders) using several THRβ structures retrieved from the Protein Data Bank. We found that two agonist-bound THRβ conformations could effectively discriminate their corresponding ligands from presumed non-binders. Moreover, one of the agonist conformations could discriminate agonists from antagonists. Finally, we have conducted virtual screening of a chemical library compiled by the EPA as part of the Tox21 program to identify potential THRβ-mediated EDCs using both QSAR models and docking. We concluded that the library is unlikely to have any EDC that would bind to the THRβ. Models developed in this study can be employed either to identify environmental chemicals interacting with the THR or, conversely, to eliminate the THR-mediated mechanism of action for chemicals of concern. - Highlights: • This is the largest curated dataset for ligand binding domain (LBD) of the THRβ. • We report the first QSAR model for antagonists of AF-2 domain of THRβ. • A combination of QSAR and docking enables

  8. Dataset size and composition impact the reliability of performance benchmarks for peptide-MHC binding predictions

    DEFF Research Database (Denmark)

    Kim, Yohan; Sidney, John; Buus, Søren

    2014-01-01

    are cross-validation, in which all available data are iteratively split into training and testing data, and the use of blind sets generated separately from the data used to construct the predictive method. In the present study, we have compared cross-validated prediction performances generated on our last...... benchmark dataset from 2009 with prediction performances generated on data subsequently added to the Immune Epitope Database (IEDB) which served as a blind set. Results: We found that cross-validated performances systematically overestimated performance on the blind set. This was found not to be due...... to the presence of similar peptides in the cross-validation dataset. Rather, we found that small size and low sequence/affinity diversity of either training or blind datasets were associated with large differences in cross-validated vs. blind prediction performances. We use these findings to derive quantitative...

  9. Problem Posing with the Multiplication Table

    Science.gov (United States)

    Dickman, Benjamin

    2014-01-01

    Mathematical problem posing is an important skill for teachers of mathematics, and relates readily to mathematical creativity. This article gives a bit of background information on mathematical problem posing, lists further references to connect problem posing and creativity, and then provides 20 problems based on the multiplication table to be…

  10. Prediction of binding affinity and efficacy of thyroid hormone receptor ligands using QSAR and structure based modeling methods

    Science.gov (United States)

    Politi, Regina; Rusyn, Ivan; Tropsha, Alexander

    2016-01-01

    The thyroid hormone receptor (THR) is an important member of the nuclear receptor family that can be activated by endocrine disrupting chemicals (EDC). Quantitative Structure-Activity Relationship (QSAR) models have been developed to facilitate the prioritization of THR-mediated EDC for the experimental validation. The largest database of binding affinities available at the time of the study for ligand binding domain (LBD) of THRβ was assembled to generate both continuous and classification QSAR models with an external accuracy of R2=0.55 and CCR=0.76, respectively. In addition, for the first time a QSAR model was developed to predict binding affinities of antagonists inhibiting the interaction of coactivators with the AF-2 domain of THRβ (R2=0.70). Furthermore, molecular docking studies were performed for a set of THRβ ligands (57 agonists and 15 antagonists of LBD, 210 antagonists of the AF-2 domain, supplemented by putative decoys/non-binders) using several THRβ structures retrieved from the Protein Data Bank. We found that two agonist-bound THRβ conformations could effectively discriminate their corresponding ligands from presumed non-binders. Moreover, one of the agonist conformations could discriminate agonists from antagonists. Finally, we have conducted virtual screening of a chemical library compiled by the EPA as part of the Tox21 program to identify potential THRβ-mediated EDCs using both QSAR models and docking. We concluded that the library is unlikely to have any EDC that would bind to the THRβ. Models developed in this study can be employed either to identify environmental chemicals interacting with the THR or, conversely, to eliminate the THR-mediated mechanism of action for chemicals of concern. PMID:25058446

  11. A molecular modeling based method to predict elution behavior and binding patches of proteins in multimodal chromatography.

    Science.gov (United States)

    Banerjee, Suvrajit; Parimal, Siddharth; Cramer, Steven M

    2017-08-18

    Multimodal (MM) chromatography provides a powerful means to enhance the selectivity of protein separations by taking advantage of multiple weak interactions that include electrostatic, hydrophobic and van der Waals interactions. In order to increase our understanding of such phenomena, a computationally efficient approach was developed that combines short molecular dynamics simulations and continuum solvent based coarse-grained free energy calculations in order to study the binding of proteins to Self Assembled Monolayers (SAM) presenting MM ligands. Using this method, the free energies of protein-MM SAM binding over a range of incident orientations of the protein can be determined. The resulting free energies were then examined to identify the more "strongly bound" orientations of different proteins with two multimodal surfaces. The overall free energy of protein-MM surface binding was then determined and correlated to retention factors from isocratic chromatography. This correlation, combined with analytical expressions from the literature, was then employed to predict protein gradient elution salt concentrations as well as selectivity reversals with different MM resin systems. Patches on protein surfaces that interacted strongly with MM surfaces were also identified by determining the frequency of heavy atom contacts with the atoms of the MM SAMs. A comparison of these patches to Electrostatic Potential and hydrophobicity maps indicated that while all of these patches contained significant positive charge, only the highest frequency sites also possessed hydrophobicity. The ability to identify key binding patches on proteins may have significant impact on process development for the separation of bioproduct related impurities. Copyright © 2017 Elsevier B.V. All rights reserved.

  12. Predictive Structure and Topology of Peroxisomal ATP-Binding Cassette (ABC) Transporters

    Science.gov (United States)

    Andreoletti, Pierre; Raas, Quentin; Gondcaille, Catherine; Cherkaoui-Malki, Mustapha; Trompier, Doriane; Savary, Stéphane

    2017-01-01

    The peroxisomal ATP-binding Cassette (ABC) transporters, which are called ABCD1, ABCD2 and ABCD3, are transmembrane proteins involved in the transport of various lipids that allow their degradation inside the organelle. Defective ABCD1 leads to the accumulation of very long-chain fatty acids and is associated with a complex and severe neurodegenerative disorder called X-linked adrenoleukodystrophy (X-ALD). Although the nucleotide-binding domain is highly conserved and characterized within the ABC transporters family, solid data are missing for the transmembrane domain (TMD) of ABCD proteins. The lack of a clear consensus on the secondary and tertiary structure of the TMDs weakens any structure-function hypothesis based on the very diverse ABCD1 mutations found in X-ALD patients. Therefore, we first reinvestigated thoroughly the structure-function data available and performed refined alignments of ABCD protein sequences. Based on the 2.85  Å resolution crystal structure of the mitochondrial ABC transporter ABCB10, here we propose a structural model of peroxisomal ABCD proteins that specifies the position of the transmembrane and coupling helices, and highlight functional motifs and putative important amino acid residues. PMID:28737695

  13. Predictive Structure and Topology of Peroxisomal ATP-Binding Cassette (ABC Transporters

    Directory of Open Access Journals (Sweden)

    Pierre Andreoletti

    2017-07-01

    Full Text Available The peroxisomal ATP-binding Cassette (ABC transporters, which are called ABCD1, ABCD2 and ABCD3, are transmembrane proteins involved in the transport of various lipids that allow their degradation inside the organelle. Defective ABCD1 leads to the accumulation of very long-chain fatty acids and is associated with a complex and severe neurodegenerative disorder called X-linked adrenoleukodystrophy (X-ALD. Although the nucleotide-binding domain is highly conserved and characterized within the ABC transporters family, solid data are missing for the transmembrane domain (TMD of ABCD proteins. The lack of a clear consensus on the secondary and tertiary structure of the TMDs weakens any structure-function hypothesis based on the very diverse ABCD1 mutations found in X-ALD patients. Therefore, we first reinvestigated thoroughly the structure-function data available and performed refined alignments of ABCD protein sequences. Based on the 2.85  Å resolution crystal structure of the mitochondrial ABC transporter ABCB10, here we propose a structural model of peroxisomal ABCD proteins that specifies the position of the transmembrane and coupling helices, and highlight functional motifs and putative important amino acid residues.

  14. Proteus: a random forest classifier to predict disorder-to-order transitioning binding regions in intrinsically disordered proteins

    Science.gov (United States)

    Basu, Sankar; Söderquist, Fredrik; Wallner, Björn

    2017-05-01

    The focus of the computational structural biology community has taken a dramatic shift over the past one-and-a-half decades from the classical protein structure prediction problem to the possible understanding of intrinsically disordered proteins (IDP) or proteins containing regions of disorder (IDPR). The current interest lies in the unraveling of a disorder-to-order transitioning code embedded in the amino acid sequences of IDPs/IDPRs. Disordered proteins are characterized by an enormous amount of structural plasticity which makes them promiscuous in binding to different partners, multi-functional in cellular activity and atypical in folding energy landscapes resembling partially folded molten globules. Also, their involvement in several deadly human diseases (e.g. cancer, cardiovascular and neurodegenerative diseases) makes them attractive drug targets, and important for a biochemical understanding of the disease(s). The study of the structural ensemble of IDPs is rather difficult, in particular for transient interactions. When bound to a structured partner, an IDPR adapts an ordered conformation in the complex. The residues that undergo this disorder-to-order transition are called protean residues, generally found in short contiguous stretches and the first step in understanding the modus operandi of an IDP/IDPR would be to predict these residues. There are a few available methods which predict these protean segments from their amino acid sequences; however, their performance reported in the literature leaves clear room for improvement. With this background, the current study presents `Proteus', a random forest classifier that predicts the likelihood of a residue undergoing a disorder-to-order transition upon binding to a potential partner protein. The prediction is based on features that can be calculated using the amino acid sequence alone. Proteus compares favorably with existing methods predicting twice as many true positives as the second best method (55

  15. Enhancing in silico protein-based vaccine discovery for eukaryotic pathogens using predicted peptide-MHC binding and peptide conservation scores.

    Directory of Open Access Journals (Sweden)

    Stephen J Goodswen

    Full Text Available Given thousands of proteins constituting a eukaryotic pathogen, the principal objective for a high-throughput in silico vaccine discovery pipeline is to select those proteins worthy of laboratory validation. Accurate prediction of T-cell epitopes on protein antigens is one crucial piece of evidence that would aid in this selection. Prediction of peptides recognised by T-cell receptors have to date proved to be of insufficient accuracy. The in silico approach is consequently reliant on an indirect method, which involves the prediction of peptides binding to major histocompatibility complex (MHC molecules. There is no guarantee nevertheless that predicted peptide-MHC complexes will be presented by antigen-presenting cells and/or recognised by cognate T-cell receptors. The aim of this study was to determine if predicted peptide-MHC binding scores could provide contributing evidence to establish a protein's potential as a vaccine. Using T-Cell MHC class I binding prediction tools provided by the Immune Epitope Database and Analysis Resource, peptide binding affinity to 76 common MHC I alleles were predicted for 160 Toxoplasma gondii proteins: 75 taken from published studies represented proteins known or expected to induce T-cell immune responses and 85 considered less likely vaccine candidates. The results show there is no universal set of rules that can be applied directly to binding scores to distinguish a vaccine from a non-vaccine candidate. We present, however, two proposed strategies exploiting binding scores that provide supporting evidence that a protein is likely to induce a T-cell immune response-one using random forest (a machine learning algorithm with a 72% sensitivity and 82.4% specificity and the other, using amino acid conservation scores with a 74.6% sensitivity and 70.5% specificity when applied to the 160 benchmark proteins. More importantly, the binding score strategies are valuable evidence contributors to the overall in silico

  16. Quantification and prediction of the detoxifying properties of humic substances related to their chemical binding to polycyclic aromatic hydrocarbons.

    Science.gov (United States)

    Perminova, I V; Grechishcheva, N Y; Kovalevskii, D V; Kudryavtsev, A V; Petrosyan, V S; Matorin, D N

    2001-10-01

    Effects of 27 different humic materials on the toxicity of polycyclic aromatic hydrocarbons (PAH) were studied for crustacean Daphnia magna. Sources included isolated humic acids, fulvic acids, and their combination from soil, peat, and freshwater. The PAH used were pyrene, fluoranthene, and anthracene. The observed reduction in toxicity of PAH in the presence of humic substances (HS) was shown to be a result of the detoxification effect caused by the chemical binding of PAH to HS and of the direct effect of HS on D. magna. An approach was developed to quantify the detoxifying impact of humic materials related to their chemical binding to PAH with a use of the "constant of detoxification" or "toxicological partition coefficient" K(oc)D. The latter was proposed to determine by fitting the experimental relationships of the detoxification effect versus concentration of HS. The obtained K(oc)D values were well tracked by the corresponding partition coefficients determined by the fluorescence quenching technique (K(oc)fq): K(oc)D=b x K(oc)fq, b (mean+/-Cl, n=26, P=95%)=2.6+/-0.3, 4.6+/-0.6, and 6.0+/-1.4 for pyrene, fluoranthene, and anthracene, respectively. The predictive relationships between the structure and detoxifying properties of humic materials in relation to PAH were developed. It was shown that the magnitude of the K(oc)D values correlated closely with the aromaticity of humic materials characterized with the 13C NMR descriptors (sigma(C)Ar, sigma(C)Ar/sigma(C)Alk) and atomic H/C ratio. The obtained relationships showed the highest detoxifying potential of the humic materials enriched with aromatics and allowed a conclusion on the chemical binding as the governing mechanism of the mitigating action of HS on the toxicity of PAH.

  17. Computational Immunology Meets Bioinformatics: The Use of Prediction Tools for Molecular Binding in the Simulation of the Immune System

    DEFF Research Database (Denmark)

    Rapin, N.; Lund, Ole; Bernaschi, M.

    2010-01-01

    We present a new approach to the study of the immune system that combines techniques of systems biology with information provided by data-driven prediction methods. To this end, we have extended an agent-based simulator of the immune response, C-IMMSIM, such that it represents pathogens, as well......-protein potential measurements, for assessing molecular binding in the context of immune complexes. We benchmark the resulting model by simulating a classical immunization experiment that reproduces the development of immune memory. We also investigate the role of major histocompatibility complex (MHC) haplotype...... proliferate more than any other. These results show that the simulator produces dynamics that are stable and consistent with basic immunological knowledge. We believe that the combination of genomic information and simulation of the dynamics of the immune system, in one single tool, can offer new perspectives...

  18. Mannose binding lectin (MBL levels predict lung function decline in severe asthma

    Directory of Open Access Journals (Sweden)

    Ilonka. H. van Veen

    2006-12-01

    Full Text Available There is increasing evidence that activation of the complement system in asthma contributes to ongoing inflammation, tissue damage and airway remodeling. Mannose binding lectin (MBL is a pattern recognition molecule that serves as the key mediator of the lectin pathway of complement activation. MBL levels are genetically determined and vary widely amongst individuals. In the present study we hypothesized that high MBL levels in asthma are associated with increased loss of lung function over time, as a consequence of inflammatory tissue damage. We measured serum MBL levels by ELISA in 68 patients with severe asthma and prospectively determined the change in post-bronchodilator (pb FEV1 over a mean period of 5.7 years. The relationship between MBL and change in pbFEV1 (FEV1 was analysed using (multiple regression analysis and corrected for possible confounders (age, sex, age of onset, asthma duration, and pbFEV1. The median (range MBL level was 332 (10.8-3587 ng·ml–1. MBL was significantly associated with FEV1 (p<0.04. Patients with a high MBL level (332 ng·ml–1 had an increased risk of lung function decline compared to those with low MBL levels (OR (CI: 3.16 (1.14-8.79, p = 0.027; the excess decline being 42 ml·yr–1 (p = 0.01. We conclude that a high MBL level is associated with an increased decline in lung function in patients with severe asthma. MBL might provide a clue towards better understanding of the pathophysiology of ongoing inflammation and subsequent decline in lung function of patients with severe asthma.

  19. Predictive models for identifying the binding activity of structurally diverse chemicals to human pregnane X receptor.

    Science.gov (United States)

    Yin, Cen; Yang, Xianhai; Wei, Mengbi; Liu, Huihui

    2017-08-01

    Toxic chemicals entered into human body would undergo a series of metabolism, transport and excretion, and the key roles played in there processes were metabolizing enzymes, which was regulated by the pregnane X receptor (PXR). However, some chemicals in environment could activate or antagonize human pregnane X receptor, thereby leading to a disturbance of normal physiological systems. In this study, based on a larger number of 2724 structurally diverse chemicals, we developed qualitative classification models by the k-nearest neighbor method. Moreover, the logarithm of 20 and 50% effective concentrations (log EC 20 and log EC 50 ) was used to establish quantitative structure-activity relationship (QSAR) models. With the classification model, two descriptors were enough to establish acceptable models, with the sensitivity, specificity, and accuracy being larger than 0.7, highlighting a high classification performance of the models. With two QSAR models, the statistics parameters with the correlation coefficient (R 2 ) of 0.702-0.749 and the cross-validation and external validation coefficient (Q 2 ) of 0.643-0.712, this indicated that the models complied with the criteria proposed in previous studies, i.e., R 2  > 0.6, Q 2  > 0.5. The small root mean square error (RMSE) of 0.254-0.414 and the good consistency between observed and predicted values proved satisfactory goodness of fit, robustness, and predictive ability of the developed QSAR models. Additionally, the applicability domains were characterized by the Euclidean distance-based approach and Williams plot, and results indicated that the current models had a wide applicability domain, which especially included a few classes of environmental contaminant, those that were not included in the previous models.

  20. Penicillin-Binding Protein Transpeptidase Signatures for Tracking and Predicting β-Lactam Resistance Levels in Streptococcus pneumoniae

    Directory of Open Access Journals (Sweden)

    Yuan Li

    2016-06-01

    Full Text Available β-Lactam antibiotics are the drugs of choice to treat pneumococcal infections. The spread of β-lactam-resistant pneumococci is a major concern in choosing an effective therapy for patients. Systematically tracking β-lactam resistance could benefit disease surveillance. Here we developed a classification system in which a pneumococcal isolate is assigned to a “PBP type” based on sequence signatures in the transpeptidase domains (TPDs of the three critical penicillin-binding proteins (PBPs, PBP1a, PBP2b, and PBP2x. We identified 307 unique PBP types from 2,528 invasive pneumococcal isolates, which had known MICs to six β-lactams based on broth microdilution. We found that increased β-lactam MICs strongly correlated with PBP types containing divergent TPD sequences. The PBP type explained 94 to 99% of variation in MICs both before and after accounting for genomic backgrounds defined by multilocus sequence typing, indicating that genomic backgrounds made little independent contribution to β-lactam MICs at the population level. We further developed and evaluated predictive models of MICs based on PBP type. Compared to microdilution MICs, MICs predicted by PBP type showed essential agreement (MICs agree within 1 dilution of >98%, category agreement (interpretive results agree of >94%, a major discrepancy (sensitive isolate predicted as resistant rate of <3%, and a very major discrepancy (resistant isolate predicted as sensitive rate of <2% for all six β-lactams. Thus, the PBP transpeptidase signatures are robust indicators of MICs to different β-lactam antibiotics in clinical pneumococcal isolates and serve as an accurate alternative to phenotypic susceptibility testing.

  1. Posing Problems that Matter: Investigating School Overcrowding

    Science.gov (United States)

    Turner, Erin E.; Font Strawhun, Beatriz T.

    2007-01-01

    This article shows how sixth graders engaged in authentic problem posing related to overcrowding at their school. Students posed authentic problems about their school space and then used mathematics as a tool to investigate and act on the situation. (Contains 6 figures.)

  2. Preparatory power posing affects nonverbal presence and job interview performance.

    Science.gov (United States)

    Cuddy, Amy J C; Wilmuth, Caroline A; Yap, Andy J; Carney, Dana R

    2015-07-01

    The authors tested whether engaging in expansive (vs. contractive) "power poses" before a stressful job interview--preparatory power posing--would enhance performance during the interview. Participants adopted high-power (i.e., expansive, open) poses or low-power (i.e., contractive, closed) poses, and then prepared and delivered a speech to 2 evaluators as part of a mock job interview. All interview speeches were videotaped and coded for overall performance and hireability and for 2 potential mediators: verbal content (e.g., structure, content) and nonverbal presence (e.g., captivating, enthusiastic). As predicted, those who prepared for the job interview with high- (vs. low-) power poses performed better and were more likely to be chosen for hire; this relation was mediated by nonverbal presence, but not by verbal content. Although previous research has focused on how a nonverbal behavior that is enacted during interactions and observed by perceivers affects how those perceivers evaluate and respond to the actor, this experiment focused on how a nonverbal behavior that is enacted before the interaction and unobserved by perceivers affects the actor's performance, which, in turn, affects how perceivers evaluate and respond to the actor. This experiment reveals a theoretically novel and practically informative result that demonstrates the causal relation between preparatory nonverbal behavior and subsequent performance and outcomes. (c) 2015 APA, all rights reserved).

  3. Predicting and analyzing DNA-binding domains using a systematic approach to identifying a set of informative physicochemical and biochemical properties

    Science.gov (United States)

    2011-01-01

    Background Existing methods of predicting DNA-binding proteins used valuable features of physicochemical properties to design support vector machine (SVM) based classifiers. Generally, selection of physicochemical properties and determination of their corresponding feature vectors rely mainly on known properties of binding mechanism and experience of designers. However, there exists a troublesome problem for designers that some different physicochemical properties have similar vectors of representing 20 amino acids and some closely related physicochemical properties have dissimilar vectors. Results This study proposes a systematic approach (named Auto-IDPCPs) to automatically identify a set of physicochemical and biochemical properties in the AAindex database to design SVM-based classifiers for predicting and analyzing DNA-binding domains/proteins. Auto-IDPCPs consists of 1) clustering 531 amino acid indices in AAindex into 20 clusters using a fuzzy c-means algorithm, 2) utilizing an efficient genetic algorithm based optimization method IBCGA to select an informative feature set of size m to represent sequences, and 3) analyzing the selected features to identify related physicochemical properties which may affect the binding mechanism of DNA-binding domains/proteins. The proposed Auto-IDPCPs identified m=22 features of properties belonging to five clusters for predicting DNA-binding domains with a five-fold cross-validation accuracy of 87.12%, which is promising compared with the accuracy of 86.62% of the existing method PSSM-400. For predicting DNA-binding sequences, the accuracy of 75.50% was obtained using m=28 features, where PSSM-400 has an accuracy of 74.22%. Auto-IDPCPs and PSSM-400 have accuracies of 80.73% and 82.81%, respectively, applied to an independent test data set of DNA-binding domains. Some typical physicochemical properties discovered are hydrophobicity, secondary structure, charge, solvent accessibility, polarity, flexibility, normalized Van Der

  4. Sex hormone binding globulin - an important biomarker for predicting PCOS risk: A systematic review and meta-analysis.

    Science.gov (United States)

    Deswal, Ritu; Yadav, Arun; Dang, Amita Suneja

    2018-02-01

    Sex hormone-binding globulin (SHBG) is a glycoprotein which regulates bioavailability of sex steroid hormones. Interest in SHBG has escalated in recent years because of its inverse association with polycystic ovary syndrome (PCOS), obesity, insulin resistance, metabolic syndrome, and diabetes type II. This meta-analysis was performed to examine the associations of SHBG with PCOS and to correlate serum SHBG levels with various PCOS associated endocrine and metabolic dysregulation as well as to determine the effects of various therapeutic agents on serum SHBG levels in PCOS patients in order to assess the true accuracy of SHBG in the prediction of PCOS. A literature search was performed using Pub-Med, Science direct, google scholar, EMBASE, and Cochrane library. A total of 675 relevant records were identified, of which 62 articles were included. Meta-analysis using a random-effects model was performed using STATA version 13 to calculate standardized mean difference (SMD) with 95% confidence intervals (95 % CIs). SHBG levels in controls were significantly higher than that of PCOS patients (SMD= -0.83, 95%CI = -1.01, -0.64), with significant heterogeneity across studies (I 2 = 93.9% and p=0.000). Our results suggest that the lower serum SHBG levels are associated with the risk of PCOS. SHBG may also play an important role in various metabolic disturbances in PCOS patients. Therapeutic interventions improved SHBG levels in PCOS women which further reduced PCOS associated complications. Therefore, SHBG levels may prove to be a useful biomarker for the diagnosis and treatment of PCOS. Systematic review registration: PROSPERO CRD42017057972 Abbreviations: PCOS: polycystic ovary syndrome; SHBG: sex hormone-binding globulin.

  5. AutoSite: an automated approach for pseudo-ligands prediction—from ligand-binding sites identification to predicting key ligand atoms

    Science.gov (United States)

    Ravindranath, Pradeep Anand; Sanner, Michel F.

    2016-01-01

    Motivation: The identification of ligand-binding sites from a protein structure facilitates computational drug design and optimization, and protein function assignment. We introduce AutoSite: an efficient software tool for identifying ligand-binding sites and predicting pseudo ligand corresponding to each binding site identified. Binding sites are reported as clusters of 3D points called fills in which every point is labelled as hydrophobic or as hydrogen bond donor or acceptor. From these fills AutoSite derives feature points: a set of putative positions of hydrophobic-, and hydrogen-bond forming ligand atoms. Results: We show that AutoSite identifies ligand-binding sites with higher accuracy than other leading methods, and produces fills that better matches the ligand shape and properties, than the fills obtained with a software program with similar capabilities, AutoLigand. In addition, we demonstrate that for the Astex Diverse Set, the feature points identify 79% of hydrophobic ligand atoms, and 81% and 62% of the hydrogen acceptor and donor hydrogen ligand atoms interacting with the receptor, and predict 81.2% of water molecules mediating interactions between ligand and receptor. Finally, we illustrate potential uses of the predicted feature points in the context of lead optimization in drug discovery projects. Availability and Implementation: http://adfr.scripps.edu/AutoDockFR/autosite.html Contact: sanner@scripps.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27354702

  6. In silico peptide-binding predictions of passerine MHC class I reveal similarities across distantly related species, suggesting convergence on the level of protein function

    DEFF Research Database (Denmark)

    Follin, Elna; Karlsson, Maria; Lundegaard, Claus

    2013-01-01

    compared to most mammals. To elucidate the reason for this large number of genes, we compared 14 MHC class I alleles (α1–α3 domains), from great reed warbler, house sparrow and tree sparrow, via phylogenetic analysis, homology modelling and in silico peptide-binding predictions to investigate...

  7. The timing of associative memory formation: frontal lobe and anterior medial temporal lobe activity at associative binding predicts memory

    Science.gov (United States)

    Hales, J. B.

    2011-01-01

    The process of associating items encountered over time and across variable time delays is fundamental for creating memories in daily life, such as for stories and episodes. Forming associative memory for temporally discontiguous items involves medial temporal lobe structures and additional neocortical processing regions, including prefrontal cortex, parietal lobe, and lateral occipital regions. However, most prior memory studies, using concurrently presented stimuli, have failed to examine the temporal aspect of successful associative memory formation to identify when activity in these brain regions is predictive of associative memory formation. In the current study, functional MRI data were acquired while subjects were shown pairs of sequentially presented visual images with a fixed interitem delay within pairs. This design allowed the entire time course of the trial to be analyzed, starting from onset of the first item, across the 5.5-s delay period, and through offset of the second item. Subjects then completed a postscan recognition test for the items and associations they encoded during the scan and their confidence for each. After controlling for item-memory strength, we isolated brain regions selectively involved in associative encoding. Consistent with prior findings, increased regional activity predicting subsequent associative memory success was found in anterior medial temporal lobe regions of left perirhinal and entorhinal cortices and in left prefrontal cortex and lateral occipital regions. The temporal separation within each pair, however, allowed extension of these findings by isolating the timing of regional involvement, showing that increased response in these regions occurs during binding but not during maintenance. PMID:21248058

  8. Learning toward practical head pose estimation

    Science.gov (United States)

    Sang, Gaoli; He, Feixiang; Zhu, Rong; Xuan, Shibin

    2017-08-01

    Head pose is useful information for many face-related tasks, such as face recognition, behavior analysis, human-computer interfaces, etc. Existing head pose estimation methods usually assume that the face images have been well aligned or that sufficient and precise training data are available. In practical applications, however, these assumptions are very likely to be invalid. This paper first investigates the impact of the failure of these assumptions, i.e., misalignment of face images, uncertainty and undersampling of training data, on head pose estimation accuracy of state-of-the-art methods. A learning-based approach is then designed to enhance the robustness of head pose estimation to these factors. To cope with misalignment, instead of using hand-crafted features, it seeks suitable features by learning from a set of training data with a deep convolutional neural network (DCNN), such that the training data can be best classified into the correct head pose categories. To handle uncertainty and undersampling, it employs multivariate labeling distributions (MLDs) with dense sampling intervals to represent the head pose attributes of face images. The correlation between the features and the dense MLD representations of face images is approximated by a maximum entropy model, whose parameters are optimized on the given training data. To estimate the head pose of a face image, its MLD representation is first computed according to the model based on the features extracted from the image by the trained DCNN, and its head pose is then assumed to be the one corresponding to the peak in its MLD. Evaluation experiments on the Pointing'04, FacePix, Multi-PIE, and CASIA-PEAL databases prove the effectiveness and efficiency of the proposed method.

  9. Binding Mode Prediction of 5-Hydroxytryptamine 2C Receptor Ligands by Homology Modeling and Molecular Docking Analysis

    International Nuclear Information System (INIS)

    Ahmed, Asif; Nagarajan, Shanthi; Doddareddy, Munikumar Reddy; Cho, Yong Seo; Pae, Ae Nim

    2011-01-01

    Serotonin or 5-hydroxytryptamine subtype 2C (5-HT 2C ) receptor belongs to class A amine subfamily of Gprotein- coupled receptor (GPCR) super family and its ligands has therapeutic promise as anti-depressant and -obesity agents. So far, bovine rhodopsin from class A opsin subfamily was the mostly used X-ray crystal template to model this receptor. Here, we explained homology model using beta 2 adrenergic receptor (β2AR), the model was energetically minimized and validated by flexible ligand docking with known agonists and antagonists. In the active site Asp134, Ser138 of transmembrane 3 (TM3), Arg195 of extracellular loop 2 (ECL2) and Tyr358 of TM7 were found as important residues to interact with agonists. In addition to these, V208 of ECL2 and N351 of TM7 was found to interact with antagonists. Several conserved residues including Trp324, Phe327 and Phe328 were also found to contribute hydrophobic interaction. The predicted ligand binding mode is in good agreement with published mutagenesis and homology model data. This new template derived homology model can be useful for further virtual screening based lead identification

  10. Binding Mode Prediction of 5-Hydroxytryptamine 2C Receptor Ligands by Homology Modeling and Molecular Docking Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Ahmed, Asif; Nagarajan, Shanthi; Doddareddy, Munikumar Reddy; Cho, Yong Seo; Pae, Ae Nim [Korea Institute of Science and Technology, Seoul (Korea, Republic of)

    2011-06-15

    Serotonin or 5-hydroxytryptamine subtype 2C (5-HT{sub 2C}) receptor belongs to class A amine subfamily of Gprotein- coupled receptor (GPCR) super family and its ligands has therapeutic promise as anti-depressant and -obesity agents. So far, bovine rhodopsin from class A opsin subfamily was the mostly used X-ray crystal template to model this receptor. Here, we explained homology model using beta 2 adrenergic receptor (β2AR), the model was energetically minimized and validated by flexible ligand docking with known agonists and antagonists. In the active site Asp134, Ser138 of transmembrane 3 (TM3), Arg195 of extracellular loop 2 (ECL2) and Tyr358 of TM7 were found as important residues to interact with agonists. In addition to these, V208 of ECL2 and N351 of TM7 was found to interact with antagonists. Several conserved residues including Trp324, Phe327 and Phe328 were also found to contribute hydrophobic interaction. The predicted ligand binding mode is in good agreement with published mutagenesis and homology model data. This new template derived homology model can be useful for further virtual screening based lead identification.

  11. Novel biomarkers predict liver fibrosis in hepatitis C patients: alpha 2 macroglobulin, vitamin D binding protein and apolipoprotein AI

    Directory of Open Access Journals (Sweden)

    Lee Jing-Ying

    2010-07-01

    Full Text Available Abstract Background The gold standard of assessing liver fibrosis is liver biopsy, which is invasive and not without risk. Therefore, searching for noninvasive serologic biomarkers for liver fibrosis is an importantly clinical issue. Methods A total of 16 healthy volunteers and 45 patients with chronic hepatitis C virus (HCV were enrolled (F0: n = 16, F1: n = 7, F2: n = 17, F3: n = 8 and F4: n = 13, according to the METAVIR classification. Three serum samples of each fibrotic stage were analyzed by two-dimension difference gel electrophoresis (2D-DIGE. The differential proteins were identified by the cooperation of MALDI-TOF/TOF and MASCOT; then western blotting and Bio-Plex Suspension Array were used to quantify the protein levels. Results Three prominent candidate biomarkers were identified: alpha 2 macroglobulin (A2M is up regulated; vitamin D binding protein (VDBP and apolipoprotein AI (ApoAI are down regulated. The serum concentration of A2M was significantly different among normal, mild (F1/F2 and advanced fibrosis (F3/F4 (p p Conclusions This study not only reveals three putative biomarkers of liver fibrosis (A2M, VDBP and ApoAI but also proves the differential expressions of those markers in different stages of fibrosis. We expect that combination of these novel biomarkers could be applied clinically to predict the stage of liver fibrosis without the need of liver biopsy.

  12. Circulating Serum Fatty Acid-Binding Protein 4 Levels Predict the Development of Diabetic Retinopathy in Type 2 Diabetic Patients.

    Science.gov (United States)

    Zhang, Xian-Zhao; Tu, Wen-Jun; Wang, Hong; Zhao, Qi; Liu, Qiang; Sun, Lei; Yu, Lei

    2018-03-01

    Fatty acid-binding protein 4 (FABP4) has been implicated in the pathology of diabetes and macrovascular diseases. Serum FABP4 levels were determined in type 2 diabetic patients without diabetic retinopathy (DR) at admission in order to investigate a possible contribution of FABP4 to the increased risk of 5-year incidence of DR. Cohort study. A total of 738 patients with type 2 diabetes without DR were consecutively enrolled and followed up prospectively. Retinopathy evaluation was annually performed by ophthalmologists in the following 5 years. Multivariate analyses were performed using logistic regression models. During the follow-up period, 152 (20.60% [95% CI: 17.68%-23.51%]) patients developed DR and 60 (8.13% [95% CI: 6.16%-10.10%]) patients developed vision-threatening DR (VTDR). Nonparametric Spearman rank correlation revealed a statistically significant positive correlation between serum FABP 4 level and international Clinical Diabetic Retinopathy Severity Scales (r = 0.348; P prediction in Chinese patients with T2DM, and strict glycemic control and more frequent retinal examination should be highlighted for T2DM patients with the highest quartile range of FABP4. Copyright © 2017 Elsevier Inc. All rights reserved.

  13. Structure–property reduced order model for viscosity prediction in single-component CO 2 -binding organic liquids

    Energy Technology Data Exchange (ETDEWEB)

    Cantu, David C.; Malhotra, Deepika; Koech, Phillip K.; Heldebrant, David J.; Zheng, Feng (Richard); Freeman, Charles J.; Rousseau, Roger; Glezakou, Vassiliki-Alexandra

    2016-01-01

    CO2 capture from power generation with aqueous solvents remains energy intensive due to the high water content of the current technology, or the high viscosity of non-aqueous alternatives. Quantitative reduced models, connecting molecular structure to bulk properties, are key for developing structure-property relationships that enable molecular design. In this work, we describe such a model that quantitatively predicts viscosities of CO2 binding organic liquids (CO2BOLs) based solely on molecular structure and the amount of bound CO2. The functional form of the model correlates the viscosity with the CO2 loading and an electrostatic term describing the charge distribution between the CO2-bearing functional group and the proton-receiving amine. Molecular simulations identify the proton shuttle between these groups within the same molecule to be the critical indicator of low viscosity. The model, developed to allow for quick screening of solvent libraries, paves the way towards the rational design of low viscosity non-aqueous solvent systems for post-combustion CO2 capture. Following these theoretical recommendations, synthetic efforts of promising candidates and viscosity measurement provide experimental validation and verification.

  14. Students’ Creativity: Problem Posing in Structured Situation

    Science.gov (United States)

    Amalina, I. K.; Amirudin, M.; Budiarto, M. T.

    2018-01-01

    This is a qualitative research concerning on students’ creativity on problem posing task. The study aimed at describing the students’ creative thinking ability to pose the mathematics problem in structured situations with varied condition of given problems. In order to find out the students’ creative thinking ability, an analysis of mathematics problem posing test based on fluency, novelty, and flexibility and interview was applied for categorizing students’ responses on that task. The data analysis used the quality of problem posing and categorized in 4 level of creativity. The results revealed from 29 secondary students grade 8, a student in CTL (Creative Thinking Level) 1 met the fluency. A student in CTL 2 met the novelty, while a student in CTL 3 met both fluency and novelty and no one in CTL 4. These results are affected by students’ mathematical experience. The findings of this study highlight that student’s problem posing creativity are dependent on their experience in mathematics learning and from the point of view of which students start to pose problem.

  15. First Trimester Maternal Glycated Hemoglobin and Sex Hormone-Binding Globulin Do Not Predict Third Trimester Glucose Intolerance of Pregnancy.

    Science.gov (United States)

    Berggren, Erica K; Boggess, Kim A; Mathew, Leny; Culhane, Jennifer

    2017-04-01

    Early pregnancy prediction of third trimester glucose intolerance may identify a population of women whose trajectory toward gestational diabetes mellitus (GDM) is modifiable. We assessed whether first trimester glycated hemoglobin (HbA1c) and sex hormone-binding globulin (SHBG), markers of insulin resistance, predicted third trimester glucose intolerance. Nondiabetic women with singleton pregnancies enrolled in a prospective observational study, 11 0/7 to 14 6/7 weeks. At enrollment, maternal characteristics, medical history, and blood samples were collected for HbA1c and SHBG. Two-step GDM screening was performed, 22 0/7 to 33 6/7 weeks. A 50 g oral glucose tolerance test ≥130 mg/dL defined screen positive, or glucose intolerance. Carpenter-Coustan criteria diagnosed GDM. Means HbA1c and SHBG were compared between glucose-intolerant versus normoglycemic women, and GDM versus no GDM women. We report unadjusted and adjusted odds ratios (OR; 95% confidence interval [CI]) of regression analyses. Adjusted models include race, enrollment body mass index, and history of GDM. Among 250 women, 29% were glucose intolerant and 6% had GDM. Among glucose-intolerant women, HbA1c was higher (5.3 ± 0.3 vs. 5.1 ± 0.3, P = .01) and associated with glucose intolerance in unadjusted, but not adjusted, models (OR: 2.9, 95% CI: 1.2-7.1; adjusted odds ratio [aOR]: 2.0, 95% CI: 0.7-5.4). Among GDM women, HbA1c was higher (5.4 ± 0.4 vs 5.2 ± 0.3, P = .002) and SHBG was lower (228 ± 72 vs 288 ± 93 mmol/L, P = .02). The HbA1c predicted GDM in unadjusted (OR: 13.2, 95% CI: 2.6-68.0) but not adjusted (aOR: 6.7, 95% CI: 0.8-55.2) models. Although metabolic alterations may well precede third trimester glucose intolerance, neither HbA1c of SHBG remained an independent predictor of glucose intolerance or GDM in adjusted models.

  16. Homology modelling of frequent HLA class-II alleles: A perspective to improve prediction of HLA binding peptide and understand the HLA associated disease susceptibility.

    Science.gov (United States)

    Kashyap, Manju; Farooq, Umar; Jaiswal, Varun

    2016-10-01

    Human leukocyte antigen (HLA) plays significant role via the regulation of immune system and contribute in the progression and protection of many diseases. HLA molecules bind and present peptides to T- cell receptors which generate the immune response. HLA peptide interaction and molecular function of HLA molecule is the key to predict peptide binding and understanding its role in different diseases. The availability of accurate three dimensional (3D) structures is the initial step towards this direction. In the present work, homology modelling of important and frequent HLA-DRB1 alleles (07:01, 11:01 and 09:01) was done and acceptable models were generated. These modelled alleles were further refined and cross validated by using several methods including Ramachandran plot, Z-score, ERRAT analysis and root mean square deviation (RMSD) calculations. It is known that numbers of allelic variants are related to the susceptibility or protection of various infectious diseases. Difference in amino acid sequences and structures of alleles were also studied to understand the association of HLA with disease susceptibility and protection. Susceptible alleles showed more amino acid variations than protective alleles in three selected diseases caused by different pathogens. Amino acid variations at binding site were found to be more than other part of alleles. RMSD values were also higher at variable positions within binding site. Higher RMSD values indicate that mutations occurring at peptide binding site alter protein structure more than rest of the protein. Hence, these findings and modelled structures can be used to design HLA-DRB1 binding peptides to overcome low prediction accuracy of HLA class II binding peptides. Furthermore, it may help to understand the allele specific molecular mechanisms involved in susceptibility/resistance against pathogenic diseases. Copyright © 2016 Elsevier B.V. All rights reserved.

  17. Analysis of a predicted nuclear localization signal: implications for the intracellular localization and function of the Saccharomyces cerevisiae RNA-binding protein Scp160.

    Science.gov (United States)

    Brykailo, Melissa A; McLane, Laura M; Fridovich-Keil, Judith; Corbett, Anita H

    2007-01-01

    Gene expression is controlled by RNA-binding proteins that modulate the synthesis, processing, transport and stability of various classes of RNA. Some RNA-binding proteins shuttle between the nucleus and cytoplasm and are thought to bind to RNA transcripts in the nucleus and remain bound during translocation to the cytoplasm. One RNA-binding protein that has been hypothesized to function in this manner is the Saccharomyces cerevisiae Scp160 protein. Although the steady-state localization of Scp160 is cytoplasmic, previous studies have identified putative nuclear localization (NLS) and nuclear export (NES) signals. The goal of this study was to test the hypothesis that Scp160 is a nucleocytoplasmic shuttling protein. We exploited a variety of yeast export mutants to capture any potential nuclear accumulation of Scp160 and found no evidence that Scp160 enters the nucleus. These localization studies were complemented by a mutational analysis of the predicted NLS. Results indicate that key basic residues within the predicted NLS of Scp160 can be altered without severely affecting Scp160 function. This finding has important implications for understanding the function of Scp160, which is likely limited to the cytoplasm. Additionally, our results provide strong evidence that the presence of a predicted nuclear localization signal within the sequence of a protein should not lead to the assumption that the protein enters the nucleus in the absence of additional experimental evidence.

  18. An improved silhouette for human pose estimation

    Science.gov (United States)

    Hawes, Anthony H.; Iftekharuddin, Khan M.

    2017-08-01

    We propose a novel method for analyzing images that exploits the natural lines of a human poses to find areas where self-occlusion could be present. Errors caused by self-occlusion cause several modern human pose estimation methods to mis-identify body parts, which reduces the performance of most action recognition algorithms. Our method is motivated by the observation that, in several cases, occlusion can be reasoned using only boundary lines of limbs. An intelligent edge detection algorithm based on the above principle could be used to augment the silhouette with information useful for pose estimation algorithms and push forward progress on occlusion handling for human action recognition. The algorithm described is applicable to computer vision scenarios involving 2D images and (appropriated flattened) 3D images.

  19. Non-standard and improperly posed problems

    CERN Document Server

    Straughan, Brian; Ames, William F

    1997-01-01

    Written by two international experts in the field, this book is the first unified survey of the advances made in the last 15 years on key non-standard and improperly posed problems for partial differential equations.This reference for mathematicians, scientists, and engineers provides an overview of the methodology typically used to study improperly posed problems. It focuses on structural stability--the continuous dependence of solutions on the initial conditions and the modeling equations--and on problems for which data are only prescribed on part of the boundary.The book addresses continuou

  20. Flexible Polyhedral Surfaces with Two Flat Poses

    Directory of Open Access Journals (Sweden)

    Hellmuth Stachel

    2015-05-01

    Full Text Available We present three types of polyhedral surfaces, which are continuously flexible and have not only an initial pose, where all faces are coplanar, but pass during their self-motion through another pose with coplanar faces (“flat pose”. These surfaces are examples of so-called rigid origami, since we only admit exact flexions, i.e., each face remains rigid during the motion; only the dihedral angles vary. We analyze the geometry behind Miura-ori and address Kokotsakis’ example of a flexible tessellation with the particular case of a cyclic quadrangle. Finally, we recall Bricard’s octahedra of Type 3 and their relation to strophoids.

  1. RNABindRPlus: a predictor that combines machine learning and sequence homology-based methods to improve the reliability of predicted RNA-binding residues in proteins.

    Science.gov (United States)

    Walia, Rasna R; Xue, Li C; Wilkins, Katherine; El-Manzalawy, Yasser; Dobbs, Drena; Honavar, Vasant

    2014-01-01

    Protein-RNA interactions are central to essential cellular processes such as protein synthesis and regulation of gene expression and play roles in human infectious and genetic diseases. Reliable identification of protein-RNA interfaces is critical for understanding the structural bases and functional implications of such interactions and for developing effective approaches to rational drug design. Sequence-based computational methods offer a viable, cost-effective way to identify putative RNA-binding residues in RNA-binding proteins. Here we report two novel approaches: (i) HomPRIP, a sequence homology-based method for predicting RNA-binding sites in proteins; (ii) RNABindRPlus, a new method that combines predictions from HomPRIP with those from an optimized Support Vector Machine (SVM) classifier trained on a benchmark dataset of 198 RNA-binding proteins. Although highly reliable, HomPRIP cannot make predictions for the unaligned parts of query proteins and its coverage is limited by the availability of close sequence homologs of the query protein with experimentally determined RNA-binding sites. RNABindRPlus overcomes these limitations. We compared the performance of HomPRIP and RNABindRPlus with that of several state-of-the-art predictors on two test sets, RB44 and RB111. On a subset of proteins for which homologs with experimentally determined interfaces could be reliably identified, HomPRIP outperformed all other methods achieving an MCC of 0.63 on RB44 and 0.83 on RB111. RNABindRPlus was able to predict RNA-binding residues of all proteins in both test sets, achieving an MCC of 0.55 and 0.37, respectively, and outperforming all other methods, including those that make use of structure-derived features of proteins. More importantly, RNABindRPlus outperforms all other methods for any choice of tradeoff between precision and recall. An important advantage of both HomPRIP and RNABindRPlus is that they rely on readily available sequence and sequence

  2. RNABindRPlus: A Predictor that Combines Machine Learning and Sequence Homology-Based Methods to Improve the Reliability of Predicted RNA-Binding Residues in Proteins

    Science.gov (United States)

    Walia, Rasna R.; Xue, Li C.; Wilkins, Katherine; El-Manzalawy, Yasser; Dobbs, Drena; Honavar, Vasant

    2014-01-01

    Protein-RNA interactions are central to essential cellular processes such as protein synthesis and regulation of gene expression and play roles in human infectious and genetic diseases. Reliable identification of protein-RNA interfaces is critical for understanding the structural bases and functional implications of such interactions and for developing effective approaches to rational drug design. Sequence-based computational methods offer a viable, cost-effective way to identify putative RNA-binding residues in RNA-binding proteins. Here we report two novel approaches: (i) HomPRIP, a sequence homology-based method for predicting RNA-binding sites in proteins; (ii) RNABindRPlus, a new method that combines predictions from HomPRIP with those from an optimized Support Vector Machine (SVM) classifier trained on a benchmark dataset of 198 RNA-binding proteins. Although highly reliable, HomPRIP cannot make predictions for the unaligned parts of query proteins and its coverage is limited by the availability of close sequence homologs of the query protein with experimentally determined RNA-binding sites. RNABindRPlus overcomes these limitations. We compared the performance of HomPRIP and RNABindRPlus with that of several state-of-the-art predictors on two test sets, RB44 and RB111. On a subset of proteins for which homologs with experimentally determined interfaces could be reliably identified, HomPRIP outperformed all other methods achieving an MCC of 0.63 on RB44 and 0.83 on RB111. RNABindRPlus was able to predict RNA-binding residues of all proteins in both test sets, achieving an MCC of 0.55 and 0.37, respectively, and outperforming all other methods, including those that make use of structure-derived features of proteins. More importantly, RNABindRPlus outperforms all other methods for any choice of tradeoff between precision and recall. An important advantage of both HomPRIP and RNABindRPlus is that they rely on readily available sequence and sequence

  3. Genome-wide expression profiling, in vivo DNA binding analysis, and probabilistic motif prediction reveal novel Abf1 target genes during fermentation, respiration, and sporulation in yeast.

    Science.gov (United States)

    Schlecht, Ulrich; Erb, Ionas; Demougin, Philippe; Robine, Nicolas; Borde, Valérie; van Nimwegen, Erik; Nicolas, Alain; Primig, Michael

    2008-05-01

    The autonomously replicating sequence binding factor 1 (Abf1) was initially identified as an essential DNA replication factor and later shown to be a component of the regulatory network controlling mitotic and meiotic cell cycle progression in budding yeast. The protein is thought to exert its functions via specific interaction with its target site as part of distinct protein complexes, but its roles during mitotic growth and meiotic development are only partially understood. Here, we report a comprehensive approach aiming at the identification of direct Abf1-target genes expressed during fermentation, respiration, and sporulation. Computational prediction of the protein's target sites was integrated with a genome-wide DNA binding assay in growing and sporulating cells. The resulting data were combined with the output of expression profiling studies using wild-type versus temperature-sensitive alleles. This work identified 434 protein-coding loci as being transcriptionally dependent on Abf1. More than 60% of their putative promoter regions contained a computationally predicted Abf1 binding site and/or were bound by Abf1 in vivo, identifying them as direct targets. The present study revealed numerous loci previously unknown to be under Abf1 control, and it yielded evidence for the protein's variable DNA binding pattern during mitotic growth and meiotic development.

  4. Head Pose Estimation from Passive Stereo Images

    DEFF Research Database (Denmark)

    Breitenstein, Michael D.; Jensen, Jeppe; Høilund, Carsten

    2009-01-01

    function. Our algorithm incorporates 2D and 3D cues to make the system robust to low-quality range images acquired by passive stereo systems. It handles large pose variations (of ±90 ° yaw and ±45 ° pitch rotation) and facial variations due to expressions or accessories. For a maximally allowed error of 30...

  5. THE CHALLENGES POSED BY INFORMATION COMMUNICATION ...

    African Journals Online (AJOL)

    THE CHALLENGES POSED BY INFORMATION COMMUNICATION TECHNOLOGIES (ICT) FACILITIES TO. STUDENTS OF TERTIARY INSTITUTIONS IN BAUCHI STATE. Umaru F. Aliyu. Page 2 all-important technological tool in the production, marketing and use of information worldwide, (Ajala, 2010). The impact of ICTs ...

  6. Improved Prediction of Bovine Leucocyte Antigens (BoLA) Presented Ligands by Use of Mass-Spectrometry-Determined Ligand and in Vitro Binding Data

    DEFF Research Database (Denmark)

    Nielsen, Morten; Connelley, Tim; Ternette, Nicola

    2018-01-01

    of the data. We here outline a general pipeline for dealing with this challenge and accurately annotate ligands to the relevant MHC-I molecule they were eluted from by use of GibbsClustering and binding motif information inferred from in silico models. We illustrate the approach here in the context of MHC......, and predictors of peptide-MHC interactions constitute an attractive alternative. Recently, an increasing amount of MHC presented peptides identified by mass spectrometry (MS ligands) has been published. Handling and interpretation of MS ligand data is, in general, challenging due to the polyspecificity nature......-I molecules (BoLA) of cattle. Next, we demonstrate how such annotated BoLA MS ligand data can readily be integrated with in vitro binding affinity data in a prediction model with very high and unprecedented performance for identification of BoLA-I restricted T-cell epitopes. The prediction model is freely...

  7. Method of orthogonally splitting imaging pose measurement

    Science.gov (United States)

    Zhao, Na; Sun, Changku; Wang, Peng; Yang, Qian; Liu, Xintong

    2018-01-01

    In order to meet the aviation's and machinery manufacturing's pose measurement need of high precision, fast speed and wide measurement range, and to resolve the contradiction between measurement range and resolution of vision sensor, this paper proposes an orthogonally splitting imaging pose measurement method. This paper designs and realizes an orthogonally splitting imaging vision sensor and establishes a pose measurement system. The vision sensor consists of one imaging lens, a beam splitter prism, cylindrical lenses and dual linear CCD. Dual linear CCD respectively acquire one dimensional image coordinate data of the target point, and two data can restore the two dimensional image coordinates of the target point. According to the characteristics of imaging system, this paper establishes the nonlinear distortion model to correct distortion. Based on cross ratio invariability, polynomial equation is established and solved by the least square fitting method. After completing distortion correction, this paper establishes the measurement mathematical model of vision sensor, and determines intrinsic parameters to calibrate. An array of feature points for calibration is built by placing a planar target in any different positions for a few times. An terative optimization method is presented to solve the parameters of model. The experimental results show that the field angle is 52 °, the focus distance is 27.40 mm, image resolution is 5185×5117 pixels, displacement measurement error is less than 0.1mm, and rotation angle measurement error is less than 0.15°. The method of orthogonally splitting imaging pose measurement can satisfy the pose measurement requirement of high precision, fast speed and wide measurement range.

  8. Parameterizing the binding properties of dissolved organic matter with default values skews the prediction of copper solution speciation and ecotoxicity in soil.

    Science.gov (United States)

    Djae, Tanalou; Bravin, Matthieu N; Garnier, Cédric; Doelsch, Emmanuel

    2017-04-01

    Parameterizing speciation models by setting the percentage of dissolved organic matter (DOM) that is reactive (% r-DOM) toward metal cations at a single 65% default value is very common in predictive ecotoxicology. The authors tested this practice by comparing the free copper activity (pCu 2+  = -log 10 [Cu 2+ ]) measured in 55 soil sample solutions with pCu 2+ predicted with the Windermere humic aqueous model (WHAM) parameterized by default. Predictions of Cu toxicity to soil organisms based on measured or predicted pCu 2+ were also compared. Default WHAM parameterization substantially skewed the prediction of measured pCu 2+ by up to 2.7 pCu 2+ units (root mean square residual = 0.75-1.3) and subsequently the prediction of Cu toxicity for microbial functions, invertebrates, and plants by up to 36%, 45%, and 59% (root mean square residuals ≤9 %, 11%, and 17%), respectively. Reparametrizing WHAM by optimizing the 2 DOM binding properties (i.e., % r-DOM and the Cu complexation constant) within a physically realistic value range much improved the prediction of measured pCu 2+ (root mean square residual = 0.14-0.25). Accordingly, this WHAM parameterization successfully predicted Cu toxicity for microbial functions, invertebrates, and plants (root mean square residual ≤3.4%, 4.4%, and 5.8%, respectively). Thus, it is essential to account for the real heterogeneity in DOM binding properties for relatively accurate prediction of Cu speciation in soil solution and Cu toxic effects on soil organisms. Environ Toxicol Chem 2017;36:898-905. © 2016 SETAC. © 2016 SETAC.

  9. Pose Sentences: A new representation for action recognition using sequence of pose words

    NARCIS (Netherlands)

    Hatun, Kardelen; Duygulu, Pinar

    2008-01-01

    We propose a method for recognizing human actions in videos. Inspired from the recent bag-of-words approaches, we represent actions as documents consisting of words, where a word refers to the pose in a frame. Histogram of oriented gradients (HOG) features are used to describe poses, which are then

  10. Tridimensional pose estimation of a person head

    International Nuclear Information System (INIS)

    Perez Berenguer, Elisa; Soria, Carlos; Nasisi, Oscar; Mut, Vicente

    2007-01-01

    In this work, we present a method for estimating 3-D motion parameters; this method provides an alternative way for 3D head pose estimation from image sequence in the current computer vision literature. This method is robust over extended sequences and large head motions and accurately extracts the orientation angles of head from a single view. Experimental results show that this tracking system works well for development a human-computer interface for people that possess severe motor incapacity

  11. Driver head pose tracking with thermal camera

    Science.gov (United States)

    Bole, S.; Fournier, C.; Lavergne, C.; Druart, G.; Lépine, T.

    2016-09-01

    Head pose can be seen as a coarse estimation of gaze direction. In automotive industry, knowledge about gaze direction could optimize Human-Machine Interface (HMI) and Advanced Driver Assistance Systems (ADAS). Pose estimation systems are often based on camera when applications have to be contactless. In this paper, we explore uncooled thermal imagery (8-14μm) for its intrinsic night vision capabilities and for its invariance versus lighting variations. Two methods are implemented and compared, both are aided by a 3D model of the head. The 3D model, mapped with thermal texture, allows to synthesize a base of 2D projected models, differently oriented and labeled in yaw and pitch. The first method is based on keypoints. Keypoints of models are matched with those of the query image. These sets of matchings, aided with the 3D shape of the model, allow to estimate 3D pose. The second method is a global appearance approach. Among all 2D models of the base, algorithm searches the one which is the closest to the query image thanks to a weighted least squares difference.

  12. Has My Algorithm Succeeded? An Evaluator for Human Pose Estimators

    OpenAIRE

    Jammalamadaka, Nataraj; Zisserman, Andrew; Eichner, M.; Ferrari, Vittorio; Jawahar, C. V.

    2012-01-01

    Most current vision algorithms deliver their output ‘as is’, without indicating whether it is correct or not. In this paper we propose evaluator algorithms that predict if a vision algorithm has succeeded. We illustrate this idea for the case of Human Pose Estimation (HPE).We describe the stages required to learn and test an evaluator, including the use of an annotated ground truth dataset for training and testing the evaluator (and we provide a new dataset for the HPE case), and the developm...

  13. Coarse-grained/molecular mechanics of the TAS2R38 bitter taste receptor: experimentally-validated detailed structural prediction of agonist binding.

    Directory of Open Access Journals (Sweden)

    Alessandro Marchiori

    Full Text Available Bitter molecules in humans are detected by ∼25 G protein-coupled receptors (GPCRs. The lack of atomic resolution structure for any of them is complicating an in depth understanding of the molecular mechanisms underlying bitter taste perception. Here, we investigate the molecular determinants of the interaction of the TAS2R38 bitter taste receptor with its agonists phenylthiocarbamide (PTC and propylthiouracil (PROP. We use the recently developed hybrid Molecular Mechanics/Coarse Grained (MM/CG method tailored specifically for GPCRs. The method, through an extensive exploration of the conformational space in the binding pocket, allows the identification of several residues important for agonist binding that would have been very difficult to capture from the standard bioinformatics/docking approach. Our calculations suggest that both agonists bind to Asn103, Phe197, Phe264 and Trp201, whilst they do not interact with the so-called extra cellular loop 2, involved in cis-retinal binding in the GPCR rhodopsin. These predictions are consistent with data sets based on more than 20 site-directed mutagenesis and functional calcium imaging experiments of TAS2R38. The method could be readily used for other GPCRs for which experimental information is currently lacking.

  14. [A precise equilibrium equation for four steps of binding between TBP and TATA-box allows for the prediction of phenotypical expression upon mutation].

    Science.gov (United States)

    Ponomarenko, P M; Suslov, V V; Savinkova, L K; Ponomarenko, M P; Kolchanov, N A

    2010-01-01

    Among the main events of transcription initiation of TATA-containing genes in eukayotes are the recognition and binding of the TATA-box by the TATA-binding protein (TBP) to start the preinitiation complex formation on the nucleosomal DNA. Using the equilibrium equation for step-by-step TBP/TATA-binding, we have analyzed 69 experimental datasets on the characteristics of biologicacally important features altered by TATA-box mutations. Among these features, the TBP/TATA-complex parameters, the transcription level, the activity of gene products, yeast colony growth at a dose of growth inhibitor (phenotype), and the heterogenity of the response of a population to unspecific environmental stress have been described. Significant correlations were found between in silico prediction for TBP/TATA affinity and experimental data for in vivo and in vitro test-systems based on 15 cell types of 19 species, RNA polymerases II and III, and natural, recombinant or mutant TBP. Such an invariant impact of the step-by-step TBP/TATA-binding on the biological activity of complex systems, from a molecule to a population, might be due to the fact that TBP/TATA-complex formation precedes specific steps of transcription machinery assembly, which provide the multivariant jigsaw puzzle according to the expression pattern of each eukaryotic gene.

  15. Predicting Allosteric Effects from Orthosteric Binding in Hsp90-Ligand Interactions: Implications for Fragment-Based Drug Design.

    Directory of Open Access Journals (Sweden)

    Arun Chandramohan

    2016-06-01

    Full Text Available A key question in mapping dynamics of protein-ligand interactions is to distinguish changes at binding sites from those associated with long range conformational changes upon binding at distal sites. This assumes a greater challenge when considering the interactions of low affinity ligands (dissociation constants, KD, in the μM range or lower. Amide hydrogen deuterium Exchange mass spectrometry (HDXMS is a robust method that can provide both structural insights and dynamics information on both high affinity and transient protein-ligand interactions. In this study, an application of HDXMS for probing the dynamics of low affinity ligands to proteins is described using the N-terminal ATPase domain of Hsp90. Comparison of Hsp90 dynamics between high affinity natural inhibitors (KD ~ nM and fragment compounds reveal that HDXMS is highly sensitive in mapping the interactions of both high and low affinity ligands. HDXMS reports on changes that reflect both orthosteric effects and allosteric changes accompanying binding. Orthosteric sites can be identified by overlaying HDXMS onto structural information of protein-ligand complexes. Regions distal to orthosteric sites indicate long range conformational changes with implications for allostery. HDXMS, thus finds powerful utility as a high throughput method for compound library screening to identify binding sites and describe allostery with important implications for fragment-based ligand discovery (FBLD.

  16. Urinary liver-type fatty acid-binding protein predicts progression to nephropathy in type 1 diabetic patients

    DEFF Research Database (Denmark)

    Nielsen, Stine Elkjaer; Sugaya, Takeshi; Hovind, Peter

    2010-01-01

    Urinary liver-type fatty acid-binding protein (u-LFABP) is a marker of tubulointerstitial inflammation and has been shown to be increased in patients with type 1 diabetes and is further increased in patients who progress to micro- and macroalbuminuria. Our aim was to evaluate u-LFABP as a predictor...... of progression to micro- and macroalbuminuria in type 1 diabetes....

  17. Urinary liver-type fatty acid-binding protein predicts progression to nephropathy in type 1 diabetic patients

    DEFF Research Database (Denmark)

    Nielsen, Stine Elkjaer; Sugaya, Takeshi; Hovind, Peter

    2010-01-01

    Urinary liver-type fatty acid-binding protein (u-LFABP) is a marker of tubulointerstitial inflammation and has been shown to be increased in patients with type 1 diabetes and is further increased in patients who progress to micro- and macroalbuminuria. Our aim was to evaluate u-LFABP as a predictor...

  18. Bedside Heart Type Fatty Acid Binding Protein (H-FABP): Is an Early Predictive Marker of Cardiac Syncope

    International Nuclear Information System (INIS)

    Sonmez, B. M.; Yilmaz, F.; Durdu, T.; Hakbilir, O.; Ongar, M.; Ozturk, D.; Altinbilek, E.; Kavalci, C.; Turhan, T.

    2015-01-01

    Objective: To determine the value of bedside heart-type fatty acid binding protein in diagnosis of cardiac syncope in patients presenting with syncope or presyncope. Methods: The prospective study was conducted at Ankara Numune Training and Research Hospital, Ankara, Turkey, between September 1, 2010, and January 1, 2011, and comprised patients aged over 18 years who presented with syncope or presyncope. Patients presenting to emergency department within 4 hours of syncope or presyncope underwent a bedside heart-type fatty acid binding protein test measurement. SPSS 16 was used for statistical analysis, Results: Of the 100 patients evaluated, 22(22 percent) were diagnosed with cardiac syncope. Of them, 13(59.1 percent) patients had a positive and 9(40.9 percent) had a negative heart-type fatty acid binding protein result. Consequently, the test result was 12.64 times more positive in patients with cardiac syncope compared to those without. Conclusions: Bedside heart-type fatty acid binding protein, particularly at early phase of myocardial injury, reduces diagnostic and therapeutic uncertainity of cardiac origin in syncope patients. (author)

  19. Numerical Regularization of Ill-Posed Problems.

    Science.gov (United States)

    1980-07-09

    a 073 CINCINNATI UNIV O DEPT OF MATHENATICAL SCIENCES P/S ll1 NUMERICAL REGULARIZATION OF ILL-POSEO PROBLENS(U) JULso C 0 GROTSCH AFOSR-9 -OO9...regularization and projection methods, Proc. Annual Conference of the Association of Computing Machinery (1973), 415-419. [7) A. Sard, Approximations based on...solving incorreitly posed problems, U.S.S.R. Computational Math. and Math. Phys. 14(1974), 24-33. 4 I, I 11 4. L. J. Lardy, A series representation of

  20. Head Pose Estimation Using Multilinear Subspace Analysis for Robot Human Awareness

    Science.gov (United States)

    Ivanov, Tonislav; Matthies, Larry; Vasilescu, M. Alex O.

    2009-01-01

    Mobile robots, operating in unconstrained indoor and outdoor environments, would benefit in many ways from perception of the human awareness around them. Knowledge of people's head pose and gaze directions would enable the robot to deduce which people are aware of the its presence, and to predict future motions of the people for better path planning. To make such inferences, requires estimating head pose on facial images that are combination of multiple varying factors, such as identity, appearance, head pose, and illumination. By applying multilinear algebra, the algebra of higher-order tensors, we can separate these factors and estimate head pose regardless of subject's identity or image conditions. Furthermore, we can automatically handle uncertainty in the size of the face and its location. We demonstrate a pipeline of on-the-move detection of pedestrians with a robot stereo vision system, segmentation of the head, and head pose estimation in cluttered urban street scenes.

  1. Accuracy of a combined insulin-like growth factor-binding protein-1/interleukin-6 test (Premaquick) in predicting delivery in women with threatened preterm labor.

    Science.gov (United States)

    Eleje, George Uchenna; Ezugwu, Euzebus Chinonye; Eke, Ahizechukwu Chigoziem; Eleje, Lydia Ijeoma; Ikechebelu, Joseph Ifeanyichukwu; Ezebialu, Ifeanyichukwu Uzoma; Obiora, Chukwudi Celestine; Nwosu, Betrand Obi; Ezeama, Chukwuemeka Okwudili; Udigwe, Gerald Okanandu; Okafor, Charles Ikechukwu; Ezugwu, Frank Okechukwu

    2017-11-27

    To determine values of combinations of interleukin-6 (IL-6)/cervical native insulin-like growth factor-binding protein-1 (IGFBP-1)/total IGFBP-1 (Premaquick©) in predicting spontaneous deliveries and spontaneous exclusive preterm deliveries in women with threatened preterm labor. Women with singleton pregnancies between gestation age (GA) of 24 weeks and 36 weeks and 6 days with preterm labor were recruited during a prospective multicenter study. Premaquick© was positive when at least two of three biomarkers were positive. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy were estimated for both prediction of spontaneous deliveries and spontaneous exclusive preterm deliveries. Ninety-seven (99.0%) out of 98 women enrolled were analyzed. Based on delivery status 7/14 days post-enrollment of general study population, Premaquick© had a sensitivity of 87.1/85.7%, a specificity of 92.4/96.8%, a PPV of 84.4/93.8% and a NPV of 93.9/92.3% for prediction of spontaneous delivery. Predictive accuracy of Premaquick© test in relation to days of enrollment were: 90.7% (≤7 days) and 92.8% (≤14 days). For women enrolled at GA preterm delivery within 7/14 days of enrollment, respectively. PPV was most significantly different in both groups when outcomes were compared between 2 days and 14 days post-enrollment (Ppreterm deliveries in threatened preterm labor in singleton pregnancies.

  2. Predicting combinatorial binding of transcription factors to regulatory elements in the human genome by association rule mining

    OpenAIRE

    Morgan, Xochitl C; Ni, Shulin; Miranker, Daniel P; Iyer, Vishwanath R

    2007-01-01

    Abstract Background Cis-acting transcriptional regulatory elements in mammalian genomes typically contain specific combinations of binding sites for various transcription factors. Although some cis-regulatory elements have been well studied, the combinations of transcription factors that regulate normal expression levels for the vast majority of the 20,000 genes in the human genome are unknown. We hypothesized that it should be possible to discover transcription factor combinations that regul...

  3. Skill Levels of Prospective Physics Teachers on Problem Posing

    Science.gov (United States)

    Cildir, Sema; Sezen, Nazan

    2011-01-01

    Problem posing is one of the topics which the educators thoroughly accentuate. Problem posing skill is defined as an introvert activity of a student's learning. In this study, skill levels of prospective physics teachers on problem posing were determined and their views on problem posing were evaluated. To this end, prospective teachers were given…

  4. Experimental validation of plant peroxisomal targeting prediction algorithms by systematic comparison of in vivo import efficiency and in vitro PTS1 binding affinity.

    Science.gov (United States)

    Skoulding, Nicola S; Chowdhary, Gopal; Deus, Mara J; Baker, Alison; Reumann, Sigrun; Warriner, Stuart L

    2015-03-13

    Most peroxisomal matrix proteins possess a C-terminal targeting signal type 1 (PTS1). Accurate prediction of functional PTS1 sequences and their relative strength by computational methods is essential for determination of peroxisomal proteomes in silico but has proved challenging due to high levels of sequence variability of non-canonical targeting signals, particularly in higher plants, and low levels of availability of experimentally validated non-canonical examples. In this study, in silico predictions were compared with in vivo targeting analyses and in vitro thermodynamic binding of mutated variants within the context of one model targeting sequence. There was broad agreement between the methods for entire PTS1 domains and position-specific single amino acid residues, including residues upstream of the PTS1 tripeptide. The hierarchy Leu>Met>Ile>Val at the C-terminal position was determined for all methods but both experimental approaches suggest that Tyr is underweighted in the prediction algorithm due to the absence of this residue in the positive training dataset. A combination of methods better defines the score range that discriminates a functional PTS1. In vitro binding to the PEX5 receptor could discriminate among strong targeting signals while in vivo targeting assays were more sensitive, allowing detection of weak functional import signals that were below the limit of detection in the binding assay. Together, the data provide a comprehensive assessment of the factors driving PTS1 efficacy and provide a framework for the more quantitative assessment of the protein import pathway in higher plants. Copyright © 2014 Elsevier Ltd. All rights reserved.

  5. bSiteFinder, an improved protein-binding sites prediction server based on structural alignment: more accurate and less time-consuming.

    Science.gov (United States)

    Gao, Jun; Zhang, Qingchen; Liu, Min; Zhu, Lixin; Wu, Dingfeng; Cao, Zhiwei; Zhu, Ruixin

    2016-01-01

    Protein-binding sites prediction lays a foundation for functional annotation of protein and structure-based drug design. As the number of available protein structures increases, structural alignment based algorithm becomes the dominant approach for protein-binding sites prediction. However, the present algorithms underutilize the ever increasing numbers of three-dimensional protein-ligand complex structures (bound protein), and it could be improved on the process of alignment, selection of templates and clustering of template. Herein, we built so far the largest database of bound templates with stringent quality control. And on this basis, bSiteFinder as a protein-binding sites prediction server was developed. By introducing Homology Indexing, Chain Length Indexing, Stability of Complex and Optimized Multiple-Templates Clustering into our algorithm, the efficiency of our server has been significantly improved. Further, the accuracy was approximately 2-10 % higher than that of other algorithms for the test with either bound dataset or unbound dataset. For 210 bound dataset, bSiteFinder achieved high accuracies up to 94.8 % (MCC 0.95). For another 48 bound/unbound dataset, bSiteFinder achieved high accuracies up to 93.8 % for bound proteins (MCC 0.95) and 85.4 % for unbound proteins (MCC 0.72). Our bSiteFinder server is freely available at http://binfo.shmtu.edu.cn/bsitefinder/, and the source code is provided at the methods page. An online bSiteFinder server is freely available at http://binfo.shmtu.edu.cn/bsitefinder/. Our work lays a foundation for functional annotation of protein and structure-based drug design. With ever increasing numbers of three-dimensional protein-ligand complex structures, our server should be more accurate and less time-consuming.Graphical Abstract bSiteFinder (http://binfo.shmtu.edu.cn/bsitefinder/) as a protein-binding sites prediction server was developed based on the largest database of bound templates so far with stringent quality

  6. Attribute And-Or Grammar for Joint Parsing of Human Pose, Parts and Attributes.

    Science.gov (United States)

    Park, Seyoung; Nie, Xiaohan; Zhu, Song-Chun

    2017-07-25

    This paper presents an attribute and-or grammar (A-AOG) model for jointly inferring human body pose and human attributes in a parse graph with attributes augmented to nodes in the hierarchical representation. In contrast to other popular methods in the current literature that train separate classifiers for poses and individual attributes, our method explicitly represents the decomposition and articulation of body parts, and account for the correlations between poses and attributes. The A-AOG model is an amalgamation of three traditional grammar formulations: (i)Phrase structure grammar representing the hierarchical decomposition of the human body from whole to parts; (ii)Dependency grammar modeling the geometric articulation by a kinematic graph of the body pose; and (iii)Attribute grammar accounting for the compatibility relations between different parts in the hierarchy so that their appearances follow a consistent style. The parse graph outputs human detection, pose estimation, and attribute prediction simultaneously, which are intuitive and interpretable. We conduct experiments on two tasks on two datasets, and experimental results demonstrate the advantage of joint modeling in comparison with computing poses and attributes independently. Furthermore, our model obtains better performance over existing methods for both pose estimation and attribute prediction tasks.

  7. Relative Pose Estimation Algorithm with Gyroscope Sensor

    Directory of Open Access Journals (Sweden)

    Shanshan Wei

    2016-01-01

    Full Text Available This paper proposes a novel vision and inertial fusion algorithm S2fM (Simplified Structure from Motion for camera relative pose estimation. Different from current existing algorithms, our algorithm estimates rotation parameter and translation parameter separately. S2fM employs gyroscopes to estimate camera rotation parameter, which is later fused with the image data to estimate camera translation parameter. Our contributions are in two aspects. (1 Under the circumstance that no inertial sensor can estimate accurately enough translation parameter, we propose a translation estimation algorithm by fusing gyroscope sensor and image data. (2 Our S2fM algorithm is efficient and suitable for smart devices. Experimental results validate efficiency of the proposed S2fM algorithm.

  8. Structural model of a putrescine-cadaverine permease from Trypanosoma cruzi predicts residues vital for transport and ligand binding.

    Science.gov (United States)

    Soysa, Radika; Venselaar, Hanka; Poston, Jacqueline; Ullman, Buddy; Hasne, Marie-Pierre

    2013-06-15

    The TcPOT1.1 gene from Trypanosoma cruzi encodes a high affinity putrescine-cadaverine transporter belonging to the APC (amino acid/polyamine/organocation) transporter superfamily. No experimental three-dimensional structure exists for any eukaryotic member of the APC family, and thus the structural determinants critical for function of these permeases are unknown. To elucidate the key residues involved in putrescine translocation and recognition by this APC family member, a homology model of TcPOT1.1 was constructed on the basis of the atomic co-ordinates of the Escherichia coli AdiC arginine/agmatine antiporter crystal structure. The TcPOT1.1 homology model consisted of 12 transmembrane helices with the first ten helices organized in two V-shaped antiparallel domains with discontinuities in the helical structures of transmembrane spans 1 and 6. The model suggests that Trp241 and a Glu247-Arg403 salt bridge participate in a gating system and that Asn245, Tyr148 and Tyr400 contribute to the putrescine-binding pocket. To test the validity of the model, 26 site-directed mutants were created and tested for their ability to transport putrescine and to localize to the parasite cell surface. These results support the robustness of the TcPOT1.1 homology model and reveal the importance of specific aromatic residues in the TcPOT1.1 putrescine-binding pocket.

  9. Crystal complexes of a predicted S-adenosylmethionine-dependent methyltransferase reveal a typical AdoMet binding domain and a substrate recognition domain

    Energy Technology Data Exchange (ETDEWEB)

    Miller, D.J.; Ouellette, N.; Evodokimova, E.; Savchenko, A.; Edwards, A.; Anderson, W.F. (Toronto); (NWU)

    2010-03-08

    S-adenosyl-L-methionine-dependent methyltransferases (MTs) are abundant, and highly conserved across phylogeny. These enzymes use the cofactor AdoMet to methylate a wide variety of molecular targets, thereby modulating important cellular and metabolic activities. Thermotoga maritima protein 0872 (TM0872) belongs to a large sequence family of predicted MTs, ranging phylogenetically from relatively simple bacteria to humans. The genes for many of the bacterial homologs are located within operons involved in cell wall synthesis and cell division. Despite preliminary biochemical studies in E. coli and B. subtilis, the substrate specificity of this group of more than 150 proteins is unknown. As part of the Midwest Center for Structural Genomics initiative (www.mcsg.anl.gov), we have determined the structure of TM0872 in complexes with AdoMet and with S-adenosyl-L-homocysteine (AdoHcy). As predicted, TM0872 has a typical MT domain, and binds endogenous AdoMet, or co-crystallized AdoHcy, in a manner consistent with other known MT structures. In addition, TM0872 has a second domain that is novel among MTs in both its location in the sequence and its structure. The second domain likely acts in substrate recognition and binding, and there is a potential substrate-binding cleft spanning the two domains. This long and narrow cleft is lined with positively charged residues which are located opposite the S{sup +}-CH{sub 3} bond, suggesting that a negatively charged molecule might be targeted for catalysis. However, AdoMet and AdoHcy are both buried, and access to the methyl group would presumably require structural rearrangement. These TM0872 crystal structures offer the first structural glimpses at this phylogenetically conserved sequence family.

  10. An experimental verification of the predicted effects of promoter TATA-box polymorphisms associated with human diseases on interactions between the TATA boxes and TATA-binding protein.

    Science.gov (United States)

    Savinkova, Ludmila; Drachkova, Irina; Arshinova, Tatyana; Ponomarenko, Petr; Ponomarenko, Mikhail; Kolchanov, Nikolay

    2013-01-01

    Human genome sequencing has resulted in a great body of data, including a stunningly large number of single nucleotide polymorphisms (SNPs) with unknown phenotypic manifestations. Identification and comprehensive analysis of regulatory SNPs in human gene promoters will help quantify the effects of these SNPs on human health. Based on our experimental and computer-aided study of SNPs in TATA boxes and the use of literature data, we have derived an equation for TBP/TATA equilibrium binding in three successive steps: TATA-binding protein (TBP) sliding along DNA due to their nonspecific affinity for each other ↔ recognition of the TATA box ↔ stabilization of the TBP/TATA complex. Using this equation, we have analyzed TATA boxes containing SNPs associated with human diseases and made in silico predictions of changes in TBP/TATA affinity. An electrophoretic mobility shift assay (EMSA)-based experimental study performed under the most standardized conditions demonstrates that the experimentally measured values are highly correlated with the predicted values: the coefficient of linear correlation, r, was 0.822 at a significance level of αTATA boxes (δ= -ln[K(D,TATAMut)]-(-ln[K(D,TATAMut)])). It has been demonstrated that the SNPs associated with increased risk of human diseases such as α-, β- and δ-thalassemia, myocardial infarction and thrombophlebitis, changes in immune response, amyotrophic lateral sclerosis, lung cancer and hemophilia B Leyden cause 2-4-fold changes in TBP/TATA affinity in most cases. The results obtained strongly suggest that the TBP/TATA equilibrium binding equation derived can be used for analysis of TATA-box sequences and identification of SNPs with a potential of being functionally important.

  11. Bioinformatic prediction of transcription factor binding sites at promoter regions of genes for photoperiod and vernalization responses in model and temperate cereal plants.

    Science.gov (United States)

    Peng, Fred Y; Hu, Zhiqiu; Yang, Rong-Cai

    2016-08-08

    Many genes involved in responses to photoperiod and vernalization have been characterized or predicted in Arabidopsis (Arabidopsis thaliana), Brachypodium (Brachypodium distachyon), wheat (Triticum aestivum) and barley (Hordeum vulgare). However, little is known about the transcription regulation of these genes, especially in the large, complex genomes of wheat and barley. We identified 68, 60, 195 and 61 genes that are known or postulated to control pathways of photoperiod (PH), vernalization (VE) and pathway integration (PI) in Arabidopsis, Brachypodium, wheat and barley for predicting transcription factor binding sites (TFBSs) in the promoters of these genes using the FIMO motif search tool of the MEME Suite. The initial predicted TFBSs were filtered to confirm the final numbers of predicted TFBSs to be 1066, 1379, 1528, and 789 in Arabidopsis, Brachypodium, wheat and barley, respectively. These TFBSs were mapped onto the PH, VE and PI pathways to infer about the regulation of gene expression in Arabidopsis and cereal species. The GC contents in promoters, untranslated regions (UTRs), coding sequences and introns were higher in the three cereal species than those in Arabidopsis. The predicted TFBSs were most abundant for two transcription factor (TF) families: MADS-box and CSD (cold shock domain). The analysis of publicly available gene expression data showed that genes with similar numbers of MADS-box and CSD TFBSs exhibited similar expression patterns across several different tissues and developmental stages. The intra-specific Tajima D-statistics of TFBS motif diversity showed different binding specificity among different TF families. The inter-specific Tajima D-statistics suggested faster TFBS divergence in TFBSs than in coding sequences and introns. Mapping TFBSs onto the PH, VE and PI pathways showed the predominance of MADS-box and CSD TFBSs in most genes of the four species, and the difference in the pathway regulations between Arabidopsis and the three

  12. ONKALO POSE experiment. Phase 1 and 2: execution and monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Johansson, E. [Saanio and Riekkola Oy, Helsinki (Finland); Siren, T. [Posiva Oy, Helsinki (Finland); Hakala, M. [KMS-Hakala Oy, Nokia (Finland); Kantia, P. [Geofcon Oy, Rovaniemi (Finland)

    2014-02-15

    Posiva has conducted in the ONKALO rock characterisation facility during 2010 - 2011 an in situ experiment named POSE (Posiva's Olkiluoto Spalling Experiment). The POSE experiment had three objectives: to establish the in situ spalling/damage strength of Olkiluoto migmatitic gneiss, to establish the state of in situ stress at the -345 m depth level, and to act as a Prediction-Outcome (P-O) exercise. The POSE experiment consisted of drilling with full-face boring machine two near fullscale deposition holes, diameter 1.52 m (compared to 1.75 m for the actual deposition holes), to a depth of 7.2 m, leaving a 0.9 m pillar between the holes. The holes were planned to be located in such way that maximum excavation-induced stresses could act in the pillar and damage could then take place. Boring of the two holes in 2010 was called Phase 1 (Pillar test). This was followed in 2011 by Phase 2 (Pillar heating test) where four heaters with a length of 7.5 m heated the test area to increase the stresses around the experimental holes. In the heating phase the other hole was back-filled with sand. The test was extensively monitored during the execution using temperature monitoring, strain gauge monitoring, video monitoring, microseismic monitoring and pressure monitoring. In addition, the holes were after the test measured using ground penetration radar (GPR) and 3D photogrammetry for detailed modelling. The outcomes from the test showed that no damage, except for three opened/sheared fractures, was noticed during the boring of the holes (Phase 1). Surface damage was, though, induced by heating (Phase 2). The damage was well localized around the holes and controlled by the foliation (mica rich layers) and rock type contacts which were known to be relatively weak. Surface type failures were not observed in the gneiss, but it was noticed in limited areas in the pegmatite-granite. The depths of the damaged areas due to heating were less than 100 mm. The depths and sizes of the

  13. Predicting treatment response in Schizophrenia: the role of stratal and frontal dopamine D2/D3 receptor binding potential

    DEFF Research Database (Denmark)

    Wulff, Sanne; Nørbak-Emig, Henrik; Nielsen, Mette Ødegaard

    2014-01-01

    relationship between frontal and striatal dopamine activity. Data also emphasize that there might be gender differences. The data analysis is ongoing. (1) Glenthøj BY, Mackeprang T et al. Frontal dopamine D2/3 receptor binding in drug-naïve first-episode schizophrenic patients correlates with positive...... cortex in antipsychotic-naïve first-episode male schizophrenia patients(1). Preclinical studies suggest an inverse relationship between frontal and striatal dopamine activity. This activity can indirectly be expressed by the BP of dopamine receptors using Single Photon Emission Computed Tomography (SPECT......-up. There was a negative correlation between striatal BP and improvement of the PANSS total score (Rho=-0,553 P=0.009). Furthermore we found a negative correlation between striatal BP and improvement of positive symptoms among the male patients only (P=0.020). The same relationship was found at trend level for the entire...

  14. Neocortical serotonin2A receptor binding predicts quetiapine associated weight gain in antipsychotic-naive first-episode schizophrenia patients

    DEFF Research Database (Denmark)

    Rasmussen, Hans; Ebdrup, Bjørn H; Oranje, B

    2014-01-01

    related serotonin2A receptor binding to weight gain before and after antipsychotic monotherapy. Fifteen antipsychotic-naive first-episode schizophrenia patients were included and investigated before and after six months of quetiapine treatment. We examined the relationship between serotonin2A receptor......Antipsychotic-induced weight gain is of major clinical importance since it is associated with severe metabolic complications and increased mortality. The serotonin2A receptor system has been suggested to be implicated in weight gain and obesity. However, no previous in vivo imaging data have...... to treatment and subsequent increase in BMI (rho = 0.59, p = 0.022). At follow-up, the serotonin2A receptor occupancy was positively correlated with BMI increase (rho = 0.54, p = 0.038). To our knowledge, these are the first in vivo receptor imaging data in initially antipsychotic-naive first...

  15. Serum testosterone, sex hormone-binding globulin and total calcium levels predict the calcaneal speed of sound in men.

    Science.gov (United States)

    Chin, Kok-Yong; Soelaiman, Ima-Nirwana; Mohamed, Isa Naina; Ngah, Wan Zurinah Wan

    2012-08-01

    Variations in sex hormones and the calcium balance can influence bone health in men. The present study aimed to examine the relationship between the calcaneal speed of sound and biochemical determinants of bone mass, such as sex hormones, parathyroid hormones and serum calcium. Data from 549 subjects from the Malaysian Aging Male Study, which included Malay and Chinese men aged 20 years and older residing in the Klang Valley, were used for analysis. The subjects' calcaneal speed of sound was measured, and their blood was collected for biochemical analysis. Two sets of multiple regression models were generated for the total/bioavailable testosterone and estradiol to avoid multicollinearity. The multiple regression results revealed that bioavailable testosterone and serum total calcium were significant predictors of the calcaneal speed of sound in the adjusted model. After adjustment for ethnicity and body mass index, only bioavailable testosterone remained significant; the total serum calcium was marginally insignificant. In a separate model, the total testosterone and sex hormone-binding globulin were significant predictors, whereas the total serum calcium was marginally insignificant. After adjustment for ethnicity and body mass index (BMI), the significance persisted for total testosterone and SHBG. After further adjustment for age, none of the serum biochemical determinants was a significant predictor of the calcaneal speed of sound. There is a significant age-dependent relationship between the calcaneal speed of sound and total testosterone, bioavailable testosterone and sex hormone-binding globulin in Chinese and Malay men in Malaysia. The relationship between total serum calcium and calcaneal speed of sound is ethnicity-dependent.

  16. Full Body Pose Estimation During Occlusion using Multiple Cameras

    DEFF Research Database (Denmark)

    Fihl, Preben; Cosar, Serhan

    Automatic estimation of the human pose enables many interesting applications and has therefore achieved much attention in recent years. One of the most successful approaches for estimating unconstrained poses has been the pictorial structures framework. However, occlusions between interacting...

  17. Relative binding affinity-serum modified access (RBA-SMA) assay predicts the relative in vivo bioactivity of the xenoestrogens bisphenol A and octylphenol.

    Science.gov (United States)

    Nagel, S C; vom Saal, F S; Thayer, K A; Dhar, M G; Boechler, M; Welshons, W V

    1997-01-01

    We have developed a relative binding affinity-serum modified access (RBA-SMA) assay to determine the effect of serum on the access of xenoestrogens to estrogen receptors within intact cultured MCF-7 human breast cancer cells. We used this assay to predict low dose activity of two xenoestrogens in mice. In serum-free medium, bisphenol A, a component of polycarbonates and of resins used to line metal food cans, showed a lower relative binding affinity (RBA; 0.006%) than octylphenol (0.072%) and nonylphenol (0.026%), which are used as surfactants in many commercial products (all RBAs are relative to estradiol, which is equal to 100%). In 100% serum from adult men, bisphenol A showed a higher RBA (0.01%) than in serum-free medium and thus enhanced access to estrogen receptors relative to estradiol. In contrast, octylphenol showed a 22-fold decrease in RBA (0.0029%) and nonylphenol showed a 5-fold decrease in RBA (0.0039%) when measured in adult serum. This indicates that, relative to estradiol, serum had less of an inhibitory effect on the cell uptake and binding in MCF-7 cells of bisphenol A, while serum had a greater inhibitory effect on octylphenol and nonylphenol relative to estradiol. Extrapolation of these relative activities in adult serum predicted that the estrogenic bioactivity of bisphenol A would be over 500-fold greater than that of octylphenol in fetal mouse serum. Bisphenol A and octylphenol were fed to pregnant mice at 2 and 20 micrograms/kg/day. Exposure of male mouse fetuses to either dose of bisphenol A, but to neither dose of octylphenol, significantly increased their adult prostate weight relative to control males, which is consistent with the higher predicted bioactivity of bisphenol A than octylphenol in the RBA-SMA assay. In addition, our findings show for the first time that fetal exposure to environmentally relevant parts-per-billion (ppb) doses of bisphenol A, in the range currently being consumed by people, can alter the adult reproductive

  18. 2D Methods for pose invariant face recognition

    CSIR Research Space (South Africa)

    Mokoena, Ntabiseng

    2016-12-01

    Full Text Available The ability to recognise face images under random pose is a task that is done effortlessly by human beings. However, for a computer system, recognising face images under varying poses still remains an open research area. Face recognition across pose...

  19. Computational Prediction and Analysis of Associations between Small Molecules and Binding-Associated S-Nitrosylation Sites.

    Science.gov (United States)

    Huang, Guohua; Li, Jincheng; Zhao, Chenglin

    2018-04-19

    Interactions between drugs and proteins occupy a central position during the process of drug discovery and development. Numerous methods have recently been developed for identifying drug⁻target interactions, but few have been devoted to finding interactions between post-translationally modified proteins and drugs. We presented a machine learning-based method for identifying associations between small molecules and binding-associated S-nitrosylated (SNO-) proteins. Namely, small molecules were encoded by molecular fingerprint, SNO-proteins were encoded by the information entropy-based method, and the random forest was used to train a classifier. Ten-fold and leave-one-out cross validations achieved, respectively, 0.7235 and 0.7490 of the area under a receiver operating characteristic curve. Computational analysis of similarity suggested that SNO-proteins associated with the same drug shared statistically significant similarity, and vice versa. This method and finding are useful to identify drug⁻SNO associations and further facilitate the discovery and development of SNO-associated drugs.

  20. Novel drug design for Chagas disease via targeting Trypanosoma cruzi tubulin: Homology modeling and binding pocket prediction on Trypanosoma cruzi tubulin polymerization inhibition by naphthoquinone derivatives.

    Science.gov (United States)

    Ogindo, Charles O; Khraiwesh, Mozna H; George, Matthew; Brandy, Yakini; Brandy, Nailah; Gugssa, Ayele; Ashraf, Mohammad; Abbas, Muneer; Southerland, William M; Lee, Clarence M; Bakare, Oladapo; Fang, Yayin

    2016-08-15

    Chagas disease, also called American trypanosomiasis, is a parasitic disease caused by Trypanosoma cruzi (T. cruzi). Recent findings have underscored the abundance of the causative organism, (T. cruzi), especially in the southern tier states of the US and the risk burden for the rural farming communities there. Due to a lack of safe and effective drugs, there is an urgent need for novel therapeutic options for treating Chagas disease. We report here our first scientific effort to pursue a novel drug design for treating Chagas disease via the targeting of T. cruzi tubulin. First, the anti T. cruzi tubulin activities of five naphthoquinone derivatives were determined and correlated to their anti-trypanosomal activities. The correlation between the ligand activities against the T. cruzi organism and their tubulin inhibitory activities was very strong with a Pearson's r value of 0.88 (P value cruzi tubulin polymerization inhibition. Subsequent molecular modeling studies were carried out to understand the mechanisms of the anti-tubulin activities, wherein, the homology model of T. cruzi tubulin dimer was generated and the putative binding site of naphthoquinone derivatives was predicted. The correlation coefficient for ligand anti-tubulin activities and their binding energies at the putative pocket was found to be r=0.79, a high correlation efficiency that was not replicated in contiguous candidate pockets. The homology model of T. cruzi tubulin and the identification of its putative binding site lay a solid ground for further structure based drug design, including molecular docking and pharmacophore analysis. This study presents a new opportunity for designing potent and selective drugs for Chagas disease. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  1. Prediction of Delivery in Women with Threatening Preterm Labour using Phosphorylated Insulin-Like Growth Factor Binding Protein-1 and Cervical Length using Transvaginal Ultrasound.

    Science.gov (United States)

    Kumari, Amrita; Saini, Vandana; Jain, P K; Gupta, Mamta

    2017-09-01

    Preterm delivery remains a challenge in Obstetrics as it is responsible for significant cause of perinatal morbidity and mortality. At present there is no standard test for prediction of preterm labour for timely referral to a center with NICU facilities. To evaluate the effectiveness of the cervical phosphorylated insulin like growth factor binding protein-1(phIGFBP-1), cervical length measurement and combination of phIGFBP-1 with cervical length for Predicting Preterm Labour (PTL). It was a observational prospective study done from January 2014 to April 2015 in Department of Obstetrics and Gynaecology, NDMC Medical College and Hindu Rao Hospital, Delhi, India. A total of 100 women with singleton pregnancy, between 24 and 36 weeks of gestation with complaint of uterine contractions were randomly selected. These women were subjected to detect phIGFBP-1 in cervical secretions and cervical length measurement by Transvaginal Sonography (TVS). Result of the test, cervical length and time lapse between test and delivery was noted and the results were analysed. The cervical length less than 25 mm was used as a cut off point for predicting pre-term delivery. Data was analysed using SPSS software version 20.0. The Negative Predictive Value (NPV) of phIGFBP-1 and cervical length was similar (95.2% vs 94.05%) respectively for prediction of preterm labour within one week of admission and 93.92% vs 94.80% at 37 weeks of gestational age. Combined test had higher NPV of 96.38% at 34 weeks of gestation and 94% within two days of admission. Positive Predictive Value (PPV) was low for both the test and combining the two-test did not have any advantage as far as PPV was concerned. Receiver Operating Characteristic (ROC) curve showed that the combined test had a superior result in predicting PTL compared to either phIGFBP-1 or cervical length. The combined test had steepest ROC curve at preterm delivery independently. The combined use of phIGFBP-1 and TVS for cervical length shows an

  2. An all-atom knowledge-based energy function for protein-DNA threading, docking decoy discrimination, and prediction of transcription-factor binding profiles

    Science.gov (United States)

    Xu, Beisi; Yang, Yuedong; Liang, Haojun; Zhou, Yaoqi

    2009-01-01

    How to make an accurate representation of protein-DNA interactions by an energy function is a long-standing unsolved problem in structural biology. Here, we modified a statistical potential based on the distance-scaled, finite ideal-gas reference state (DFIRE) so that it is optimized for protein-DNA interactions. The changes include a volume-fraction correction to account for unmixable atom types in proteins and DNA in addition to the usage of a low-count correction, residue/base-specific atom types, and a shorter cutoff distance for protein-DNA interactions. The new statistical energy functions are tested in threading and docking decoy discriminations and prediction of protein-DNA binding affinities and transcription-factor binding profiles. Results indicate that new proposed energy functions are among the best in existing energy functions for protein-DNA interactions. The new energy functions are available as a web-server called DDNA 2.0 at http://sparks.informatics.iupui.edu. The server version was trained by the entire 212 protein-DNA complexes. PMID:19274740

  3. Prediction and experimental validation of a putative non-consensus binding site for transcription factor STAT3 in serum amyloid A gene promoter.

    Science.gov (United States)

    Tiwari, Prabha; Tripathi, Lokesh P; Nishikawa-Matsumura, Teppei; Ahmad, Shandar; Song, Soken-Nakazawa J; Isobe, Tomoyasu; Mizuguchi, Kenji; Yoshizaki, Kazuyuki

    2013-06-01

    We previously demonstrated that though the human SAA1 gene shows no typical STAT3 response element (STAT3-RE) in its promoter region, STAT3 and the nuclear factor (NF-κB) p65 first form a complex following interleukin IL-1 and IL-6 (IL-1+6) stimulation, after which STAT3 interacts with a region downstream of the NF-κB RE in the SAA1 promoter. In this study, we employed a computational approach based on indirect read outs of protein-DNA contacts to identify a set of candidates for non-consensus STAT3 transcription factor binding sites (TFBSs). The binding of STAT3 to one of the predicted non-consensus TFBSs was experimentally confirmed through a dual luciferase assay and DNA affinity chromatography. The present study defines a novel STAT3 non-consensus TFBS at nt -75/-66 downstream of the NF-κB RE in the SAA1 promoter region that is required for NF-κB p65 and STAT3 to activate SAA1 transcription in human HepG2 liver cells. Our analysis builds upon the current understanding of STAT3 function, suggesting a wider array of mechanisms of STAT3 function in inflammatory response, and provides a useful framework for investigating novel TF-target associations with potential therapeutic implications. Copyright © 2013 Elsevier B.V. All rights reserved.

  4. Predicting the functionally distinct residues in the heme, cation, and substrate-binding sites of peroxidase from stress-tolerant mangrove specie, Avicennia marina.

    Science.gov (United States)

    Jabeen, Uzma; Abbasi, Atiya; Salim, Asmat

    2011-11-01

    Recent work was conducted to predict the structure of functionally distinct regions of Avicennia marina peroxidase (AP) by using the structural coordinates of barley grains peroxidase as the template. This enzyme is utilized by all living organisms in many biosynthetic or degradable processes and in defense against oxidative stress. The homology model showed some distinct structural changes in the heme, calcium, and substrate-binding regions. Val53 was found to be an important coordinating residue between distal calcium ion and the distal heme site while Ser176 is coordinated to the proximal histidine through Ala174 and Leu172. Different ionic and hydrogen-bonded interactions were also observed in AP. Analyses of various substrate-enzyme interactions revealed that the substrate-binding pocket is provided by the residues, His41, Phe70, Gly71, Asp138, His139, and Lys176; the later three residues are not conserved in the peroxidase family. We have also performed structural comparison of the A. marina peroxidase with that of two class III salt-sensitive species, peanut and soybean. Four loop regions were found to have largest structural deviation. The overall protein sequence was also analyzed for the presence of probable post-translational modification sites and the functional significance of these sites were outlined.

  5. Pose Estimation with a Kinect for Ergonomic Studies: Evaluation of the Accuracy Using a Virtual Mannequin

    Directory of Open Access Journals (Sweden)

    Pierre Plantard

    2015-01-01

    Full Text Available Analyzing human poses with a Kinect is a promising method to evaluate potentials risks of musculoskeletal disorders at workstations. In ecological situations, complex 3D poses and constraints imposed by the environment make it difficult to obtain reliable kinematic information. Thus, being able to predict the potential accuracy of the measurement for such complex 3D poses and sensor placements is challenging in classical experimental setups. To tackle this problem, we propose a new evaluation method based on a virtual mannequin. In this study, we apply this method to the evaluation of joint positions (shoulder, elbow, and wrist, joint angles (shoulder and elbow, and the corresponding RULA (a popular ergonomics assessment grid upper-limb score for a large set of poses and sensor placements. Thanks to this evaluation method, more than 500,000 configurations have been automatically tested, which would be almost impossible to evaluate with classical protocols. The results show that the kinematic information obtained by the Kinect software is generally accurate enough to fill in ergonomic assessment grids. However inaccuracy strongly increases for some specific poses and sensor positions. Using this evaluation method enabled us to report configurations that could lead to these high inaccuracies. As a supplementary material, we provide a software tool to help designers to evaluate the expected accuracy of this sensor for a set of upper-limb configurations. Results obtained with the virtual mannequin are in accordance with those obtained from a real subject for a limited set of poses and sensor placements.

  6. Pose Planning for the Feed Support System of FAST

    Directory of Open Access Journals (Sweden)

    Rui Yao

    2014-01-01

    Full Text Available A six-cable driven parallel manipulator and an A-B rotator in the feed support system of the Five-hundred-meter Aperture Spherical radio Telescope (FAST are adopted for realizing the position and pose of nine feeds. The six-cable driven parallel manipulator is a flexible mechanism, which may not be stably controlled due to a small cable tension. The A-B rotator is a rigid mechanism, and its stability and accuracy can be improved by small pose angle. Based on the different characteristics, a pose planning function is presented. The optimization target of the pose planning function is to get the smallest pose angle of the A-B rotator, and the constraint condition can reflect the controllability of the six-cable driven parallel manipulator. Then, the pose planning realization process of the feed support system is proposed. Based on the pose planning method, optimized pose angles of the feed support system for the nine feeds are obtained, which suggests that the pose angle of the six-cable driven parallel manipulator changes from 0° to 14° and the pose angle of the A-B rotator changes from 0° to 26.4°.

  7. Effective transcription factor binding site prediction using a combination of optimization, a genetic algorithm and discriminant analysis to capture distant interactions.

    Science.gov (United States)

    Levitsky, Victor G; Ignatieva, Elena V; Ananko, Elena A; Turnaev, Igor I; Merkulova, Tatyana I; Kolchanov, Nikolay A; Hodgman, T C

    2007-12-19

    Reliable transcription factor binding site (TFBS) prediction methods are essential for computer annotation of large amount of genome sequence data. However, current methods to predict TFBSs are hampered by the high false-positive rates that occur when only sequence conservation at the core binding-sites is considered. To improve this situation, we have quantified the performance of several Position Weight Matrix (PWM) algorithms, using exhaustive approaches to find their optimal length and position. We applied these approaches to bio-medically important TFBSs involved in the regulation of cell growth and proliferation as well as in inflammatory, immune, and antiviral responses (NF-kappaB, ISGF3, IRF1, STAT1), obesity and lipid metabolism (PPAR, SREBP, HNF4), regulation of the steroidogenic (SF-1) and cell cycle (E2F) genes expression. We have also gained extra specificity using a method, entitled SiteGA, which takes into account structural interactions within TFBS core and flanking regions, using a genetic algorithm (GA) with a discriminant function of locally positioned dinucleotide (LPD) frequencies. To ensure a higher confidence in our approach, we applied resampling-jackknife and bootstrap tests for the comparison, it appears that, optimized PWM and SiteGA have shown similar recognition performances. Then we applied SiteGA and optimized PWMs (both separately and together) to sequences in the Eukaryotic Promoter Database (EPD). The resulting SiteGA recognition models can now be used to search sequences for BSs using the web tool, SiteGA. Analysis of dependencies between close and distant LPDs revealed by SiteGA models has shown that the most significant correlations are between close LPDs, and are generally located in the core (footprint) region. A greater number of less significant correlations are mainly between distant LPDs, which spanned both core and flanking regions. When SiteGA and optimized PWM models were applied together, this substantially reduced

  8. Head pose estimation algorithm based on deep learning

    Science.gov (United States)

    Cao, Yuanming; Liu, Yijun

    2017-05-01

    Head pose estimation has been widely used in the field of artificial intelligence, pattern recognition and intelligent human-computer interaction and so on. Good head pose estimation algorithm should deal with light, noise, identity, shelter and other factors robustly, but so far how to improve the accuracy and robustness of attitude estimation remains a major challenge in the field of computer vision. A method based on deep learning for pose estimation is presented. Deep learning with a strong learning ability, it can extract high-level image features of the input image by through a series of non-linear operation, then classifying the input image using the extracted feature. Such characteristics have greater differences in pose, while they are robust of light, identity, occlusion and other factors. The proposed head pose estimation is evaluated on the CAS-PEAL data set. Experimental results show that this method is effective to improve the accuracy of pose estimation.

  9. Local Feature Learning for Face Recognition under Varying Poses

    DEFF Research Database (Denmark)

    Duan, Xiaodong; Tan, Zheng-Hua

    2015-01-01

    In this paper, we present a local feature learning method for face recognition to deal with varying poses. As opposed to the commonly used approaches of recovering frontal face images from profile views, the proposed method extracts the subject related part from a local feature by removing the pose...... related part in it on the basis of a pose feature. The method has a closed-form solution, hence being time efficient. For performance evaluation, cross pose face recognition experiments are conducted on two public face recognition databases FERET and FEI. The proposed method shows a significant...... recognition improvement under varying poses over general local feature approaches and outperforms or is comparable with related state-of-the-art pose invariant face recognition approaches. Copyright ©2015 by IEEE....

  10. Are predefined decoy sets of ligand poses able to quantify scoring function accuracy?

    Science.gov (United States)

    Korb, Oliver; ten Brink, Tim; Victor Paul Raj, Fredrick Robin Devadoss; Keil, Matthias; Exner, Thomas E.

    2012-02-01

    Due to the large number of different docking programs and scoring functions available, researchers are faced with the problem of selecting the most suitable one when starting a structure-based drug discovery project. To guide the decision process, several studies comparing different docking and scoring approaches have been published. In the context of comparing scoring function performance, it is common practice to use a predefined, computer-generated set of ligand poses (decoys) and to reevaluate their score using the set of scoring functions to be compared. But are predefined decoy sets able to unambiguously evaluate and rank different scoring functions with respect to pose prediction performance? This question arose when the pose prediction performance of our piecewise linear potential derived scoring functions (Korb et al. in J Chem Inf Model 49:84-96, 2009) was assessed on a standard decoy set (Cheng et al. in J Chem Inf Model 49:1079-1093, 2009). While they showed excellent pose identification performance when they were used for rescoring of the predefined decoy conformations, a pronounced degradation in performance could be observed when they were directly applied in docking calculations using the same test set. This implies that on a discrete set of ligand poses only the rescoring performance can be evaluated. For comparing the pose prediction performance in a more rigorous manner, the search space of each scoring function has to be sampled extensively as done in the docking calculations performed here. We were able to identify relative strengths and weaknesses of three scoring functions (ChemPLP, GoldScore, and Astex Statistical Potential) by analyzing the performance for subsets of the complexes grouped by different properties of the active site. However, reasons for the overall poor performance of all three functions on this test set compared to other test sets of similar size could not be identified.

  11. The lighter side of advertising: investigating posing and lighting biases.

    Science.gov (United States)

    Thomas, Nicole A; Burkitt, Jennifer A; Patrick, Regan E; Elias, Lorin J

    2008-11-01

    People tend to display the left cheek when posing for a portrait; however, this effect does not appear to generalise to advertising. The amount of body visible in the image and the sex of the poser might also contribute to the posing bias. Portraits also exhibit lateral lighting biases, with most images being lit from the left. This effect might also be present in advertisements. A total of 2801 full-page advertisements were sampled and coded for posing direction, lighting direction, sex of model, and amount of body showing. Images of females showed an overall leftward posing bias, but the biases in males depended on the amount of body visible. Males demonstrated rightward posing biases for head-only images. Overall, images tended to be lit from the top left corner. The two factors of posing and lighting biases appear to influence one another. Leftward-lit images had more leftward poses than rightward, while the opposite occurred for rightward-lit images. Collectively, these results demonstrate that the posing biases in advertisements are dependent on the amount of body showing in the image, and that biases in lighting direction interact with these posing biases.

  12. Mendalami Dasar-Dasar dalam Pengambilan Pose pada Pemotretan Model

    Directory of Open Access Journals (Sweden)

    Agnes Paulina Gunawan

    2013-04-01

    Full Text Available There are many activities and numerous objects in this universe, which make them interesting for photographers to explore as their masterpiece. One of the things that has been enjoyed and is always developing over time is the use of human as an object, whether as a candid photography or as a posing model in accordance to photographer's concept and theme. Using human being as an object is always popular among beginners and professional photographers. Even nowadays people often hold photo shoot as a media in many social network sites. And so if they understand the simple theories in basic knowledge of using human object, the results will be maximized, and of course, much more interesting. The more a photographer does his job, the better his experience is, and his work will develop. Thus, it makes him more alert to the situation and character of a model, which will then become more observant in predicting their outcome in photography.   

  13. Estimating 2D Upper Body Poses from Monocular Images

    NARCIS (Netherlands)

    Broekhuijsen, Jeroen; Poppe, Ronald Walter; Poel, Mannes

    2006-01-01

    Automatic estimation and recognition of poses from video allows for a whole range of applications. The research described here is an important step towards automatic extraction of 3D poses. We describe our research to extract the 2D joint locations of the people in meeting videos. The key point of

  14. Helping Young Students to Better Pose an Environmental Problem

    Science.gov (United States)

    Pruneau, Diane; Freiman, Viktor; Barbier, Pierre-Yves; Langis, Joanne

    2009-01-01

    Grade 3 students were asked to solve a sedimentation problem in a local river. With scientists, students explored many aspects of the problem and proposed solutions. Graphic representation tools were used to help students to better pose the problem. Using questionnaires and interviews, researchers observed students' capacity to pose the problem…

  15. Posing Problems to Understand Children's Learning of Fractions

    Science.gov (United States)

    Cheng, Lu Pien

    2013-01-01

    In this study, ways in which problem posing activities aid our understanding of children's learning of addition of unlike fractions and product of proper fractions was examined. In particular, how a simple problem posing activity helps teachers take a second, deeper look at children's understanding of fraction concepts will be discussed. The…

  16. Formulas in inverse and ill-posed problems

    CERN Document Server

    Anikonov, Yu E

    1997-01-01

    The Inverse and Ill-Posed Problems Series is a series of monographs publishing postgraduate level information on inverse and ill-posed problems for an international readership of professional scientists and researchers. The series aims to publish works which involve both theory and applications in, e.g., physics, medicine, geophysics, acoustics, electrodynamics, tomography, and ecology.

  17. Insulin-like growth factor II mRNA-binding protein 3 (IMP3) is a marker that predicts presence of invasion in papillary biliary tumors.

    Science.gov (United States)

    Sasaki, Motoko; Sato, Yasunori

    2017-04-01

    Biliary tumors showing intraductal papillary growth (Pap-BTs) include intraductal papillary neoplasm of the bile duct (IPNB) and papillary cholangiocarcinoma (CC). A differential diagnosis between IPNB and papillary CC currently remains challenging. The aim of the present study is to identify histological features and immunohistochemical markers of malignant potential such as tumor invasion in Pap-BTs. Subjects comprised 37 patients with Pap-BT (intrahepatic and perihilar [proximal], 27: 17 noninvasive and 10 invasive; distal, 10: all invasive). We examined histological features and the expression of p53, enhancer of zeste homolog 2, insulin-like growth factor II mRNA-binding protein 3 (IMP3), and DNA methyltransferase-1 in the intraductal area in Pap-BTs. Noninvasive Pap-BT was characterized by the presence of a low-grade dysplastic area, edematous stroma, and the absence of necrosis. The expression of p53, enhancer of zeste homolog 2, IMP3, and DNA methyltransferase-1 was significantly weaker in noninvasive Pap-BTs than in invasive Pap-BTs (PBTs. IMP3 showed the greatest specificity to predict a presence of invasion. A heatmap demonstrated that proximal noninvasive Pap-BTs and distal Pap-BTs may be completely different. In bile duct biopsies, the expression of IMP3 was the most precise predictor of invasion in Pap-BTs. In conclusion, Pap-BTs may be separated into 3 subgroups: (1) proximal noninvasive Pap-BT, corresponding to IPNB; (2) distal invasive Pap-BT, corresponding to papillary CC; and (3) the remaining Pap-BT including IPNB with associated adenocarcinomas, based on histological and immunohistochemical features. IMP3 may be a useful marker for predicting invasion in Pap-BT. Copyright © 2017 Elsevier Inc. All rights reserved.

  18. Importin alpha binding and nuclear localization of PARP-2 is dependent on lysine 36, which is located within a predicted classical NLS

    Directory of Open Access Journals (Sweden)

    Valovka Taras

    2008-07-01

    Full Text Available Abstract Background The enzymes responsible for the synthesis of poly-ADP-ribose are named poly-ADP-ribose polymerases (PARP. PARP-2 is a nuclear protein, which regulates a variety of cellular functions that are mainly controlled by protein-protein interactions. A previously described non-conventional bipartite nuclear localization sequence (NLS lies in the amino-terminal DNA binding domain of PARP-2 between amino acids 1–69; however, this targeting sequence has not been experimentally examined or validated. Results Using a site-directed mutagenesis approach, we found that lysines 19 and 20, located within a previously described bipartite NLS, are not required for nuclear localization of PARP-2. In contrast, lysine 36, which is located within a predicted classical monopartite NLS, was required for PARP-2 nuclear localization. While wild type PARP-2 interacted with importin α3 and to a very weak extent with importin α1 and importin α5, the mutant PARP-2 (K36R did not interact with importin α3, providing a molecular explanation why PARP-2 (K36R is not targeted to the nucleus. Conclusion Our results provide strong evidence that lysine 36 of PARP-2 is a critical residue for proper nuclear targeting of PARP-2 and consequently for the execution of its biological functions.

  19. Response surface modeling to predict fluid loss from beef strip loins and steaks injected with salt and phosphate with or without a dehydrated beef protein water binding adjunct.

    Science.gov (United States)

    Lowder, Austin C; Goad, Carla L; Lou, Xingqiu; Morgan, J Brad; Koh, Chern Lin; Deakins, Alisha Parsons; Mireles DeWitt, Christina A

    2013-05-01

    This study was conducted using response surface methodology to predict fluid loss from injected beef strip steaks as influenced by levels of salt and sodium phosphates (SP) in the injection brine. Also, a beef-based dehydrated beef protein (DBP) water binding ingredient was evaluated. Paired U.S. select beef strip loins were quartered before being injected with 110% of initial weight with brines containing various concentrations of salt and SP (CON) or salt, SP and 5% DBP. Steaks were sliced, overwrapped and stored in the dark for 4d. Purge values ranged from 0.6% to 4.6% for CON and 0.3% to 2.1% for DBP. Fluid losses when accounting for the fluid lost from injection to slicing were as high as 6.8% for CON brines, but only 2.8% for DBP brines. The equations generated here and the DBP product could help producers achieve acceptable purge while reducing sodium use. Copyright © 2012 Elsevier Ltd. All rights reserved.

  20. Face pose tracking using the four-point algorithm

    Science.gov (United States)

    Fung, Ho Yin; Wong, Kin Hong; Yu, Ying Kin; Tsui, Kwan Pang; Kam, Ho Chuen

    2017-06-01

    In this paper, we have developed an algorithm to track the pose of a human face robustly and efficiently. Face pose estimation is very useful in many applications such as building virtual reality systems and creating an alternative input method for the disabled. Firstly, we have modified a face detection toolbox called DLib for the detection of a face in front of a camera. The detected face features are passed to a pose estimation method, known as the four-point algorithm, for pose computation. The theory applied and the technical problems encountered during system development are discussed in the paper. It is demonstrated that the system is able to track the pose of a face in real time using a consumer grade laptop computer.

  1. Real-Time Head Pose Estimation on Mobile Platforms

    Directory of Open Access Journals (Sweden)

    Jianfeng Ren

    2010-06-01

    Full Text Available Many computer vision applications such as augmented reality require head pose estimation. As far as the real-time implementation of head pose estimation on relatively resource limited mobile platforms is concerned, it is required to satisfy real-time constraints while maintaining reasonable head pose estimation accuracy. The introduced head pose estimation approach in this paper is an attempt to meet this objective. The approach consists of the following components: Viola-Jones face detection, color-based face tracking using an online calibration procedure, and head pose estimation using Hu moment features and Fisher linear discriminant. Experimental results running on an actual mobile device are reported exhibiting both the real- time and accuracy aspects of the developed approach.

  2. Person-Independent Head Pose Estimation Using Biased Manifold Embedding

    Directory of Open Access Journals (Sweden)

    Sethuraman Panchanathan

    2008-02-01

    Full Text Available Head pose estimation has been an integral problem in the study of face recognition systems and human-computer interfaces, as part of biometric applications. A fine estimate of the head pose angle is necessary and useful for several face analysis applications. To determine the head pose, face images with varying pose angles can be considered to be lying on a smooth low-dimensional manifold in high-dimensional image feature space. However, when there are face images of multiple individuals with varying pose angles, manifold learning techniques often do not give accurate results. In this work, we propose a framework for a supervised form of manifold learning called Biased Manifold Embedding to obtain improved performance in head pose angle estimation. This framework goes beyond pose estimation, and can be applied to all regression applications. This framework, although formulated for a regression scenario, unifies other supervised approaches to manifold learning that have been proposed so far. Detailed studies of the proposed method are carried out on the FacePix database, which contains 181 face images each of 30 individuals with pose angle variations at a granularity of 1∘. Since biometric applications in the real world may not contain this level of granularity in training data, an analysis of the methodology is performed on sparsely sampled data to validate its effectiveness. We obtained up to 2∘ average pose angle estimation error in the results from our experiments, which matched the best results obtained for head pose estimation using related approaches.

  3. The Antinociceptive Agent SBFI-26 Binds to Anandamide Transporters FABP5 and FABP7 at Two Different Sites

    Energy Technology Data Exchange (ETDEWEB)

    Hsu, Hao-Chi [Cryo-EM Structural; Tong, Simon [Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States; Zhou, Yuchen [Department of Applied Mathematics; Elmes, Matthew W. [Department of Biochemistry and; Yan, Su [Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States; Kaczocha, Martin [Department of Biochemistry and; Department of Anesthesiology, Stony Brook University, Stony; Deutsch, Dale G. [Department of Biochemistry and; Institute of Chemical Biology and; Rizzo, Robert C. [Department of Applied Mathematics; Institute of Chemical Biology and; Laufer; Ojima, Iwao [Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States; Institute of Chemical Biology and; Li, Huilin [Cryo-EM Structural; Institute of Chemical Biology and

    2017-06-28

    Human FABP5 and FABP7 are intracellular endocannabinoid transporters. SBFI-26 is an α-truxillic acid 1-naphthyl monoester that competitively inhibits the activities of FABP5 and FABP7 and produces antinociceptive and anti-inflammatory effects in mice. The synthesis of SBFI-26 yields several stereoisomers, and it is not known how the inhibitor binds the transporters. Here we report co-crystal structures of SBFI-26 in complex with human FABP5 and FABP7 at 2.2 and 1.9 Å resolution, respectively. We found that only (S)-SBFI-26 was present in the crystal structures. The inhibitor largely mimics the fatty acid binding pattern, but it also has several unique interactions. Notably, the FABP7 complex corroborates key aspects of the ligand binding pose at the canonical site previously predicted by virtual screening. In FABP5, SBFI-26 was unexpectedly found to bind at the substrate entry portal region in addition to binding at the canonical ligand-binding pocket. Our structural and binding energy analyses indicate that both R and S forms appear to bind the transporter equally well. We suggest that the S enantiomer observed in the crystal structures may be a result of the crystallization process selectively incorporating the (S)-SBFI-26–FABP complexes into the growing lattice, or that the S enantiomer may bind to the portal site more rapidly than to the canonical site, leading to an increased local concentration of the S enantiomer for binding to the canonical site. Our work reveals two binding poses of SBFI-26 in its target transporters. This knowledge will guide the development of more potent FABP inhibitors based upon the SBFI-26 scaffold.

  4. Animated pose templates for modeling and detecting human actions.

    Science.gov (United States)

    Yao, Benjamin Z; Nie, Bruce X; Liu, Zicheng; Zhu, Song-Chun

    2014-03-01

    This paper presents animated pose templates (APTs) for detecting short-term, long-term, and contextual actions from cluttered scenes in videos. Each pose template consists of two components: 1) a shape template with deformable parts represented in an And-node whose appearances are represented by the Histogram of Oriented Gradient (HOG) features, and 2) a motion template specifying the motion of the parts by the Histogram of Optical-Flows (HOF) features. A shape template may have more than one motion template represented by an Or-node. Therefore, each action is defined as a mixture (Or-node) of pose templates in an And-Or tree structure. While this pose template is suitable for detecting short-term action snippets in two to five frames, we extend it in two ways: 1) For long-term actions, we animate the pose templates by adding temporal constraints in a Hidden Markov Model (HMM), and 2) for contextual actions, we treat contextual objects as additional parts of the pose templates and add constraints that encode spatial correlations between parts. To train the model, we manually annotate part locations on several keyframes of each video and cluster them into pose templates using EM. This leaves the unknown parameters for our learning algorithm in two groups: 1) latent variables for the unannotated frames including pose-IDs and part locations, 2) model parameters shared by all training samples such as weights for HOG and HOF features, canonical part locations of each pose, coefficients penalizing pose-transition and part-deformation. To learn these parameters, we introduce a semi-supervised structural SVM algorithm that iterates between two steps: 1) learning (updating) model parameters using labeled data by solving a structural SVM optimization, and 2) imputing missing variables (i.e., detecting actions on unlabeled frames) with parameters learned from the previous step and progressively accepting high-score frames as newly labeled examples. This algorithm belongs to a

  5. Binding free energy predictions of farnesoid X receptor (FXR) agonists using a linear interaction energy (LIE) approach with reliability estimation: application to the D3R Grand Challenge 2

    Science.gov (United States)

    Rifai, Eko Aditya; van Dijk, Marc; Vermeulen, Nico P. E.; Geerke, Daan P.

    2018-01-01

    Computational protein binding affinity prediction can play an important role in drug research but performing efficient and accurate binding free energy calculations is still challenging. In the context of phase 2 of the Drug Design Data Resource (D3R) Grand Challenge 2 we used our automated eTOX ALLIES approach to apply the (iterative) linear interaction energy (LIE) method and we evaluated its performance in predicting binding affinities for farnesoid X receptor (FXR) agonists. Efficiency was obtained by our pre-calibrated LIE models and molecular dynamics (MD) simulations at the nanosecond scale, while predictive accuracy was obtained for a small subset of compounds. Using our recently introduced reliability estimation metrics, we could classify predictions with higher confidence by featuring an applicability domain (AD) analysis in combination with protein-ligand interaction profiling. The outcomes of and agreement between our AD and interaction-profile analyses to distinguish and rationalize the performance of our predictions highlighted the relevance of sufficiently exploring protein-ligand interactions during training and it demonstrated the possibility to quantitatively and efficiently evaluate if this is achieved by using simulation data only.

  6. Binding free energy predictions of farnesoid X receptor (FXR) agonists using a linear interaction energy (LIE) approach with reliability estimation: application to the D3R Grand Challenge 2.

    Science.gov (United States)

    Rifai, Eko Aditya; van Dijk, Marc; Vermeulen, Nico P E; Geerke, Daan P

    2018-01-01

    Computational protein binding affinity prediction can play an important role in drug research but performing efficient and accurate binding free energy calculations is still challenging. In the context of phase 2 of the Drug Design Data Resource (D3R) Grand Challenge 2 we used our automated eTOX ALLIES approach to apply the (iterative) linear interaction energy (LIE) method and we evaluated its performance in predicting binding affinities for farnesoid X receptor (FXR) agonists. Efficiency was obtained by our pre-calibrated LIE models and molecular dynamics (MD) simulations at the nanosecond scale, while predictive accuracy was obtained for a small subset of compounds. Using our recently introduced reliability estimation metrics, we could classify predictions with higher confidence by featuring an applicability domain (AD) analysis in combination with protein-ligand interaction profiling. The outcomes of and agreement between our AD and interaction-profile analyses to distinguish and rationalize the performance of our predictions highlighted the relevance of sufficiently exploring protein-ligand interactions during training and it demonstrated the possibility to quantitatively and efficiently evaluate if this is achieved by using simulation data only.

  7. On Pose Estimation for Human-Robot Symbiosis

    Directory of Open Access Journals (Sweden)

    Md. Al-Amin Bhuiyan

    2008-03-01

    Full Text Available This paper presents a vision based pose estimation system using knowledge based approach for human-robot symbiosis. The system is based on visual information of the face by connected component analysis of the skin color segmentation of images in HSV color model and is commenced with the face recognition and pose classification scheme using subspace PCA based pattern-matching strategies. With the knowledge of the known user's profile, face poses are then classified by multilayer perceptron. Based on the frame-based knowledge representation approach, face poses are being interpreted using the Software Platform for Agent and Knowledge (SPAK management. On face pose recognition, robot is then instructed to perform some specific tasks by issuing pose commands. Experimental results demonstrate that the subspace method is better than that of the standard PCA method for face pose classification. The system has been demonstrated with the implementation of the algorithm to interact with an entertainment robot named, AIBO for human-robot symbiotic relationship.

  8. Methods of RVD object pose estimation and experiments

    Science.gov (United States)

    Shang, Yang; He, Yan; Wang, Weihua; Yu, Qifeng

    2007-11-01

    Methods of measuring a RVD (rendezvous and docking) cooperative object's pose from monocular and binocular images respectively are presented. The methods solve the initial values first and optimize the object pose parameters by bundle adjustment. In the disturbance-rejecting binocular method, chosen measurement system parameters of one camera's exterior parameters are modified simultaneously. The methods need three or more cooperative target points to measure the object's pose accurately. Experimental data show that the methods converge quickly and stably, provide accurate results and do not need accurate initial values. Even when the chosen measurement system parameters are subjected to some amount of disturbance, the binocular method manages to provide fairly accurate results.

  9. A modern approach for epitope prediction: identification of foot-and-mouth disease virus peptides binding bovine leukocyte antigen (BoLA) class I molecules

    Science.gov (United States)

    Major histocompatibility complex (MHC) class I molecules regulate adaptive immune responses through the presentation of antigenic peptides to CD8positive T-cells. Polymorphisms in the peptide binding region of class I molecules determine peptide binding affinity and stability during antigen presenta...

  10. NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data

    DEFF Research Database (Denmark)

    Jurtz, Vanessa Isabell; Paul, Sinu; Andreatta, Massimo

    2017-01-01

    Cytotoxic T cells are of central importance in the immune system's response to disease. They recognize defective cells by binding to peptides presented on the cell surface by MHC class I molecules. Peptide binding to MHC molecules is the single most selective step in the Ag-presentation pathway...

  11. NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets

    DEFF Research Database (Denmark)

    Nielsen, Morten; Andreatta, Massimo

    2016-01-01

    Background: Binding of peptides to MHC class I molecules (MHC-I) is essential for antigen presentation to cytotoxic T-cells.Results: Here, we demonstrate how a simple alignment step allowing insertions and deletions in a pan-specific MHC-I binding machine-learning model enables combining informat...

  12. Prediction of the Occurrence of the ADP-binding βαβ-fold in Proteins, Using an Amino Acid Sequence Fingerprint

    NARCIS (Netherlands)

    Wierenga, Rik K.; Terpstra, Peter; Hol, Wim G.J.

    1986-01-01

    An amino acid sequence "fingerprint” has been derived that can be used to test if a particular sequence will fold into a βαβ-unit with ADP-binding properties. It was deduced from a careful analysis of the known three-dimensional structures of ADP-binding βαβ-folds. This fingerprint is in fact a set

  13. Studies on the competitive binding of cleviprex and flavonoids to plasma protein by multi-spectroscopic methods: A prediction of food-drug interaction.

    Science.gov (United States)

    Wang, Xin; Guo, Xue-Yuan; Xu, Liang; Liu, Bin; Zhou, Ling-Ling; Wang, Xiao-Fang; Wang, Dan; Sun, Ting

    2017-10-01

    Cleviprex is a short-acting dihydropyridine calcium channel antagonist used as an antihypertensive drug. In this work, the binding characterization of cleviprex to human serum albumin (HSA) and the competitive binding to HSA between cleviprex and two flavonoids, baicalin and rutin, were studied using multi-spectroscopic techniques and molecular docking method. The fluorescence quenching of HSA by cleviprex was initiated by the formation of HSA-cleviprex complex, which was confirmed by UV-vis spectra measurements. The results of thermodynamic analysis and molecular docking revealed that the hydrophobic interactions and hydrogen bonding were the major acting forces in stabilizing HSA-cleviprex complex. The results of substitution experiments and molecular docking demonstrated that cleviprex was mainly situated within the site I of HSA. Baicalin and rutin could reduce the values of binding constant and enhance the values of binding distance of cleviprex binding to HSA because they bind to the same binding site. The results of synchronous fluorescence and CD spectra suggested that the binding reaction of cleviprex to HSA could give rise to the changes of protein conformation and the combined actions of cleviprex and flavonoids could cause further changes of HSA conformation. Consequently, the intakes of flavonoid-rich foods and beverages should be lessened under the treatment of cleviprex to avoid food-drug interactions. Copyright © 2017 Elsevier B.V. All rights reserved.

  14. Serum Wisteria Floribunda Agglutinin-Positive Mac-2 Binding Protein Values Predict the Development of Hepatocellular Carcinoma among Patients with Chronic Hepatitis C after Sustained Virological Response.

    Directory of Open Access Journals (Sweden)

    Ryu Sasaki

    Full Text Available Measurement of Wisteria floribunda agglutinin-positive human Mac-2 binding protein (WFA+-M2BP in serum was recently shown to be a noninvasive method to assess liver fibrosis. The aim of this study was to evaluate the utility of serum WFA+-M2BP values to predict the development of hepatocellular carcinoma (HCC in patients who achieved a sustained virological response (SVR by interferon treatment. For this purpose, we retrospectively analyzed 238 patients with SVR who were treated with interferon in our department. Serum WFA+-M2BP values were measured at pre-treatment (pre-Tx, post-treatment (24 weeks after completion of interferon; post-Tx, the time of HCC diagnosis, and the last clinical visit. Of 238 patients with SVR, HCC developed in 16 (6.8% patients. The average follow-up period was 9.1 years. The cumulative incidence of HCC was 3.4% at 5 years and 7.5% at 10 years. The median pre-Tx and post-Tx WFA+-M2BP values were 1.69 (range: 0.28 to 12.04 cutoff index (COI and 0.80 (range: 0.17 to 5.29 COI, respectively. The WFA+-M2BP values decreased significantly after SVR (P 60 years, sex (male, pre-Tx platelet count ( 2.0 COI were associated with the development of HCC after SVR. Conclusion: Post-Tx WFA+-M2BP (> 2.0 COI is associated with the risk for development of HCC among patients with SVR. The WFA+-M2BP values could be a new predictor for HCC after SVR.

  15. Association of Serum Adipocyte-Specific Fatty Acid Binding Protein with Fatty Liver Index as a Predictive Indicator of Nonalcoholic Fatty Liver Disease

    Directory of Open Access Journals (Sweden)

    Won Seon Jeon

    2013-12-01

    Full Text Available BackgroundAdipocyte-specific fatty acid-binding protein (A-FABP is a cytoplasmic protein expressed in macrophages and adipocytes and it plays a role in insulin resistance and metabolic syndrome. Recently, the fatty liver index (FLI was introduced as an indicator of nonalcoholic fatty liver disease (NAFLD. In this study, we aimed to investigate the relationship between baseline serum A-FABP levels and FLI after 4 years in apparently healthy subjects.MethodsA total of 238 subjects without a past history of alcoholism or hepatitis were recruited from a medical check-up program. The NAFLD state was evaluated 4 years later in the same subjects using FLI. Fatty liver disease was diagnosed as diffusely increased echogenicity of the hepatic parenchyma compared to the kidneys, vascular blurring, and deep-echo attenuation. NAFLD was defined as subjects with fatty liver and no history of alcohol consumption (>20 g/day.ResultsBaseline serum A-FABP levels were significantly associated with FLI after adjustment for age and sex (P<0.001. The subjects with higher A-FABP levels had a higher mean FLI (P for trend=0.006. After adjusting for age and sex, serum A-FABP levels at baseline were shown to be significantly associated with FLI as a marker of development of NAFLD after 4 years (odds ratio, 2.68; 95% confidence interval, 1.24 to 5.80 for highest tertile vs. lowest tertile; P=0.012.ConclusionThis study demonstrated that higher baseline serum A-FABP levels were associated with FLI as a predictive indicator of NAFLD after 4 years of follow-up in healthy Korean adults.

  16. RELATIVE CAMERA POSE ESTIMATION METHOD USING OPTIMIZATION ON THE MANIFOLD

    Directory of Open Access Journals (Sweden)

    C. Cheng

    2017-05-01

    Full Text Available To solve the problem of relative camera pose estimation, a method using optimization with respect to the manifold is proposed. Firstly from maximum-a-posteriori (MAP model to nonlinear least squares (NLS model, the general state estimation model using optimization is derived. Then the camera pose estimation model is applied to the general state estimation model, while the parameterization of rigid body transformation is represented by Lie group/algebra. The jacobian of point-pose model with respect to Lie group/algebra is derived in detail and thus the optimization model of rigid body transformation is established. Experimental results show that compared with the original algorithms, the approaches with optimization can obtain higher accuracy both in rotation and translation, while avoiding the singularity of Euler angle parameterization of rotation. Thus the proposed method can estimate relative camera pose with high accuracy and robustness.

  17. Mathematical Thinking and Creativity through Mathematical Problem Posing and Solving

    Directory of Open Access Journals (Sweden)

    María F. Ayllón

    2016-04-01

    Full Text Available This work shows the relationship between the development of mathematical thinking and creativity with mathematical problem posing and solving. Creativity and mathematics are disciplines that do not usually appear together. Both concepts constitute complex processes sharing elements, such as fluency (number of ideas, flexibility (range of ideas, novelty (unique idea and elaboration (idea development. These factors contribute, among others, to the fact that schoolchildren are competent in mathematics. The problem solving and posing are a very powerful evaluation tool that shows the mathematical reasoning and creative level of a person. Creativity is part of the mathematics education and is a necessary ingredient to perform mathematical assignments. This contribution presents some important research works about problem posing and solving related to the development of mathematical knowledge and creativity. To that end, it is based on various beliefs reflected in the literature with respect to notions of creativity, problem solving and posing.

  18. Pose estimation of industrial objects towards robot operation

    Science.gov (United States)

    Niu, Jie; Zhou, Fuqiang; Tan, Haishu; Cao, Yu

    2017-10-01

    With the advantages of wide range, non-contact and high flexibility, the visual estimation technology of target pose has been widely applied in modern industry, robot guidance and other engineering practices. However, due to the influence of complicated industrial environment, outside interference factors, lack of object characteristics, restrictions of camera and other limitations, the visual estimation technology of target pose is still faced with many challenges. Focusing on the above problems, a pose estimation method of the industrial objects is developed based on 3D models of targets. By matching the extracted shape characteristics of objects with the priori 3D model database of targets, the method realizes the recognition of target. Thus a pose estimation of objects can be determined based on the monocular vision measuring model. The experimental results show that this method can be implemented to estimate the position of rigid objects based on poor images information, and provides guiding basis for the operation of the industrial robot.

  19. Health Issues: Do Cell Phones Pose a Health Hazard?

    Science.gov (United States)

    ... Procedures Home, Business, and Entertainment Products Cell Phones Health Issues Share Tweet Linkedin Pin it More sharing ... it Email Print Do cell phones pose a health hazard? Many people are concerned that cell phone ...

  20. ESPRIT: Exercise Sensing and Pose Recovery Inference Tool, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — We propose to develop ESPRIT: an Exercise Sensing and Pose Recovery Inference Tool, in support of NASA's effort in developing crew exercise technologies for...

  1. Assessing the Biological Threat Posed by Suicide Bombers

    Science.gov (United States)

    2016-02-01

    ASSESSING THE BIOLOGICAL THREAT POSED BY SUICIDE BOMBERS ECBC-TR-1363 Jerry B. Cabalo Jana Kesavan David W...Assessing the Biological Threat Posed by Suicide Bombers 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Cabalo ...RDCB-DRI-T ATTN: J. Cabalo J. Kesavan D. Sickenberger A. Goad RDCB-DEJ-P ATTN: G. Diviacchi Defense

  2. Inverse and Ill-posed Problems Theory and Applications

    CERN Document Server

    Kabanikhin, S I

    2011-01-01

    The text demonstrates the methods for proving the existence (if et all) and finding of inverse and ill-posed problems solutions in linear algebra, integral and operator equations, integral geometry, spectral inverse problems, and inverse scattering problems. It is given comprehensive background material for linear ill-posed problems and for coefficient inverse problems for hyperbolic, parabolic, and elliptic equations. A lot of examples for inverse problems from physics, geophysics, biology, medicine, and other areas of application of mathematics are included.

  3. Structural interface parameters are discriminatory in recognising near-native poses of protein-protein interactions.

    Directory of Open Access Journals (Sweden)

    Sony Malhotra

    Full Text Available Interactions at the molecular level in the cellular environment play a very crucial role in maintaining the physiological functioning of the cell. These molecular interactions exist at varied levels viz. protein-protein interactions, protein-nucleic acid interactions or protein-small molecules interactions. Presently in the field, these interactions and their mechanisms mark intensively studied areas. Molecular interactions can also be studied computationally using the approach named as Molecular Docking. Molecular docking employs search algorithms to predict the possible conformations for interacting partners and then calculates interaction energies. However, docking proposes number of solutions as different docked poses and hence offers a serious challenge to identify the native (or near native structures from the pool of these docked poses. Here, we propose a rigorous scoring scheme called DockScore which can be used to rank the docked poses and identify the best docked pose out of many as proposed by docking algorithm employed. The scoring identifies the optimal interactions between the two protein partners utilising various features of the putative interface like area, short contacts, conservation, spatial clustering and the presence of positively charged and hydrophobic residues. DockScore was first trained on a set of 30 protein-protein complexes to determine the weights for different parameters. Subsequently, we tested the scoring scheme on 30 different protein-protein complexes and native or near-native structure were assigned the top rank from a pool of docked poses in 26 of the tested cases. We tested the ability of DockScore to discriminate likely dimer interactions that differ substantially within a homologous family and also demonstrate that DOCKSCORE can distinguish correct pose for all 10 recent CAPRI targets.

  4. A deep learning approach for pose estimation from volumetric OCT data.

    Science.gov (United States)

    Gessert, Nils; Schlüter, Matthias; Schlaefer, Alexander

    2018-03-10

    Tracking the pose of instruments is a central problem in image-guided surgery. For microscopic scenarios, optical coherence tomography (OCT) is increasingly used as an imaging modality. OCT is suitable for accurate pose estimation due to its micrometer range resolution and volumetric field of view. However, OCT image processing is challenging due to speckle noise and reflection artifacts in addition to the images' 3D nature. We address pose estimation from OCT volume data with a new deep learning-based tracking framework. For this purpose, we design a new 3D convolutional neural network (CNN) architecture to directly predict the 6D pose of a small marker geometry from OCT volumes. We use a hexapod robot to automatically acquire labeled data points which we use to train 3D CNN architectures for multi-output regression. We use this setup to provide an in-depth analysis on deep learning-based pose estimation from volumes. Specifically, we demonstrate that exploiting volume information for pose estimation yields higher accuracy than relying on 2D representations with depth information. Supporting this observation, we provide quantitative and qualitative results that 3D CNNs effectively exploit the depth structure of marker objects. Regarding the deep learning aspect, we present efficient design principles for 3D CNNs, making use of insights from the 2D deep learning community. In particular, we present Inception3D as a new architecture which performs best for our application. We show that our deep learning approach reaches errors at our ground-truth label's resolution. We achieve a mean average error of 14.89 ± 9.3 µm and 0.096 ± 0.072° for position and orientation learning, respectively. Copyright © 2018 Elsevier B.V. All rights reserved.

  5. Contactless and pose invariant biometric identification using hand surface.

    Science.gov (United States)

    Kanhangad, Vivek; Kumar, Ajay; Zhang, David

    2011-05-01

    This paper presents a novel approach for hand matching that achieves significantly improved performance even in the presence of large hand pose variations. The proposed method utilizes a 3-D digitizer to simultaneously acquire intensity and range images of the user's hand presented to the system in an arbitrary pose. The approach involves determination of the orientation of the hand in 3-D space followed by pose normalization of the acquired 3-D and 2-D hand images. Multimodal (2-D as well as 3-D) palmprint and hand geometry features, which are simultaneously extracted from the user's pose normalized textured 3-D hand, are used for matching. Individual matching scores are then combined using a new dynamic fusion strategy. Our experimental results on the database of 114 subjects with significant pose variations yielded encouraging results. Consistent (across various hand features considered) performance improvement achieved with the pose correction demonstrates the usefulness of the proposed approach for hand based biometric systems with unconstrained and contact-free imaging. The experimental results also suggest that the dynamic fusion approach employed in this work helps to achieve performance improvement of 60% (in terms of EER) over the case when matching scores are combined using the weighted sum rule.

  6. Perspective projection for variance pose face recognition from camera calibration

    Science.gov (United States)

    Fakhir, M. M.; Woo, W. L.; Chambers, J. A.; Dlay, S. S.

    2016-04-01

    Variance pose is an important research topic in face recognition. The alteration of distance parameters across variance pose face features is a challenging. We provide a solution for this problem using perspective projection for variance pose face recognition. Our method infers intrinsic camera parameters of the image which enable the projection of the image plane into 3D. After this, face box tracking and centre of eyes detection can be identified using our novel technique to verify the virtual face feature measurements. The coordinate system of the perspective projection for face tracking allows the holistic dimensions for the face to be fixed in different orientations. The training of frontal images and the rest of the poses on FERET database determine the distance from the centre of eyes to the corner of box face. The recognition system compares the gallery of images against different poses. The system initially utilises information on position of both eyes then focuses principally on closest eye in order to gather data with greater reliability. Differentiation between the distances and position of the right and left eyes is a unique feature of our work with our algorithm outperforming other state of the art algorithms thus enabling stable measurement in variance pose for each individual.

  7. Robust head pose estimation via supervised manifold learning.

    Science.gov (United States)

    Wang, Chao; Song, Xubo

    2014-05-01

    Head poses can be automatically estimated using manifold learning algorithms, with the assumption that with the pose being the only variable, the face images should lie in a smooth and low-dimensional manifold. However, this estimation approach is challenging due to other appearance variations related to identity, head location in image, background clutter, facial expression, and illumination. To address the problem, we propose to incorporate supervised information (pose angles of training samples) into the process of manifold learning. The process has three stages: neighborhood construction, graph weight computation and projection learning. For the first two stages, we redefine inter-point distance for neighborhood construction as well as graph weight by constraining them with the pose angle information. For Stage 3, we present a supervised neighborhood-based linear feature transformation algorithm to keep the data points with similar pose angles close together but the data points with dissimilar pose angles far apart. The experimental results show that our method has higher estimation accuracy than the other state-of-art algorithms and is robust to identity and illumination variations. Copyright © 2014 Elsevier Ltd. All rights reserved.

  8. Neuromorphic Event-Based 3D Pose Estimation

    Directory of Open Access Journals (Sweden)

    David eReverter Valeiras

    2016-01-01

    Full Text Available Pose estimation is a fundamental step in many artificial vision tasks. It consists of estimating the 3D pose of an object with respect to a camera from the object's 2D projection. Current state of the art implementations operate on images. These implementations are computationally expensive, especially for real-time applications. Scenes with fast dynamics exceeding 30-60Hz can rarely be processed in real-time using conventional hardware. This paper presents a new method for event-based 3D object pose estimation, making full use of the high temporal resolution (1textmu s of asynchronous visual events output from a single neuromorphic camera. Given an initial estimate of the pose, each incoming event is used to update the pose by combining both 3D and 2D criteria. We show that the asynchronous high temporal resolution of the neuromorphic camera allows us to solve the problem in an incremental manner, achieving real-time performance at an update rate of several hundreds kHz on a conventional laptop. We show that the high temporal resolution of neuromorphic cameras is a key feature for performing accurate pose estimation. Experiments are provided showing the performance of the algorithm on real data, including fast moving objects, occlusions, and cases where the neuromorphic camera and the object are both in motion.

  9. Yoga Poses Increase Subjective Energy and State Self-Esteem in Comparison to ‘Power Poses’

    Science.gov (United States)

    Golec de Zavala, Agnieszka; Lantos, Dorottya; Bowden, Deborah

    2017-01-01

    Research on beneficial consequences of yoga focuses on the effects of yogic breathing and meditation. Less is known about the psychological effects of performing yoga postures. The present study investigated the effects of yoga poses on subjective sense of energy and self-esteem. The effects of yoga postures were compared to the effects of ‘power poses,’ which arguably increase the sense of power and self-confidence due to their association with interpersonal dominance (Carney et al., 2010). The study tested the novel prediction that yoga poses, which are not associated with interpersonal dominance but increase bodily energy, would increase the subjective feeling of energy and therefore increase self-esteem compared to ‘high power’ and ‘low power’ poses. A two factorial, between participants design was employed. Participants performed either two standing yoga poses with open front of the body (n = 19), two standing yoga poses with covered front of the body (n = 22), two expansive, high power poses (n = 21), or two constrictive, low power poses (n = 20) for 1-min each. The results showed that yoga poses in comparison to ‘power poses’ increased self-esteem. This effect was mediated by an increased subjective sense of energy and was observed when baseline trait self-esteem was controlled for. These results suggest that the effects of performing open, expansive body postures may be driven by processes other than the poses’ association with interpersonal power and dominance. This study demonstrates that positive effects of yoga practice can occur after performing yoga poses for only 2 min. PMID:28553249

  10. Yoga Poses Increase Subjective Energy and State Self-Esteem in Comparison to ‘Power Poses’

    Directory of Open Access Journals (Sweden)

    Agnieszka Golec de Zavala

    2017-05-01

    Full Text Available Research on beneficial consequences of yoga focuses on the effects of yogic breathing and meditation. Less is known about the psychological effects of performing yoga postures. The present study investigated the effects of yoga poses on subjective sense of energy and self-esteem. The effects of yoga postures were compared to the effects of ‘power poses,’ which arguably increase the sense of power and self-confidence due to their association with interpersonal dominance (Carney et al., 2010. The study tested the novel prediction that yoga poses, which are not associated with interpersonal dominance but increase bodily energy, would increase the subjective feeling of energy and therefore increase self-esteem compared to ‘high power’ and ‘low power’ poses. A two factorial, between participants design was employed. Participants performed either two standing yoga poses with open front of the body (n = 19, two standing yoga poses with covered front of the body (n = 22, two expansive, high power poses (n = 21, or two constrictive, low power poses (n = 20 for 1-min each. The results showed that yoga poses in comparison to ‘power poses’ increased self-esteem. This effect was mediated by an increased subjective sense of energy and was observed when baseline trait self-esteem was controlled for. These results suggest that the effects of performing open, expansive body postures may be driven by processes other than the poses’ association with interpersonal power and dominance. This study demonstrates that positive effects of yoga practice can occur after performing yoga poses for only 2 min.

  11. Do Fragments and Crystallization Additives Bind Similarly to Drug-like Ligands?

    Science.gov (United States)

    Drwal, Malgorzata N; Jacquemard, Célien; Perez, Carlos; Desaphy, Jérémy; Kellenberger, Esther

    2017-05-22

    The success of fragment-based drug design (FBDD) hinges upon the optimization of low-molecular-weight compounds (MW additives such as cryoprotectants or buffer components, which are highly abundant in crystal structures, are frequently ignored. Thus, the aim of this study was to investigate the information present in protein complexes with fragments as well as those with additives and how they relate to the binding modes of their drug-like counterparts. We present a thorough analysis of the binding modes of crystallographic additives, fragments, and drug-like ligands bound to four diverse targets of wide interest in drug discovery and highly represented in the Protein Data Bank: cyclin-dependent kinase 2, β-secretase 1, carbonic anhydrase 2, and trypsin. We identified a total of 630 unique molecules bound to the catalytic binding sites, among them 31 additives, 222 fragments, and 377 drug-like ligands. In general, we observed that, independent of the target, protein-fragment interaction patterns are highly similar to those of drug-like ligands and mostly cover the residues crucial for binding. Crystallographic additives are also able to show conserved binding modes and recover the residues important for binding in some of the cases. Moreover, we show evidence that the information from fragments and drug-like ligands can be applied to rescore docking poses in order to improve the prediction of binding modes.

  12. Optimal accelerometer placement on a robot arm for pose estimation

    Science.gov (United States)

    Wijayasinghe, Indika B.; Sanford, Joseph D.; Abubakar, Shamsudeen; Saadatzi, Mohammad Nasser; Das, Sumit K.; Popa, Dan O.

    2017-05-01

    The performance of robots to carry out tasks depends in part on the sensor information they can utilize. Usually, robots are fitted with angle joint encoders that are used to estimate the position and orientation (or the pose) of its end-effector. However, there are numerous situations, such as in legged locomotion, mobile manipulation, or prosthetics, where such joint sensors may not be present at every, or any joint. In this paper we study the use of inertial sensors, in particular accelerometers, placed on the robot that can be used to estimate the robot pose. Studying accelerometer placement on a robot involves many parameters that affect the performance of the intended positioning task. Parameters such as the number of accelerometers, their size, geometric placement and Signal-to-Noise Ratio (SNR) are included in our study of their effects for robot pose estimation. Due to the ubiquitous availability of inexpensive accelerometers, we investigated pose estimation gains resulting from using increasingly large numbers of sensors. Monte-Carlo simulations are performed with a two-link robot arm to obtain the expected value of an estimation error metric for different accelerometer configurations, which are then compared for optimization. Results show that, with a fixed SNR model, the pose estimation error decreases with increasing number of accelerometers, whereas for a SNR model that scales inversely to the accelerometer footprint, the pose estimation error increases with the number of accelerometers. It is also shown that the optimal placement of the accelerometers depends on the method used for pose estimation. The findings suggest that an integration-based method favors placement of accelerometers at the extremities of the robot links, whereas a kinematic-constraints-based method favors a more uniformly distributed placement along the robot links.

  13. Informing the Human Plasma Protein Binding of Environmental Chemicals by Machine Learning in the Pharmaceutical Space: Applicability Domain and Limits of Predictability

    Science.gov (United States)

    The free fraction of a xenobiotic in plasma (Fub) is an important determinant of chemical adsorption, distribution, metabolism, elimination, and toxicity, yet experimental plasma protein binding data is scarce for environmentally relevant chemicals. The presented work explores th...

  14. A modern approach for epitope prediction: identification of foot-and-mouth disease virus peptides binding bovine leukocyte antigen (BoLA) class I molecules

    DEFF Research Database (Denmark)

    Pandya, Mital; Rasmussen, Michael; Hansen, Andreas

    2015-01-01

    Major histocompatibility complex (MHC) class I molecules regulate adaptive immune responses through the presentation of antigenic peptides to CD8+ T cells. Polymorphisms in the peptide binding region of class I molecules determine peptide binding affinity and stability during antigen presentation......, and different antigen peptide motifs are associated with specific genetic sequences of class I molecules. Understanding bovine leukocyte antigen (BoLA), peptide-MHC class I binding specificities may facilitate development of vaccines or reagents for quantifying the adaptive immune response to intracellular...... pathogens, such as foot-and-mouth disease virus (FMDV). Six synthetic BoLA class I (BoLA-I) molecules were produced, and the peptide binding motif was generated for five of the six molecules using a combined approach of positional scanning combinatorial peptide libraries (PSCPLs) and neural network...

  15. Students’ Mathematical Creative Thinking through Problem Posing Learning

    Science.gov (United States)

    Ulfah, U.; Prabawanto, S.; Jupri, A.

    2017-09-01

    The research aims to investigate the differences in enhancement of students’ mathematical creative thinking ability of those who received problem posing approach assisted by manipulative media and students who received problem posing approach without manipulative media. This study was a quasi experimental research with non-equivalent control group design. Population of this research was third-grade students of a primary school in Bandung city in 2016/2017 academic year. Sample of this research was two classes as experiment class and control class. The instrument used is a test of mathematical creative thinking ability. Based on the results of the research, it is known that the enhancement of the students’ mathematical creative thinking ability of those who received problem posing approach with manipulative media aid is higher than the ability of those who received problem posing approach without manipulative media aid. Students who get learning problem posing learning accustomed in arranging mathematical sentence become matter of story so it can facilitate students to comprehend about story

  16. Multi-task pose-invariant face recognition.

    Science.gov (United States)

    Ding, Changxing; Xu, Chang; Tao, Dacheng

    2015-03-01

    Face images captured in unconstrained environments usually contain significant pose variation, which dramatically degrades the performance of algorithms designed to recognize frontal faces. This paper proposes a novel face identification framework capable of handling the full range of pose variations within ±90° of yaw. The proposed framework first transforms the original pose-invariant face recognition problem into a partial frontal face recognition problem. A robust patch-based face representation scheme is then developed to represent the synthesized partial frontal faces. For each patch, a transformation dictionary is learnt under the proposed multi-task learning scheme. The transformation dictionary transforms the features of different poses into a discriminative subspace. Finally, face matching is performed at patch level rather than at the holistic level. Extensive and systematic experimentation on FERET, CMU-PIE, and Multi-PIE databases shows that the proposed method consistently outperforms single-task-based baselines as well as state-of-the-art methods for the pose problem. We further extend the proposed algorithm for the unconstrained face verification problem and achieve top-level performance on the challenging LFW data set.

  17. Candidate SNP markers of reproductive potential are predicted by a significant change in the affinity of TATA-binding protein for human gene promoters.

    Science.gov (United States)

    Chadaeva, Irina V; Ponomarenko, Petr M; Rasskazov, Dmitry A; Sharypova, Ekaterina B; Kashina, Elena V; Zhechev, Dmitry A; Drachkova, Irina A; Arkova, Olga V; Savinkova, Ludmila K; Ponomarenko, Mikhail P; Kolchanov, Nikolay A; Osadchuk, Ludmila V; Osadchuk, Alexandr V

    2018-02-09

    The progress of medicine, science, technology, education, and culture improves, year by year, quality of life and life expectancy of the populace. The modern human has a chance to further improve the quality and duration of his/her life and the lives of his/her loved ones by bringing their lifestyle in line with their sequenced individual genomes. With this in mind, one of genome-based developments at the junction of personalized medicine and bioinformatics will be considered in this work, where we used two Web services: (i) SNP_TATA_Comparator to search for alleles with a single nucleotide polymorphism (SNP) that alters the affinity of TATA-binding protein (TBP) for the TATA boxes of human gene promoters and (ii) PubMed to look for retrospective clinical reviews on changes in physiological indicators of reproductive potential in carriers of these alleles. A total of 126 SNP markers of female reproductive potential, capable of altering the affinity of TBP for gene promoters, were found using the two above-mentioned Web services. For example, 10 candidate SNP markers of thrombosis (e.g., rs563763767) can cause overproduction of coagulation inducers. In pregnant women, Hughes syndrome provokes thrombosis with a fatal outcome although this syndrome can be diagnosed and eliminated even at the earliest stages of its development. Thus, in women carrying any of the above SNPs, preventive treatment of this syndrome before a planned pregnancy can reduce the risk of death. Similarly, seven SNP markers predicted here (e.g., rs774688955) can elevate the risk of myocardial infarction. In line with Bowles' lifespan theory, women carrying any of these SNPs may modify their lifestyle to improve their longevity if they can take under advisement that risks of myocardial infarction increase with age of the mother, total number of pregnancies, in multiple pregnancies, pregnancies under the age of 20, hypertension, preeclampsia, menstrual cycle irregularity, and in women smokers

  18. Pose-Invariant Face Recognition via RGB-D Images.

    Science.gov (United States)

    Sang, Gaoli; Li, Jing; Zhao, Qijun

    2016-01-01

    Three-dimensional (3D) face models can intrinsically handle large pose face recognition problem. In this paper, we propose a novel pose-invariant face recognition method via RGB-D images. By employing depth, our method is able to handle self-occlusion and deformation, both of which are challenging problems in two-dimensional (2D) face recognition. Texture images in the gallery can be rendered to the same view as the probe via depth. Meanwhile, depth is also used for similarity measure via frontalization and symmetric filling. Finally, both texture and depth contribute to the final identity estimation. Experiments on Bosphorus, CurtinFaces, Eurecom, and Kiwi databases demonstrate that the additional depth information has improved the performance of face recognition with large pose variations and under even more challenging conditions.

  19. Pose Estimation of Interacting People using Pictorial Structures

    DEFF Research Database (Denmark)

    Fihl, Preben; Moeslund, Thomas B.

    2010-01-01

    Pose estimation of people have had great progress in recent years but so far research has dealt with single persons. In this paper we address some of the challenges that arise when doing pose estimation of interacting people. We build on the pictorial structures framework and make important...... contributions by combining color-based appearance and edge information using a measure of the local quality of the appearance feature. In this way we not only combine the two types of features but dynamically find the optimal weighting of them. We further enable the method to handle occlusions by searching...

  20. A direct method for nonlinear ill-posed problems

    Science.gov (United States)

    Lakhal, A.

    2018-02-01

    We propose a direct method for solving nonlinear ill-posed problems in Banach-spaces. The method is based on a stable inversion formula we explicitly compute by applying techniques for analytic functions. Furthermore, we investigate the convergence and stability of the method and prove that the derived noniterative algorithm is a regularization. The inversion formula provides a systematic sensitivity analysis. The approach is applicable to a wide range of nonlinear ill-posed problems. We test the algorithm on a nonlinear problem of travel-time inversion in seismic tomography. Numerical results illustrate the robustness and efficiency of the algorithm.

  1. Present and potential security threats posed to civil aviation

    Directory of Open Access Journals (Sweden)

    Stanislav SZABO

    2012-06-01

    Full Text Available Aircraft presents ideal object for terrorist attack. Apart from the risks posed by possible terrorist attacks on airborne aircraft, air terrorism includes the threats to general aviation on the ground, including airports and surrounding infrastructure. Air oriented terrorism in all of its forms can undermine public confidence in the safety of air travel, which could result in negative effects for certain airlines and other firms in aviation industry due to decline in passenger travel and cargo shipment. This article is giving an overview about the redoubtable present and potential future threats posed to in-flight security, and possibilities and solutions how to mitigate the risks on acceptable level.

  2. Winners, losers, and posers: The effect of power poses on testosterone and risk-taking following competition.

    Science.gov (United States)

    Smith, Kristopher M; Apicella, Coren L

    2017-06-01

    A contribution to a special issue on Hormones and Human Competition. The effect of postural power displays (i.e. power poses) on hormone levels and decision-making has recently been challenged. While Carney et al. (2010) found that holding brief postural displays of power leads to increased testosterone, decreased cortisol and greater economic risk taking, this failed to replicate in a recent high-powered study (Ranehill et al. 2015). It has been put forward that subtle differences in social context may account for the differences in results. Power displays naturally occur within the context of competitions, as do changes in hormones, and researchers have yet to examine the effects of poses within this ecologically relevant context. Using a large sample of 247 male participants, natural winners and losers of a physical competition were randomly assigned to hold a low, neutral or high-power postural display. We found no main effect of pose type on testosterone, cortisol, risk or feelings of power. Winners assigned to a high-power pose had a relative, albeit small, rise in testosterone compared to winners who held neutral or low-power poses. For losers, we found little evidence that high-power poses lead to increased testosterone relative to those holding neutral or low-powered poses. If anything, the reverse was observed - losers had a reduction in testosterone after holding high-power poses. To the extent that changes in testosterone modulate social behaviors adaptively, it is possible that the relative reduction in testosterone observed in losers taking high-powered poses is designed to inhibit further "winner-like" behavior that could result in continued defeat and harm. Still, effects were small, multiple comparisons were made, and the results ran counter to our predictions. We thus treat these conclusions as preliminary. Copyright © 2016 Elsevier Inc. All rights reserved.

  3. Peptide-binding motif prediction by using phage display library for SasaUBA*0301, a resistance haplotype of MHC class I molecule from Atlantic Salmon (Salmo salar)

    DEFF Research Database (Denmark)

    Zhao, Heng; Hermsen, Trudi; Stet, Rene J M

    2008-01-01

    The structure of the peptide-binding specificity of major histocompatibility complex (MHC) class I has been analyzed extensively in human and mouse. For fish, there are no crystallographic models of MHC molecules, neither are there data on the peptide-binding specificity. In this study, we descri...... and there is a significant association between MHC polymorphism and the disease resistance. Therefore, our study might contribute to designing a peptide vaccine against this viral disease....... class I molecule might have a very similar binding motif at the C-terminus compared with a known mouse class I molecule H2-Kb which has L, or I, V, M at p8. Previous work showed that Atlantic Salmon carrying the allele SasaUBA*0301 are resistant to infectious Salmon aneamia virus...

  4. Problem Posing with Realistic Mathematics Education Approach in Geometry Learning

    Science.gov (United States)

    Mahendra, R.; Slamet, I.; Budiyono

    2017-09-01

    One of the difficulties of students in the learning of geometry is on the subject of plane that requires students to understand the abstract matter. The aim of this research is to determine the effect of Problem Posing learning model with Realistic Mathematics Education Approach in geometry learning. This quasi experimental research was conducted in one of the junior high schools in Karanganyar, Indonesia. The sample was taken using stratified cluster random sampling technique. The results of this research indicate that the model of Problem Posing learning with Realistic Mathematics Education Approach can improve students’ conceptual understanding significantly in geometry learning especially on plane topics. It is because students on the application of Problem Posing with Realistic Mathematics Education Approach are become to be active in constructing their knowledge, proposing, and problem solving in realistic, so it easier for students to understand concepts and solve the problems. Therefore, the model of Problem Posing learning with Realistic Mathematics Education Approach is appropriately applied in mathematics learning especially on geometry material. Furthermore, the impact can improve student achievement.

  5. Introduced organisms pose the most significant threat to the ...

    African Journals Online (AJOL)

    spamer

    Introduced organisms pose the most significant threat to the conservation status of oceanic islands (e.g.. Williamson 1996). Subantarctic Prince Edward Island, the smaller of the two islands in the Prince Edward. Island group, has few introduced organisms; it is cur- rently known to support only three introduced animals.

  6. Developing teachers' subject didactic competence through problem posing

    Czech Academy of Sciences Publication Activity Database

    Tichá, Marie; Hošpesová, A.

    2013-01-01

    Roč. 83, č. 1 (2013), s. 133-143 ISSN 0013-1954 Institutional support: RVO:67985840 Keywords : professional development * primary school teacher s * problem posing Subject RIV: AM - Education Impact factor: 0.639, year: 2013 http://link.springer.com/article/10.1007%2Fs10649-012-9455-1

  7. Mathematical Thinking and Creativity through Mathematical Problem Posing and Solving

    Science.gov (United States)

    Ayllón, María F.; Gómez, Isabel A.; Ballesta-Claver, Julio

    2016-01-01

    This work shows the relationship between the development of mathematical thinking and creativity with mathematical problem posing and solving. Creativity and mathematics are disciplines that do not usually appear together. Both concepts constitute complex processes sharing elements, such as fluency (number of ideas), flexibility (range of ideas),…

  8. Enhancing Students' Communication Skills through Problem Posing and Presentation

    Science.gov (United States)

    Sugito; E. S., Sri Mulyani; Hartono; Supartono

    2017-01-01

    This study was to explore how enhance communication skill through problem posing and presentation method. The subjects of this research were the seven grade students Junior High School, including 20 male and 14 female. This research was conducted in two cycles and each cycle consisted of four steps, they were: planning, action, observation, and…

  9. Astronaut Linda Godwin poses with spacesuit she wore for launch

    Science.gov (United States)

    1994-01-01

    Astronaut Linda M. Godwin, STS-59 payload commander, poses with the spacesuit she wore for launch. She will eventually wear the partial pressure suit for the entry phase of the Space Shuttle Endeavour's week and a half mission in Earth orbit.

  10. Regularization of Ill-Posed Point Neuron Models.

    Science.gov (United States)

    Nielsen, Bjørn Fredrik

    2017-12-01

    Point neuron models with a Heaviside firing rate function can be ill-posed. That is, the initial-condition-to-solution map might become discontinuous in finite time. If a Lipschitz continuous but steep firing rate function is employed, then standard ODE theory implies that such models are well-posed and can thus, approximately, be solved with finite precision arithmetic. We investigate whether the solution of this well-posed model converges to a solution of the ill-posed limit problem as the steepness parameter of the firing rate function tends to infinity. Our argument employs the Arzelà-Ascoli theorem and also yields the existence of a solution of the limit problem. However, we only obtain convergence of a subsequence of the regularized solutions. This is consistent with the fact that models with a Heaviside firing rate function can have several solutions, as we show. Our analysis assumes that the vector-valued limit function v, provided by the Arzelà-Ascoli theorem, is threshold simple: That is, the set containing the times when one or more of the component functions of v equal the threshold value for firing, has zero Lebesgue measure. If this assumption does not hold, we argue that the regularized solutions may not converge to a solution of the limit problem with a Heaviside firing function.

  11. Effects of pose and image resolution on automatic face recognition

    NARCIS (Netherlands)

    Mahmood, Zahid; Ali, Tauseef; Khan, Samee U.

    The popularity of face recognition systems have increased due to their use in widespread applications. Driven by the enormous number of potential application domains, several algorithms have been proposed for face recognition. Face pose and image resolutions are among the two important factors that

  12. 3D Facial Landmarking under Expression, Pose, and Occlusion Variations

    NARCIS (Netherlands)

    H. Dibeklioğ lu; A.A. Salah (Albert Ali); L. Akarun

    2008-01-01

    htmlabstractAutomatic localization of 3D facial features is important for face recognition, tracking, modeling and expression analysis. Methods developed for 2D images were shown to have problems working across databases acquired with different illumination conditions. Expression variations, pose

  13. Problem Posing Based on Investigation Activities by University Students

    Science.gov (United States)

    da Ponte, Joao Pedro; Henriques, Ana

    2013-01-01

    This paper reports a classroom-based study involving investigation activities in a university numerical analysis course. The study aims to analyse students' mathematical processes and to understand how these activities provide opportunities for problem posing. The investigations were intended to stimulate students in asking questions, to trigger…

  14. Pose-invariant face recognition using Markov random fields.

    Science.gov (United States)

    Ho, Huy Tho; Chellappa, Rama

    2013-04-01

    One of the key challenges for current face recognition techniques is how to handle pose variations between the probe and gallery face images. In this paper, we present a method for reconstructing the virtual frontal view from a given nonfrontal face image using Markov random fields (MRFs) and an efficient variant of the belief propagation algorithm. In the proposed approach, the input face image is divided into a grid of overlapping patches, and a globally optimal set of local warps is estimated to synthesize the patches at the frontal view. A set of possible warps for each patch is obtained by aligning it with images from a training database of frontal faces. The alignments are performed efficiently in the Fourier domain using an extension of the Lucas-Kanade algorithm that can handle illumination variations. The problem of finding the optimal warps is then formulated as a discrete labeling problem using an MRF. The reconstructed frontal face image can then be used with any face recognition technique. The two main advantages of our method are that it does not require manually selected facial landmarks or head pose estimation. In order to improve the performance of our pose normalization method in face recognition, we also present an algorithm for classifying whether a given face image is at a frontal or nonfrontal pose. Experimental results on different datasets are presented to demonstrate the effectiveness of the proposed approach.

  15. Developing teachers' subject didactic competence through problem posing

    Czech Academy of Sciences Publication Activity Database

    Tichá, Marie; Hošpesová, A.

    2013-01-01

    Roč. 83, č. 1 (2013), s. 133-143 ISSN 0013-1954 Institutional support: RVO:67985840 Keywords : professional development * primary school teachers * problem posing Subject RIV: AM - Education Impact factor: 0.639, year: 2013 http://link.springer.com/article/10.1007%2Fs10649-012-9455-1

  16. Zebra Mussels Pose a Threat to Virginia's Waters

    OpenAIRE

    Helfrich, Louis A. (Louis Anthony), 1942-; Weigmann, Diana L.; Speenburgh, Renee M.; Neves, Richard J.; Kitchel, Lisie; Bruenderman, Sue A., 1962-

    2005-01-01

    Provides an brief introduction to the invasion of the zebra mussel into American waters, explains the economic consequences they pose, and discusses if Virginia will inherit the problem, what the public can do to help, the general lifecycle of the zebra mussel and if they can be controlled, and who is working on the zebra mussel problem.

  17. Meanings Given to Algebraic Symbolism in Problem-Posing

    Science.gov (United States)

    Cañadas, María C.; Molina, Marta; del Río, Aurora

    2018-01-01

    Some errors in the learning of algebra suggest that students might have difficulties giving meaning to algebraic symbolism. In this paper, we use problem posing to analyze the students' capacity to assign meaning to algebraic symbolism and the difficulties that students encounter in this process, depending on the characteristics of the algebraic…

  18. Optical neural network system for pose determination of spinning satellites

    Science.gov (United States)

    Lee, Andrew; Casasent, David

    1990-01-01

    An optical neural network architecture and algorithm based on a Hopfield optimization network are presented for multitarget tracking. This tracker utilizes a neuron for every possible target track, and a quadratic energy function of neural activities which is minimized using gradient descent neural evolution. The neural net tracker is demonstrated as part of a system for determining position and orientation (pose) of spinning satellites with respect to a robotic spacecraft. The input to the system is time sequence video from a single camera. Novelty detection and filtering are utilized to locate and segment novel regions from the input images. The neural net multitarget tracker determines the correspondences (or tracks) of the novel regions as a function of time, and hence the paths of object (satellite) parts. The path traced out by a given part or region is approximately elliptical in image space, and the position, shape and orientation of the ellipse are functions of the satellite geometry and its pose. Having a geometric model of the satellite, and the elliptical path of a part in image space, the three-dimensional pose of the satellite is determined. Digital simulation results using this algorithm are presented for various satellite poses and lighting conditions.

  19. Docking-based three-dimensional quantitative structure-activity relationship (3D-QSAR) predicts binding affinities to aryl hydrocarbon receptor for polychlorinated dibenzodioxins, dibenzofurans, and biphenyls.

    Science.gov (United States)

    Yuan, Jintao; Pu, Yuepu; Yin, Lihong

    2013-07-01

    Polychlorinated dibenzodioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), and polychlorinated biphenyls (PCBs) cause toxic effects after binding to an intracellular cytosolic receptor called the aryl hydrocarbon receptor (AhR). Thymic atrophy, weight loss, immunotoxicity, acute lethality, and induction of cytochrome P4501A1 have all been correlated with the binding affinity to AhR. To study the key molecular features for determining binding affinity to AhR, a homology model of AhR ligand-binding domains was developed, a molecular docking approach was employed to obtain docking-based conformations of all molecules in the whole set, and 3-dimensional quantitative structure-activity relationship (3D-QSAR) methodology, namely, comparative molecular field analysis (CoMFA), was applied. A partial least square analysis was performed, and QSAR models were generated for a training set of 59 compounds. The generated QSAR model showed good internal and external statistical reliability, and in a comparison with other reported CoMFA models using different alignment methods, the docking-based CoMFA model showed some advantages. Copyright © 2013 SETAC.

  20. Epitope identification and in silico prediction of the specificity of antibodies binding to the coat proteins of Potato Virus Y strains

    NARCIS (Netherlands)

    Keller, H.J.H.G.; Pomp, H.; Bakker, J.; Schots, A.

    2005-01-01

    A phage library containing 2.7 × 10(9) randomly expressed peptides was used to determine the epitopes of three monoclonal antibodies that bind to the coat protein of Potato Virus Y. Construction of the consensus sequences for the peptides obtained after three selection rounds indicated that each

  1. Pose tracking for augmented reality applications in outdoor archaeological sites

    Science.gov (United States)

    Younes, Georges; Asmar, Daniel; Elhajj, Imad; Al-Harithy, Howayda

    2017-01-01

    In recent years, agencies around the world have invested huge amounts of effort toward digitizing many aspects of the world's cultural heritage. Of particular importance is the digitization of outdoor archaeological sites. In the spirit of valorization of this digital information, many groups have developed virtual or augmented reality (AR) computer applications themed around a particular archaeological object. The problem of pose tracking in outdoor AR applications is addressed. Different positional systems are analyzed, resulting in the selection of a monocular camera-based user tracker. The limitations that challenge this technique from map generation, scale, anchoring, to lighting conditions are analyzed and systematically addressed. Finally, as a case study, our pose tracking system is implemented within an AR experience in the Byblos Roman theater in Lebanon.

  2. A New Full Pose Measurement Method for Robot Calibration

    Directory of Open Access Journals (Sweden)

    Hee-Jun Kang

    2013-07-01

    Full Text Available Identification of robot kinematic errors during the calibration process often requires accurate full pose measurements (position and orientation of robot end-effectors in Cartesian space. This paper proposes a new method of full pose measurement of robot end-effectors for calibration. This method is based on an analysis of the features of a set of target points (placed on a rotating end-effector on a circular trajectory. The accurate measurement is validated by computational simulation results from the Puma robot. Moreover, experimental calibration and validation results for the Hyundai HA-06 robot prove the effectiveness, correctness, and reliability of the proposed method. This method can be applied to robots that have entirely revolute joints or to robots for which only the last joint is revolute.

  3. Strategic management of health risks posed by buried transuranic wastes

    Energy Technology Data Exchange (ETDEWEB)

    Jump, R.A. [Department of Energy, Washington, DC (United States)

    1994-12-31

    A strategy is presented for reducing health risks at sites contaminated with buried transuranic (TRU) wastes by first taking measures to immobilize the contaminants until the second step, final action, becomes cost-effective and poses less risk to the remediation workers. The first step of this strategy does not preclude further action if it is warranted and is in harmony with environmental laws and regulations.

  4. Sensing Strategies for Disambiguating among Multiple Objects in Known Poses.

    Science.gov (United States)

    1985-08-01

    ELEMENT. PROIECT. TASK Artificial Inteligence Laboratory AE OKUI UBR 545 Technology Square Cambridge, MA 021.39 11. CONTROLLING OFFICE NAME AND ADDRESS 12...AD-Ali65 912 SENSING STRATEGIES FOR DISAMBIGURTING MONG MULTIPLE 1/1 OBJECTS IN KNOWN POSES(U) MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL ...or Dist Special 1 ’ MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY A. I. Memo 855 August, 1985 Sensing Strategies for

  5. Robustifying Correspondence Based 6D Object Pose Estimation

    DEFF Research Database (Denmark)

    Hietanen, Antti; Halme, Jussi; Buch, Anders Glent

    2017-01-01

    We propose two methods to robustify point correspondence based 6D object pose estimation. The first method, curvature filtering, is based on the assumption that low curvature regions provide false matches, and removing points in these regions improves robustness. The second method, region pruning....... For the experiments, we evaluated three correspondence selection methods, Geometric Consistency (GC) [1], Hough Grouping (HG) [2] and Search of Inliers (SI) [3] and report systematic improvements for their robustified versions with two distinct datasets....

  6. A new L-curve for ill-posed problems

    Science.gov (United States)

    Reichel, Lothar; Sadok, Hassane

    2008-10-01

    The truncated singular value decomposition is a popular method for the solution of linear ill-posed problems. The method requires the choice of a truncation index, which affects the quality of the computed approximate solution. This paper proposes that an L-curve, which is determined by how well the given data (right-hand side) can be approximated by a linear combination of the first (few) left singular vectors (or functions), be used as an aid for determining the truncation index.

  7. Dual Path Networks for Multi-Person Human Pose Estimation

    OpenAIRE

    Ning, Guanghan; He, Zhihai

    2017-01-01

    The task of multi-person human pose estimation in natural scenes is quite challenging. Existing methods include both top-down and bottom-up approaches. The main advantage of bottom-up methods is its excellent tradeoff between estimation accuracy and computational cost. We follow this path and aim to design smaller, faster, and more accurate neural networks for the regression of keypoints and limb association vectors. These two regression tasks are naturally dependent on each other. In this wo...

  8. Iterative regularization methods for nonlinear ill-posed problems

    CERN Document Server

    Scherzer, Otmar; Kaltenbacher, Barbara

    2008-01-01

    Nonlinear inverse problems appear in many applications, and typically they lead to mathematical models that are ill-posed, i.e., they are unstable under data perturbations. Those problems require a regularization, i.e., a special numerical treatment. This book presents regularization schemes which are based on iteration methods, e.g., nonlinear Landweber iteration, level set methods, multilevel methods and Newton type methods.

  9. How to measure the pose robustness of object views

    Czech Academy of Sciences Publication Activity Database

    Peters, G.; Zitová, Barbara; von der Malsburg, C.

    2002-01-01

    Roč. 20, č. 4 (2002), s. 249-256 ISSN 0262-8856 R&D Projects: GA AV ČR KSK1019101 Institutional research plan: CEZ:AV0Z1075907 Keywords : object perception * pose robustness * matching/tracking object features Subject RIV: JD - Comput er Applications, Robotics Impact factor: 1.029, year: 2002 http://library.utia.cas.cz/prace/20020006.pdf

  10. Molecular modeling of human APOBEC3G to predict the binding modes of the inhibitor compounds IMB26 and IMB35

    Directory of Open Access Journals (Sweden)

    Zhixin Zhang

    2013-07-01

    Full Text Available APOBEC3G(A3G is a host cytidine deaminase that incorporates into HIV-1 virions and efficiently inhibits viral replication. The virally encoded protein Vif binds to A3G and induces its degradation, thereby counteracting the antiviral activity of A3G. Vif-mediated A3G degradation clearly represents a potential target for anti-HIV drug development. Currently, there is an urgent need for understanding the three dimensional structure of full-length A3G. In this work, we use a homology modeling approach to propose a structure for A3G based on the crystal structure of APOBEC2 (APO2 and the catalytic domain structure of A3G. Two compounds, IMB26 and IMB35, which have been shown to bind to A3G and block degradation by Vif, were docked into the A3G model and the binding modes were generated for further analysis. The results may be used to design or optimize molecules targeting Vif–A3G interaction, and lead to the development of novel anti-HIV drugs.

  11. Synthesis, characterization, in silico ADMET prediction, and protein binding analysis of a novel zinc(II) Schiff-base complex: Application of multi-spectroscopic and computational techniques.

    Science.gov (United States)

    Shahraki, Somaye; Shiri, Fereshteh; Saeidifar, Maryam

    2017-06-22

    By reaction of 1,2-diaminocyclohexane with the 2,3-butanedione monoxime in the presence of ZnCl 2 , a new Schiff base complex was obtained. This complex was characterized by elemental analyses, FT-IR, 1 H NMR, UV-Vis, and conductivity measurements. The reactivity of this complex to human serum albumin (HSA) under simulative physiological conditions was studied by spectroscopic and molecular docking analysis. Experimental results at various temperatures indicated that the intrinsic fluorescence of protein was quenched through a static quenching mechanism. The negative value of enthalpy change and positive value of entropy change indicated that both hydrogen bonding and hydrophobic forces played a major role in the binding of Zn(II) complex to HSA. FT-IR, three-dimensional fluorescence, and UV-Vis absorption results showed that the secondary structure of HSA changed after Zn(II) complex bound to protein. The binding distance was calculated to be 4.96 nm, according to fluorescence resonance energy transfer. Molecular docking results confirmed the spectroscopic results and showed that above complex is embedded into subdomain IIA of protein. All these experimental and computational results clarified that Zn(II) complex could bind with HSA effectively, which could be a useful guideline for efficient Schiff-base drug design.

  12. Position and pose detection of active camera-head in a nuclear power plant

    Energy Technology Data Exchange (ETDEWEB)

    Kita, Yasuyo; Kita, Nobuyuki [National Institute of Advanced Industrial Science and Technology, Research Institute of Intelligent Systems, Tsukuba, Ibaraki (Japan)

    2002-01-01

    A method to determine the position and pose of an active camera-head by aligning a 3D model of its surrounding environment with an observed 2D image is proposed. The camera-head is mounted on a mobile robot and freely moves in a 3D space. We aim at visual feedback to correct the estimation error of its position and pose obtained from dead reckoning. Since the nuclear power plant where the robot moves about consists of many pipes without particular marks, most of features in the observed images are occluding edges of the pipes. For robustly finding 3D-2D point correspondences on the occluding edges, two-type predicted images which are calculated from the 3D environmental model by a graphics system (eg. OpenGL etc) are used as follows: 1) 3D model points which correspond to the observed occluding edges are quickly obtained from the predicted depth image; 2) The predicted intensity image is used to select only the 3D model points which are expected to appear clearly in the observed image. As a result, point correspondences between the observed image and the 3D model can be robustly found even in complicated scenes. Preliminary experiments using actual plant mock-up have shown that the method is promising. (author)

  13. LEVELING STUDENTS’ CREATIVE THINKING IN SOLVING AND POSING MATHEMATICAL PROBLEM

    Directory of Open Access Journals (Sweden)

    Tatag Yuli Eko Siswono

    2010-07-01

    Full Text Available Many researchers assume that people are creative, but their degree ofcreativity is different. The notion of creative thinking level has beendiscussed .by experts. The perspective of mathematics creative thinkingrefers to a combination of logical and divergent thinking which is basedon intuition but has a conscious aim. The divergent thinking is focusedon flexibility, fluency, and novelty in mathematical problem solving andproblem posing. As students have various backgrounds and differentabilities, they possess different potential in thinking patterns,imagination, fantasy and performance; therefore, students have differentlevels of creative thinking. A research study was conducted in order todevelop a framework for students’ levels of creative thinking inmathematics. This research used a qualitative approach to describe thecharacteristics of the levels of creative thinking. Task-based interviewswere conducted to collect data with ten 8thgrade junior secondary schoolstudents. The results distinguished five levels of creative thinking,namely level 0 to level 4 with different characteristics in each level.These differences are based on fluency, flexibility, and novelty inmathematical problem solving and problem posing.Keywords: student’s creative thinking, problem posing, flexibility,fluency, novelty DOI: http://dx.doi.org/10.22342/jme.1.1.794.17-40

  14. LIGAND-BINDING SITES ON THE MYCOBACTERIUM TUBERCULOSIS UREASE

    Directory of Open Access Journals (Sweden)

    Lisnyak Yu. V.

    2017-10-01

    Full Text Available Introduction. Mycobacterium tuberculosis is the causative agent of tuberculosis that remains a serious medical and social health problem. Despite intensive efforts have been made in the past decade, there are no new efficient anti-tuberculosis drugs today, and that need is growing due to the spread of drug-resistant strains of M.tuberculosis. M. tuberculosis urease (MTU, being an important factor of the bacterium viability and virulence, is an attractive target for anti-tuberculosis drugs acting by inhibition of urease activity. However, the commercially available urease inhibitors are toxic and unstable, that prevent their clinical use. Therefore, new more potent anti-tuberculosis drugs inhibiting new targets are urgently needed. A useful tool for the search of novel inhibitors is a computational drug design. The inhibitor design is significantly easier if binding sites on the enzyme are identified in advance. This paper aimed to determine the probable ligand binding sites on the surface of M. tuberculosis urease. Methods. To identify ligand binding sites on MTU surface, сomputational solvent mapping method FTSite was applied by the use of MTU homology model we have built earlier. The method places molecular probes (small organic molecules containing various functional groups on a dense grid defined around the enzyme, and for each probe finds favorable positions. The selected poses are refined by free energy minimization, the low energy conformations are clustered, and the clusters are ranked on the basis of the average free energy. FTSite server outputs the protein residues delineating a binding sites and the probe molecules representing each cluster. To predict allosteric pockets on MTU, AlloPred and AlloSite servers were applied. AlloPred uses the normal mode analysis (NMA and models how the dynamics of a protein would be altered in the presence of a modulator at a specific pocket. Pockets on the enzyme are predicted using the Fpocket

  15. Ill Posed Problems: Numerical and Statistical Methods for Mildly, Moderately and Severely Ill Posed Problems with Noisy Data.

    Science.gov (United States)

    1980-02-01

    AD-A 7 .SA92 925 WISCONSIN UN ! V-MADISON DEFT OF STATISTICS F/S 11,𔃻 ILL POSED PRORLEMS: NUMERICAL ANn STATISTICAL METHODS FOR MILOL-ETC(U FEB 80 a...Numerical aspects of some regularization methods and apolication to data collected in isolated dog heart experiments. Laboratorio di Analisi Numerica...minimum d’une fonction convexe sur une intersection de convexes. Proceedings of the Symposium on Optimization, held in Nice, France, June 23-July 5

  16. Is Attribute-Based Zero-Shot Learning an Ill-Posed Strategy?

    KAUST Repository

    Alabdulmohsin, Ibrahim

    2016-09-03

    One transfer learning approach that has gained a wide popularity lately is attribute-based zero-shot learning. Its goal is to learn novel classes that were never seen during the training stage. The classical route towards realizing this goal is to incorporate a prior knowledge, in the form of a semantic embedding of classes, and to learn to predict classes indirectly via their semantic attributes. Despite the amount of research devoted to this subject lately, no known algorithm has yet reported a predictive accuracy that could exceed the accuracy of supervised learning with very few training examples. For instance, the direct attribute prediction (DAP) algorithm, which forms a standard baseline for the task, is known to be as accurate as supervised learning when as few as two examples from each hidden class are used for training on some popular benchmark datasets! In this paper, we argue that this lack of significant results in the literature is not a coincidence; attribute-based zero-shot learning is fundamentally an ill-posed strategy. The key insight is the observation that the mechanical task of predicting an attribute is, in fact, quite different from the epistemological task of learning the “correct meaning” of the attribute itself. This renders attribute-based zero-shot learning fundamentally ill-posed. In more precise mathematical terms, attribute-based zero-shot learning is equivalent to the mirage goal of learning with respect to one distribution of instances, with the hope of being able to predict with respect to any arbitrary distribution. We demonstrate this overlooked fact on some synthetic and real datasets. The data and software related to this paper are available at https://mine. kaust.edu.sa/Pages/zero-shot-learning.aspx. © Springer International Publishing AG 2016.

  17. Regularization theory for ill-posed problems selected topics

    CERN Document Server

    Lu, Shuai

    2013-01-01

    Thismonograph is a valuable contribution to thehighly topical and extremly productive field ofregularisationmethods for inverse and ill-posed problems. The author is an internationally outstanding and acceptedmathematicianin this field. In his book he offers a well-balanced mixtureof basic and innovative aspects.He demonstrates new,differentiatedviewpoints, and important examples for applications. The bookdemontrates thecurrent developments inthe field of regularization theory,such as multiparameter regularization and regularization in learning theory. The book is written for graduate and PhDs

  18. Promiscuous prediction and conservancy analysis of CTL binding epitopes of HCV 3a viral proteome from Punjab Pakistan: an In Silico Approach

    Directory of Open Access Journals (Sweden)

    Idrees Muhammad

    2011-02-01

    Full Text Available Abstract Background HCV is a positive sense RNA virus affecting approximately 180 million people world wide and about 10 million Pakistani populations. HCV genotype 3a is the major cause of infection in Pakistani population. One of the major problems of HCV infection especially in the developing countries that limits the limits the antiviral therapy is the long term treatment, high dosage and side effects. Studies of antigenic epitopes of viral sequences of a specific origin can provide an effective way to overcome the mutation rate and to determine the promiscuous binders to be used for epitope based subunit vaccine design. An in silico approach was applied for the analysis of entire HCV proteome of Pakistani origin, aimed to identify the viral epitopes and their conservancy in HCV genotypes 1, 2 and 3 of diverse origin. Results Immunoinformatic tools were applied for the predictive analysis of HCV 3a antigenic epitopes of Pakistani origin. All the predicted epitopes were then subjected for their conservancy analysis in HCV genotypes 1, 2 and 3 of diverse origin (worldwide. Using freely available web servers, 150 MHC II epitopes were predicted as promiscuous binders against 51 subjected alleles. E2 protein represented the 20% of all the predicted MHC II epitopes. 75.33% of the predicted MHC II epitopes were (77-100% conserve in genotype 3; 47.33% and 40.66% in genotype 1 and 2 respectively. 69 MHC I epitopes were predicted as promiscuous binders against 47 subjected alleles. NS4b represented 26% of all the MHC I predicted epitopes. Significantly higher epitope conservancy was represented by genotype 3 i.e. 78.26% and 21.05% for genotype 1 and 2. Conclusions The study revealed comprehensive catalogue of potential HCV derived CTL epitopes from viral proteome of Pakistan origin. A considerable number of predicted epitopes were found to be conserved in different HCV genotype. However, the number of conserved epitopes in HCV genotype 3 was

  19. Probabilistic Mapping of Human Visual Attention from Head Pose Estimation

    Directory of Open Access Journals (Sweden)

    Andrea Veronese

    2017-10-01

    Full Text Available Effective interaction between a human and a robot requires the bidirectional perception and interpretation of actions and behavior. While actions can be identified as a directly observable activity, this might not be sufficient to deduce actions in a scene. For example, orienting our face toward a book might suggest the action toward “reading.” For a human observer, this deduction requires the direction of gaze, the object identified as a book and the intersection between gaze and book. With this in mind, we aim to estimate and map human visual attention as directed to a scene, and assess how this relates to the detection of objects and their related actions. In particular, we consider human head pose as measurement to infer the attention of a human engaged in a task and study which prior knowledge should be included in such a detection system. In a user study, we show the successful detection of attention to objects in a typical office task scenario (i.e., reading, working with a computer, studying an object. Our system requires a single external RGB camera for head pose measurements and a pre-recorded 3D point cloud of the environment.

  20. Toward active pose estimation of a grasped object

    Science.gov (United States)

    Abbott, A. Lynn

    1993-08-01

    This paper concerns the use of visual feedback to verify whether an object has been properly grasped by a manipulator. The work is motivated by the fact that many general-purpose manipulators are equipped with very simple grippers which may not be well suited to grasping common objects. Furthermore, many robotic systems do not verify that a grasp operation has been successfully executed. This paper describes a system under development at Virginia Tech which utilizes visual feedback to guide relative camera-object movements for the purpose of estimating the pose of the object. The goal is to assist in computing object pose relative to a coordinate system embedded in the gripper. Object shape is assumed to be known in advance. Two methods are discussed, both of which utilize visually guided movements to search for a minimum in an objective function. The first method is to align the gripper with the image plane, facilitating the computation of object orientation about the normal to the image plane. The second involves moving the object to align its image with a desired view of the object. Extensive calibration of the camera or manipulator is not required. The methods discussed here are still at the conceptual stage, but illustrate the potential of the active approach.

  1. Dysfunctional Crohn's disease-associated NOD2 polymorphisms cannot be reliably predicted on the basis of RIPK2-binding or membrane association

    Directory of Open Access Journals (Sweden)

    Rhiannon eParkhouse

    2015-10-01

    Full Text Available Polymorphisms in NOD2 represent the single greatest genetic risk factor for the development of Crohn's disease. Three different non-synonomous NOD2 polymorphisms - R702W, G908R, L1007fsincC - account for roughly 80 % of all NOD2-associated cases of Crohn's disease and are reported to result in a loss of receptor function in response to muramyl dipeptide stimulation. Loss of NOD2 signalling can result from a failure to detect ligand; alterations in cellular localization; and changes in protein interactions, such as an inability to interact with downstream adaptor protein RIPK2. Using an overexpression system we analysed approximately 50 NOD2 polymorphisms reportedly connected to Crohn's disease to determine if they also displayed loss of function and if this could be related to alterations in protein localization and/or association with RIPK2. Just under half the polymorphisms displayed a significant reduction in signalling capacity following ligand stimulation, with nine of them showing near complete ablation. Only two polymorphisms, R38M and R138Q, lost the ability to interact with RIPK2. However, both these polymorphisms still associated with cellular membranes. In contrast, L248R, W355stop, L550V, N825K, L1007fsinC, L1007P and R1019stopstill bound RIPK2, but showed impaired membrane association and were unable to signal in response to muramyl dipeptide. This highlights the complex contributions of NOD2 polymorphisms to Crohn’s disease and reiterates the importance of both RIPK2-binding and membrane association in NOD2 signalling. Simply ascertaining whether or not NOD2 polymorphisms bind RIPK2 or associate with cellular membranes is not sufficient for determining their signalling competency.

  2. Pyranose dehydrogenase ligand promiscuity: a generalized approach to simulate monosaccharide solvation, binding, and product formation.

    Directory of Open Access Journals (Sweden)

    Michael M H Graf

    2014-12-01

    Full Text Available The flavoenzyme pyranose dehydrogenase (PDH from the litter decomposing fungus Agaricus meleagris oxidizes many different carbohydrates occurring during lignin degradation. This promiscuous substrate specificity makes PDH a promising catalyst for bioelectrochemical applications. A generalized approach to simulate all 32 possible aldohexopyranoses in the course of one or a few molecular dynamics (MD simulations is reported. Free energy calculations according to the one-step perturbation (OSP method revealed the solvation free energies (ΔGsolv of all 32 aldohexopyranoses in water, which have not yet been reported in the literature. The free energy difference between β- and α-anomers (ΔGβ-α of all d-stereoisomers in water were compared to experimental values with a good agreement. Moreover, the free-energy differences (ΔG of the 32 stereoisomers bound to PDH in two different poses were calculated from MD simulations. The relative binding free energies (ΔΔGbind were calculated and, where available, compared to experimental values, approximated from Km values. The agreement was very good for one of the poses, in which the sugars are positioned in the active site for oxidation at C1 or C2. Distance analysis between hydrogens of the monosaccharide and the reactive N5-atom of the flavin adenine dinucleotide (FAD revealed that oxidation is possible at HC1 or HC2 for pose A, and at HC3 or HC4 for pose B. Experimentally detected oxidation products could be rationalized for the majority of monosaccharides by combining ΔΔGbind and a reweighted distance analysis. Furthermore, several oxidation products were predicted for sugars that have not yet been tested experimentally, directing further analyses. This study rationalizes the relationship between binding free energies and substrate promiscuity in PDH, providing novel insights for its applicability in bioelectrochemistry. The results suggest that a similar approach could be applied to study

  3. Macrobend optical sensing for pose measurement in soft robot arms

    International Nuclear Information System (INIS)

    Sareh, Sina; Noh, Yohan; Liu, Hongbin; Althoefer, Kaspar; Li, Min; Ranzani, Tommaso

    2015-01-01

    This paper introduces a pose-sensing system for soft robot arms integrating a set of macrobend stretch sensors. The macrobend sensory design in this study consists of optical fibres and is based on the notion that bending an optical fibre modulates the intensity of the light transmitted through the fibre. This sensing method is capable of measuring bending, elongation and compression in soft continuum robots and is also applicable to wearable sensing technologies, e.g. pose sensing in the wrist joint of a human hand. In our arrangement, applied to a cylindrical soft robot arm, the optical fibres for macrobend sensing originate from the base, extend to the tip of the arm, and then loop back to the base. The connectors that link the fibres to the necessary opto-electronics are all placed at the base of the arm, resulting in a simplified overall design. The ability of this custom macrobend stretch sensor to flexibly adapt its configuration allows preserving the inherent softness and compliance of the robot which it is installed on. The macrobend sensing system is immune to electrical noise and magnetic fields, is safe (because no electricity is needed at the sensing site), and is suitable for modular implementation in multi-link soft continuum robotic arms. The measurable light outputs of the proposed stretch sensor vary due to bend-induced light attenuation (macrobend loss), which is a function of the fibre bend radius as well as the number of repeated turns. The experimental study conducted as part of this research revealed that the chosen bend radius has a far greater impact on the measured light intensity values than the number of turns (if greater than five). Taking into account that the bend radius is the only significantly influencing design parameter, the macrobend stretch sensors were developed to create a practical solution to the pose sensing in soft continuum robot arms. Henceforward, the proposed sensing design was benchmarked against an electromagnetic

  4. Predicting the most appropriate wood biomass for selected industrial applications: comparison of wood, pulping, and enzymatic treatments using fluorescent-tagged carbohydrate-binding modules.

    Science.gov (United States)

    Bombeck, Pierre-Louis; Khatri, Vinay; Meddeb-Mouelhi, Fatma; Montplaisir, Daniel; Richel, Aurore; Beauregard, Marc

    2017-01-01

    Lignocellulosic biomass will progressively become the main source of carbon for a number of products as the Earth's oil reservoirs disappear. Technology for conversion of wood fiber into bioproducts (wood biorefining) continues to flourish, and access to reliable methods for monitoring modification of such fibers is becoming an important issue. Recently, we developed a simple, rapid approach for detecting four different types of polymer on the surface of wood fibers. Named fluorescent-tagged carbohydrate-binding module (FTCM), this method is based on the fluorescence signal from carbohydrate-binding modules-based probes designed to recognize specific polymers such as crystalline cellulose, amorphous cellulose, xylan, and mannan. Here we used FTCM to characterize pulps made from softwood and hardwood that were prepared using Kraft or chemical-thermo-mechanical pulping. Comparison of chemical analysis (NREL protocol) and FTCM revealed that FTCM results were consistent with chemical analysis of the hemicellulose composition of both hardwood and softwood samples. Kraft pulping increased the difference between softwood and hardwood surface mannans, and increased xylan exposure. This suggests that Kraft pulping leads to exposure of xylan after removal of both lignin and mannan. Impact of enzyme cocktails from Trichoderma reesei (Celluclast 1.5L) and from Aspergillus sp. (Carezyme 1000L) was investigated by analysis of hydrolyzed sugars and by FTCM. Both enzymes preparations released cellobiose and glucose from pulps, with the cocktail from Trichoderma being the most efficient. Enzymatic treatments were not as effective at converting chemical-thermomechanical pulps to simple sugars, regardless of wood type. FTCM revealed that amorphous cellulose was the primary target of either enzyme preparation, which resulted in a higher proportion of crystalline cellulose on the surface after enzymatic treatment. FTCM confirmed that enzymes from Aspergillus had little impact on

  5. Interpolation spaces in the resolution of ill-posed problems

    International Nuclear Information System (INIS)

    Logon, T.B.

    1995-11-01

    A number of applied problems connected with the interpretation of geophysical data leads to the resolution of ill-posed problems of the form A x = y δ , where A is an integral operator and y δ - some measurements. In the resolution of these problems by the Tikhonov's variational method, the choice of the stabilizing functional is crucial and needs some a-priori informations about the exact solution. Here the norm of the interpolation spaces X θ,q, which depends on two parameters 0 < θ < 1, 1 ≤ q < ∞ is proposed as a stabilizing functional. The a-priori information about the exact solution is characterized by its membership in one of the interpolation spaces. (author). 9 refs

  6. EFEKTIVITAS PEMBELAJARAN MATEMATIKA DENGAN METODE PROBLEM POSING BERBASIS PENDIDIKAN KARAKTER

    Directory of Open Access Journals (Sweden)

    Eka Lia Susanti

    2012-06-01

    Full Text Available Abstract Tujuan penelitian ini adalah untuk mengetahui apakah pembelajaran matematika dengan metode Problem Posing berbasis pendidikan karakter di laboratorium TeenZania pada materi garis singgung lingkaran efektif. Populasi dalam penelitian ini adalah peserta didik di SMP N 2 Pati. Sampel dalam penelitian ini diambil dengan teknik cluster random sampling. Variabel dalam penelitian ini yaitu keaktifan sebagai variabel independen dan prestasi belajar sebagai variabel dependen. Cara pengambilan data dengan lembar pengamatan dan tes. Data diolah dengan uji banding t dan uji pengaruh regresi. Hasil penelitian menunjukkan bahwa prestasi belajar kelas eksperimen (82,74 secara statistik melebihi KKM (75. Dengan uji regresi linear sederhana diperoleh persamaan regresi ?=-15,847 + 1,194X dan R^2=0,829. Koefisien X merupakan bilangan positif sehingga keaktifan berpengaruh positif pada prestasi belajar sebesar 82,9%. Rata-rata prestasi belajar kelas eksperimen (82,74 dan rata-rata prestasi belajar kelas kontrol (72,91. Secara uji stastistik prestasi belajar kelas eksperimen lebih baik daripada prestasi belajar kelas kontrol. Berdasarkan hasil analisis disimpulkan (1 pembelajaran mencapai tuntas belajar; (2 adanya pengaruh positif pada keaktifan terhadap prestasi belajar; dan (3 prestasi belajar kelas eksperimen lebih baik daripada prestasi belajar kelas kontrol; sehingga pembelajaran matematika dengan metode problem posing berbasis pendidikan karakter di laboratorium TeenZania merupakan pembelajaran yang efektif. The purpose of this study was to determine whether the learning of mathematics by Problem Posing method in a TeenZania laboratory based character education in circle tangent material effectively. The population in this study were students in SMP N 2 Pati. The sample in this study were drawn by cluster random sampling technique. The variables in this study is the activity as an independent variable and learning achievement as the dependent variable

  7. Level of environmental threat posed by horticultural trade in Cactaceae.

    Science.gov (United States)

    Novoa, Ana; Le Roux, Johannes J; Richardson, David M; Wilson, John R U

    2017-10-01

    Ornamental horticulture has been identified as an important threat to plant biodiversity and is a major pathway for plant invasions worldwide. In this context, the family Cactaceae is particularly challenging because it is considered the fifth most threatened large taxonomic group in the world; several species are among the most widespread and damaging invasive species; and Cactaceae is one of the most popular horticultural plant groups. Based on the Convention on International Trade in Endangered Species of Wild Flora and Fauna and the 11 largest online auction sites selling cacti, we documented the international cactus trade. To provide an in-depth look at the dynamics of the industry, we surveyed the businesses involved in the cactus trade in South Africa (a hotspot of cactus trade and invasions). We purchased seeds of every available species and used DNA barcoding to identify species to the genus level. Although <20% of this trade involved threatened species and <3% involved known invasive species, many species were identified by a common name. However, only 0.02% of the globally traded cacti were collected from wild populations. Despite a large commercial network, all South African imports (of which 15% and 1.5% were of species listed as threatened and invasive, respectively) came from the same source. With DNA barcoding, we identified 24% of the species to genus level. Based on our results, we believe that if trade restrictions are placed on the small proportion of cacti that are invasive and there is no major increase in harvesting of native populations, then the commercial trade in cactus poses a negligible environmental threat. However, there are currently no effective methods for easily identifying which cacti are traded, and both the illicit harvesting of cacti from the wild and the informal trade in invasive taxa pose on-going conservation challenges. © 2017 Society for Conservation Biology.

  8. Extrapolative prediction using physically-based QSAR

    Science.gov (United States)

    Cleves, Ann E.; Jain, Ajay N.

    2016-02-01

    Surflex-QMOD integrates chemical structure and activity data to produce physically-realistic models for binding affinity prediction . Here, we apply QMOD to a 3D-QSAR benchmark dataset and show broad applicability to a diverse set of targets. Testing new ligands within the QMOD model employs automated flexible molecular alignment, with the model itself defining the optimal pose for each ligand. QMOD performance was compared to that of four approaches that depended on manual alignments (CoMFA, two variations of CoMSIA, and CMF). QMOD showed comparable performance to the other methods on a challenging, but structurally limited, test set. The QMOD models were also applied to test a large and structurally diverse dataset of ligands from ChEMBL, nearly all of which were synthesized years after those used for model construction. Extrapolation across diverse chemical structures was possible because the method addresses the ligand pose problem and provides structural and geometric means to quantitatively identify ligands within a model's applicability domain. Predictions for such ligands for the four tested targets were highly statistically significant based on rank correlation. Those molecules predicted to be highly active (pK_i ≥ 7.5) had a mean experimental pK_i of 7.5, with potent and structurally novel ligands being identified by QMOD for each target.

  9. Candidate SNP Markers of Chronopathologies Are Predicted by a Significant Change in the Affinity of TATA-Binding Protein for Human Gene Promoters.

    Science.gov (United States)

    Ponomarenko, Petr; Rasskazov, Dmitry; Suslov, Valentin; Sharypova, Ekaterina; Savinkova, Ludmila; Podkolodnaya, Olga; Podkolodny, Nikolay L; Tverdokhleb, Natalya N; Chadaeva, Irina; Ponomarenko, Mikhail; Kolchanov, Nikolay

    2016-01-01

    Variations in human genome (e.g., single nucleotide polymorphisms, SNPs) may be associated with hereditary diseases, their complications, comorbidities, and drug responses. Using Web service SNP_TATA_Comparator presented in our previous paper, here we analyzed immediate surroundings of known SNP markers of diseases and identified several candidate SNP markers that can significantly change the affinity of TATA-binding protein for human gene promoters, with circadian consequences. For example, rs572527200 may be related to asthma, where symptoms are circadian (worse at night), and rs367732974 may be associated with heart attacks that are characterized by a circadian preference (early morning). By the same method, we analyzed the 90 bp proximal promoter region of each protein-coding transcript of each human gene of the circadian clock core. This analysis yielded 53 candidate SNP markers, such as rs181985043 (susceptibility to acute Q fever in male patients), rs192518038 (higher risk of a heart attack in patients with diabetes), and rs374778785 (emphysema and lung cancer in smokers). If they are properly validated according to clinical standards, these candidate SNP markers may turn out to be useful for physicians (to select optimal treatment for each patient) and for the general population (to choose a lifestyle preventing possible circadian complications of diseases).

  10. Candidate SNP Markers of Gender-Biased Autoimmune Complications of Monogenic Diseases Are Predicted by a Significant Change in the Affinity of TATA-Binding Protein for Human Gene Promoters

    Science.gov (United States)

    Ponomarenko, Mikhail P.; Arkova, Olga; Rasskazov, Dmitry; Ponomarenko, Petr; Savinkova, Ludmila; Kolchanov, Nikolay

    2016-01-01

    Some variations of human genome [for example, single nucleotide polymorphisms (SNPs)] are markers of hereditary diseases and drug responses. Analysis of them can help to improve treatment. Computer-based analysis of millions of SNPs in the 1000 Genomes project makes a search for SNP markers more targeted. Here, we combined two computer-based approaches: DNA sequence analysis and keyword search in databases. In the binding sites for TATA-binding protein (TBP) in human gene promoters, we found candidate SNP markers of gender-biased autoimmune diseases, including rs1143627 [cachexia in rheumatoid arthritis (double prevalence among women)]; rs11557611 [demyelinating diseases (thrice more prevalent among young white women than among non-white individuals)]; rs17231520 and rs569033466 [both: atherosclerosis comorbid with related diseases (double prevalence among women)]; rs563763767 [Hughes syndrome-related thrombosis (lethal during pregnancy)]; rs2814778 [autoimmune diseases (excluding multiple sclerosis and rheumatoid arthritis) underlying hypergammaglobulinemia in women]; rs72661131 and rs562962093 (both: preterm delivery in pregnant diabetic women); and rs35518301, rs34166473, rs34500389, rs33981098, rs33980857, rs397509430, rs34598529, rs33931746, rs281864525, and rs63750953 (all: autoimmune diseases underlying hypergammaglobulinemia in women). Validation of these predicted candidate SNP markers using the clinical standards may advance personalized medicine. PMID:27092142

  11. Enrichment and assessment of the health risks posed by heavy metals in PM1 in Changji, Xinjiang, China.

    Science.gov (United States)

    Liu, Yu Y; Shen, Ya X; Liu, Cheng; Liu, Hao F

    2017-04-16

    The present study aims to investigate the influence of human activity on heavy metals in a typical arid urban area of China and assess human health risks posed by heavy metals in PM 1 (particles <1.0 μm in diameter) for different people. In this paper, Changji (Xinjiang, China) was selected as the study area, and samples were collected from March 2014 to March 2015. A total 14 elements in PM 1 were quantified using ICP-MS. An enrichment factor (EF) was used to assess the influence of human activity on the contamination of these metals. The results indicated that Mn was not enriched; Co, Cu, Cr, Ni, Tl, and V were slightly enriched; Mo, Pb, and Sb were moderately enriched; and Ag, As, and Cd were strongly enriched. To assess the health risks associated with inhaling PM 1 , the risk assessment code and loss in life expectancy based on the individual metals were calculated. The results showed that the elements Ag, Cu, Mo, Pb, Sb, Tl, and V in PM 1 posed low levels of non-carcinogenic risks, but these metals may still pose risks to certain susceptible populations. In addition, the results also showed that As, Co, and Cr posed an appreciable carcinogenic risk, while Cd and Ni posed low levels of carcinogenic risk. The total predicted loss of life expectancy caused by the three metals As, Co, and Ni was 63.67 d for the elderly, 30.95 d for adult males, 26.62 d for adult females, and 48.22 d for children. Therefore, the safety of the elderly and children exposed to PM 1 should be given more attention than the safety of adults. The results from this study demonstrate that the health risks posed by heavy metals in PM 1 in Changji, Xinjiang, China should be examined.

  12. Predicting growth in angus bulls: the use of GHRH challenge, insulin-like growth factor-I, and insulin-like growth factor binding proteins.

    Science.gov (United States)

    Connor, E E; Barao, S M; Kimrey, A S; Parlier, A B; Douglass, L W; Dahl, G E

    2000-11-01

    Plasma IGF-I, IGF binding protein-2 (IGFBP-2), and IGFBP-3 were quantified in growing Angus bulls (n = 56) to determine their relationship with postweaning growth and carcass ultrasound measurements. In addition, GH response to GHRH challenge (area-under-the-curve GH [AUC-GH) was determined for each bull as part of a previous study. Blood was collected by jugular venipuncture at the start of a 140-d postweaning growth performance test and at 28 d intervals for plasma IGF-I determination by RIA. Plasma IGFBP-2 and -3 content was measured at the start of the study, on d 70, and d 140 by Western ligand blotting. Individual weights and hip heights were measured every 28 d during the study and carcass longissimus muscle area, intramuscular fat percentage, and carcass backfat were estimated by ultrasound on d 140. Greater plasma IGF-I at the start of the performance test was associated with reduced postweaning ADG and increased longissimus area. Throughout the performance test period, the correlations between plasma IGF-I and hip height were consistently positive, ranging from 0.10 to 0.38, but the correlations between ADG and IGF-I varied from -0.32 to 0.31. Age-adjusted d-1 plasma IGFBP-2 was related to ADG during the performance test, explaining nearly 30% of the variation in ADG. A model combining weaning age, IGFBP-2, and AUC-GH showed a strong relationship with ADG (R2 = 0.40). Plasma IGFBP-2 and -3 were not related to carcass characteristics, and IGFBP-3 was not related to growth rates. This study provides additional evidence for the variable relationship between plasma IGF-I and growth rates in cattle. A significant positive relationship between plasma IGFBP-2, AUC-GH, and postweaning ADG warrants further investigation.

  13. Clinical application of immunomagnetic reduction for quantitative measurement of insulin-like growth factor binding protein-1 in the prediction of pregnant women with preterm premature rupture of membranes.

    Science.gov (United States)

    Chen, Chen-Yu; Chang, Chia-Chen; Lin, Chii-Wann

    2015-01-01

    Insulin-like growth factor binding protein-1 (IGFBP-1) constitutes a subgroup of the insulin-like growth factor binding protein systems, and its concentration in amniotic fluid is 100-1000 times higher than the concentration in other body fluids. The aim of this study was to evaluate the clinical application of a novel method immunomagnetic reduction (IMR) for quantitative measurement of IGFBP-1 concentrations in the cervicovaginal secretions to diagnose pregnant women with preterm premature rupture of membranes (PPROM). We established a standard calibration curve of IMR intensity against IGFBP-1 concentration based on standard IGFBP-1 samples. We used the IMR assay to detect IGFBP-1 concentrations in the cervicovaginal secretions of pregnant women which were divided into two groups according to the presence or absence of PPROM. The calibration curve extended from 0.1ng/mL to 10000ng/mL with an excellent correlation (R(2)=0.999). Twenty-two pregnant women between 22 and 34weeks of gestation were analyzed in this prospective study, of whom 10 were clinical evidence of PPROM, and 12 were intact membranes. Through the analysis of receiver-operating characteristic curve, the cut-off point for IMR to differentiate intact membranes from PPROM is 1.015%, which resulted in 90.0, 83.3, 81.8, and 90.9% for sensitivity, specificity, positive predictive value, and negative predictive value, respectively. It is evidenced that IMR assay can quantitatively analyze IGFBP-1 concentrations, and the results show the possibility to diagnose pregnant women with PPROM by IMR assay. Copyright © 2014 Elsevier B.V. All rights reserved.

  14. Asynchronous vehicle pose correction using visual detection of ground features

    International Nuclear Information System (INIS)

    Harnarinesingh, Randy E S; Syan, Chanan S

    2014-01-01

    The inherent noise associated with odometry manifests itself as errors in localization for autonomous vehicles. Visual odometry has been previously used in order to supplement classical vehicle odometry. However, visual odometry is limited in its ability to reduce errors in localization for large travel distances that entail the cumulative summing of individual frame-to-frame image errors. In this paper, a novel machine vision approach for tiled surfaces is proposed to address this problem. Tile edges in a laboratory environment are used to define a travel trajectory for the Quansar Qbot (autonomous vehicle) built on the iRobot iRoomba platform with a forward facing camera. Tile intersections are used to enable asynchronous error recovery for vehicle position and orientation. The proposed approach employs real-time image classification and is feasible for error mitigation for large travel distances. The average position error for an 8m travel distance using classical odometry was measured to be 0.28m. However, implementation of the proposed approach resulted in an error of 0.028m. The proposed approach therefore significantly reduces pose estimation error and could be used to supplement existing modalities such as GPS and Laser-based range sensors

  15. The role of the posed smile in overall facial esthetics.

    Science.gov (United States)

    Havens, David C; McNamara, James A; Sigler, Lauren M; Baccetti, Tiziano

    2010-03-01

    To evaluate the role of the posed smile in overall facial esthetics, as determined by laypersons and orthodontists. Twenty orthodontists and 20 lay evaluators were asked to perform six Q-sorts on different photographs of 48 white female subjects. The six Q-sorts consisted of three different photographs for each of two time points (pre- and posttreatment), as follows: (1) smile-only, (2) face without the smile, and (3) face with the smile. The evaluators determined a split-line for attractive and unattractive images at the end of each Q-sort. The proportions of attractive patients were compared across Q-sorts using a Wilcoxon signed-rank test for paired data. The evaluators also ranked nine facial/dental characteristics at the completion of the six Q-sorts. Evaluators found the pretreatment face without the smile to be significantly more attractive than the face with the smile or the smile-only photographs. Dissimilar results were seen posttreatment; there was not a significant difference between the three posttreatment photographs. The two panels agreed on the proportion of "attractive" subjects but differed on the attractiveness level of each individual subject. The presence of a malocclusion has a negative impact on facial attractiveness. Orthodontic correction of a malocclusion affects overall facial esthetics positively. Laypeople and orthodontists agree on what is attractive. Overall facial harmony is the most important characteristic used in deciding facial attractiveness.

  16. Robust Sonar ATR Through Bayesian Pose-Corrected Sparse Classification

    Science.gov (United States)

    McKay, John; Monga, Vishal; Raj, Raghu G.

    2017-10-01

    Sonar imaging has seen vast improvements over the last few decades due in part to advances in synthetic aperture Sonar (SAS). Sophisticated classification techniques can now be used in Sonar automatic target recognition (ATR) to locate mines and other threatening objects. Among the most promising of these methods is sparse reconstruction-based classification (SRC) which has shown an impressive resiliency to noise, blur, and occlusion. We present a coherent strategy for expanding upon SRC for Sonar ATR that retains SRC's robustness while also being able to handle targets with diverse geometric arrangements, bothersome Rayleigh noise, and unavoidable background clutter. Our method, pose corrected sparsity (PCS), incorporates a novel interpretation of a spike and slab probability distribution towards use as a Bayesian prior for class-specific discrimination in combination with a dictionary learning scheme for localized patch extractions. Additionally, PCS offers the potential for anomaly detection in order to avoid false identifications of tested objects from outside the training set with no additional training required. Compelling results are shown using a database provided by the United States Naval Surface Warfare Center.

  17. The STS-93 crew pose in front of Columbia

    Science.gov (United States)

    1999-01-01

    The STS-93 crew pose in front of the Space Shuttle orbiter Columbia following their landing on runway 33 at the Shuttle Landing Facility. Main gear touchdown occurred at 11:20:35 p.m. EDT on July 27. From left to right, they are Mission Specialists Catherine G. Coleman (Ph.D.) and Stephen A. Hawley (Ph.D.), Pilot Jeffrey S. Ashby, Commander Eileen Collins, and Mission Specialist Michel Tognini of France, with the Centre National d'Etudes Spatiales (CNES). The mission's primary objective was to deploy the Chandra X-ray Observatory, which will allow scientists from around the world to study some of the most distant, powerful and dynamic objects in the universe. This was the 95th flight in the Space Shuttle program and the 26th for Columbia. The landing was the 19th consecutive Shuttle landing in Florida and the 12th night landing in Shuttle program history. On this mission, Collins became the first woman to serve as a Shuttle commander.

  18. STS-93 Commander Collins poses in front of Columbia

    Science.gov (United States)

    1999-01-01

    STS-93 Commander Eileen Collins poses in front of the Space Shuttle orbiter Columbia following her textbook landing on runway 33 at the Shuttle Landing Facility. Main gear touchdown occurred at 11:20:35 p.m. EDT on July 27. On this mission, Collins became the first woman to serve as a Shuttle commander. Also on board were her fellow STS-93 crew members: Pilot Jeffrey S. Ashby and Mission Specialists Stephen A. Hawley (Ph.D.), Catherine G. Coleman (Ph.D.) and Michel Tognini of France, with the Centre National d'Etudes Spatiales (CNES). The mission's primary objective was to deploy the Chandra X-ray Observatory, which will allow scientists from around the world to study some of the most distant, powerful and dynamic objects in the universe. This was the 95th flight in the Space Shuttle program and the 26th for Columbia. The landing was the 19th consecutive Shuttle landing in Florida and the 12th night landing in Shuttle program history.

  19. The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction.

    Science.gov (United States)

    Li, Hongjian; Peng, Jiangjun; Leung, Yee; Leung, Kwong-Sak; Wong, Man-Hon; Lu, Gang; Ballester, Pedro J

    2018-03-14

    It has recently been claimed that the outstanding performance of machine-learning scoring functions (SFs) is exclusively due to the presence of training complexes with highly similar proteins to those in the test set. Here, we revisit this question using 24 similarity-based training sets, a widely used test set, and four SFs. Three of these SFs employ machine learning instead of the classical linear regression approach of the fourth SF (X-Score which has the best test set performance out of 16 classical SFs). We have found that random forest (RF)-based RF-Score-v3 outperforms X-Score even when 68% of the most similar proteins are removed from the training set. In addition, unlike X-Score, RF-Score-v3 is able to keep learning with an increasing training set size, becoming substantially more predictive than X-Score when the full 1105 complexes are used for training. These results show that machine-learning SFs owe a substantial part of their performance to training on complexes with dissimilar proteins to those in the test set, against what has been previously concluded using the same data. Given that a growing amount of structural and interaction data will be available from academic and industrial sources, this performance gap between machine-learning SFs and classical SFs is expected to enlarge in the future.

  20. Counterfeit phosphodiesterase type 5 inhibitors pose significant safety risks.

    Science.gov (United States)

    Jackson, G; Arver, S; Banks, I; Stecher, V J

    2010-03-01

    Counterfeit drugs are inherently dangerous and a growing problem; counterfeiters are becoming increasingly sophisticated. Growth of the counterfeit medication market is attributable in part to phosphodiesterase type 5 inhibitor (PDE5i) medications for erectile dysfunction (ED). Millions of counterfeit PDE5is are seized yearly and account for the bulk of all counterfeit pharmaceutical product seizures. It has been estimated that up to 2.5 million men in Europe are exposed to illicit sildenafil, suggesting that there may be as many illegal as legal users of sildenafil. Analysis of the contents of counterfeit PDE5is shows inconsistent doses of active pharmaceutical ingredients (from 0% to > 200% of labelled dose), contaminants (including talcum powder, commercial paint and printer ink) and alternative ingredients that are potentially hazardous. In one analysis, only 10.1% of samples were within 10% of the labelled tablet strength. Estimates place the proportion of counterfeit medications sold over the Internet from 44% to 90%. Of men who purchase prescription-only medication for ED without a prescription, 67% do so using the Internet. Counterfeit PDE5is pose direct and indirect risks to health, including circumvention of the healthcare system. More than 30% of men reported no healthcare interaction when purchasing ED medications. Because > 65% actually had ED, these men missed an opportunity for evaluation of comorbidities (e.g. diabetes and hypertension). Globally, increased obstacles for counterfeiters are necessary to combat pharmaceutical counterfeiting, including fines and penalties. The worldwide nature of the counterfeit problem requires proper coordination between countries to ensure adequate enforcement. Locally, physicians who treat ED need to inform patients of the dangers of ordering PDE5is via the Internet.

  1. Internet poses multiple risks to children and adolescents.

    Science.gov (United States)

    McColgan, Maria D; Giardino, Angelo P

    2005-05-01

    Computers and Internet usage, whether by children at home or at public places such as schools and libraries, are here to stay. Tremendous benefits in terms of educational opportunities, communication, and recreation can be expected. With all the benefits that such information technology provides, however, there is an element of risk that should not inhibit its use but must be attended to and managed. The methods child sexual offenders use to pursue their criminal interests will continue to evolve as technology evolves. The first and most important line of defense calls for parents and other caregivers to remain directly responsible for the safety of the children in their care. Parents, teachers, healthcare providers, and other caregivers need to learn continually about the Internet and remain aware of how best to protect children who use the computer and the Internet. Law enforcement agencies must also continue to prepare for advances in computer technology, to better anticipate the behavior of child sexual offenders, and to investigate and prosecute offenders. All law enforcement, medical, and social services personnel who have contact with children on a regular basis must continue to educate children and their parents or guardians about the dangers posed by the Internet. After a child is victimized, law enforcement, medical, and social services personnel also must remain cognizant that the victim's computer may contain evidence that may help identify and prosecute the offender. In short, all those charged with the protection of children and the prosecution of child sexual offenders must continue to adapt to our ever-evolving computer technology.

  2. Crisis planning to manage risks posed by animal rights extremists.

    Science.gov (United States)

    Bailey, Matthew R; Rich, Barbara A; Bennett, B Taylor

    2010-01-01

    Among the multitude of crises that US research institutions may face are those caused by animal rights activists. While most activists opposed to animal research use peaceful and lawful means of expressing their opinions, some extremists resort to illegal methods. Arson, break-ins, and theft with significant property damage at US animal research facilities began in the 1980s. The most troubling trend to develop in the past decade is the targeting of individuals associated with animal research, whether directly or indirectly, and the use of violent scare tactics to intimidate researchers and their families. The National Association for Biomedical Research has a 30-year history of monitoring the animal rights movement and assisting member institutions with crisis situations. In this article we discuss attacks on researchers at their homes, cyber crimes, exploitation of new media formats, infiltration of research facilities, and the targeting of external research stakeholders and business partners. We describe the need for a well-conceived crisis management plan and strong leadership to mitigate crisis situations. Institutions with well-informed leaders and crisis management teams ready to take timely action are best equipped to protect staff, laboratory animals, and research programs. They act on early warnings, provide support for targeted staff, seek legal remedies, thoughtfully control access to research facilities, and identify and enlist new research supporters. We underscore the importance of up-to-date crisis planning so that institutions are not only aware of ongoing risks posed by animal rights extremists but also better prepared to take preemptive action and able to manage those risks successfully.

  3. Image-based aircraft pose estimation: a comparison of simulations and real-world data

    Science.gov (United States)

    Breuers, Marcel G. J.; de Reus, Nico

    2001-10-01

    The problem of estimating aircraft pose information from mono-ocular image data is considered using a Fourier descriptor based algorithm. The dependence of pose estimation accuracy on image resolution and aspect angle is investigated through simulations using sets of synthetic aircraft images. Further evaluation shows that god pose estimation accuracy can be obtained in real world image sequences.

  4. Pose Estimation and Adaptive Robot Behaviour for Human-Robot Interaction

    DEFF Research Database (Denmark)

    Svenstrup, Mikael; Hansen, Søren Tranberg; Andersen, Hans Jørgen

    2009-01-01

    ’s pose. The resulting pose estimates are used to identify humans who wish to be approached and interacted with. The interaction motion of the robot is based on adaptive potential functions centered around the person that respect the persons social spaces. The method is tested in experiments...... that demonstrate the potential of the combined pose estimation and adaptive potential function approach....

  5. Multi-view 3D Human Pose Estimation in Complex Environment

    NARCIS (Netherlands)

    Hofmann, K.M.; Gavrila, D.M.

    2012-01-01

    We introduce a framework for unconstrained 3D human upper body pose estimation from multiple camera views in complex environment. Its main novelty lies in the integration of three components: single-frame pose recovery, temporal integration and model texture adaptation. Single-frame pose recovery

  6. Coupled Gaussian Process Regression for pose-invariant facial expression recognition

    NARCIS (Netherlands)

    Rudovic, Ognjen; Patras, Ioannis; Pantic, Maja; Daniilidis, Kostas; Maragos, Petros; Paragios, Nikos

    2010-01-01

    We present a novel framework for the recognition of facial expressions at arbitrary poses that is based on 2D geometric features. We address the problem by first mapping the 2D locations of landmark points of facial expressions in non-frontal poses to the corresponding locations in the frontal pose.

  7. Solute-vacancy binding in aluminum

    International Nuclear Information System (INIS)

    Wolverton, C.

    2007-01-01

    Previous efforts to understand solute-vacancy binding in aluminum alloys have been hampered by a scarcity of reliable, quantitative experimental measurements. Here, we report a large database of solute-vacancy binding energies determined from first-principles density functional calculations. The calculated binding energies agree well with accurate measurements where available, and provide an accurate predictor of solute-vacancy binding in other systems. We find: (i) some common solutes in commercial Al alloys (e.g., Cu and Mg) possess either very weak (Cu), or even repulsive (Mg), binding energies. Hence, we assert that some previously reported large binding energies for these solutes are erroneous. (ii) Large binding energies are found for Sn, Cd and In, confirming the proposed mechanism for the reduced natural aging in Al-Cu alloys containing microalloying additions of these solutes. (iii) In addition, we predict that similar reduction in natural aging should occur with additions of Si, Ge and Au. (iv) Even larger binding energies are found for other solutes (e.g., Pb, Bi, Sr, Ba), but these solutes possess essentially no solubility in Al. (v) We have explored the physical effects controlling solute-vacancy binding in Al. We find that there is a strong correlation between binding energy and solute size, with larger solute atoms possessing a stronger binding with vacancies. (vi) Most transition-metal 3d solutes do not bind strongly with vacancies, and some are even energetically strongly repelled from vacancies, particularly for the early 3d solutes, Ti and V

  8. Multiple binding modes of ibuprofen in human serum albumin identified by absolute binding free energy calculations

    KAUST Repository

    Evoli, Stefania

    2016-11-10

    Human serum albumin possesses multiple binding sites and transports a wide range of ligands that include the anti-inflammatory drug ibuprofen. A complete map of the binding sites of ibuprofen in albumin is difficult to obtain in traditional experiments, because of the structural adaptability of this protein in accommodating small ligands. In this work, we provide a set of predictions covering the geometry, affinity of binding and protonation state for the pharmaceutically most active form (S-isomer) of ibuprofen to albumin, by using absolute binding free energy calculations in combination with classical molecular dynamics (MD) simulations and molecular docking. The most favorable binding modes correctly reproduce several experimentally identified binding locations, which include the two Sudlow\\'s drug sites (DS2 and DS1) and the fatty acid binding sites 6 and 2 (FA6 and FA2). Previously unknown details of the binding conformations were revealed for some of them, and formerly undetected binding modes were found in other protein sites. The calculated binding affinities exhibit trends which seem to agree with the available experimental data, and drastically degrade when the ligand is modeled in a protonated (neutral) state, indicating that ibuprofen associates with albumin preferentially in its charged form. These findings provide a detailed description of the binding of ibuprofen, help to explain a wide range of results reported in the literature in the last decades, and demonstrate the possibility of using simulation methods to predict ligand binding to albumin.

  9. Antibiotics as CECs: An Overview of the Hazards Posed by Antibiotics and Antibiotic Resistance

    Directory of Open Access Journals (Sweden)

    Geoffrey Ivan Scott

    2016-04-01

    Full Text Available ABSTRACTMonitoring programs have traditionally monitored legacy contaminants but are shifting focus to Contaminants of Emerging Concern (CECs. CECs present many challenges for monitoring and assessment, because measurement methods don't always exist nor have toxicological studies been fully conducted to place results in proper context. Also some CECs affect metabolic pathways to produce adverse outcomes that are not assessed through traditional toxicological evaluations. Antibiotics are CECs that pose significant environmental risks including development of both toxic effects at high doses and antibiotic resistance at doses well below the Minimum Inhibitory Concentration (MIC which kill bacteria and have been found in nearly half of all sites monitored in the US. Antimicrobial resistance has generally been attributed to the use of antibiotics in medicine for humans and livestock as well as aquaculture operations. The objective of this study was to assess the extent and magnitude of antibiotics in the environment and estimate their potential hazards in the environment. Antibiotics concentrations were measured in a number of monitoring studies which included Waste Water Treatment Plants (WWTP effluent, surface waters, sediments and biota. A number of studies reported levels of Antibiotic Resistant Microbes (ARM in surface waters and some studies found specific ARM genes (e.g. the blaM-1 gene in E. coli which may pose additional environmental risk. High levels of this gene were found to survive WWTP disinfection and accumulated in sediment at levels 100-1000 times higher than in the sewerage effluent, posing potential risks for gene transfer to other bacteria.in aquatic and marine ecosystems. Antibiotic risk assessment approaches were developed based on the use of MICs and MIC Ratios [High (Antibiotic Resistant/Low (Antibiotic Sensitive MIC] for each antibiotic indicating the range of bacterial adaptability to each antibiotic to help define the No

  10. Particle Filter with Binary Gaussian Weighting and Support Vector Machine for Human Pose Interpretation

    Directory of Open Access Journals (Sweden)

    Indah Agustien

    2010-10-01

    Full Text Available Human pose interpretation using Particle filter with Binary Gaussian Weighting and Support Vector Machine is proposed. In the proposed system, Particle filter is used to track human object, then this human object is skeletonized using thinning algorithm and classified using Support Vector Machine. The classification is to identify human pose, whether a normal or abnormal behavior. Here Particle filter is modified through weight calculation using Gaussiandistribution to reduce the computational time. The modified particle filter consists of four main phases. First, particles are generated to predict target’s location. Second, weight of certain particles is calculated and these particles are used to build Gaussian distribution. Third, weight of all particles is calculated based on Gaussian distribution. Fourth, update particles based on each weight. The modified particle filter could reduce computational time of object tracking since this method does not have to calculate particle’s weight one by one. To calculate weight, the proposed method builds Gaussian distribution and calculates particle’s weight using this distribution. Through experiment using video data taken in front of cashier of convenient store, the proposed method reduced computational time in tracking process until 68.34% in average compare to the conventional one, meanwhile the accuracy of tracking with this new method is comparable with particle filter method i.e. 90.3%. Combination particle filter with binary Gaussian weighting and support vector machine is promising for advanced early crime scene investigation.

  11. Ligand pose and orientational sampling in molecular docking.

    Directory of Open Access Journals (Sweden)

    Ryan G Coleman

    Full Text Available Molecular docking remains an important tool for structure-based screening to find new ligands and chemical probes. As docking ambitions grow to include new scoring function terms, and to address ever more targets, the reliability and extendability of the orientation sampling, and the throughput of the method, become pressing. Here we explore sampling techniques that eliminate stochastic behavior in DOCK3.6, allowing us to optimize the method for regularly variable sampling of orientations. This also enabled a focused effort to optimize the code for efficiency, with a three-fold increase in the speed of the program. This, in turn, facilitated extensive testing of the method on the 102 targets, 22,805 ligands and 1,411,214 decoys of the Directory of Useful Decoys-Enhanced (DUD-E benchmarking set, at multiple levels of sampling. Encouragingly, we observe that as sampling increases from 50 to 500 to 2000 to 5000 to 20,000 molecular orientations in the binding site (and so from about 1×10(10 to 4×10(10 to 1×10(11 to 2×10(11 to 5×10(11 mean atoms scored per target, since multiple conformations are sampled per orientation, the enrichment of ligands over decoys monotonically increases for most DUD-E targets. Meanwhile, including internal electrostatics in the evaluation ligand conformational energies, and restricting aromatic hydroxyls to low energy rotamers, further improved enrichment values. Several of the strategies used here to improve the efficiency of the code are broadly applicable in the field.

  12. Heart-type fatty acid binding protein and high-sensitivity troponin T are myocardial damage markers that could predict adverse clinical outcomes in patients with peripheral artery disease.

    Science.gov (United States)

    Otaki, Yoichiro; Takahashi, Hiroki; Watanabe, Tetsu; Yamaura, Gensai; Funayama, Akira; Arimoto, Takanori; Shishido, Tetsuro; Miyamoto, Takuya; Kubota, Isao

    2015-12-01

    Despite many recent advances in endovascular therapy (EVT), peripheral artery disease (PAD) is an increasing health problem with high mortality. Heart-type fatty acid-binding protein (H-FABP) and high-sensitivity troponin T (hsTnT) are markers of ongoing myocardial damage and have been reported to be useful indicators of future cardiovascular events. However, it remains to be determined whether H-FABP and hsTnT can predict adverse clinical outcomes in patients with PAD. We enrolled 208 de novo PAD patients who underwent EVT. Serum H-FABP and hsTnT were measured in all patients before EVT. During the median follow-up period of 694 days, there were 40 major adverse cardiovascular and cerebrovascular events (MACCEs) including all-cause deaths, and re-hospitalizations due to cardiovascular and cerebrovascular diseases and amputations. H-FABP and hsTnT were found to be higher in patients with critical limb ischemia (CLI) compared to those without this condition. Multivariate Cox proportional hazard regression analysis revealed that both H-FABP and hsTnT were independent predictors of MACCEs after adjustment for confounding factors. Kaplan-Meier analysis demonstrated that patients in the highest tertile according to H-FABP levels, as well as those in the highest hsTnT tertile, were at greatest risk for MACCEs. The net reclassification index was significantly improved by the addition of H-FABP as well as the addition of hsTnT to traditional risk factors. The myocardial damage markers H-FABP and hsTnT were increased in PAD patients with CLI and could predict MACCEs in PAD patients.

  13. Predicting the affinity of Farnesoid X Receptor ligands through a hierarchical ranking protocol: a D3R Grand Challenge 2 case study

    Science.gov (United States)

    Réau, Manon; Langenfeld, Florent; Zagury, Jean-François; Montes, Matthieu

    2018-01-01

    The Drug Design Data Resource (D3R) Grand Challenges are blind contests organized to assess the state-of-the-art methods accuracy in predicting binding modes and relative binding free energies of experimentally validated ligands for a given target. The second stage of the D3R Grand Challenge 2 (GC2) was focused on ranking 102 compounds according to their predicted affinity for Farnesoid X Receptor. In this task, our workflow was ranked 5th out of the 77 submissions in the structure-based category. Our strategy consisted in (1) a combination of molecular docking using AutoDock 4.2 and manual edition of available structures for binding poses generation using SeeSAR, (2) the use of HYDE scoring for pose selection, and (3) a hierarchical ranking using HYDE and MM/GBSA. In this report, we detail our pose generation and ligands ranking protocols and provide guidelines to be used in a prospective computer aided drug design program.

  14. Creativity of Field-dependent and Field-independent Students in Posing Mathematical Problems

    Science.gov (United States)

    Azlina, N.; Amin, S. M.; Lukito, A.

    2018-01-01

    This study aims at describing the creativity of elementary school students with different cognitive styles in mathematical problem-posing. The posed problems were assessed based on three components of creativity, namely fluency, flexibility, and novelty. The free-type problem posing was used in this study. This study is a descriptive research with qualitative approach. Data collections were conducted through written task and task-based interviews. The subjects were two elementary students. One of them is Field Dependent (FD) and the other is Field Independent (FI) which were measured by GEFT (Group Embedded Figures Test). Further, the data were analyzed based on creativity components. The results show thatFD student’s posed problems have fulfilled the two components of creativity namely fluency, in which the subject posed at least 3 mathematical problems, and flexibility, in whichthe subject posed problems with at least 3 different categories/ideas. Meanwhile,FI student’s posed problems have fulfilled all three components of creativity, namely fluency, in which thesubject posed at least 3 mathematical problems, flexibility, in which thesubject posed problems with at least 3 different categories/ideas, and novelty, in which the subject posed problems that are purely the result of her own ideas and different from problems they have known.

  15. Minimal residual disease monitoring by quantitative RT-PCR in core binding factor AML allows risk stratification and predicts relapse: results of the United Kingdom MRC AML-15 trial.

    Science.gov (United States)

    Yin, John A Liu; O'Brien, Michelle A; Hills, Robert K; Daly, Sarah B; Wheatley, Keith; Burnett, Alan K

    2012-10-04

    The clinical value of serial minimal residual disease (MRD) monitoring in core binding factor (CBF) acute myeloid leukemia (AML) by quantitative RT-PCR was prospectively assessed in 278 patients [163 with t(8;21) and 115 with inv(16)] entered in the United Kingdom MRC AML 15 trial. CBF transcripts were normalized to 10(5) ABL copies. At remission, after course 1 induction chemotherapy, a > 3 log reduction in RUNX1-RUNX1T1 transcripts in BM in t(8;21) patients and a > 10 CBFB-MYH11 copy number in peripheral blood (PB) in inv(16) patients were the most useful prognostic variables for relapse risk on multivariate analysis. MRD levels after consolidation (course 3) were also informative. During follow-up, cut-off MRD thresholds in BM and PB associated with a 100% relapse rate were identified: for t(8;21) patients BM > 500 copies, PB > 100 copies; for inv(16) patients, BM > 50 copies and PB > 10 copies. Rising MRD levels on serial monitoring accurately predicted hematologic relapse. During follow-up, PB sampling was equally informative as BM for MRD detection. We conclude that MRD monitoring by quantitative RT-PCR at specific time points in CBF AML allows identification of patients at high risk of relapse and could now be incorporated in clinical trials to evaluate the role of risk directed/preemptive therapy.

  16. DNA-Aptamers Binding Aminoglycoside Antibiotics

    Directory of Open Access Journals (Sweden)

    Nadia Nikolaus

    2014-02-01

    Full Text Available Aptamers are short, single stranded DNA or RNA oligonucleotides that are able to bind specifically and with high affinity to their non-nucleic acid target molecules. This binding reaction enables their application as biorecognition elements in biosensors and assays. As antibiotic residues pose a problem contributing to the emergence of antibiotic-resistant pathogens and thereby reducing the effectiveness of the drug to fight human infections, we selected aptamers targeted against the aminoglycoside antibiotic kanamycin A with the aim of constructing a robust and functional assay that can be used for water analysis. With this work we show that aptamers that were derived from a Capture-SELEX procedure targeting against kanamycin A also display binding to related aminoglycoside antibiotics. The binding patterns differ among all tested aptamers so that there are highly substance specific aptamers and more group specific aptamers binding to a different variety of aminoglycoside antibiotics. Also the region of the aminoglycoside antibiotics responsible for aptamer binding can be estimated. Affinities of the different aptamers for their target substance, kanamycin A, are measured with different approaches and are in the micromolar range. Finally, the proof of principle of an assay for detection of kanamycin A in a real water sample is given.

  17. Performance of HADDOCK and a simple contact-based protein-ligand binding affinity predictor in the D3R Grand Challenge 2

    Science.gov (United States)

    Kurkcuoglu, Zeynep; Koukos, Panagiotis I.; Citro, Nevia; Trellet, Mikael E.; Rodrigues, J. P. G. L. M.; Moreira, Irina S.; Roel-Touris, Jorge; Melquiond, Adrien S. J.; Geng, Cunliang; Schaarschmidt, Jörg; Xue, Li C.; Vangone, Anna; Bonvin, A. M. J. J.

    2018-01-01

    We present the performance of HADDOCK, our information-driven docking software, in the second edition of the D3R Grand Challenge. In this blind experiment, participants were requested to predict the structures and binding affinities of complexes between the Farnesoid X nuclear receptor and 102 different ligands. The models obtained in Stage1 with HADDOCK and ligand-specific protocol show an average ligand RMSD of 5.1 Å from the crystal structure. Only 6/35 targets were within 2.5 Å RMSD from the reference, which prompted us to investigate the limiting factors and revise our protocol for Stage2. The choice of the receptor conformation appeared to have the strongest influence on the results. Our Stage2 models were of higher quality (13 out of 35 were within 2.5 Å), with an average RMSD of 4.1 Å. The docking protocol was applied to all 102 ligands to generate poses for binding affinity prediction. We developed a modified version of our contact-based binding affinity predictor PRODIGY, using the number of interatomic contacts classified by their type and the intermolecular electrostatic energy. This simple structure-based binding affinity predictor shows a Kendall's Tau correlation of 0.37 in ranking the ligands (7th best out of 77 methods, 5th/25 groups). Those results were obtained from the average prediction over the top10 poses, irrespective of their similarity/correctness, underscoring the robustness of our simple predictor. This results in an enrichment factor of 2.5 compared to a random predictor for ranking ligands within the top 25%, making it a promising approach to identify lead compounds in virtual screening.

  18. Attributed Relational Graph Based Feature Extraction of Body Poses In Indian Classical Dance Bharathanatyam

    OpenAIRE

    Athira. Sugathan; Suganya. R

    2014-01-01

    Articulated body pose estimation in computer vision is an important problem because of convolution of the models. It is useful in real time applications such as surveillance camera, computer games, human computer interaction etc. Feature extraction is the main part in pose estimation which helps for a successful classification. In this paper, we propose a system for extracting the features from the relational graph of articulated upper body poses of basic Bharatanatyam steps, ...

  19. Vehicle Pose Estimation for Vehicle Detection and Tracking Based on Road Direction

    OpenAIRE

    Prahara, Adhi; Azhari, Ahmad; Murinto, Murinto

    2017-01-01

    Vehicle has several types and each of them has different color, size, and shape. The appearance of vehicle also changes if viewed from different viewpoint of traffic surveillance camera. This situation can create many possibilities of vehicle poses. However, the one in common, vehicle pose usually follows road direction. Therefore, this research proposes a method to estimate the pose of vehicle for vehicle detection and tracking based on road direction. Vehicle training data are generated fro...

  20. Addressing socioeconomic and political challenges posed by climate change

    Science.gov (United States)

    Fernando, Harindra Joseph; Klaic, Zvjezdana Bencetic

    2011-08-01

    NATO Advanced Research Workshop: Climate Change, Human Health and National Security; Dubrovnik, Croatia, 28-30 April 2011; Climate change has been identified as one of the most serious threats to humanity. It not only causes sea level rise, drought, crop failure, vector-borne diseases, extreme events, degradation of water and air quality, heat waves, and other phenomena, but it is also a threat multiplier wherein concatenation of multiple events may lead to frequent human catastrophes and intranational and international conflicts. In particular, urban areas may bear the brunt of climate change because of the amplification of climate effects that cascade down from global to urban scales, but current modeling and downscaling capabilities are unable to predict these effects with confidence. These were the main conclusions of a NATO Advanced Research Workshop (ARW) sponsored by the NATO Science for Peace and Security program. Thirty-two invitees from 17 counties, including leading modelers; natural, political, and social scientists; engineers; politicians; military experts; urban planners; industry analysts; epidemiologists; and health care professionals, parsed the topic on a common platform.

  1. Using Online Modelled Spatial Constraints for Pose Estimation in an Industrial Setting

    DEFF Research Database (Denmark)

    Meyer, Kenneth Korsgaard; Wolniakowski, Adam; Hagelskjær, Frederik

    2017-01-01

    We introduce a vision system that is able to on-line learn spatial constraints to improve pose estimation in terms of correct recognition as well as computational speed. By making use of a simulated industrial robot system performing various pick and place tasks, we show the effect of model...... building when making use of visual knowledge in terms of visually extracted pose hypotheses as well as action knowledge in terms of pose hypotheses verified by action execution. We show that the use of action knowledge significantly improves the pose estimation process....

  2. ROS wrapper for real-time multi-person pose estimation with a single camera

    OpenAIRE

    Arduengo García, Miguel; Jorgensen, Steven Jens; Hambuchen, Kimberly; Sentis, Luis; Moreno-Noguer, Francesc; Alenyà Ribas, Guillem

    2017-01-01

    For robots to be deployable in human occupied environments, the robots must have human-awareness and generate human-aware behaviors and policies. OpenPose is a library for real-time multi-person keypoint detection. We have considered the implementation of a ROS package that would allow the estimation of 2d pose from simple RGB images, for which we have introduced a ROS wrapper that automatically recovers the pose of several people from a single camera using OpenPose. Additionally, a ROS node ...

  3. Screening Mixtures of Small Molecules for Binding to Multiple Sites on the Surface Tetanus Toxin C Fragment by Bioaffinity NMR

    Energy Technology Data Exchange (ETDEWEB)

    Cosman, M; Zeller, L; Lightstone, F C; Krishnan, V V; Balhorn, R

    2002-01-01

    The clostridial neurotoxins include the closely related tetanus (TeNT) and botulinum (BoNT) toxins. Botulinum toxin is used to treat severe muscle disorders and as a cosmetic wrinkle reducer. Large quantities of botulinum toxin have also been produced by terrorists for use as a biological weapon. Because there are no known antidotes for these toxins, they thus pose a potential threat to human health whether by an accidental overdose or by a hostile deployment. Thus, the discovery of high specificity and affinity compounds that can inhibit their binding to neural cells can be used as antidotes or in the design of chemical detectors. Using the crystal structure of the C fragment of the tetanus toxin (TetC), which is the cell recognition and cell surface binding domain, and the computational program DOCK, sets of small molecules have been predicted to bind to two different sites located on the surface of this protein. While Site-1 is common to the TeNT and BoNTs, Site-2 is unique to TeNT. Pairs of these molecules from each site can then be linked together synthetically to thereby increase the specificity and affinity for this toxin. Electrospray ionization mass spectroscopy was used to experimentally screen each compound for binding. Mixtures containing binders were further screened for activity under biologically relevant conditions using nuclear magnetic resonance (NMR) methods. The screening of mixtures of compounds offers increased efficiency and throughput as compared to testing single compounds and can also evaluate how possible structural changes induced by the binding of one ligand can influence the binding of the second ligand. In addition, competitive binding experiments with mixtures containing ligands predicted to bind the same site could identify the best binder for that site. NMR transfer nuclear Overhauser effect (trNOE) confirm that TetC binds doxorubicin but that this molecule is displaced by N-acetylneuraminic acid (sialic acid) in a mixture that

  4. Screening Mixtures of Small Molecules for Binding to Multiple Sites on the Surface Tetanus Toxin C Fragment by Bioaffinity NMR

    International Nuclear Information System (INIS)

    Cosman, M; Zeller, L; Lightstone, F C; Krishnan, V V; Balhorn, R

    2002-01-01

    The clostridial neurotoxins include the closely related tetanus (TeNT) and botulinum (BoNT) toxins. Botulinum toxin is used to treat severe muscle disorders and as a cosmetic wrinkle reducer. Large quantities of botulinum toxin have also been produced by terrorists for use as a biological weapon. Because there are no known antidotes for these toxins, they thus pose a potential threat to human health whether by an accidental overdose or by a hostile deployment. Thus, the discovery of high specificity and affinity compounds that can inhibit their binding to neural cells can be used as antidotes or in the design of chemical detectors. Using the crystal structure of the C fragment of the tetanus toxin (TetC), which is the cell recognition and cell surface binding domain, and the computational program DOCK, sets of small molecules have been predicted to bind to two different sites located on the surface of this protein. While Site-1 is common to the TeNT and BoNTs, Site-2 is unique to TeNT. Pairs of these molecules from each site can then be linked together synthetically to thereby increase the specificity and affinity for this toxin. Electrospray ionization mass spectroscopy was used to experimentally screen each compound for binding. Mixtures containing binders were further screened for activity under biologically relevant conditions using nuclear magnetic resonance (NMR) methods. The screening of mixtures of compounds offers increased efficiency and throughput as compared to testing single compounds and can also evaluate how possible structural changes induced by the binding of one ligand can influence the binding of the second ligand. In addition, competitive binding experiments with mixtures containing ligands predicted to bind the same site could identify the best binder for that site. NMR transfer nuclear Overhauser effect (trNOE) confirm that TetC binds doxorubicin but that this molecule is displaced by N-acetylneuraminic acid (sialic acid) in a mixture that

  5. Improved docking, screening and selectivity prediction for small molecule nuclear receptor modulators using conformational ensembles.

    Science.gov (United States)

    Park, So-Jung; Kufareva, Irina; Abagyan, Ruben

    2010-05-01

    Nuclear receptors (NRs) are ligand dependent transcriptional factors and play a key role in reproduction, development, and homeostasis of organism. NRs are potential targets for treatment of cancer and other diseases such as inflammatory diseases, and diabetes. In this study, we present a comprehensive library of pocket conformational ensembles of thirteen human nuclear receptors (NRs), and test the ability of these ensembles to recognize their ligands in virtual screening, as well as predict their binding geometry, functional type, and relative binding affinity. 157 known NR modulators and 66 structures were used as a benchmark. Our pocket ensemble library correctly predicted the ligand binding poses in 94% of the cases. The models were also highly selective for the active ligands in virtual screening, with the areas under the ROC curves ranging from 82 to a remarkable 99%. Using the computationally determined receptor-specific binding energy offsets, we showed that the ensembles can be used for predicting selectivity profiles of NR ligands. Our results evaluate and demonstrate the advantages of using receptor ensembles for compound docking, screening, and profiling.

  6. HLA-DPβ1 Asp84-Lys69 antigen-binding signature predicts event-free survival in childhood B-cell precursor acute lymphoblastic leukaemia: results from the MRC UKALL XI childhood ALL trial.

    Science.gov (United States)

    Taylor, G M; Wade, R; Hussain, A; Thompson, P; Hann, I; Gibson, B; Eden, T; Richards, S

    2012-07-01

    We previously reported that children in the UKALL XI ALL trial with HLA-DP 1 and -DP 3 supertypes had significantly worse event-free survival (EFS) than children with other DP supertypes. As DP 1 and DP 3 share two of four key antigen-binding amino-acid polymorphisms (aspartic acid84-lysine69), we asked whether Asp84-Lys69 or Asp84 alone were independent prognostic indicators in childhood acute lymphoblastic leukemia (ALL). We analysed EFS in 798 UKALL XI patients, stratified by Asp84-Lys69 vs non-Asp84-Lys69, for a median follow-up of 12.5 years. Asp84-Lys69 was associated with a significantly worse EFS than non-Asp84-Lys69 (5-year EFS: Asp84-Lys69: 58.8% (95% CI (confidence of interval): 52.7-64.9%); non-Asp84-Lys69: 67.3% (63.4-71.2%); 2P=0.007). Post-relapse EFS was 10% less in Asp84-Lys69 than non-Asp84-Lys69 patients. EFS was significantly worse (P=0.03) and post-relapse EFS marginally worse (P=0.06) in patients with Asp84 compared with Gly84. These results suggest that Asp84-Lys69 predicted adverse EFS in the context of UKALL XI because of Asp84, and may have influenced post-relapse EFS. We speculate that this may be due to the recruitment of Asp84-Lys69-restricted regulatory T cells in the context of this regimen, leading to the re-emergence of residual disease. However, functional and molecular studies of the prognostic value of this and other HLA molecular signatures in other childhood ALL trials are needed.

  7. Interleukin 6, lipopolysaccharide-binding protein and interleukin 10 in the prediction of risk and etiologic patterns in patients with community-acquired pneumonia: results from the German competence network CAPNETZ

    Science.gov (United States)

    2012-01-01

    Background The aim of our study was to investigate the predictive value of the biomarkers interleukin 6 (IL-6), interleukin 10 (IL-10) and lipopolysaccharide-binding protein (LBP) compared with clinical CRB and CRB-65 severity scores in patients with community-acquired pneumonia (CAP). Methods Samples and data were obtained from patients enrolled into the German CAPNETZ study group. Samples (blood, sputum and urine) were collected within 24 h of first presentation and inclusion in the CAPNETZ study, and CRB and CRB-65 scores were determined for all patients at the time of enrollment. The combined end point representative of a severe course of CAP was defined as mechanical ventilation, intensive care unit treatment and/or death within 30 days. Overall, a total of 1,000 patients were enrolled in the study. A severe course of CAP was observed in 105 (10.5%) patients. Results The highest IL-6, IL-10 and LBP concentrations were found in patients with CRB-65 scores of 3-4 or CRB scores of 2-3. IL-6 and LBP levels on enrollment in the study were significantly higher for patients with a severe course of CAP than for those who did not have severe CAP. In receiver operating characteristic analyses, the area under the curve values for of IL-6 (0.689), IL-10 (0.665) and LPB (0.624) in a severe course of CAP were lower than that of CRB-65 (0.764) and similar to that of CRB (0.69). The accuracy of both CRB and CRB-65 was increased significantly by including IL-6 measurements. In addition, higher cytokine concentrations were found in patients with typical bacterial infections compared with patients with atypical or viral infections and those with infection of unknown etiology. LBP showed the highest discriminatory power with respect to the etiology of infection. Conclusions IL-6, IL-10 and LBP concentrations were increased in patients with a CRB-65 score of 3-4 and a severe course of CAP. The concentrations of IL-6 and IL-10 reflected the severity of disease in patients with CAP

  8. 21 CFR 740.18 - Coal tar hair dyes posing a risk of cancer.

    Science.gov (United States)

    2010-04-01

    ... 21 Food and Drugs 7 2010-04-01 2010-04-01 false Coal tar hair dyes posing a risk of cancer. 740.18... posing a risk of cancer. (a) The principal display panel of the label and any labeling accompanying a... your skin and has been determined to cause cancer in laboratory animals. (b) Hair dyes containing any...

  9. An Investigation of Relationships between Students' Mathematical Problem-Posing Abilities and Their Mathematical Content Knowledge

    Science.gov (United States)

    Van Harpen, Xianwei Y.; Presmeg, Norma C.

    2013-01-01

    The importance of students' problem-posing abilities in mathematics has been emphasized in the K-12 curricula in the USA and China. There are claims that problem-posing activities are helpful in developing creative approaches to mathematics. At the same time, there are also claims that students' mathematical content knowledge could be highly…

  10. An Analysis of Problem-Posing Tasks in Chinese and US Elementary Mathematics Textbooks

    Science.gov (United States)

    Cai, Jinfa; Jiang, Chunlian

    2017-01-01

    This paper reports on 2 studies that examine how mathematical problem posing is integrated in Chinese and US elementary mathematics textbooks. Study 1 involved a historical analysis of the problem-posing (PP) tasks in 3 editions of the most widely used elementary mathematics textbook series published by People's Education Press in China over 3…

  11. The Problems Posed and Models Employed by Primary School Teachers in Subtraction with Fractions

    Science.gov (United States)

    Iskenderoglu, Tuba Aydogdu

    2017-01-01

    Students have difficulties in solving problems of fractions in almost all levels, and in problem posing. Problem posing skills influence the process of development of the behaviors observed at the level of comprehension. That is why it is very crucial for teachers to develop activities for student to have conceptual comprehension of fractions and…

  12. Example-based pose estimation in monocular images using compact fourier descriptors

    NARCIS (Netherlands)

    Poppe, Ronald Walter; Poel, Mannes

    2005-01-01

    Automatically estimating human poses from visual input is useful but challenging due to variations in image space and the high dimensionality of the pose space. In this paper, we assume that a human silhouette can be extracted from monocular visual input. We compare the recovery performance of

  13. The Effects of Problem Posing on Student Mathematical Learning: A Meta-Analysis

    Science.gov (United States)

    Rosli, Roslinda; Capraro, Mary Margaret; Capraro, Robert M.

    2014-01-01

    The purpose of the study was to meta-synthesize research findings on the effectiveness of problem posing and to investigate the factors that might affect the incorporation of problem posing in the teaching and learning of mathematics. The eligibility criteria for inclusion of literature in the meta-analysis was: published between 1989 and 2011,…

  14. Making 2D face recognition more robust using AAMs for pose compensation

    NARCIS (Netherlands)

    Huisman, Peter; Munster, Ruud; Moro-Ellenberger, Stephanie; Veldhuis, Raymond N.J.; Bazen, A.M.

    2006-01-01

    The problem of pose in 2D face recognition is widely acknowledged. Commercial systems are limited to near frontal face images and cannot deal with pose deviations larger than 15 degrees from the frontal view. This is a problem, when using face recognition for surveillance applications in which

  15. Prospective Middle School Mathematics Teachers' Knowledge of Linear Graphs in Context of Problem-Posing

    Science.gov (United States)

    Kar, Tugrul

    2016-01-01

    This study examined prospective middle school mathematics teachers' problem-posing skills by investigating their ability to associate linear graphs with daily life situations. Prospective teachers were given linear graphs and asked to pose problems that could potentially be represented by the graphs. Their answers were analyzed in two stages. In…

  16. A preliminary approach to quantifying the overall environmental risks posed by development projects during environmental impact assessment.

    Science.gov (United States)

    Nicol, Sam; Chadès, Iadine

    2017-01-01

    Environmental impact assessment (EIA) is used globally to manage the impacts of development projects on the environment, so there is an imperative to demonstrate that it can effectively identify risky projects. However, despite the widespread use of quantitative predictive risk models in areas such as toxicology, ecosystem modelling and water quality, the use of predictive risk tools to assess the overall expected environmental impacts of major construction and development proposals is comparatively rare. A risk-based approach has many potential advantages, including improved prediction and attribution of cause and effect; sensitivity analysis; continual learning; and optimal resource allocation. In this paper we investigate the feasibility of using a Bayesian belief network (BBN) to quantify the likelihood and consequence of non-compliance of new projects based on the occurrence probabilities of a set of expert-defined features. The BBN incorporates expert knowledge and continually improves its predictions based on new data as it is collected. We use simulation to explore the trade-off between the number of data points and the prediction accuracy of the BBN, and find that the BBN could predict risk with 90% accuracy using approximately 1000 data points. Although a further pilot test with real project data is required, our results suggest that a BBN is a promising method to monitor overall risks posed by development within an existing EIA process given a modest investment in data collection.

  17. A preliminary approach to quantifying the overall environmental risks posed by development projects during environmental impact assessment.

    Directory of Open Access Journals (Sweden)

    Sam Nicol

    Full Text Available Environmental impact assessment (EIA is used globally to manage the impacts of development projects on the environment, so there is an imperative to demonstrate that it can effectively identify risky projects. However, despite the widespread use of quantitative predictive risk models in areas such as toxicology, ecosystem modelling and water quality, the use of predictive risk tools to assess the overall expected environmental impacts of major construction and development proposals is comparatively rare. A risk-based approach has many potential advantages, including improved prediction and attribution of cause and effect; sensitivity analysis; continual learning; and optimal resource allocation. In this paper we investigate the feasibility of using a Bayesian belief network (BBN to quantify the likelihood and consequence of non-compliance of new projects based on the occurrence probabilities of a set of expert-defined features. The BBN incorporates expert knowledge and continually improves its predictions based on new data as it is collected. We use simulation to explore the trade-off between the number of data points and the prediction accuracy of the BBN, and find that the BBN could predict risk with 90% accuracy using approximately 1000 data points. Although a further pilot test with real project data is required, our results suggest that a BBN is a promising method to monitor overall risks posed by development within an existing EIA process given a modest investment in data collection.

  18. Trajectory Planning with Pose Feedback for a Dual-Arm Space Robot

    Directory of Open Access Journals (Sweden)

    Yicheng Liu

    2016-01-01

    Full Text Available In order to obtain high precision path tracking for a dual-arm space robot, a trajectory planning method with pose feedback is proposed to be introduced into the design process in this paper. Firstly, pose error kinematic models are derived from the related kinematics and desired pose command for the end-effector and the base, respectively. On this basis, trajectory planning with pose feedback is proposed from a control perspective. Theoretical analyses show that the proposed trajectory planning algorithm can guarantee that pose error converges to zero exponentially for both the end-effector and the base when the robot is out of singular configuration. Compared with the existing algorithms, the proposed algorithm can lead to higher precision path tracking for the end-effector. Furthermore, the algorithm renders the system good anti-interference property for the base. Simulation results demonstrate the effectiveness of the proposed trajectory planning algorithm.

  19. Multi-view space object recognition and pose estimation based on kernel regression

    Directory of Open Access Journals (Sweden)

    Zhang Haopeng

    2014-10-01

    Full Text Available The application of high-performance imaging sensors in space-based space surveillance systems makes it possible to recognize space objects and estimate their poses using vision-based methods. In this paper, we proposed a kernel regression-based method for joint multi-view space object recognition and pose estimation. We built a new simulated satellite image dataset named BUAA-SID 1.5 to test our method using different image representations. We evaluated our method for recognition-only tasks, pose estimation-only tasks, and joint recognition and pose estimation tasks. Experimental results show that our method outperforms the state-of-the-arts in space object recognition, and can recognize space objects and estimate their poses effectively and robustly against noise and lighting conditions.

  20. Coupled bias-variance tradeoff for cross-pose face recognition.

    Science.gov (United States)

    Li, Annan; Shan, Shiguang; Gao, Wen

    2012-01-01

    Subspace-based face representation can be looked as a regression problem. From this viewpoint, we first revisited the problem of recognizing faces across pose differences, which is a bottleneck in face recognition. Then, we propose a new approach for cross-pose face recognition using a regressor with a coupled bias-variance tradeoff. We found that striking a coupled balance between bias and variance in regression for different poses could improve the regressor-based cross-pose face representation, i.e., the regressor can be more stable against a pose difference. With the basic idea, ridge regression and lasso regression are explored. Experimental results on CMU PIE, the FERET, and the Multi-PIE face databases show that the proposed bias-variance tradeoff can achieve considerable reinforcement in recognition performance.

  1. Ranking docking poses by graph matching of protein-ligand interactions: lessons learned from the D3R Grand Challenge 2

    Science.gov (United States)

    da Silva Figueiredo Celestino Gomes, Priscila; Da Silva, Franck; Bret, Guillaume; Rognan, Didier

    2018-01-01

    A novel docking challenge has been set by the Drug Design Data Resource (D3R) in order to predict the pose and affinity ranking of a set of Farnesoid X receptor (FXR) agonists, prior to the public release of their bound X-ray structures and potencies. In a first phase, 36 agonists were docked to 26 Protein Data Bank (PDB) structures of the FXR receptor, and next rescored using the in-house developed GRIM method. GRIM aligns protein-ligand interaction patterns of docked poses to those of available PDB templates for the target protein, and rescore poses by a graph matching method. In agreement with results obtained during the previous 2015 docking challenge, we clearly show that GRIM rescoring improves the overall quality of top-ranked poses by prioritizing interaction patterns already visited in the PDB. Importantly, this challenge enables us to refine the applicability domain of the method by better defining the conditions of its success. We notably show that rescoring apolar ligands in hydrophobic pockets leads to frequent GRIM failures. In the second phase, 102 FXR agonists were ranked by decreasing affinity according to the Gibbs free energy of the corresponding GRIM-selected poses, computed by the HYDE scoring function. Interestingly, this fast and simple rescoring scheme provided the third most accurate ranking method among 57 contributions. Although the obtained ranking is still unsuitable for hit to lead optimization, the GRIM-HYDE scoring scheme is accurate and fast enough to post-process virtual screening data.

  2. Total iron binding capacity

    Science.gov (United States)

    ... page: //medlineplus.gov/ency/article/003489.htm Total iron binding capacity To use the sharing features on this page, please enable JavaScript. Total iron binding capacity (TIBC) is a blood test to ...

  3. Cue integration and the perception of action in intentional binding

    DEFF Research Database (Denmark)

    Wolpe, Noham; Haggard, Patrick; Siebner, Hartwig R

    2013-01-01

    , further analyses showed that cue integration accounted for changes in action binding, but not tone binding. These findings establish a role for cue integration in action binding and support the growing evidence suggesting that action and tone binding are, at least in part, driven by distinct mechanisms....... that binding results from cue integration, in which a voluntary action provides information about the timing of its consequences or vice versa. The perception of the timing of either event is then a weighted average, determined according to the reliability of each of these two cues. Here we tested...... the contribution of cue integration to the perception of action and its sensory effect in binding, that is, action and tone binding, by manipulating the sensory reliability of the outcome tone. As predicted, when tone reliability was reduced, action binding was diminished and tone binding was increased. However...

  4. Assessing exposure risks for aquatic organisms posed by Tamiflu use under seasonal influenza and pandemic conditions

    International Nuclear Information System (INIS)

    Chen, Wei-Yu; Lin, Chia-Jung; Liao, Chung-Min

    2014-01-01

    Environmental pollution by anti-influenza drugs is increasingly recognized as a threat to aquatic environments. However, little is known about empirical data on risk effects posed by environmentally relevant concentrations of anti-influenza drug based on recently published ecotoxicological researches in Taiwan. Here we linked ecotoxicology models with an epidemiological scheme to assess exposure risks of aquatic organisms and environmental hazards posed by antiviral oseltamivir (Tamiflu) use in Taiwan. Built on published bioassays, we used probabilistic risk assessment model to estimate potential threats of environmentally relevant hazards on algae, daphnid, and zerbrafish. We found that Tamiflu use was unlikely to pose a significant chronic environmental risk to daphnia and zebrafish during seasonal influenza. However, the chronic environmental risk posed by Tamiflu use during pandemic was alarming. We conclude that no significant risk to algal growth was found during seasonal influenza and high pandemic Tamiflu use. -- Highlights: • Environmentally relevant concentrations of anti-influenza drug have ecotoxicologically important effects. • Tamiflu is unlikely to pose a significant chronic environmental risk during seasonal influenza. • Chronic environmental risk posed by Tamiflu during pandemic is alarming. • Tertiary process in sewage treatment plants is crucial in mitigating Tamiflu exposure risk. -- A probabilistic framework can be used for assessing exposure risks posed by environmentally relevant concentrations of anti-influenza drug in aquatic ecosystems

  5. Coupled multiview autoencoders with locality sensitivity for three-dimensional human pose estimation

    Science.gov (United States)

    Yu, Jialin; Sun, Jifeng; Luo, Shasha; Duan, Bichao

    2017-09-01

    Estimating three-dimensional (3D) human poses from a single camera is usually implemented by searching pose candidates with image descriptors. Existing methods usually suppose that the mapping from feature space to pose space is linear, but in fact, their mapping relationship is highly nonlinear, which heavily degrades the performance of 3D pose estimation. We propose a method to recover 3D pose from a silhouette image. It is based on the multiview feature embedding (MFE) and the locality-sensitive autoencoders (LSAEs). On the one hand, we first depict the manifold regularized sparse low-rank approximation for MFE and then the input image is characterized by a fused feature descriptor. On the other hand, both the fused feature and its corresponding 3D pose are separately encoded by LSAEs. A two-layer back-propagation neural network is trained by parameter fine-tuning and then used to map the encoded 2D features to encoded 3D poses. Our LSAE ensures a good preservation of the local topology of data points. Experimental results demonstrate the effectiveness of our proposed method.

  6. Head Pose Estimation on Eyeglasses Using Line Detection and Classification Approach

    Science.gov (United States)

    Setthawong, Pisal; Vannija, Vajirasak

    This paper proposes a unique approach for head pose estimation of subjects with eyeglasses by using a combination of line detection and classification approaches. Head pose estimation is considered as an important non-verbal form of communication and could also be used in the area of Human-Computer Interface. A major improvement of the proposed approach is that it allows estimation of head poses at a high yaw/pitch angle when compared with existing geometric approaches, does not require expensive data preparation and training, and is generally fast when compared with other approaches.

  7. Robustness of Input features from Noisy Silhouettes in Human Pose Estimation

    DEFF Research Database (Denmark)

    Gong, Wenjuan; Fihl, Preben; Gonzàlez, Jordi

    2014-01-01

    Silhouettes are frequently extracted and described to compose inputs for learning methods in solving human pose estimation problem. Although silhouettes extracted from background subtraction methods are usually noisy, the effect of noisy inputs to pose estimation accuracies is seldom studied....... In this paper, we explore this problem. First, We compare performances of several image features widely used for human pose estimation and explore their performances against each other and select one with best performance. Second, iterative closest point algorithm is introduced for a new quantitative...... of silhouette samples of different noise levels and compare with the selected feature on a public dataset: Human Eva dataset....

  8. On Mathematical Problem Posing by Elementary Pre-teachers: The Case of Spreadsheets

    Directory of Open Access Journals (Sweden)

    Sergei Abramovich

    2008-07-01

    Full Text Available This article concerns the use of an electronic spreadsheet in mathematical problem posing by prospective elementary teachers. It introduces a didactic construct dealing with three types of a problem's coherence -- numerical, contextual and pedagogical. The main thesis of the article is that technological support of problem posing proves to be insufficient without one's use of this construct. The article reflects on work done with the teachers in a number of education courses. It suggests that including mathematics problem posing with spreadsheets into a coursework for the teachers provides them with research-like experience in curriculum development.

  9. Druggability of methyl-lysine binding sites

    Science.gov (United States)

    Santiago, C.; Nguyen, K.; Schapira, M.

    2011-12-01

    Structural modules that specifically recognize—or read—methylated or acetylated lysine residues on histone peptides are important components of chromatin-mediated signaling and epigenetic regulation of gene expression. Deregulation of epigenetic mechanisms is associated with disease conditions, and antagonists of acetyl-lysine binding bromodomains are efficacious in animal models of cancer and inflammation, but little is known regarding the druggability of methyl-lysine binding modules. We conducted a systematic structural analysis of readers of methyl marks and derived a predictive druggability landscape of methyl-lysine binding modules. We show that these target classes are generally less druggable than bromodomains, but that some proteins stand as notable exceptions.

  10. Recent life events pose greatest risk for onset of major depressive disorder during mid-life

    Science.gov (United States)

    Stegenga, Bauke T.; Nazareth, Irwin; Grobbee, Diederick E.; Torres-González, Francisco; Švab, Igor; Maaroos, Heidi-Ingrid; Xavier, Miguel; Saldivia, Sandra; Bottomley, Christian; King, Michael; Geerlings, Mirjam I.

    2012-01-01

    Background The authors examined an additive model for the association of life events and age with onset of major depressive disorder (MDD) and whether the combination of life events and age posed greater risk than the sum of their independent effects. Methods Data were used from a prospective cohort study of 10,045 general practice attendees (PredictD). We included those without MDD at baseline (N = 8293). We examined age divided into tertiles and into 10 year groups. Life events were assessed at baseline using the List of Threatening Life Experiences Questionnaire and categorized according to type. Main outcome measure was onset of DSM-IV MDD at 6 or 12 months of follow-up. The authors calculated Relative Excess Risks due to Interaction (RERI). Results 6910 persons (83.3%) had a complete follow-up, of whom 589 (8.5%) had an onset of MDD (166 younger, 254 middle aged and 169 older). The combined effect of personal problems (RERI = 1.30; 95% CI 0.29 to 2.32), events in family or friends (RERI = 1.23; 95% CI 0.28 to 2.19), or problems with law (RERI = 1.57; 95% CI 0.33 to 2.82) and middle age was larger than the sum of individual effects. Limitations Lower response to recruitment in the UK and the Netherlands. Conclusions Recent life events carry the largest risk of onset of MDD in mid-life. Understanding the different vulnerability to life events according to age may help to indicate groups at a particular risk and assist in preventive strategies. PMID:22119082

  11. Well-posed continuum equations for granular flow with compressibility and μ(I)-rheology

    Science.gov (United States)

    Schaeffer, D. G.; Shearer, M.; Gray, J. M. N. T.

    2017-01-01

    Continuum modelling of granular flow has been plagued with the issue of ill-posed dynamic equations for a long time. Equations for incompressible, two-dimensional flow based on the Coulomb friction law are ill-posed regardless of the deformation, whereas the rate-dependent μ(I)-rheology is ill-posed when the non-dimensional inertial number I is too high or too low. Here, incorporating ideas from critical-state soil mechanics, we derive conditions for well-posedness of partial differential equations that combine compressibility with I-dependent rheology. When the I-dependence comes from a specific friction coefficient μ(I), our results show that, with compressibility, the equations are well-posed for all deformation rates provided that μ(I) satisfies certain minimal, physically natural, inequalities. PMID:28588402

  12. The Tendency of Turkish Pre-service Teachers’ to Pose Word Problems

    Directory of Open Access Journals (Sweden)

    Çiğdem Kılıç

    2015-08-01

    Full Text Available The aim of this study was to identify the problem posing tendency of preservice teachers (primary and mathematics in structured problem posing situations. Participants were selected using a two-step sampling process in order to prevent bias. In the first sampling process, a total of 109 pre-service teachers participated in the study. Of these participants, 48 were pre-service primary school mathematics teachers and 61 were pre-service primary teachers who were in their sixth term of school. In the second sampling process, 10 volunteer participants were selected using purposeful sampling. It was found that participants had a tendency to pose result-centered problems (contextually inappropriate and irrelevant result-focused problems and context-centered problems (standard and non-standard word problems. In some cases, participants did not pose any word problems.

  13. Estimating aquatic hazards posed by prescription pharmaceutical residues from municipal wastewater

    Science.gov (United States)

    Risks posed by pharmaceuticals in the environment are hard to estimate due to limited monitoring capacity and difficulty interpreting monitoring results. In order to partially address these issues, we suggest a method for prioritizing pharmaceuticals for monitoring, and a framewo...

  14. Consistently Showing Your Best Side? Intra-individual Consistency in #Selfie Pose Orientation

    Science.gov (United States)

    Lindell, Annukka K.

    2017-01-01

    Painted and photographic portraits of others show an asymmetric bias: people favor their left cheek. Both experimental and database studies confirm that the left cheek bias extends to selfies. To date all such selfie studies have been cross-sectional; whether individual selfie-takers tend to consistently favor the same pose orientation, or switch between multiple poses, remains to be determined. The present study thus examined intra-individual consistency in selfie pose orientations. Two hundred selfie-taking participants (100 male and 100 female) were identified by searching #selfie on Instagram. The most recent 10 single-subject selfies for the each of the participants were selected and coded for type of selfie (normal; mirror) and pose orientation (left, midline, right), resulting in a sample of 2000 selfies. Results indicated that selfie-takers do tend to consistently adopt a preferred pose orientation (α = 0.72), with more participants showing an overall left cheek bias (41%) than would be expected by chance (overall right cheek bias = 31.5%; overall midline bias = 19.5%; no overall bias = 8%). Logistic regression modellng, controlling for the repeated measure of participant identity, indicated that sex did not affect pose orientation. However, selfie type proved a significant predictor when comparing left and right cheek poses, with a stronger left cheek bias for mirror than normal selfies. Overall, these novel findings indicate that selfie-takers show intra-individual consistency in pose orientation, and in addition, replicate the previously reported left cheek bias for selfies and other types of portrait, confirming that the left cheek bias also presents within individuals’ selfie corpora. PMID:28270790

  15. A review of cooperative and uncooperative spacecraft pose determination techniques for close-proximity operations

    Science.gov (United States)

    Opromolla, Roberto; Fasano, Giancarmine; Rufino, Giancarlo; Grassi, Michele

    2017-08-01

    The capability of an active spacecraft to accurately estimate its relative position and attitude (pose) with respect to an active/inactive, artificial/natural space object (target) orbiting in close-proximity is required to carry out various activities like formation flying, on-orbit servicing, active debris removal, and space exploration. According to the specific mission scenario, the pose determination task involves both theoretical and technological challenges related to the search for the most suitable algorithmic solution and sensor architecture, respectively. As regards the latter aspect, electro-optical sensors represent the best option as their use is compatible with mass and power limitation of micro and small satellites, and their measurements can be processed to estimate all the pose parameters. Overall, the degree of complexity of the challenges related to pose determination largely varies depending on the nature of the targets, which may be actively/passively cooperative, uncooperative but known, or uncooperative and unknown space objects. In this respect, while cooperative pose determination has been successfully demonstrated in orbit, the uncooperative case is still under study by universities, research centers, space agencies and private companies. However, in both the cases, the demand for space applications involving relative navigation maneuvers, also in close-proximity, for which pose determination capabilities are mandatory, is significantly increasing. In this framework, a review of state-of-the-art techniques and algorithms developed in the last decades for cooperative and uncooperative pose determination by processing data provided by electro-optical sensors is herein presented. Specifically, their main advantages and drawbacks in terms of achieved performance, computational complexity, and sensitivity to variability of pose and target geometry, are highlighted.

  16. A Well-Posed Kelvin-Helmholtz Instability Test and Comparison

    OpenAIRE

    McNally, Colin P.; Lyra, Wladimir; Passy, Jean-Claude

    2011-01-01

    Recently, there has been a significant level of discussion of the correct treatment of Kelvin-Helmholtz instability in the astrophysical community. This discussion relies largely on how the KHI test is posed and analyzed. We pose a stringent test of the initial growth of the instability. The goal is to provide a rigorous methodology for verifying a code on two dimensional Kelvin-Helmholtz instability. We ran the problem in the Pencil Code, Athena, Enzo, NDSPHMHD, and Phurbas. A strict compari...

  17. Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition

    Science.gov (United States)

    Yin, Xi; Liu, Xiaoming

    2018-02-01

    This paper explores multi-task learning (MTL) for face recognition. We answer the questions of how and why MTL can improve the face recognition performance. First, we propose a multi-task Convolutional Neural Network (CNN) for face recognition where identity classification is the main task and pose, illumination, and expression estimations are the side tasks. Second, we develop a dynamic-weighting scheme to automatically assign the loss weight to each side task, which is a crucial problem in MTL. Third, we propose a pose-directed multi-task CNN by grouping different poses to learn pose-specific identity features, simultaneously across all poses. Last but not least, we propose an energy-based weight analysis method to explore how CNN-based MTL works. We observe that the side tasks serve as regularizations to disentangle the variations from the learnt identity features. Extensive experiments on the entire Multi-PIE dataset demonstrate the effectiveness of the proposed approach. To the best of our knowledge, this is the first work using all data in Multi-PIE for face recognition. Our approach is also applicable to in-the-wild datasets for pose-invariant face recognition and achieves comparable or better performance than state of the art on LFW, CFP, and IJB-A datasets.

  18. Muscle utilization patterns vary by skill levels of the practitioners across specific yoga poses (asanas).

    Science.gov (United States)

    Ni, Meng; Mooney, Kiersten; Balachandran, Anoop; Richards, Luca; Harriell, Kysha; Signorile, Joseph F

    2014-08-01

    To compare muscle activation patterns in 14 dominant side muscles during different yoga poses across three skill levels. Mixed repeated-measures descriptive study. University neuromuscular research laboratory, Miami, US. A group of 36 yoga practitioners (9 M/27 F; mean ± SD, 31.6 ± 12.6 years) with at least 3 months yoga practice experience. Each of the 11 surya namaskar poses A and B was performed separately for 15s and the surface electromyography for 14 muscles were recorded. Normalized root mean square of the electromyographic signal (NrmsEMG) for 14 muscles (5 upper body, 4 trunk, 5 lower body). There were significant main effects of pose for all fourteen muscles except middle trapezius (p<.02) and of skill level for the vastus medialis; p=.027). A significant skill level × pose interaction existed for five muscles (pectoralis major sternal head, anterior deltoid, medial deltoid, upper rectus abdominis and gastrocnemius lateralis; p<.05). Post hoc analyses using Bonferroni comparisons indicated that different poses activated specific muscle groups; however, this varied by skill level. Our results indicate that different poses can produce specific muscle activation patterns which may vary due to practitioners' skill levels. This information can be used in designing rehabilitation and training programs and for cuing during yoga training. Copyright © 2014 Elsevier Ltd. All rights reserved.

  19. Postural Communication of Emotion: Perception of Distinct Poses of Five Discrete Emotions.

    Science.gov (United States)

    Lopez, Lukas D; Reschke, Peter J; Knothe, Jennifer M; Walle, Eric A

    2017-01-01

    Emotion can be communicated through multiple distinct modalities. However, an often-ignored channel of communication is posture. Recent research indicates that bodily posture plays an important role in the perception of emotion. However, research examining postural communication of emotion is limited by the variety of validated emotion poses and unknown cohesion of categorical and dimensional ratings. The present study addressed these limitations. Specifically, we examined individuals' (1) categorization of emotion postures depicting 5 discrete emotions (joy, sadness, fear, anger, and disgust), (2) categorization of different poses depicting the same discrete emotion, and (3) ratings of valence and arousal for each emotion pose. Findings revealed that participants successfully categorized each posture as the target emotion, including disgust. Moreover, participants accurately identified multiple distinct poses within each emotion category. In addition to the categorical responses, dimensional ratings of valence and arousal revealed interesting overlap and distinctions between and within emotion categories. These findings provide the first evidence of an identifiable posture for disgust and instantiate the principle of equifinality of emotional communication through the inclusion of distinct poses within emotion categories. Additionally, the dimensional ratings corroborated the categorical data and provide further granularity for future researchers to consider in examining how distinct emotion poses are perceived.

  20. Computation of pH-Dependent Binding Free Energies

    Science.gov (United States)

    Kim, M. Olivia; McCammon, J. Andrew

    2015-01-01

    Protein-ligand binding accompanies changes in the surrounding electrostatic environments of the two binding partners and may lead to changes in protonation upon binding. In cases where the complex formation results in a net transfer of protons, the binding process is pH-dependent. However, conventional free energy computations or molecular docking protocols typically employ fixed protonation states for the titratable groups in both binding partners set a priori, which are identical for the free and bound states. In this review, we draw attention to these important yet largely ignored binding-induced protonation changes in protein-ligand association by outlining physical origins and prevalence of the protonation changes upon binding. Following a summary of various theoretical methods for pKa prediction, we discuss the theoretical framework to examine the pH dependence of protein-ligand binding processes. PMID:26202905

  1. The Effect of Problem Solving and Problem Posing Models and Innate Ability to Students Achievement

    Directory of Open Access Journals (Sweden)

    Ratna Kartika Irawati

    2015-04-01

    Full Text Available Pengaruh Model Problem Solving dan Problem Posing serta Kemampuan Awal terhadap Hasil Belajar Siswa   Abstract: Chemistry concepts understanding features abstract quality and requires higher order thinking skills. Yet, the learning on chemistry has not boost the higher order thinking skills of the students. The use of the learning model of Problem Solving and Problem Posing in observing the innate ability of the student is expected to resolve the issue. This study aims to determine the learning model which is effective to improve the study of the student with different level of innate ability. This study used the quasi-experimental design. The research data used in this research is the quiz/test of the class which consist of 14 multiple choice questions and 5 essay questions. The data analysis used is ANOVA Two Ways. The results showed that Problem Posing is more effective to improve the student compared to Problem Solving, students with high level of innate ability have better outcomes in learning rather than the students with low level of innate ability after being applied with the Problem solving and Problem posing model, further, Problem Solving and Problem Posing is more suitable to be applied to the students with high level of innate ability. Key Words: problem solving, problem posing, higher order thinking skills, innate ability, learning outcomes   Abstrak: Pemahaman konsep-konsep kimia yang bersifat abstrak membutuhkan keterampilan berpikir tingkat tinggi. Pembelajaran kimia belum mendorong siswa melakukan keterampilan berpikir tingkat tinggi. Penggunaan model pembelajaran Problem Solving dan Problem Posing dengan memperhatikan kemampuan awal siswa diduga dapat mengatasi masalah tersebut. Penelitian ini bertujuan untuk mengetahui model pembelajaran yang efektif dalam meningkatkan hasil belajar dengan kemampuan awal siswa yang berbeda. Penelitian ini menggunakan rancangan eksperimen semu. Data penelitian menggunakan tes hasil belajar

  2. Feature Binding in Zebrafish

    Directory of Open Access Journals (Sweden)

    P Neri

    2012-07-01

    Full Text Available Binding operations are primarily ascribed to cortex or similarly complex avian structures. My experiments show that the zebrafish, a lower vertebrate lacking cortex, supports visual feature binding of form and motion for the purpose of social behavior. These results challenge the notion that feature binding may require highly evolved neural structures and demonstrate that the nervous system of lower vertebrates can afford unexpectedly complex computations.

  3. Protein docking prediction using predicted protein-protein interface

    Directory of Open Access Journals (Sweden)

    Li Bin

    2012-01-01

    Full Text Available Abstract Background Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations. Results We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm, is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering. Conclusion We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.

  4. DNS & Bind Cookbook

    CERN Document Server

    Liu, Cricket

    2011-01-01

    The DNS & BIND Cookbook presents solutions to the many problems faced by network administrators responsible for a name server. Following O'Reilly's popular problem-and-solution cookbook format, this title is an indispensable companion to DNS & BIND, 4th Edition, the definitive guide to the critical task of name server administration. The cookbook contains dozens of code recipes showing solutions to everyday problems, ranging from simple questions, like, "How do I get BIND?" to more advanced topics like providing name service for IPv6 addresses. It's full of BIND configuration files that yo

  5. Generalized Hough transform based time invariant action recognition with 3D pose information

    Science.gov (United States)

    Muench, David; Huebner, Wolfgang; Arens, Michael

    2014-10-01

    Human action recognition has emerged as an important field in the computer vision community due to its large number of applications such as automatic video surveillance, content based video-search and human robot interaction. In order to cope with the challenges that this large variety of applications present, recent research has focused more on developing classifiers able to detect several actions in more natural and unconstrained video sequences. The invariance discrimination tradeoff in action recognition has been addressed by utilizing a Generalized Hough Transform. As a basis for action representation we transform 3D poses into a robust feature space, referred to as pose descriptors. For each action class a one-dimensional temporal voting space is constructed. Votes are generated from associating pose descriptors with their position in time relative to the end of an action sequence. Training data consists of manually segmented action sequences. In the detection phase valid human 3D poses are assumed as input, e.g. originating from 3D sensors or monocular pose reconstruction methods. The human 3D poses are normalized to gain view-independence and transformed into (i) relative limb-angle space to ensure independence of non-adjacent joints or (ii) geometric features. In (i) an action descriptor consists of the relative angles between limbs and their temporal derivatives. In (ii) the action descriptor consists of different geometric features. In order to circumvent the problem of time-warping we propose to use a codebook of prototypical 3D poses which is generated from sample sequences of 3D motion capture data. This idea is in accordance with the concept of equivalence classes in action space. Results of the codebook method are presented using the Kinect sensor and the CMU Motion Capture Database.

  6. Pose measurement method with six parameters for microassembly based on an optical micrometer

    Science.gov (United States)

    Ye, Xin; Wang, Qiang; Zhang, Zhi-jing; Sun, Yuan; Zhang, Xiao-feng

    2009-07-01

    This paper presents a new pose measurement method of microminiature parts that is capable of transforming one dimension (1D) contour size obtained by optical micrometer to three dimension (3D) data with six parameters for microassembly. Pose measurement is one of the most important processes for microminiature parts' alignment and insertion in microassembly. During the past few years, researchers have developed their microassembly systems focusing on visual identification to obtain two or three dimension data with no more than three parameters. Scanning electronic microscope (SEM), optical microscope, and stereomicroscope are applied in their systems. However, as structures of microminiature parts become increasingly complex, six parameters to represent their position and orientation are specifically needed. Firstly, The pose measurement model is established based on the introduction of measuring objects and measuring principle of optical micrometer. The measuring objects are microminiature parts with complex 3D structure. Two groups of two dimension (2D) data are gathered at two different measurement positions. Then part pose with 6 parameters is calculated, including 3 position parameters of feature point of the part and 3 orientation parameters of the part axis. Secondly, pose measurement process for a small shaft, vertical orientation determination, and position parameters obtaining are presented. 2D data is gathered by scanning the generatrix of the part, and valid data is extracted and saved in arrays. A vertical orientation criterion is proposed to determine whether the part is parallel to the Z-axis of the coordinate. If not, 2D data will be fixed into a linear equation using least square algorithm. Then orientation parameters are calculated. Center of Part End (CPE) is selected as feature point of the part, and its position parameters are extracted form two group of 2D data. Finally, a fast pose measurement device is developed and representative

  7. Combining Front Vehicle Detection with 3D Pose Estimation for a Better Driver Assistance

    Directory of Open Access Journals (Sweden)

    Yu Peng

    2012-09-01

    Full Text Available Driver assistant systems enhance traffic safety and efficiency. The accurate 3D pose of a front vehicle can help a driver to make the right decision on the road. We propose a novel real-time system to estimate the 3D pose of the front vehicle. This system consists of two parallel threads: vehicle rear tracking and mapping. The vehicle rear is first identified in the video captured by an onboard camera, after license plate localization and foreground extraction. The 3D pose estimation technique is then employed with respect to the extracted vehicle rear. Most current 3D pose estimation techniques need prior models or a stereo initialization with user cooperation. It is extremely difficult to obtain prior models due to the varying appearance of vehicles' rears. Moreover, it is unsafe to ask for drivers' cooperation when a vehicle is running. In our system, two initial keyframes for stereo algorithms are automatically extracted by vehicle rear detection and tracking. Map points are defined as a collection of point features extracted from the vehicle's rear with their 3D information. These map points are inferences that relate the 2D features detected in following vehicles' rears with the 3D world. The relative 3D pose of the onboard camera to the front vehicle rear is then estimated through matching the map points with point features detected on the front vehicle rear. We demonstrate the capabilities of our system by testing on real-time and synthesized videos. In order to make the experimental analysis visible, we demonstrated an estimated 3D pose through augmented reality, which needs accurate and real-time 3D pose estimation.

  8. Prediction of the outcome of growth hormone provocative testing in short children by measurement of serum levels of insulin-like growth factor I and insulin-like growth factor binding protein 3

    DEFF Research Database (Denmark)

    Juul, A; Skakkebaek, N E

    1997-01-01

    Serum levels of insulin-like growth factor I (IGF-I) and insulin-like growth factor binding protein 3 (IGFBP-3) reflect the secretion of endogenous growth hormone (GH) in healthy children and exhibit little diurnal variation, which makes them potential candidates for screening of GH deficiency (G...

  9. Inertial measurement unit–based iterative pose compensation algorithm for low-cost modular manipulator

    Directory of Open Access Journals (Sweden)

    Yunhan Lin

    2016-01-01

    Full Text Available It is a necessary mean to realize the accurate motion control of the manipulator which uses end-effector pose correction method and compensation method. In this article, first, we established the kinematic model and error model of the modular manipulator (WUST-ARM, and then we discussed the measurement methods and precision of the inertial measurement unit sensor. The inertial measurement unit sensor is mounted on the end-effector of modular manipulator, to get the real-time pose of the end-effector. At last, a new inertial measurement unit–based iterative pose compensation algorithm is proposed. By applying this algorithm in the pose compensation experiment of modular manipulator which is composed of low-cost rotation joints, the results show that the inertial measurement unit can obtain a higher precision when in static state; it will accurately feedback to the control system with an accurate error compensation angle after a brief delay when the end-effector moves to the target point, and after compensation, the precision errors of roll angle, pitch angle, and yaw angle are reached at 0.05°, 0.01°, and 0.27° respectively. It proves that this low-cost method provides a new solution to improve the end-effector pose of low-cost modular manipulator.

  10. Mobile Robot Aided Silhouette Imaging and Robust Body Pose Recognition for Elderly-Fall Detection

    Directory of Open Access Journals (Sweden)

    Tong Liu

    2014-03-01

    Full Text Available This article introduces a mobile infrared silhouette imaging and sparse representation-based pose recognition for building an elderly-fall detection system. The proposed imaging paradigm exploits the novel use of the pyroelectric infrared (PIR sensor in pursuit of body silhouette imaging. A mobile robot carrying a vertical column of multi-PIR detectors is organized for the silhouette acquisition. Then we express the fall detection problem in silhouette image-based pose recognition. For the pose recognition, we use a robust sparse representation-based method for fall detection. The normal and fall poses are sparsely represented in the basis space spanned by the combinations of a pose training template and an error template. The ℓ1 norm minimizations with linear programming (LP and orthogonal matching pursuit (OMP are used for finding the sparsest solution, and the entity with the largest amplitude encodes the class of the testing sample. The application of the proposed sensing paradigm to fall detection is addressed in the context of three scenarios, including: ideal non-obstruction, simulated random pixel obstruction and simulated random block obstruction. Experimental studies are conducted to validate the effectiveness of the proposed method for nursing and homeland healthcare.

  11. Did Buddha turn the other cheek too? A comparison of posing biases between Jesus and Buddha.

    Science.gov (United States)

    Duerksen, Kari N; Friedrich, Trista E; Elias, Lorin J

    2015-10-02

    People tend to exhibit a leftward bias in posing. Various studies suggest that posing to the left portrays a stronger emotion, whereas posing to the right portrays a more neutral emotion. Religions such as Christianity emphasize the role of strong emotions in religious experience, whereas religions such as Buddhism emphasize the calming of emotions as being important. In the present study, we investigated if the emphasis on emotionality of a religion influences the depiction of their religious figures. Specifically, we coded 484 paintings of Jesus and Buddha from online art databases for whether the deity exhibited a left bias, right bias, or central face presentation. The posing biases were analysed to discover whether paintings of Jesus would more frequently depict a leftward bias than paintings of Buddha. Jesus is more commonly depicted with a leftward bias than Buddha, and Buddha is more commonly depicted with a central face presentation than Jesus. These findings support the idea that the amount of emotionality that is to be conveyed in artwork influences the whether the subject is posed with a leftward bias.

  12. A Bayesian framework for human body pose tracking from depth image sequences.

    Science.gov (United States)

    Zhu, Youding; Fujimura, Kikuo

    2010-01-01

    This paper addresses the problem of accurate and robust tracking of 3D human body pose from depth image sequences. Recovering the large number of degrees of freedom in human body movements from a depth image sequence is challenging due to the need to resolve the depth ambiguity caused by self-occlusions and the difficulty to recover from tracking failure. Human body poses could be estimated through model fitting using dense correspondences between depth data and an articulated human model (local optimization method). Although it usually achieves a high accuracy due to dense correspondences, it may fail to recover from tracking failure. Alternately, human pose may be reconstructed by detecting and tracking human body anatomical landmarks (key-points) based on low-level depth image analysis. While this method (key-point based method) is robust and recovers from tracking failure, its pose estimation accuracy depends solely on image-based localization accuracy of key-points. To address these limitations, we present a flexible Bayesian framework for integrating pose estimation results obtained by methods based on key-points and local optimization. Experimental results are shown and performance comparison is presented to demonstrate the effectiveness of the proposed approach.

  13. Fringe-reflection photogrammetry based on poses calibration with planar mirror reflection

    Science.gov (United States)

    Xiao, Yong-Liang; Zhong, Jianxin; Zhang, Qican; Su, Xianyu; You, Zhisheng

    2017-10-01

    Since liquid crystal display (LCD) screen locates outside of the camera's field of view in fringe-reflection photogrammetry, fringes displayed on LCD screen are obtained through specular reflection by a fixed camera. Thus, the pose calibration between camera and LCD screen is one of the main challenges in fringe-reflection photogrammetry. A markerless planar mirror is used to reflect the LCD screen more than three times, and the fringes are mapped into the fixed camera. The geometrical calibration can be accomplished by estimating the pose between the camera and virtual image of fringes. With the help of the relation between their pose, incidence and reflection ray can be unified in the camera frame, forward triangulation intersection can be operated in the camera frame to measure 3D coordinate of specular surface. In the final optimization, constraint bundle adjustment is operated to refine simultaneously the camera intrinsic parameters including distortion coefficients, estimated geometrical pose between LCD screen and camera, 3D coordinate of specular surface, with the help of absolute phase collinear constraint. Results of simulations and experiments demonstrate that the pose calibration with planar mirror reflection is simple, feasible and constraint bundle adjustment can enhance the three-dimensional coordinate measurement accuracy in fringe-reflection photogrammetry.

  14. Constructing a Database from Multiple 2D Images for Camera Pose Estimation and Robot Localization

    Science.gov (United States)

    Wolf, Michael; Ansar, Adnan I.; Brennan, Shane; Clouse, Daniel S.; Padgett, Curtis W.

    2012-01-01

    The LMDB (Landmark Database) Builder software identifies persistent image features (landmarks) in a scene viewed multiple times and precisely estimates the landmarks 3D world positions. The software receives as input multiple 2D images of approximately the same scene, along with an initial guess of the camera poses for each image, and a table of features matched pair-wise in each frame. LMDB Builder aggregates landmarks across an arbitrarily large collection of frames with matched features. Range data from stereo vision processing can also be passed to improve the initial guess of the 3D point estimates. The LMDB Builder aggregates feature lists across all frames, manages the process to promote selected features to landmarks, and iteratively calculates the 3D landmark positions using the current camera pose estimations (via an optimal ray projection method), and then improves the camera pose estimates using the 3D landmark positions. Finally, it extracts image patches for each landmark from auto-selected key frames and constructs the landmark database. The landmark database can then be used to estimate future camera poses (and therefore localize a robotic vehicle that may be carrying the cameras) by matching current imagery to landmark database image patches and using the known 3D landmark positions to estimate the current pose.

  15. Enhancing students’ mathematical problem posing skill through writing in performance tasks strategy

    Science.gov (United States)

    Kadir; Adelina, R.; Fatma, M.

    2018-01-01

    Many researchers have studied the Writing in Performance Task (WiPT) strategy in learning, but only a few paid attention on its relation to the problem-posing skill in mathematics. The problem-posing skill in mathematics covers problem reformulation, reconstruction, and imitation. The purpose of the present study was to examine the effect of WiPT strategy on students’ mathematical problem-posing skill. The research was conducted at a Public Junior Secondary School in Tangerang Selatan. It used a quasi-experimental method with randomized control group post-test. The samples were 64 students consists of 32 students of the experiment group and 32 students of the control. A cluster random sampling technique was used for sampling. The research data were obtained by testing. The research shows that the problem-posing skill of students taught by WiPT strategy is higher than students taught by a conventional strategy. The research concludes that the WiPT strategy is more effective in enhancing the students’ mathematical problem-posing skill compared to the conventional strategy.

  16. Improving attitudes toward mathematics learning with problem posing in class VIII

    Science.gov (United States)

    Vionita, Alfha; Purboningsih, Dyah

    2017-08-01

    This research is classroom action research which is collaborated to improve student's behavior toward math and mathematics learning at class VIII by using problem posing approach. The subject of research is all of students grade VIIIA which consist of 32 students. This research has been held on two period, first period is about 3 times meeting, and second period is about 4 times meeting. The instrument of this research is implementation of learning observation's guidance by using problem posing approach. Cycle test has been used to measure cognitive competence, and questionnaire to measure the students' behavior in mathematics learning process. The result of research shows the students' behavior has been improving after using problem posing approach. It is showed by the behavior's criteria of students that has increasing result from the average in first period to high in second period. Furthermore, the percentage of test result is also improve from 68,75% in first period to 78,13% in second period. On the other hand, the implementation of learning observation by using problem posing approach has also improving and it is showed by the average percentage of teacher's achievement in first period is 89,2% and student's achievement 85,8%. These results get increase in second period for both teacher and students' achievement which are 94,4% and 91,11%. As a result, students' behavior toward math learning process in class VIII has been improving by using problem posing approach.

  17. The Mirror to Our Soul? Comparisons of Spontaneous and Posed Vocal Expression of Emotion.

    Science.gov (United States)

    Juslin, Patrik N; Laukka, Petri; Bänziger, Tanja

    2018-01-01

    It has been the subject of much debate in the study of vocal expression of emotions whether posed expressions (e.g., actor portrayals) are different from spontaneous expressions. In the present investigation, we assembled a new database consisting of 1877 voice clips from 23 datasets, and used it to systematically compare spontaneous and posed expressions across 3 experiments. Results showed that (a) spontaneous expressions were generally rated as more genuinely emotional than were posed expressions, even when controlling for differences in emotion intensity, (b) there were differences between the two stimulus types with regard to their acoustic characteristics, and (c) spontaneous expressions with a high emotion intensity conveyed discrete emotions to listeners to a similar degree as has previously been found for posed expressions, supporting a dose-response relationship between intensity of expression and discreteness in perceived emotions. Our conclusion is that there are reliable differences between spontaneous and posed expressions, though not necessarily in the ways commonly assumed. Implications for emotion theories and the use of emotion portrayals in studies of vocal expression are discussed.

  18. Single leg balancing in ballet: effects of shoe conditions and poses.

    Science.gov (United States)

    Lobo da Costa, Paula H; Azevedo Nora, Fernanda G S; Vieira, Marcus Fraga; Bosch, Kerstin; Rosenbaum, Dieter

    2013-03-01

    The purpose of this study was to describe the effects of lower limb positioning and shoe conditions on stability levels of selected single leg ballet poses performed in demi-pointe position. Fourteen female non-professional ballet dancers (mean age of 18.4±2.8 years and mean body mass index of 21.5±2.8kg/m(2)) who had practiced ballet for at least seven years, without any musculoskeletal impairment volunteered to participate in this study. A capacitive pressure platform allowed for the assessment of center of pressure variables related to the execution of three single leg ballet poses in demi pointé position: attitude devant, attitude derriére, and attitude a la second. Peak pressures, contact areas, COP oscillation areas, anterior-posterior and medio-lateral COP oscillations and velocities were compared between two shoe conditions (barefoot versus slippers) and among the different poses. Barefoot performances produced more stable poses with significantly higher plantar contact areas, smaller COP oscillation areas and smaller anterior-posterior COP oscillations. COP oscillation areas, anterior-posterior COP oscillations and medio-lateral COP velocities indicated that attitude a la second is the least challenging and attitude derriére the most challenging pose. Copyright © 2012 Elsevier B.V. All rights reserved.

  19. DNS BIND Server Configuration

    Directory of Open Access Journals (Sweden)

    Radu MARSANU

    2011-01-01

    Full Text Available After a brief presentation of the DNS and BIND standard for Unix platforms, the paper presents an application which has a principal objective, the configuring of the DNS BIND 9 server. The general objectives of the application are presented, follow by the description of the details of designing the program.

  20. DNS BIND Server Configuratio

    OpenAIRE

    Radu MARSANU

    2011-01-01

    After a brief presentation of the DNS and BIND standard for Unix platforms, the paper presents an application which has a principal objective, the configuring of the DNS BIND 9 server. The general objectives of the application are presented, follow by the description of the details of designing the program.

  1. DNS BIND Server Configuration

    OpenAIRE

    Radu MARSANU

    2011-01-01

    After a brief presentation of the DNS and BIND standard for Unix platforms, the paper presents an application which has a principal objective, the configuring of the DNS BIND 9 server. The general objectives of the application are presented, follow by the description of the details of designing the program.

  2. Melanin-binding radiopharmaceuticals

    Energy Technology Data Exchange (ETDEWEB)

    Packer, S; Fairchild, R G; Watts, K P; Greenberg, D; Hannon, S J

    1980-01-01

    The scope of this paper is limited to an analysis of the factors that are important to the relationship of radiopharmaceuticals to melanin. While the authors do not attempt to deal with differences between melanin-binding vs. melanoma-binding, a notable variance is assumed. (PSB)

  3. Melanin-binding radiopharmaceuticals

    International Nuclear Information System (INIS)

    Packer, S.; Fairchild, R.G.; Watts, K.P.; Greenberg, D.; Hannon, S.J.

    1980-01-01

    The scope of this paper is limited to an analysis of the factors that are important to the relationship of radiopharmaceuticals to melanin. While the authors do not attempt to deal with differences between melanin-binding vs. melanoma-binding, a notable variance is assumed

  4. Accurate three-dimensional pose recognition from monocular images using template matched filtering

    Science.gov (United States)

    Picos, Kenia; Diaz-Ramirez, Victor H.; Kober, Vitaly; Montemayor, Antonio S.; Pantrigo, Juan J.

    2016-06-01

    An accurate algorithm for three-dimensional (3-D) pose recognition of a rigid object is presented. The algorithm is based on adaptive template matched filtering and local search optimization. When a scene image is captured, a bank of correlation filters is constructed to find the best correspondence between the current view of the target in the scene and a target image synthesized by means of computer graphics. The synthetic image is created using a known 3-D model of the target and an iterative procedure based on local search. Computer simulation results obtained with the proposed algorithm in synthetic and real-life scenes are presented and discussed in terms of accuracy of pose recognition in the presence of noise, cluttered background, and occlusion. Experimental results show that our proposal presents high accuracy for 3-D pose estimation using monocular images.

  5. Critical steps in camera pose estimation: an evaluation using LTI-LIB2 library

    Directory of Open Access Journals (Sweden)

    Laura Cabrera-Quirós

    2014-03-01

    Full Text Available An evaluation of camera pose estimation methods using a chessboard pattern is presented. Steps evaluated in the estimation process are landmark point detection and camera parameter estimation, due to their critical role in the entire process. The ChESS method and a custom heuristic method are compared for chessboard pattern detection.  Both methods are objectively contrasted using True Positive and False Negative criteria. Meanwhile, Zhang’s method for pose estimation based on planar surface point distribution is used as a first approach, and then refined with a nonlinear regression through the Levenberg-Marquardt algorithm. This pose estimation algorithm is evaluated through a comparison with a stable tool, such as the Camera Calibration Toolbox for Matlab®.

  6. Hierarchical graphical-based human pose estimation via local multi-resolution convolutional neural network

    Science.gov (United States)

    Zhu, Aichun; Wang, Tian; Snoussi, Hichem

    2018-03-01

    This paper addresses the problems of the graphical-based human pose estimation in still images, including the diversity of appearances and confounding background clutter. We present a new architecture for estimating human pose using a Convolutional Neural Network (CNN). Firstly, a Relative Mixture Deformable Model (RMDM) is defined by each pair of connected parts to compute the relative spatial information in the graphical model. Secondly, a Local Multi-Resolution Convolutional Neural Network (LMR-CNN) is proposed to train and learn the multi-scale representation of each body parts by combining different levels of part context. Thirdly, a LMR-CNN based hierarchical model is defined to explore the context information of limb parts. Finally, the experimental results demonstrate the effectiveness of the proposed deep learning approach for human pose estimation.

  7. Hierarchical graphical-based human pose estimation via local multi-resolution convolutional neural network

    Directory of Open Access Journals (Sweden)

    Aichun Zhu

    2018-03-01

    Full Text Available This paper addresses the problems of the graphical-based human pose estimation in still images, including the diversity of appearances and confounding background clutter. We present a new architecture for estimating human pose using a Convolutional Neural Network (CNN. Firstly, a Relative Mixture Deformable Model (RMDM is defined by each pair of connected parts to compute the relative spatial information in the graphical model. Secondly, a Local Multi-Resolution Convolutional Neural Network (LMR-CNN is proposed to train and learn the multi-scale representation of each body parts by combining different levels of part context. Thirdly, a LMR-CNN based hierarchical model is defined to explore the context information of limb parts. Finally, the experimental results demonstrate the effectiveness of the proposed deep learning approach for human pose estimation.

  8. Pose optimization and port placement for robot-assisted minimally invasive surgery in cholecystectomy.

    Science.gov (United States)

    Feng, Mei; Jin, Xingze; Tong, Weihua; Guo, Xiaoyu; Zhao, Ji; Fu, Yili

    2017-12-01

    Pose optimization and port placement are critical issues for preoperative preparation in robot-assisted minimally invasive surgery (RMIS), and affect the robot performance and surgery quality. This paper proposes a method for pose optimization and port placement for RMIS in cholecystectomy that considers both the robot and surgery requirements. The robot pose optimization was divided into optimization of the positioning joint configuration and optimization of the end effector configuration. To determine the optimal location for the trocar port placement, the operational workspace was defined as the evaluation index. The port area was divided into many sub-areas, and that with the maximum operational workspace was selected as the location for the port placement. Considering the left robotic arm as an example, the location for the port placement and joints angles for robotic arm configuration were discussed and simulated using the proposed method. This research can provide guidelines for surgeons in preoperative preparation. Copyright © 2017 John Wiley & Sons, Ltd.

  9. PoseShop: human image database construction and personalized content synthesis.

    Science.gov (United States)

    Chen, Tao; Tan, Ping; Ma, Li-Qian; Cheng, Ming-Ming; Shamir, Ariel; Hu, Shi-Min

    2013-05-01

    We present PoseShop--a pipeline to construct segmented human image database with minimal manual intervention. By downloading, analyzing, and filtering massive amounts of human images from the Internet, we achieve a database which contains 400 thousands human figures that are segmented out of their background. The human figures are organized based on action semantic, clothes attributes, and indexed by the shape of their poses. They can be queried using either silhouette sketch or a skeleton to find a given pose. We demonstrate applications for this database for multiframe personalized content synthesis in the form of comic-strips, where the main character is the user or his/her friends. We address the two challenges of such synthesis, namely personalization and consistency over a set of frames, by introducing head swapping and clothes swapping techniques. We also demonstrate an action correlation analysis application to show the usefulness of the database for vision application.

  10. Methods for intraoperative, sterile pose-setting of patient-specific microstereotactic frames

    Science.gov (United States)

    Vollmann, Benjamin; Müller, Samuel; Kundrat, Dennis; Ortmaier, Tobias; Kahrs, Lüder A.

    2015-03-01

    This work proposes new methods for a microstereotactic frame based on bone cement fixation. Microstereotactic frames are under investigation for minimal invasive temporal bone surgery, e.g. cochlear implantation, or for deep brain stimulation, where products are already on the market. The correct pose of the microstereotactic frame is either adjusted outside or inside the operating room and the frame is used for e.g. drill or electrode guidance. We present a patientspecific, disposable frame that allows intraoperative, sterile pose-setting. Key idea of our approach is bone cement between two plates that cures while the plates are positioned with a mechatronics system in the desired pose. This paper includes new designs of microstereotactic frames, a system for alignment and first measurements to analyze accuracy and applicable load.

  11. An investigation of U.S. and Chinese students' mathematical problem posing and problem solving

    Science.gov (United States)

    Cai, Jinfa

    1998-04-01

    This study explored the mathematical problem posing and problem solving of 181 U.S. and 223 Chinese sixth-grade students. It is part of a continuing effort to examine U.S. and Chinese students' performance by conducting a cognitive analysis of student responses to mathematical problem-posing and problem-solving tasks. The findings of this study provide further evidence that, while Chinese students outperform U.S. students on computational tasks, there are many similarities and differences between U.S. and Chinese students in performing relatively novel tasks. Moreover, the findings of this study suggest that a direct link between mathematical problem posing and problem solving found in earlier studies for U.S. students is true for Chinese students as well.

  12. POSE ESTIMATION OF UNMANNED AERIAL VEHICLES BASED ON A VISION-AIDED MULTI-SENSOR FUSION

    Directory of Open Access Journals (Sweden)

    G. Abdi

    2016-06-01

    Full Text Available GNSS/IMU navigation systems offer low-cost and robust solution to navigate UAVs. Since redundant measurements greatly improve the reliability of navigation systems, extensive researches have been made to enhance the efficiency and robustness of GNSS/IMU by additional sensors. This paper presents a method for integrating reference data, images taken from UAVs, barometric height data and GNSS/IMU data to estimate accurate and reliable pose parameters of UAVs. We provide improved pose estimations by integrating multi-sensor observations in an EKF algorithm with IMU motion model. The implemented methodology has demonstrated to be very efficient and reliable for automatic pose estimation. The calculated position and attitude of the UAV especially when we removed the GNSS from the working cycle clearly indicate the ability of the purposed methodology.

  13. Categorization of questions posed before and after inquiry-based learning

    Directory of Open Access Journals (Sweden)

    Sandra Milena García González

    2014-07-01

    Full Text Available Posing research questions is the central ability of the scientific thought. This article examines the ability of sixth grade children to pose researchable questions before and after a three months’ work on a didactic sequence based on the inquiry school model. According to their purpose, the questions asked by children, after reading a text, were classified into researchable questions -susceptible to be empirically explored-, questions about a cause, and questions on a piece of data. The results show that the amount and the type of questions the students were able to pose during the intervention changed, from most of questions on data or information, to most of researchable questions, subsequently, the importance of designing teaching approaches to foster this ability was proved.

  14. Adaptive relative pose control of spacecraft with model couplings and uncertainties

    Science.gov (United States)

    Sun, Liang; Zheng, Zewei

    2018-02-01

    The spacecraft pose tracking control problem for an uncertain pursuer approaching to a space target is researched in this paper. After modeling the nonlinearly coupled dynamics for relative translational and rotational motions between two spacecraft, position tracking and attitude synchronization controllers are developed independently by using a robust adaptive control approach. The unknown kinematic couplings, parametric uncertainties, and bounded external disturbances are handled with adaptive updating laws. It is proved via Lyapunov method that the pose tracking errors converge to zero asymptotically. Spacecraft close-range rendezvous and proximity operations are introduced as an example to validate the effectiveness of the proposed control approach.

  15. Simultaneous Estimation of Material Properties and Pose for Deformable Objects from Depth and Color Images

    DEFF Research Database (Denmark)

    Fugl, Andreas Rune; Jordt, Andreas; Petersen, Henrik Gordon

    2012-01-01

    In this paper we consider the problem of estimating 6D pose and material properties of a deformable object grasped by a robot grip- per. To estimate the parameters we minimize an error function incorpo- rating visual and physical correctness. Through simulated and real-world experiments we demons...... demonstrate that we are able to find realistic 6D poses and elasticity parameters like Young’s modulus. This makes it possible to perform subsequent manipulation tasks, where accurate modelling of the elastic behaviour is important....

  16. Robust Pose Estimation using the SwissRanger SR-3000 Camera

    DEFF Research Database (Denmark)

    Gudmundsson, Sigurjon Arni; Larsen, Rasmus; Ersbøll, Bjarne Kjær

    2007-01-01

    In this paper a robust method is presented to classify and estimate an objects pose from a real time range image and a low dimensional model. The model is made from a range image training set which is reduced dimensionally by a nonlinear manifold learning method named Local Linear Embedding (LLE)......). New range images are then projected to this model giving the low dimensional coordinates of the object pose in an efficient manner. The range images are acquired by a state of the art SwissRanger SR-3000 camera making the projection process work in real-time....

  17. A modified quasi-boundary value method for an abstract ill-posed biparabolic problem

    Directory of Open Access Journals (Sweden)

    Besma Khelili

    2017-12-01

    Full Text Available In this paper, we are concerned with the problem of approximating a solution of an ill-posed biparabolic problem in the abstract setting. In order to overcome the instability of the original problem, we propose a modified quasi-boundary value method to construct approximate stable solutions for the original ill-posed boundary value problem. Finally, some other convergence results including some explicit convergence rates are also established under a priori bound assumptions on the exact solution. Moreover, numerical tests are presented to illustrate the accuracy and efficiency of this method.

  18. STS-47 crew poses for portrait after having been named to the SLJ mission

    Science.gov (United States)

    1990-01-01

    STS-47 crewmembers pose for portrait after having been named to the Spacelab Japan (SLJ) mission scheduled for flight aboard Endeavour, Orbiter Vehicle (OV) 105. NASA and the National Space Development Agency of Japan (NASDA) recently named the four to the mission. Posing in front of the flags of the United States (U.S.) and Japan are (left to right) Mission Specialist (MS) Mae C. Jemison, Japanese NASDA Payload Specialist Mamoru Mohri, MS N. Jan Davis, and MS and Payload Commander (PLC) Mark C. Lee.

  19. Human Pose Estimation and Activity Recognition from Multi-View Videos

    DEFF Research Database (Denmark)

    Holte, Michael Boelstoft; Tran, Cuong; Trivedi, Mohan

    2012-01-01

    –computer interaction (HCI), assisted living, gesture-based interactive games, intelligent driver assistance systems, movies, 3D TV and animation, physical therapy, autonomous mental development, smart environments, sport motion analysis, video surveillance, and video annotation. Next, we review and categorize recent......This paper presents a review and comparative study of recent multi-view approaches for human 3D pose estimation and activity recognition. We discuss the application domain of human pose estimation and activity recognition and the associated requirements, covering: advanced human...

  20. Using a single RGB frame for real time 3D hand pose estimation in the wild

    OpenAIRE

    Panteleris, Paschalis; Oikonomidis, Iason; Argyros, Antonis

    2017-01-01

    We present a method for the real-time estimation of the full 3D pose of one or more human hands using a single commodity RGB camera. Recent work in the area has displayed impressive progress using RGBD input. However, since the introduction of RGBD sensors, there has been little progress for the case of monocular color input. We capitalize on the latest advancements of deep learning, combining them with the power of generative hand pose estimation techniques to achieve real-time monocular 3D ...

  1. Estimation of Antenna Pose in the Earth Frame Using Camera and IMU Data from Mobile Phones.

    Science.gov (United States)

    Wang, Zhen; Jin, Bingwen; Geng, Weidong

    2017-04-08

    The poses of base station antennas play an important role in cellular network optimization. Existing methods of pose estimation are based on physical measurements performed either by tower climbers or using additional sensors attached to antennas. In this paper, we present a novel non-contact method of antenna pose measurement based on multi-view images of the antenna and inertial measurement unit (IMU) data captured by a mobile phone. Given a known 3D model of the antenna, we first estimate the antenna pose relative to the phone camera from the multi-view images and then employ the corresponding IMU data to transform the pose from the camera coordinate frame into the Earth coordinate frame. To enhance the resulting accuracy, we improve existing camera-IMU calibration models by introducing additional degrees of freedom between the IMU sensors and defining a new error metric based on both the downtilt and azimuth angles, instead of a unified rotational error metric, to refine the calibration. In comparison with existing camera-IMU calibration methods, our method achieves an improvement in azimuth accuracy of approximately 1.0 degree on average while maintaining the same level of downtilt accuracy. For the pose estimation in the camera coordinate frame, we propose an automatic method of initializing the optimization solver and generating bounding constraints on the resulting pose to achieve better accuracy. With this initialization, state-of-the-art visual pose estimation methods yield satisfactory results in more than 75% of cases when plugged into our pipeline, and our solution, which takes advantage of the constraints, achieves even lower estimation errors on the downtilt and azimuth angles, both on average (0.13 and 0.3 degrees lower, respectively) and in the worst case (0.15 and 7.3 degrees lower, respectively), according to an evaluation conducted on a dataset consisting of 65 groups of data. We show that both of our enhancements contribute to the performance

  2. Real-Time Biologically Inspired Action Recognition from Key Poses Using a Neuromorphic Architecture.

    Science.gov (United States)

    Layher, Georg; Brosch, Tobias; Neumann, Heiko

    2017-01-01

    Intelligent agents, such as robots, have to serve a multitude of autonomous functions. Examples are, e.g., collision avoidance, navigation and route planning, active sensing of its environment, or the interaction and non-verbal communication with people in the extended reach space. Here, we focus on the recognition of the action of a human agent based on a biologically inspired visual architecture of analyzing articulated movements. The proposed processing architecture builds upon coarsely segregated streams of sensory processing along different pathways which separately process form and motion information (Layher et al., 2014). Action recognition is performed in an event-based scheme by identifying representations of characteristic pose configurations (key poses) in an image sequence. In line with perceptual studies, key poses are selected unsupervised utilizing a feature-driven criterion which combines extrema in the motion energy with the horizontal and the vertical extendedness of a body shape. Per class representations of key pose frames are learned using a deep convolutional neural network consisting of 15 convolutional layers. The network is trained using the energy-efficient deep neuromorphic networks ( Eedn ) framework (Esser et al., 2016), which realizes the mapping of the trained synaptic weights onto the IBM Neurosynaptic System platform (Merolla et al., 2014). After the mapping, the trained network achieves real-time capabilities for processing input streams and classify input images at about 1,000 frames per second while the computational stages only consume about 70 mW of energy (without spike transduction). Particularly regarding mobile robotic systems, a low energy profile might be crucial in a variety of application scenarios. Cross-validation results are reported for two different datasets and compared to state-of-the-art action recognition approaches. The results demonstrate, that (I) the presented approach is on par with other key pose based

  3. RBPmap: a web server for mapping binding sites of RNA-binding proteins.

    Science.gov (United States)

    Paz, Inbal; Kosti, Idit; Ares, Manuel; Cline, Melissa; Mandel-Gutfreund, Yael

    2014-07-01

    Regulation of gene expression is executed in many cases by RNA-binding proteins (RBPs) that bind to mRNAs as well as to non-coding RNAs. RBPs recognize their RNA target via specific binding sites on the RNA. Predicting the binding sites of RBPs is known to be a major challenge. We present a new webserver, RBPmap, freely accessible through the website http://rbpmap.technion.ac.il/ for accurate prediction and mapping of RBP binding sites. RBPmap has been developed specifically for mapping RBPs in human, mouse and Drosophila melanogaster genomes, though it supports other organisms too. RBPmap enables the users to select motifs from a large database of experimentally defined motifs. In addition, users can provide any motif of interest, given as either a consensus or a PSSM. The algorithm for mapping the motifs is based on a Weighted-Rank approach, which considers the clustering propensity of the binding sites and the overall tendency of regulatory regions to be conserved. In addition, RBPmap incorporates a position-specific background model, designed uniquely for different genomic regions, such as splice sites, 5' and 3' UTRs, non-coding RNA and intergenic regions. RBPmap was tested on high-throughput RNA-binding experiments and was proved to be highly accurate. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

  4. Sequence diversity between class I MHC loci of African native and introduced Bos taurus cattle in Theileria parva endemic regions: in silico peptide binding prediction identifies distinct functional clusters.

    Science.gov (United States)

    Obara, Isaiah; Nielsen, Morten; Jeschek, Marie; Nijhof, Ard; Mazzoni, Camila J; Svitek, Nicholas; Steinaa, Lucilla; Awino, Elias; Olds, Cassandra; Jabbar, Ahmed; Clausen, Peter-Henning; Bishop, Richard P

    2016-05-01

    There is strong evidence that the immunity induced by live vaccination for control of the protozoan parasite Theileria parva is mediated by class I MHC-restricted CD8(+) T cells directed against the schizont stage of the parasite that infects bovine lymphocytes. The functional competency of class I MHC genes is dependent on the presence of codons specifying certain critical amino acid residues that line the peptide binding groove. Compared with European Bos taurus in which class I MHC allelic polymorphisms have been examined extensively, published data on class I MHC transcripts in African taurines in T. parva endemic areas is very limited. We utilized the multiplexing capabilities of 454 pyrosequencing to make an initial assessment of class I MHC allelic diversity in a population of Ankole cattle. We also typed a population of exotic Holstein cattle from an African ranch for class I MHC and investigated the extent, if any, that their peptide-binding motifs overlapped with those of Ankole cattle. We report the identification of 18 novel allelic sequences in Ankole cattle and provide evidence of positive selection for sequence diversity, including in residues that predominantly interact with peptides. In silico functional analysis resulted in peptide binding specificities that were largely distinct between the two breeds. We also demonstrate that CD8(+) T cells derived from Ankole cattle that are seropositive for T. parva do not recognize vaccine candidate antigens originally identified in Holstein and Boran (Bos indicus) cattle breeds.

  5. Quantification of Cooperativity in Heterodimer-DNA Binding Improves the Accuracy of Binding Specificity Models*

    Science.gov (United States)

    Isakova, Alina; Berset, Yves; Hatzimanikatis, Vassily; Deplancke, Bart

    2016-01-01

    Many transcription factors (TFs) have the ability to cooperate on DNA elements as heterodimers. Despite the significance of TF heterodimerization for gene regulation, a quantitative understanding of cooperativity between various TF dimer partners and its impact on heterodimer DNA binding specificity models is still lacking. Here, we used a novel integrative approach, combining microfluidics-steered measurements of dimer-DNA assembly with mechanistic modeling of the implicated protein-protein-DNA interactions to quantitatively interrogate the cooperative DNA binding behavior of the adipogenic peroxisome proliferator-activated receptor γ (PPARγ):retinoid X receptor α (RXRα) heterodimer. Using the high throughput MITOMI (mechanically induced trapping of molecular interactions) platform, we derived equilibrium DNA binding data for PPARγ, RXRα, as well as the PPARγ:RXRα heterodimer to more than 300 target DNA sites and variants thereof. We then quantified cooperativity underlying heterodimer-DNA binding and derived an integrative heterodimer DNA binding constant. Using this cooperativity-inclusive constant, we were able to build a heterodimer-DNA binding specificity model that has superior predictive power than the one based on a regular one-site equilibrium. Our data further revealed that individual nucleotide substitutions within the target site affect the extent of cooperativity in PPARγ:RXRα-DNA binding. Our study therefore emphasizes the importance of assessing cooperativity when generating DNA binding specificity models for heterodimers. PMID:26912662

  6. Quantification of Cooperativity in Heterodimer-DNA Binding Improves the Accuracy of Binding Specificity Models.

    Science.gov (United States)

    Isakova, Alina; Berset, Yves; Hatzimanikatis, Vassily; Deplancke, Bart

    2016-05-06

    Many transcription factors (TFs) have the ability to cooperate on DNA elements as heterodimers. Despite the significance of TF heterodimerization for gene regulation, a quantitative understanding of cooperativity between various TF dimer partners and its impact on heterodimer DNA binding specificity models is still lacking. Here, we used a novel integrative approach, combining microfluidics-steered measurements of dimer-DNA assembly with mechanistic modeling of the implicated protein-protein-DNA interactions to quantitatively interrogate the cooperative DNA binding behavior of the adipogenic peroxisome proliferator-activated receptor γ (PPARγ):retinoid X receptor α (RXRα) heterodimer. Using the high throughput MITOMI (mechanically induced trapping of molecular interactions) platform, we derived equilibrium DNA binding data for PPARγ, RXRα, as well as the PPARγ:RXRα heterodimer to more than 300 target DNA sites and variants thereof. We then quantified cooperativity underlying heterodimer-DNA binding and derived an integrative heterodimer DNA binding constant. Using this cooperativity-inclusive constant, we were able to build a heterodimer-DNA binding specificity model that has superior predictive power than the one based on a regular one-site equilibrium. Our data further revealed that individual nucleotide substitutions within the target site affect the extent of cooperativity in PPARγ:RXRα-DNA binding. Our study therefore emphasizes the importance of assessing cooperativity when generating DNA binding specificity models for heterodimers. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

  7. Incorporating evolution of transcription factor binding sites into ...

    Indian Academy of Sciences (India)

    PRAKASH KUMAR

    Incorporating evolution of transcription factor binding sites into annotated alignments. 841. J. Biosci. 32(5), August 2007. 1. Introduction. A majority of computational approaches that aim to predict transcription factor binding sites employ cross- species comparison to focus on conserved locations. Such a comparison helps in ...

  8. Hierarchical online appearance-based tracking for 3D head pose, eyebrows, lips, eyelids, and irises

    NARCIS (Netherlands)

    Orozco, Javier; Rudovic, Ognjen; Gonzalez Garcia, Jordi; Pantic, Maja

    In this paper, we propose an On-line Appearance-Based Tracker (OABT) for simultaneous tracking of 3D head pose, lips, eyebrows, eyelids and irises in monocular video sequences. In contrast to previously proposed tracking approaches, which deal with face and gaze tracking separately, our OABT can

  9. Morozov-type discrepancy principle for nonlinear ill-posed problems ...

    Indian Academy of Sciences (India)

    For proving the existence of a regularization parameter under a Morozov-type discrepancy principle for Tikhonov regularization of nonlinear ill-posed problems, it is required to impose additional nonlinearity assumptions on the forward operator. Lipschitz continuity of the Freéchet derivative and requirement of the Lipschitz ...

  10. Pose Measurement Method and Experiments for High-Speed Rolling Targets in a Wind Tunnel

    Directory of Open Access Journals (Sweden)

    Zhenyuan Jia

    2014-12-01

    Full Text Available High-precision wind tunnel simulation tests play an important role in aircraft design and manufacture. In this study, a high-speed pose vision measurement method is proposed for high-speed and rolling targets in a supersonic wind tunnel. To obtain images with high signal-to-noise ratio and avoid impacts on the aerodynamic shape of the rolling targets, a high-speed image acquisition method based on ultrathin retro-reflection markers is presented. Since markers are small-sized and some of them may be lost when the target is rolling, a novel markers layout with which markers are distributed evenly on the surface is proposed based on a spatial coding method to achieve highly accurate pose information. Additionally, a pose acquisition is carried out according to the mentioned markers layout after removing mismatching points by Case Deletion Diagnostics. Finally, experiments on measuring the pose parameters of high-speed targets in the laboratory and in a supersonic wind tunnel are conducted to verify the feasibility and effectiveness of the proposed method. Experimental results indicate that the position measurement precision is less than 0.16 mm, the pitching and yaw angle precision less than 0.132° and the roll angle precision 0.712°.

  11. Teach it Yourself - Fast Modeling of Industrial Objects for 6D Pose Estimation

    DEFF Research Database (Denmark)

    Sølund, Thomas; Rajeeth Savarimuthu, Thiusius; Glent Buch, Anders

    2015-01-01

    In this paper, we present a vision system that allows a human to create new 3D models of novel industrial parts by placing the part in two different positions in the scene. The two shot modeling framework generates models with a precision that allows the model to be used for 6D pose estimation wi...

  12. A problem-posing approach to teaching the topic of radioactivity

    NARCIS (Netherlands)

    Klaassen, C.W.J.M.

    1995-01-01

    This thesis highlights a problem-posing approach to science education. By this is meant an approach that explicitly aims at providing students with content-related motives for extending their existing conceptual resources, experiential base and belief system in a certain direction, such that a

  13. Minimization of Linear Functionals Defined on| Solutions of Large-Scale Discrete Ill-Posed Problems

    DEFF Research Database (Denmark)

    Elden, Lars; Hansen, Per Christian; Rojas, Marielba

    2003-01-01

    The minimization of linear functionals de ned on the solutions of discrete ill-posed problems arises, e.g., in the computation of con dence intervals for these solutions. In 1990, Elden proposed an algorithm for this minimization problem based on a parametric-programming reformulation involving...

  14. Exploiting residual information in the parameter choice for discrete ill-posed problems

    DEFF Research Database (Denmark)

    Hansen, Per Christian; Kilmer, Misha E.; Kjeldsen, Rikke Høj

    2006-01-01

    Most algorithms for choosing the regularization parameter in a discrete ill-posed problem are based on the norm of the residual vector. In this work we propose a different approach, where we seek to use all the information available in the residual vector. We present important relations between...

  15. Morozov-type discrepancy principle for nonlinear ill-posed problems ...

    Indian Academy of Sciences (India)

    [3] Engl H W, Kunisch K and Neubauer A, Convergence rates for Tikhonov regularization of nonliner problems, Inverse Problems 5 (1989) 523–540. [4] Hanke M, Neubauer A and Scherzer O, A convergence analysis of Landweber iteration for nonlinear ill-posed problems, Numer. Math. 72 (1995) 21–37. [5] Hofmann B and ...

  16. Utilizing Semantic Interpretation of Junctions for 3D-2D Pose Estimation

    DEFF Research Database (Denmark)

    Pilz, Florian; Yan, Shi; Grest, Daniel

    2007-01-01

    In this paper we investigate the quality of 3D-2D pose estimates using hand labeled line and point correspondences. We select point correspondences from junctions in the image, allowing to construct a meaningful interpretation about how the junction is formed, as proposed in e.g. [1], [2], [3]. W...

  17. Multispectral embedding-based deep neural network for three-dimensional human pose recovery

    Science.gov (United States)

    Yu, Jialin; Sun, Jifeng

    2018-01-01

    Monocular image-based three-dimensional (3-D) human pose recovery aims to retrieve 3-D poses using the corresponding two-dimensional image features. Therefore, the pose recovery performance highly depends on the image representations. We propose a multispectral embedding-based deep neural network (MSEDNN) to automatically obtain the most discriminative features from multiple deep convolutional neural networks and then embed their penultimate fully connected layers into a low-dimensional manifold. This compact manifold can explore not only the optimum output from multiple deep networks but also the complementary properties of them. Furthermore, the distribution of each hierarchy discriminative manifold is sufficiently smooth so that the training process of our MSEDNN can be effectively implemented only using few labeled data. Our proposed network contains a body joint detector and a human pose regressor that are jointly trained. Extensive experiments conducted on four databases show that our proposed MSEDNN can achieve the best recovery performance compared with the state-of-the-art methods.

  18. Simulated Lidar Images of Human Pose using a 3DS Max Virtual Laboratory

    Science.gov (United States)

    2015-12-01

    Images of Human Pose using a 3DS Max Virtual Laboratory 5a. CONTRACT NUMBER In-House 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 62202F 6...tight-fitting garment worn by the volunteer, based on a modified Helen Hayes type marker set. Marker trajectories are captured during the subject’s

  19. The Analysis of the Problems the Pre-Service Teachers Experience in Posing Problems about Equations

    Science.gov (United States)

    Isik, Cemalettin; Kar, Tugrul

    2012-01-01

    The present study aimed to analyse the potential difficulties in the problems posed by pre-service teachers about first degree equations with one unknown and equation pairs with two unknowns. It was carried out with 20 pre-service teachers studying in the Department of Elementary Mathematics Educations at a university in Eastern Turkey. The…

  20. Towards real-time body pose estimation for presenters in meeting environments

    NARCIS (Netherlands)

    Poppe, Ronald Walter; Heylen, Dirk K.J.; Nijholt, Antinus; Poel, Mannes

    2005-01-01

    This paper describes a computer vision-based approach to body pose estimation. The algorithm can be executed in real-time and processes low resolution, monocular image sequences. A silhouette is extracted and matched against a projection of a 16 DOF human body model. In addition, skin color is used