Protein secondary structure prediction using modular reciprocal bidirectional recurrent neural networks.

Science.gov (United States)

Babaei, Sepideh; Geranmayeh, Amir; Seyyedsalehi, Seyyed Ali

2010-12-01

The supervised learning of recurrent neural networks well-suited for prediction of protein secondary structures from the underlying amino acids sequence is studied. Modular reciprocal recurrent neural networks (MRR-NN) are proposed to model the strong correlations between adjacent secondary structure elements. Besides, a multilayer bidirectional recurrent neural network (MBR-NN) is introduced to capture the long-range intramolecular interactions between amino acids in formation of the secondary structure. The final modular prediction system is devised based on the interactive integration of the MRR-NN and the MBR-NN structures to arbitrarily engage the neighboring effects of the secondary structure types concurrent with memorizing the sequential dependencies of amino acids along the protein chain. The advanced combined network augments the percentage accuracy (Q₃) to 79.36% and boosts the segment overlap (SOV) up to 70.09% when tested on the PSIPRED dataset in three-fold cross-validation. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Predicting Protein Secondary Structure with Markov Models

DEFF Research Database (Denmark)

Fischer, Paul; Larsen, Simon; Thomsen, Claus

2004-01-01

we are considering here, is to predict the secondary structure from the primary one. To this end we train a Markov model on training data and then use it to classify parts of unknown protein sequences as sheets, helices or coils. We show how to exploit the directional information contained...... in the Markov model for this task. Classifications that are purely based on statistical models might not always be biologically meaningful. We present combinatorial methods to incorporate biological background knowledge to enhance the prediction performance....

Statistical properties of thermodynamically predicted RNA secondary structures in viral genomes

Science.gov (United States)

Spanò, M.; Lillo, F.; Miccichè, S.; Mantegna, R. N.

2008-10-01

By performing a comprehensive study on 1832 segments of 1212 complete genomes of viruses, we show that in viral genomes the hairpin structures of thermodynamically predicted RNA secondary structures are more abundant than expected under a simple random null hypothesis. The detected hairpin structures of RNA secondary structures are present both in coding and in noncoding regions for the four groups of viruses categorized as dsDNA, dsRNA, ssDNA and ssRNA. For all groups, hairpin structures of RNA secondary structures are detected more frequently than expected for a random null hypothesis in noncoding rather than in coding regions. However, potential RNA secondary structures are also present in coding regions of dsDNA group. In fact, we detect evolutionary conserved RNA secondary structures in conserved coding and noncoding regions of a large set of complete genomes of dsDNA herpesviruses.

Protein Secondary Structure Prediction Using AutoEncoder Network and Bayes Classifier

Science.gov (United States)

Wang, Leilei; Cheng, Jinyong

2018-03-01

Protein secondary structure prediction is belong to bioinformatics,and it's important in research area. In this paper, we propose a new prediction way of protein using bayes classifier and autoEncoder network. Our experiments show some algorithms including the construction of the model, the classification of parameters and so on. The data set is a typical CB513 data set for protein. In terms of accuracy, the method is the cross validation based on the 3-fold. Then we can get the Q3 accuracy. Paper results illustrate that the autoencoder network improved the prediction accuracy of protein secondary structure.

RNA Secondary Structure Prediction by Using Discrete Mathematics: An Interdisciplinary Research Experience for Undergraduate Students

Science.gov (United States)

Ellington, Roni; Wachira, James; Nkwanta, Asamoah

2010-01-01

The focus of this Research Experience for Undergraduates (REU) project was on RNA secondary structure prediction by using a lattice walk approach. The lattice walk approach is a combinatorial and computational biology method used to enumerate possible secondary structures and predict RNA secondary structure from RNA sequences. The method uses…

Cascaded bidirectional recurrent neural networks for protein secondary structure prediction.

Science.gov (United States)

Chen, Jinmiao; Chaudhari, Narendra

2007-01-01

Protein secondary structure (PSS) prediction is an important topic in bioinformatics. Our study on a large set of non-homologous proteins shows that long-range interactions commonly exist and negatively affect PSS prediction. Besides, we also reveal strong correlations between secondary structure (SS) elements. In order to take into account the long-range interactions and SS-SS correlations, we propose a novel prediction system based on cascaded bidirectional recurrent neural network (BRNN). We compare the cascaded BRNN against another two BRNN architectures, namely the original BRNN architecture used for speech recognition as well as Pollastri's BRNN that was proposed for PSS prediction. Our cascaded BRNN achieves an overall three state accuracy Q3 of 74.38\\%, and reaches a high Segment OVerlap (SOV) of 66.0455. It outperforms the original BRNN and Pollastri's BRNN in both Q3 and SOV. Specifically, it improves the SOV score by 4-6%.

Parallel protein secondary structure prediction based on neural networks.

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Zhong, Wei; Altun, Gulsah; Tian, Xinmin; Harrison, Robert; Tai, Phang C; Pan, Yi

2004-01-01

Protein secondary structure prediction has a fundamental influence on today's bioinformatics research. In this work, binary and tertiary classifiers of protein secondary structure prediction are implemented on Denoeux belief neural network (DBNN) architecture. Hydrophobicity matrix, orthogonal matrix, BLOSUM62 and PSSM (position specific scoring matrix) are experimented separately as the encoding schemes for DBNN. The experimental results contribute to the design of new encoding schemes. New binary classifier for Helix versus not Helix ( approximately H) for DBNN produces prediction accuracy of 87% when PSSM is used for the input profile. The performance of DBNN binary classifier is comparable to other best prediction methods. The good test results for binary classifiers open a new approach for protein structure prediction with neural networks. Due to the time consuming task of training the neural networks, Pthread and OpenMP are employed to parallelize DBNN in the hyperthreading enabled Intel architecture. Speedup for 16 Pthreads is 4.9 and speedup for 16 OpenMP threads is 4 in the 4 processors shared memory architecture. Both speedup performance of OpenMP and Pthread is superior to that of other research. With the new parallel training algorithm, thousands of amino acids can be processed in reasonable amount of time. Our research also shows that hyperthreading technology for Intel architecture is efficient for parallel biological algorithms.

Bi-objective integer programming for RNA secondary structure prediction with pseudoknots.

Science.gov (United States)

Legendre, Audrey; Angel, Eric; Tahi, Fariza

2018-01-15

RNA structure prediction is an important field in bioinformatics, and numerous methods and tools have been proposed. Pseudoknots are specific motifs of RNA secondary structures that are difficult to predict. Almost all existing methods are based on a single model and return one solution, often missing the real structure. An alternative approach would be to combine different models and return a (small) set of solutions, maximizing its quality and diversity in order to increase the probability that it contains the real structure. We propose here an original method for predicting RNA secondary structures with pseudoknots, based on integer programming. We developed a generic bi-objective integer programming algorithm allowing to return optimal and sub-optimal solutions optimizing simultaneously two models. This algorithm was then applied to the combination of two known models of RNA secondary structure prediction, namely MEA and MFE. The resulting tool, called BiokoP, is compared with the other methods in the literature. The results show that the best solution (structure with the highest F 1 -score) is, in most cases, given by BiokoP. Moreover, the results of BiokoP are homogeneous, regardless of the pseudoknot type or the presence or not of pseudoknots. Indeed, the F 1 -scores are always higher than 70% for any number of solutions returned. The results obtained by BiokoP show that combining the MEA and the MFE models, as well as returning several optimal and several sub-optimal solutions, allow to improve the prediction of secondary structures. One perspective of our work is to combine better mono-criterion models, in particular to combine a model based on the comparative approach with the MEA and the MFE models. This leads to develop in the future a new multi-objective algorithm to combine more than two models. BiokoP is available on the EvryRNA platform: https://EvryRNA.ibisc.univ-evry.fr .

Bayesian Inference using Neural Net Likelihood Models for Protein Secondary Structure Prediction

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Seong-Gon Kim

2011-06-01

Full Text Available Several techniques such as Neural Networks, Genetic Algorithms, Decision Trees and other statistical or heuristic methods have been used to approach the complex non-linear task of predicting Alpha-helicies, Beta-sheets and Turns of a proteins secondary structure in the past. This project introduces a new machine learning method by using an offline trained Multilayered Perceptrons (MLP as the likelihood models within a Bayesian Inference framework to predict secondary structures proteins. Varying window sizes are used to extract neighboring amino acid information and passed back and forth between the Neural Net models and the Bayesian Inference process until there is a convergence of the posterior secondary structure probability.

Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments.

Science.gov (United States)

Zheng, Ce; Kurgan, Lukasz

2008-10-10

beta-turn is a secondary protein structure type that plays significant role in protein folding, stability, and molecular recognition. To date, several methods for prediction of beta-turns from protein sequences were developed, but they are characterized by relatively poor prediction quality. The novelty of the proposed sequence-based beta-turn predictor stems from the usage of a window based information extracted from four predicted three-state secondary structures, which together with a selected set of position specific scoring matrix (PSSM) values serve as an input to the support vector machine (SVM) predictor. We show that (1) all four predicted secondary structures are useful; (2) the most useful information extracted from the predicted secondary structure includes the structure of the predicted residue, secondary structure content in a window around the predicted residue, and features that indicate whether the predicted residue is inside a secondary structure segment; (3) the PSSM values of Asn, Asp, Gly, Ile, Leu, Met, Pro, and Val were among the top ranked features, which corroborates with recent studies. The Asn, Asp, Gly, and Pro indicate potential beta-turns, while the remaining four amino acids are useful to predict non-beta-turns. Empirical evaluation using three nonredundant datasets shows favorable Q total, Q predicted and MCC values when compared with over a dozen of modern competing methods. Our method is the first to break the 80% Q total barrier and achieves Q total = 80.9%, MCC = 0.47, and Q predicted higher by over 6% when compared with the second best method. We use feature selection to reduce the dimensionality of the feature vector used as the input for the proposed prediction method. The applied feature set is smaller by 86, 62 and 37% when compared with the second and two third-best (with respect to MCC) competing methods, respectively. Experiments show that the proposed method constitutes an improvement over the competing prediction

Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments

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Kurgan Lukasz

2008-10-01

Full Text Available Abstract Background β-turn is a secondary protein structure type that plays significant role in protein folding, stability, and molecular recognition. To date, several methods for prediction of β-turns from protein sequences were developed, but they are characterized by relatively poor prediction quality. The novelty of the proposed sequence-based β-turn predictor stems from the usage of a window based information extracted from four predicted three-state secondary structures, which together with a selected set of position specific scoring matrix (PSSM values serve as an input to the support vector machine (SVM predictor. Results We show that (1 all four predicted secondary structures are useful; (2 the most useful information extracted from the predicted secondary structure includes the structure of the predicted residue, secondary structure content in a window around the predicted residue, and features that indicate whether the predicted residue is inside a secondary structure segment; (3 the PSSM values of Asn, Asp, Gly, Ile, Leu, Met, Pro, and Val were among the top ranked features, which corroborates with recent studies. The Asn, Asp, Gly, and Pro indicate potential β-turns, while the remaining four amino acids are useful to predict non-β-turns. Empirical evaluation using three nonredundant datasets shows favorable Qtotal, Qpredicted and MCC values when compared with over a dozen of modern competing methods. Our method is the first to break the 80% Qtotal barrier and achieves Qtotal = 80.9%, MCC = 0.47, and Qpredicted higher by over 6% when compared with the second best method. We use feature selection to reduce the dimensionality of the feature vector used as the input for the proposed prediction method. The applied feature set is smaller by 86, 62 and 37% when compared with the second and two third-best (with respect to MCC competing methods, respectively. Conclusion Experiments show that the proposed method constitutes an

Predicting beta-turns and their types using predicted backbone dihedral angles and secondary structures.

Science.gov (United States)

Kountouris, Petros; Hirst, Jonathan D

2010-07-31

Beta-turns are secondary structure elements usually classified as coil. Their prediction is important, because of their role in protein folding and their frequent occurrence in protein chains. We have developed a novel method that predicts beta-turns and their types using information from multiple sequence alignments, predicted secondary structures and, for the first time, predicted dihedral angles. Our method uses support vector machines, a supervised classification technique, and is trained and tested on three established datasets of 426, 547 and 823 protein chains. We achieve a Matthews correlation coefficient of up to 0.49, when predicting the location of beta-turns, the highest reported value to date. Moreover, the additional dihedral information improves the prediction of beta-turn types I, II, IV, VIII and "non-specific", achieving correlation coefficients up to 0.39, 0.33, 0.27, 0.14 and 0.38, respectively. Our results are more accurate than other methods. We have created an accurate predictor of beta-turns and their types. Our method, called DEBT, is available online at http://comp.chem.nottingham.ac.uk/debt/.

Integrating chemical footprinting data into RNA secondary structure prediction.

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Kourosh Zarringhalam

Full Text Available Chemical and enzymatic footprinting experiments, such as shape (selective 2'-hydroxyl acylation analyzed by primer extension, yield important information about RNA secondary structure. Indeed, since the [Formula: see text]-hydroxyl is reactive at flexible (loop regions, but unreactive at base-paired regions, shape yields quantitative data about which RNA nucleotides are base-paired. Recently, low error rates in secondary structure prediction have been reported for three RNAs of moderate size, by including base stacking pseudo-energy terms derived from shape data into the computation of minimum free energy secondary structure. Here, we describe a novel method, RNAsc (RNA soft constraints, which includes pseudo-energy terms for each nucleotide position, rather than only for base stacking positions. We prove that RNAsc is self-consistent, in the sense that the nucleotide-specific probabilities of being unpaired in the low energy Boltzmann ensemble always become more closely correlated with the input shape data after application of RNAsc. From this mathematical perspective, the secondary structure predicted by RNAsc should be 'correct', in as much as the shape data is 'correct'. We benchmark RNAsc against the previously mentioned method for eight RNAs, for which both shape data and native structures are known, to find the same accuracy in 7 out of 8 cases, and an improvement of 25% in one case. Furthermore, we present what appears to be the first direct comparison of shape data and in-line probing data, by comparing yeast asp-tRNA shape data from the literature with data from in-line probing experiments we have recently performed. With respect to several criteria, we find that shape data appear to be more robust than in-line probing data, at least in the case of asp-tRNA.

Artificial Intelligence in Prediction of Secondary Protein Structure Using CB513 Database

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Avdagic, Zikrija; Purisevic, Elvir; Omanovic, Samir; Coralic, Zlatan

2009-01-01

In this paper we describe CB513 a non-redundant dataset, suitable for development of algorithms for prediction of secondary protein structure. A program was made in Borland Delphi for transforming data from our dataset to make it suitable for learning of neural network for prediction of secondary protein structure implemented in MATLAB Neural-Network Toolbox. Learning (training and testing) of neural network is researched with different sizes of windows, different number of neurons in the hidden layer and different number of training epochs, while using dataset CB513. PMID:21347158

Exploiting the Past and the Future in Protein Secondary Structure Prediction

DEFF Research Database (Denmark)

Baldi, Pierre; Brunak, Søren; Frasconi, P

1999-01-01

predictions based on variable ranges of dependencies. These architectures extend recurrent neural networks, introducing non-causal bidirectional dynamics to capture both upstream and downstream information. The prediction algorithm is completed by the use of mixtures of estimators that leverage evolutionary......Motivation: Predicting the secondary structure of a protein (alpha-helix, beta-sheet, coil) is an important step towards elucidating its three-dimensional structure, as well as its function. Presently, the best predictors are based on machine learning approaches, in particular neural network...

MUFOLD-SS: New deep inception-inside-inception networks for protein secondary structure prediction.

Science.gov (United States)

Fang, Chao; Shang, Yi; Xu, Dong

2018-05-01

Protein secondary structure prediction can provide important information for protein 3D structure prediction and protein functions. Deep learning offers a new opportunity to significantly improve prediction accuracy. In this article, a new deep neural network architecture, named the Deep inception-inside-inception (Deep3I) network, is proposed for protein secondary structure prediction and implemented as a software tool MUFOLD-SS. The input to MUFOLD-SS is a carefully designed feature matrix corresponding to the primary amino acid sequence of a protein, which consists of a rich set of information derived from individual amino acid, as well as the context of the protein sequence. Specifically, the feature matrix is a composition of physio-chemical properties of amino acids, PSI-BLAST profile, and HHBlits profile. MUFOLD-SS is composed of a sequence of nested inception modules and maps the input matrix to either eight states or three states of secondary structures. The architecture of MUFOLD-SS enables effective processing of local and global interactions between amino acids in making accurate prediction. In extensive experiments on multiple datasets, MUFOLD-SS outperformed the best existing methods and other deep neural networks significantly. MUFold-SS can be downloaded from http://dslsrv8.cs.missouri.edu/~cf797/MUFoldSS/download.html. © 2018 Wiley Periodicals, Inc.

A Comparative Taxonomy of Parallel Algorithms for RNA Secondary Structure Prediction

Science.gov (United States)

Al-Khatib, Ra’ed M.; Abdullah, Rosni; Rashid, Nur’Aini Abdul

2010-01-01

RNA molecules have been discovered playing crucial roles in numerous biological and medical procedures and processes. RNA structures determination have become a major problem in the biology context. Recently, computer scientists have empowered the biologists with RNA secondary structures that ease an understanding of the RNA functions and roles. Detecting RNA secondary structure is an NP-hard problem, especially in pseudoknotted RNA structures. The detection process is also time-consuming; as a result, an alternative approach such as using parallel architectures is a desirable option. The main goal in this paper is to do an intensive investigation of parallel methods used in the literature to solve the demanding issues, related to the RNA secondary structure prediction methods. Then, we introduce a new taxonomy for the parallel RNA folding methods. Based on this proposed taxonomy, a systematic and scientific comparison is performed among these existing methods. PMID:20458364

CentroidFold: a web server for RNA secondary structure prediction

OpenAIRE

Sato, Kengo; Hamada, Michiaki; Asai, Kiyoshi; Mituyama, Toutai

2009-01-01

The CentroidFold web server (http://www.ncrna.org/centroidfold/) is a web application for RNA secondary structure prediction powered by one of the most accurate prediction engine. The server accepts two kinds of sequence data: a single RNA sequence and a multiple alignment of RNA sequences. It responses with a prediction result shown as a popular base-pair notation and a graph representation. PDF version of the graph representation is also available. For a multiple alignment sequence, the ser...

A fast and robust iterative algorithm for prediction of RNA pseudoknotted secondary structures

Science.gov (United States)

2014-01-01

Background Improving accuracy and efficiency of computational methods that predict pseudoknotted RNA secondary structures is an ongoing challenge. Existing methods based on free energy minimization tend to be very slow and are limited in the types of pseudoknots that they can predict. Incorporating known structural information can improve prediction accuracy; however, there are not many methods for prediction of pseudoknotted structures that can incorporate structural information as input. There is even less understanding of the relative robustness of these methods with respect to partial information. Results We present a new method, Iterative HFold, for pseudoknotted RNA secondary structure prediction. Iterative HFold takes as input a pseudoknot-free structure, and produces a possibly pseudoknotted structure whose energy is at least as low as that of any (density-2) pseudoknotted structure containing the input structure. Iterative HFold leverages strengths of earlier methods, namely the fast running time of HFold, a method that is based on the hierarchical folding hypothesis, and the energy parameters of HotKnots V2.0. Our experimental evaluation on a large data set shows that Iterative HFold is robust with respect to partial information, with average accuracy on pseudoknotted structures steadily increasing from roughly 54% to 79% as the user provides up to 40% of the input structure. Iterative HFold is much faster than HotKnots V2.0, while having comparable accuracy. Iterative HFold also has significantly better accuracy than IPknot on our HK-PK and IP-pk168 data sets. Conclusions Iterative HFold is a robust method for prediction of pseudoknotted RNA secondary structures, whose accuracy with more than 5% information about true pseudoknot-free structures is better than that of IPknot, and with about 35% information about true pseudoknot-free structures compares well with that of HotKnots V2.0 while being significantly faster. Iterative HFold and all data used in

Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields.

Science.gov (United States)

Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo

2016-01-11

Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.

SAAS: Short Amino Acid Sequence - A Promising Protein Secondary Structure Prediction Method of Single Sequence

Directory of Open Access Journals (Sweden)

Zhou Yuan Wu

2013-07-01

Full Text Available In statistical methods of predicting protein secondary structure, many researchers focus on single amino acid frequencies in α-helices, β-sheets, and so on, or the impact near amino acids on an amino acid forming a secondary structure. But the paper considers a short sequence of amino acids (3, 4, 5 or 6 amino acids as integer, and statistics short sequence's probability forming secondary structure. Also, many researchers select low homologous sequences as statistical database. But this paper select whole PDB database. In this paper we propose a strategy to predict protein secondary structure using simple statistical method. Numerical computation shows that, short amino acids sequence as integer to statistics, which can easy see trend of short sequence forming secondary structure, and it will work well to select large statistical database (whole PDB database without considering homologous, and Q3 accuracy is ca. 74% using this paper proposed simple statistical method, but accuracy of others statistical methods is less than 70%.

Combining sequence-based prediction methods and circular dichroism and infrared spectroscopic data to improve protein secondary structure determinations

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Lees Jonathan G

2008-01-01

Full Text Available Abstract Background A number of sequence-based methods exist for protein secondary structure prediction. Protein secondary structures can also be determined experimentally from circular dichroism, and infrared spectroscopic data using empirical analysis methods. It has been proposed that comparable accuracy can be obtained from sequence-based predictions as from these biophysical measurements. Here we have examined the secondary structure determination accuracies of sequence prediction methods with the empirically determined values from the spectroscopic data on datasets of proteins for which both crystal structures and spectroscopic data are available. Results In this study we show that the sequence prediction methods have accuracies nearly comparable to those of spectroscopic methods. However, we also demonstrate that combining the spectroscopic and sequences techniques produces significant overall improvements in secondary structure determinations. In addition, combining the extra information content available from synchrotron radiation circular dichroism data with sequence methods also shows improvements. Conclusion Combining sequence prediction with experimentally determined spectroscopic methods for protein secondary structure content significantly enhances the accuracy of the overall results obtained.

Protein secondary structure prediction for a single-sequence using hidden semi-Markov models

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Borodovsky Mark

2006-03-01

Full Text Available Abstract Background The accuracy of protein secondary structure prediction has been improving steadily towards the 88% estimated theoretical limit. There are two types of prediction algorithms: Single-sequence prediction algorithms imply that information about other (homologous proteins is not available, while algorithms of the second type imply that information about homologous proteins is available, and use it intensively. The single-sequence algorithms could make an important contribution to studies of proteins with no detected homologs, however the accuracy of protein secondary structure prediction from a single-sequence is not as high as when the additional evolutionary information is present. Results In this paper, we further refine and extend the hidden semi-Markov model (HSMM initially considered in the BSPSS algorithm. We introduce an improved residue dependency model by considering the patterns of statistically significant amino acid correlation at structural segment borders. We also derive models that specialize on different sections of the dependency structure and incorporate them into HSMM. In addition, we implement an iterative training method to refine estimates of HSMM parameters. The three-state-per-residue accuracy and other accuracy measures of the new method, IPSSP, are shown to be comparable or better than ones for BSPSS as well as for PSIPRED, tested under the single-sequence condition. Conclusions We have shown that new dependency models and training methods bring further improvements to single-sequence protein secondary structure prediction. The results are obtained under cross-validation conditions using a dataset with no pair of sequences having significant sequence similarity. As new sequences are added to the database it is possible to augment the dependency structure and obtain even higher accuracy. Current and future advances should contribute to the improvement of function prediction for orphan proteins inscrutable

Tchebichef image moment approach to the prediction of protein secondary structures based on circular dichroism.

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Li, Sha Sha; Li, Bao Qiong; Liu, Jin Jin; Lu, Shao Hua; Zhai, Hong Lin

2018-04-20

Circular dichroism (CD) spectroscopy is a widely used technique for the evaluation of protein secondary structures that has a significant impact for the understanding of molecular biology. However, the quantitative analysis of protein secondary structures based on CD spectra is still a hard work due to the serious overlap of the spectra corresponding to different structural motifs. Here, Tchebichef image moment (TM) approach is introduced for the first time, which can effectively extract the chemical features in CD spectra for the quantitative analysis of protein secondary structures. The proposed approach was applied to analyze reference set. and the obtained results were evaluated by the strict statistical parameters such as correlation coefficient, cross-validation correlation coefficient and root mean squared error. Compared with several specialized prediction methods, TM approach provided satisfactory results, especially for turns and unordered structures. Our study indicates that TM approach can be regarded as a feasible tool for the analysis of the secondary structures of proteins based on CD spectra. An available TMs package is provided and can be used directly for secondary structures prediction. This article is protected by copyright. All rights reserved. © 2018 Wiley Periodicals, Inc.

RNA Secondary Structure Prediction by Using Discrete Mathematics: An Interdisciplinary Research Experience for Undergraduate Students

Science.gov (United States)

Ellington, Roni; Wachira, James

2010-01-01

The focus of this Research Experience for Undergraduates (REU) project was on RNA secondary structure prediction by using a lattice walk approach. The lattice walk approach is a combinatorial and computational biology method used to enumerate possible secondary structures and predict RNA secondary structure from RNA sequences. The method uses discrete mathematical techniques and identifies specified base pairs as parameters. The goal of the REU was to introduce upper-level undergraduate students to the principles and challenges of interdisciplinary research in molecular biology and discrete mathematics. At the beginning of the project, students from the biology and mathematics departments of a mid-sized university received instruction on the role of secondary structure in the function of eukaryotic RNAs and RNA viruses, RNA related to combinatorics, and the National Center for Biotechnology Information resources. The student research projects focused on RNA secondary structure prediction on a regulatory region of the yellow fever virus RNA genome and on an untranslated region of an mRNA of a gene associated with the neurological disorder epilepsy. At the end of the project, the REU students gave poster and oral presentations, and they submitted written final project reports to the program director. The outcome of the REU was that the students gained transferable knowledge and skills in bioinformatics and an awareness of the applications of discrete mathematics to biological research problems. PMID:20810968

RNA secondary structure prediction by using discrete mathematics: an interdisciplinary research experience for undergraduate students.

Science.gov (United States)

Ellington, Roni; Wachira, James; Nkwanta, Asamoah

2010-01-01

The focus of this Research Experience for Undergraduates (REU) project was on RNA secondary structure prediction by using a lattice walk approach. The lattice walk approach is a combinatorial and computational biology method used to enumerate possible secondary structures and predict RNA secondary structure from RNA sequences. The method uses discrete mathematical techniques and identifies specified base pairs as parameters. The goal of the REU was to introduce upper-level undergraduate students to the principles and challenges of interdisciplinary research in molecular biology and discrete mathematics. At the beginning of the project, students from the biology and mathematics departments of a mid-sized university received instruction on the role of secondary structure in the function of eukaryotic RNAs and RNA viruses, RNA related to combinatorics, and the National Center for Biotechnology Information resources. The student research projects focused on RNA secondary structure prediction on a regulatory region of the yellow fever virus RNA genome and on an untranslated region of an mRNA of a gene associated with the neurological disorder epilepsy. At the end of the project, the REU students gave poster and oral presentations, and they submitted written final project reports to the program director. The outcome of the REU was that the students gained transferable knowledge and skills in bioinformatics and an awareness of the applications of discrete mathematics to biological research problems.

RDNAnalyzer: A tool for DNA secondary structure prediction and sequence analysis.

Science.gov (United States)

Afzal, Muhammad; Shahid, Ahmad Ali; Shehzadi, Abida; Nadeem, Shahid; Husnain, Tayyab

2012-01-01

RDNAnalyzer is an innovative computer based tool designed for DNA secondary structure prediction and sequence analysis. It can randomly generate the DNA sequence or user can upload the sequences of their own interest in RAW format. It uses and extends the Nussinov dynamic programming algorithm and has various application for the sequence analysis. It predicts the DNA secondary structure and base pairings. It also provides the tools for routinely performed sequence analysis by the biological scientists such as DNA replication, reverse compliment generation, transcription, translation, sequence specific information as total number of nucleotide bases, ATGC base contents along with their respective percentages and sequence cleaner. RDNAnalyzer is a unique tool developed in Microsoft Visual Studio 2008 using Microsoft Visual C# and Windows Presentation Foundation and provides user friendly environment for sequence analysis. It is freely available. http://www.cemb.edu.pk/sw.html RDNAnalyzer - Random DNA Analyser, GUI - Graphical user interface, XAML - Extensible Application Markup Language.

Protein 8-class secondary structure prediction using conditional neural fields.

Science.gov (United States)

Wang, Zhiyong; Zhao, Feng; Peng, Jian; Xu, Jinbo

2011-10-01

Compared with the protein 3-class secondary structure (SS) prediction, the 8-class prediction gains less attention and is also much more challenging, especially for proteins with few sequence homologs. This paper presents a new probabilistic method for 8-class SS prediction using conditional neural fields (CNFs), a recently invented probabilistic graphical model. This CNF method not only models the complex relationship between sequence features and SS, but also exploits the interdependency among SS types of adjacent residues. In addition to sequence profiles, our method also makes use of non-evolutionary information for SS prediction. Tested on the CB513 and RS126 data sets, our method achieves Q8 accuracy of 64.9 and 64.7%, respectively, which are much better than the SSpro8 web server (51.0 and 48.0%, respectively). Our method can also be used to predict other structure properties (e.g. solvent accessibility) of a protein or the SS of RNA. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

RNAspa: a shortest path approach for comparative prediction of the secondary structure of ncRNA molecules

Directory of Open Access Journals (Sweden)

Michaeli Shulamit

2007-10-01

Full Text Available Abstract Background In recent years, RNA molecules that are not translated into proteins (ncRNAs have drawn a great deal of attention, as they were shown to be involved in many cellular functions. One of the most important computational problems regarding ncRNA is to predict the secondary structure of a molecule from its sequence. In particular, we attempted to predict the secondary structure for a set of unaligned ncRNA molecules that are taken from the same family, and thus presumably have a similar structure. Results We developed the RNAspa program, which comparatively predicts the secondary structure for a set of ncRNA molecules in linear time in the number of molecules. We observed that in a list of several hundred suboptimal minimal free energy (MFE predictions, as provided by the RNAsubopt program of the Vienna package, it is likely that at least one suggested structure would be similar to the true, correct one. The suboptimal solutions of each molecule are represented as a layer of vertices in a graph. The shortest path in this graph is the basis for structural predictions for the molecule. We also show that RNA secondary structures can be compared very rapidly by a simple string Edit-Distance algorithm with a minimal loss of accuracy. We show that this approach allows us to more deeply explore the suboptimal structure space. Conclusion The algorithm was tested on three datasets which include several ncRNA families taken from the Rfam database. These datasets allowed for comparison of the algorithm with other methods. In these tests, RNAspa performed better than four other programs.

CNNH_PSS: protein 8-class secondary structure prediction by convolutional neural network with highway.

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Zhou, Jiyun; Wang, Hongpeng; Zhao, Zhishan; Xu, Ruifeng; Lu, Qin

2018-05-08

Protein secondary structure is the three dimensional form of local segments of proteins and its prediction is an important problem in protein tertiary structure prediction. Developing computational approaches for protein secondary structure prediction is becoming increasingly urgent. We present a novel deep learning based model, referred to as CNNH_PSS, by using multi-scale CNN with highway. In CNNH_PSS, any two neighbor convolutional layers have a highway to deliver information from current layer to the output of the next one to keep local contexts. As lower layers extract local context while higher layers extract long-range interdependencies, the highways between neighbor layers allow CNNH_PSS to have ability to extract both local contexts and long-range interdependencies. We evaluate CNNH_PSS on two commonly used datasets: CB6133 and CB513. CNNH_PSS outperforms the multi-scale CNN without highway by at least 0.010 Q8 accuracy and also performs better than CNF, DeepCNF and SSpro8, which cannot extract long-range interdependencies, by at least 0.020 Q8 accuracy, demonstrating that both local contexts and long-range interdependencies are indeed useful for prediction. Furthermore, CNNH_PSS also performs better than GSM and DCRNN which need extra complex model to extract long-range interdependencies. It demonstrates that CNNH_PSS not only cost less computer resource, but also achieves better predicting performance. CNNH_PSS have ability to extracts both local contexts and long-range interdependencies by combing multi-scale CNN and highway network. The evaluations on common datasets and comparisons with state-of-the-art methods indicate that CNNH_PSS is an useful and efficient tool for protein secondary structure prediction.

A folding algorithm for extended RNA secondary structures.

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Höner zu Siederdissen, Christian; Bernhart, Stephan H; Stadler, Peter F; Hofacker, Ivo L

2011-07-01

RNA secondary structure contains many non-canonical base pairs of different pair families. Successful prediction of these structural features leads to improved secondary structures with applications in tertiary structure prediction and simultaneous folding and alignment. We present a theoretical model capturing both RNA pair families and extended secondary structure motifs with shared nucleotides using 2-diagrams. We accompany this model with a number of programs for parameter optimization and structure prediction. All sources (optimization routines, RNA folding, RNA evaluation, extended secondary structure visualization) are published under the GPLv3 and available at www.tbi.univie.ac.at/software/rnawolf/.

Prediction of RNA secondary structures: from theory to models and real molecules

International Nuclear Information System (INIS)

Schuster, Peter

2006-01-01

RNA secondary structures are derived from RNA sequences, which are strings built form the natural four letter nucleotide alphabet, {AUGC}. These coarse-grained structures, in turn, are tantamount to constrained strings over a three letter alphabet. Hence, the secondary structures are discrete objects and the number of sequences always exceeds the number of structures. The sequences built from two letter alphabets form perfect structures when the nucleotides can form a base pair, as is the case with {GC} or {AU}, but the relation between the sequences and structures differs strongly from the four letter alphabet. A comprehensive theory of RNA structure is presented, which is based on the concepts of sequence space and shape space, being a space of structures. It sets the stage for modelling processes in ensembles of RNA molecules like evolutionary optimization or kinetic folding as dynamical phenomena guided by mappings between the two spaces. The number of minimum free energy (mfe) structures is always smaller than the number of sequences, even for two letter alphabets. Folding of RNA molecules into mfe energy structures constitutes a non-invertible mapping from sequence space onto shape space. The preimage of a structure in sequence space is defined as its neutral network. Similarly the set of suboptimal structures is the preimage of a sequence in shape space. This set represents the conformation space of a given sequence. The evolutionary optimization of structures in populations is a process taking place in sequence space, whereas kinetic folding occurs in molecular ensembles that optimize free energy in conformation space. Efficient folding algorithms based on dynamic programming are available for the prediction of secondary structures for given sequences. The inverse problem, the computation of sequences for predefined structures, is an important tool for the design of RNA molecules with tailored properties. Simultaneous folding or cofolding of two or more RNA

SVM-PB-Pred: SVM based protein block prediction method using sequence profiles and secondary structures.

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Suresh, V; Parthasarathy, S

2014-01-01

We developed a support vector machine based web server called SVM-PB-Pred, to predict the Protein Block for any given amino acid sequence. The input features of SVM-PB-Pred include i) sequence profiles (PSSM) and ii) actual secondary structures (SS) from DSSP method or predicted secondary structures from NPS@ and GOR4 methods. There were three combined input features PSSM+SS(DSSP), PSSM+SS(NPS@) and PSSM+SS(GOR4) used to test and train the SVM models. Similarly, four datasets RS90, DB433, LI1264 and SP1577 were used to develop the SVM models. These four SVM models developed were tested using three different benchmarking tests namely; (i) self consistency, (ii) seven fold cross validation test and (iii) independent case test. The maximum possible prediction accuracy of ~70% was observed in self consistency test for the SVM models of both LI1264 and SP1577 datasets, where PSSM+SS(DSSP) input features was used to test. The prediction accuracies were reduced to ~53% for PSSM+SS(NPS@) and ~43% for PSSM+SS(GOR4) in independent case test, for the SVM models of above two same datasets. Using our method, it is possible to predict the protein block letters for any query protein sequence with ~53% accuracy, when the SP1577 dataset and predicted secondary structure from NPS@ server were used. The SVM-PB-Pred server can be freely accessed through http://bioinfo.bdu.ac.in/~svmpbpred.

CompaRNA: a server for continuous benchmarking of automated methods for RNA secondary structure prediction

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Puton, Tomasz; Kozlowski, Lukasz P.; Rother, Kristian M.; Bujnicki, Janusz M.

2013-01-01

We present a continuous benchmarking approach for the assessment of RNA secondary structure prediction methods implemented in the CompaRNA web server. As of 3 October 2012, the performance of 28 single-sequence and 13 comparative methods has been evaluated on RNA sequences/structures released weekly by the Protein Data Bank. We also provide a static benchmark generated on RNA 2D structures derived from the RNAstrand database. Benchmarks on both data sets offer insight into the relative performance of RNA secondary structure prediction methods on RNAs of different size and with respect to different types of structure. According to our tests, on the average, the most accurate predictions obtained by a comparative approach are generated by CentroidAlifold, MXScarna, RNAalifold and TurboFold. On the average, the most accurate predictions obtained by single-sequence analyses are generated by CentroidFold, ContextFold and IPknot. The best comparative methods typically outperform the best single-sequence methods if an alignment of homologous RNA sequences is available. This article presents the results of our benchmarks as of 3 October 2012, whereas the rankings presented online are continuously updated. We will gladly include new prediction methods and new measures of accuracy in the new editions of CompaRNA benchmarks. PMID:23435231

CompaRNA: a server for continuous benchmarking of automated methods for RNA secondary structure prediction.

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Puton, Tomasz; Kozlowski, Lukasz P; Rother, Kristian M; Bujnicki, Janusz M

2013-04-01

We present a continuous benchmarking approach for the assessment of RNA secondary structure prediction methods implemented in the CompaRNA web server. As of 3 October 2012, the performance of 28 single-sequence and 13 comparative methods has been evaluated on RNA sequences/structures released weekly by the Protein Data Bank. We also provide a static benchmark generated on RNA 2D structures derived from the RNAstrand database. Benchmarks on both data sets offer insight into the relative performance of RNA secondary structure prediction methods on RNAs of different size and with respect to different types of structure. According to our tests, on the average, the most accurate predictions obtained by a comparative approach are generated by CentroidAlifold, MXScarna, RNAalifold and TurboFold. On the average, the most accurate predictions obtained by single-sequence analyses are generated by CentroidFold, ContextFold and IPknot. The best comparative methods typically outperform the best single-sequence methods if an alignment of homologous RNA sequences is available. This article presents the results of our benchmarks as of 3 October 2012, whereas the rankings presented online are continuously updated. We will gladly include new prediction methods and new measures of accuracy in the new editions of CompaRNA benchmarks.

A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction.

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Spencer, Matt; Eickholt, Jesse; Jianlin Cheng

2015-01-01

Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80 percent and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test dataset of 198 proteins, achieving a Q3 accuracy of 80.7 percent and a Sov accuracy of 74.2 percent.

A systematic review on popularity, application and characteristics of protein secondary structure prediction tools.

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Kashani-Amin, Elaheh; Tabatabaei-Malazy, Ozra; Sakhteman, Amirhossein; Larijani, Bagher; Ebrahim-Habibi, Azadeh

2018-02-27

Prediction of proteins' secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple secondary structure prediction (SSP) options is challenging. The current study is an insight onto currently favored methods and tools, within various contexts. A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools. Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of 209 studies were finally found eligible to extract data. Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating a SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields. This study provides a comprehensive insight about the recent usage of SSP tools which could be helpful for selecting a proper tool's choice. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Facilitating RNA structure prediction with microarrays.

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Kierzek, Elzbieta; Kierzek, Ryszard; Turner, Douglas H; Catrina, Irina E

2006-01-17

Determining RNA secondary structure is important for understanding structure-function relationships and identifying potential drug targets. This paper reports the use of microarrays with heptamer 2'-O-methyl oligoribonucleotides to probe the secondary structure of an RNA and thereby improve the prediction of that secondary structure. When experimental constraints from hybridization results are added to a free-energy minimization algorithm, the prediction of the secondary structure of Escherichia coli 5S rRNA improves from 27 to 92% of the known canonical base pairs. Optimization of buffer conditions for hybridization and application of 2'-O-methyl-2-thiouridine to enhance binding and improve discrimination between AU and GU pairs are also described. The results suggest that probing RNA with oligonucleotide microarrays can facilitate determination of secondary structure.

Correlation of MFOLD-predicted DNA secondary structures with separation patterns obtained by capillary electrophoresis single-strand conformation polymorphism (CE-SSCP) analysis.

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Glavac, Damjan; Potocnik, Uros; Podpecnik, Darja; Zizek, Teofil; Smerkolj, Sava; Ravnik-Glavac, Metka

2002-04-01

We have studied 57 different mutations within three beta-globin gene promoter fragments with sizes 52 bp, 77 bp, and 193 bp by fluorescent capillary electrophoresis CE-SSCP analysis. For each mutation and wild type, energetically most-favorable predicted secondary structures were calculated for sense and antisense strands using the MFOLD DNA-folding algorithm in order to investigate if any correlation exists between predicted DNA structures and actual CE migration time shifts. The overall CE-SSCP detection rate was 100% for all mutations in three studied DNA fragments. For shorter 52 bp and 77 bp DNA fragments we obtained a positive correlation between the migration time shifts and difference in free energy values of predicted secondary structures at all temperatures. For longer 193 bp beta-globin gene fragments with 46 mutations MFOLD predicted different secondary structures for 89% of mutated strands at 25 degrees C and 40 degrees C. However, the magnitude of the mobility shifts did not necessarily correlate with their secondary structures and free energy values except for the sense strand at 40 degrees C where this correlation was statistically significant (r = 0.312, p = 0.033). Results of this study provided more direct insight into the mechanism of CE-SSCP and showed that MFOLD prediction could be helpful in making decisions about the running temperatures and in prediction of CE-SSCP data patterns, especially for shorter (50-100 bp) DNA fragments. Copyright 2002 Wiley-Liss, Inc.

Neural Networks for protein Structure Prediction

DEFF Research Database (Denmark)

Bohr, Henrik

1998-01-01

This is a review about neural network applications in bioinformatics. Especially the applications to protein structure prediction, e.g. prediction of secondary structures, prediction of surface structure, fold class recognition and prediction of the 3-dimensional structure of protein backbones...

Evaluation of the suitability of free-energy minimization using nearest-neighbor energy parameters for RNA secondary structure prediction

Directory of Open Access Journals (Sweden)

Cobaugh Christian W

2004-08-01

Full Text Available Abstract Background A detailed understanding of an RNA's correct secondary and tertiary structure is crucial to understanding its function and mechanism in the cell. Free energy minimization with energy parameters based on the nearest-neighbor model and comparative analysis are the primary methods for predicting an RNA's secondary structure from its sequence. Version 3.1 of Mfold has been available since 1999. This version contains an expanded sequence dependence of energy parameters and the ability to incorporate coaxial stacking into free energy calculations. We test Mfold 3.1 by performing the largest and most phylogenetically diverse comparison of rRNA and tRNA structures predicted by comparative analysis and Mfold, and we use the results of our tests on 16S and 23S rRNA sequences to assess the improvement between Mfold 2.3 and Mfold 3.1. Results The average prediction accuracy for a 16S or 23S rRNA sequence with Mfold 3.1 is 41%, while the prediction accuracies for the majority of 16S and 23S rRNA structures tested are between 20% and 60%, with some having less than 20% prediction accuracy. The average prediction accuracy was 71% for 5S rRNA and 69% for tRNA. The majority of the 5S rRNA and tRNA sequences have prediction accuracies greater than 60%. The prediction accuracy of 16S rRNA base-pairs decreases exponentially as the number of nucleotides intervening between the 5' and 3' halves of the base-pair increases. Conclusion Our analysis indicates that the current set of nearest-neighbor energy parameters in conjunction with the Mfold folding algorithm are unable to consistently and reliably predict an RNA's correct secondary structure. For 16S or 23S rRNA structure prediction, Mfold 3.1 offers little improvement over Mfold 2.3. However, the nearest-neighbor energy parameters do work well for shorter RNA sequences such as tRNA or 5S rRNA, or for larger rRNAs when the contact distance between the base-pairs is less than 100 nucleotides.

Capturing alternative secondary structures of RNA by decomposition of base-pairing probabilities.

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Hagio, Taichi; Sakuraba, Shun; Iwakiri, Junichi; Mori, Ryota; Asai, Kiyoshi

2018-02-19

It is known that functional RNAs often switch their functions by forming different secondary structures. Popular tools for RNA secondary structures prediction, however, predict the single 'best' structures, and do not produce alternative structures. There are bioinformatics tools to predict suboptimal structures, but it is difficult to detect which alternative secondary structures are essential. We proposed a new computational method to detect essential alternative secondary structures from RNA sequences by decomposing the base-pairing probability matrix. The decomposition is calculated by a newly implemented software tool, RintW, which efficiently computes the base-pairing probability distributions over the Hamming distance from arbitrary reference secondary structures. The proposed approach has been demonstrated on ROSE element RNA thermometer sequence and Lysine RNA ribo-switch, showing that the proposed approach captures conformational changes in secondary structures. We have shown that alternative secondary structures are captured by decomposing base-paring probabilities over Hamming distance. Source code is available from http://www.ncRNA.org/RintW .

Secondary Structure Prediction of Protein using Resilient Back Propagation Learning Algorithm

Directory of Open Access Journals (Sweden)

Jyotshna Dongardive

2015-12-01

Full Text Available The paper proposes a neural network based approach to predict secondary structure of protein. It uses Multilayer Feed Forward Network (MLFN with resilient back propagation as the learning algorithm. Point Accepted Mutation (PAM is adopted as the encoding scheme and CB396 data set is used for the training and testing of the network. Overall accuracy of the network has been experimentally calculated with different window sizes for the sliding window scheme and by varying the number of units in the hidden layer. The best results were obtained with eleven as the window size and seven as the number of units in the hidden layer.

Characteristics and Prediction of RNA Structure

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Hengwu Li

2014-01-01

Full Text Available RNA secondary structures with pseudoknots are often predicted by minimizing free energy, which is NP-hard. Most RNAs fold during transcription from DNA into RNA through a hierarchical pathway wherein secondary structures form prior to tertiary structures. Real RNA secondary structures often have local instead of global optimization because of kinetic reasons. The performance of RNA structure prediction may be improved by considering dynamic and hierarchical folding mechanisms. This study is a novel report on RNA folding that accords with the golden mean characteristic based on the statistical analysis of the real RNA secondary structures of all 480 sequences from RNA STRAND, which are validated by NMR or X-ray. The length ratios of domains in these sequences are approximately 0.382L, 0.5L, 0.618L, and L, where L is the sequence length. These points are just the important golden sections of sequence. With this characteristic, an algorithm is designed to predict RNA hierarchical structures and simulate RNA folding by dynamically folding RNA structures according to the above golden section points. The sensitivity and number of predicted pseudoknots of our algorithm are better than those of the Mfold, HotKnots, McQfold, ProbKnot, and Lhw-Zhu algorithms. Experimental results reflect the folding rules of RNA from a new angle that is close to natural folding.

RNA STRAND: The RNA Secondary Structure and Statistical Analysis Database

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Andronescu Mirela

2008-08-01

Full Text Available Abstract Background The ability to access, search and analyse secondary structures of a large set of known RNA molecules is very important for deriving improved RNA energy models, for evaluating computational predictions of RNA secondary structures and for a better understanding of RNA folding. Currently there is no database that can easily provide these capabilities for almost all RNA molecules with known secondary structures. Results In this paper we describe RNA STRAND – the RNA secondary STRucture and statistical ANalysis Database, a curated database containing known secondary structures of any type and organism. Our new database provides a wide collection of known RNA secondary structures drawn from public databases, searchable and downloadable in a common format. Comprehensive statistical information on the secondary structures in our database is provided using the RNA Secondary Structure Analyser, a new tool we have developed to analyse RNA secondary structures. The information thus obtained is valuable for understanding to which extent and with which probability certain structural motifs can appear. We outline several ways in which the data provided in RNA STRAND can facilitate research on RNA structure, including the improvement of RNA energy models and evaluation of secondary structure prediction programs. In order to keep up-to-date with new RNA secondary structure experiments, we offer the necessary tools to add solved RNA secondary structures to our database and invite researchers to contribute to RNA STRAND. Conclusion RNA STRAND is a carefully assembled database of trusted RNA secondary structures, with easy on-line tools for searching, analyzing and downloading user selected entries, and is publicly available at http://www.rnasoft.ca/strand.

IRSS: a web-based tool for automatic layout and analysis of IRES secondary structure prediction and searching system in silico

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Hong Jun-Jie

2009-05-01

Full Text Available Abstract Background Internal ribosomal entry sites (IRESs provide alternative, cap-independent translation initiation sites in eukaryotic cells. IRES elements are important factors in viral genomes and are also useful tools for bi-cistronic expression vectors. Most existing RNA structure prediction programs are unable to deal with IRES elements. Results We designed an IRES search system, named IRSS, to obtain better results for IRES prediction. RNA secondary structure prediction and comparison software programs were implemented to construct our two-stage strategy for the IRSS. Two software programs formed the backbone of IRSS: the RNAL fold program, used to predict local RNA secondary structures by minimum free energy method; and the RNA Align program, used to compare predicted structures. After complete viral genome database search, the IRSS have low error rate and up to 72.3% sensitivity in appropriated parameters. Conclusion IRSS is freely available at this website http://140.135.61.9/ires/. In addition, all source codes, precompiled binaries, examples and documentations are downloadable for local execution. This new search approach for IRES elements will provide a useful research tool on IRES related studies.

JNSViewer-A JavaScript-based Nucleotide Sequence Viewer for DNA/RNA secondary structures.

Science.gov (United States)

Shi, Jieming; Li, Xi; Dong, Min; Graham, Mitchell; Yadav, Nehul; Liang, Chun

2017-01-01

Many tools are available for visualizing RNA or DNA secondary structures, but there is scarce implementation in JavaScript that provides seamless integration with the increasingly popular web computational platforms. We have developed JNSViewer, a highly interactive web service, which is bundled with several popular tools for DNA/RNA secondary structure prediction and can provide precise and interactive correspondence among nucleotides, dot-bracket data, secondary structure graphs, and genic annotations. In JNSViewer, users can perform RNA secondary structure predictions with different programs and settings, add customized genic annotations in GFF format to structure graphs, search for specific linear motifs, and extract relevant structure graphs of sub-sequences. JNSViewer also allows users to choose a transcript or specific segment of Arabidopsis thaliana genome sequences and predict the corresponding secondary structure. Popular genome browsers (i.e., JBrowse and BrowserGenome) were integrated into JNSViewer to provide powerful visualizations of chromosomal locations, genic annotations, and secondary structures. In addition, we used StructureFold with default settings to predict some RNA structures for Arabidopsis by incorporating in vivo high-throughput RNA structure profiling data and stored the results in our web server, which might be a useful resource for RNA secondary structure studies in plants. JNSViewer is available at http://bioinfolab.miamioh.edu/jnsviewer/index.html.

JNSViewer—A JavaScript-based Nucleotide Sequence Viewer for DNA/RNA secondary structures

Science.gov (United States)

Dong, Min; Graham, Mitchell; Yadav, Nehul

2017-01-01

Many tools are available for visualizing RNA or DNA secondary structures, but there is scarce implementation in JavaScript that provides seamless integration with the increasingly popular web computational platforms. We have developed JNSViewer, a highly interactive web service, which is bundled with several popular tools for DNA/RNA secondary structure prediction and can provide precise and interactive correspondence among nucleotides, dot-bracket data, secondary structure graphs, and genic annotations. In JNSViewer, users can perform RNA secondary structure predictions with different programs and settings, add customized genic annotations in GFF format to structure graphs, search for specific linear motifs, and extract relevant structure graphs of sub-sequences. JNSViewer also allows users to choose a transcript or specific segment of Arabidopsis thaliana genome sequences and predict the corresponding secondary structure. Popular genome browsers (i.e., JBrowse and BrowserGenome) were integrated into JNSViewer to provide powerful visualizations of chromosomal locations, genic annotations, and secondary structures. In addition, we used StructureFold with default settings to predict some RNA structures for Arabidopsis by incorporating in vivo high-throughput RNA structure profiling data and stored the results in our web server, which might be a useful resource for RNA secondary structure studies in plants. JNSViewer is available at http://bioinfolab.miamioh.edu/jnsviewer/index.html. PMID:28582416

JNSViewer-A JavaScript-based Nucleotide Sequence Viewer for DNA/RNA secondary structures.

Directory of Open Access Journals (Sweden)

Jieming Shi

Full Text Available Many tools are available for visualizing RNA or DNA secondary structures, but there is scarce implementation in JavaScript that provides seamless integration with the increasingly popular web computational platforms. We have developed JNSViewer, a highly interactive web service, which is bundled with several popular tools for DNA/RNA secondary structure prediction and can provide precise and interactive correspondence among nucleotides, dot-bracket data, secondary structure graphs, and genic annotations. In JNSViewer, users can perform RNA secondary structure predictions with different programs and settings, add customized genic annotations in GFF format to structure graphs, search for specific linear motifs, and extract relevant structure graphs of sub-sequences. JNSViewer also allows users to choose a transcript or specific segment of Arabidopsis thaliana genome sequences and predict the corresponding secondary structure. Popular genome browsers (i.e., JBrowse and BrowserGenome were integrated into JNSViewer to provide powerful visualizations of chromosomal locations, genic annotations, and secondary structures. In addition, we used StructureFold with default settings to predict some RNA structures for Arabidopsis by incorporating in vivo high-throughput RNA structure profiling data and stored the results in our web server, which might be a useful resource for RNA secondary structure studies in plants. JNSViewer is available at http://bioinfolab.miamioh.edu/jnsviewer/index.html.

Predicting RNA Structure Using Mutual Information

DEFF Research Database (Denmark)

Freyhult, E.; Moulton, V.; Gardner, P. P.

2005-01-01

, to display and predict conserved RNA secondary structure (including pseudoknots) from an alignment. Results: We show that MIfold can be used to predict simple pseudoknots, and that the performance can be adjusted to make it either more sensitive or more selective. We also demonstrate that the overall...... package. Conclusion: MIfold provides a useful supplementary tool to programs such as RNA Structure Logo, RNAalifold and COVE, and should be useful for automatically generating structural predictions for databases such as Rfam. Availability: MIfold is freely available from http......Background: With the ever-increasing number of sequenced RNAs and the establishment of new RNA databases, such as the Comparative RNA Web Site and Rfam, there is a growing need for accurately and automatically predicting RNA structures from multiple alignments. Since RNA secondary structure...

A search for H/ACA snoRNAs in yeast using MFE secondary structure prediction.

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Edvardsson, Sverker; Gardner, Paul P; Poole, Anthony M; Hendy, Michael D; Penny, David; Moulton, Vincent

2003-05-01

Noncoding RNA genes produce functional RNA molecules rather than coding for proteins. One such family is the H/ACA snoRNAs. Unlike the related C/D snoRNAs these have resisted automated detection to date. We develop an algorithm to screen the yeast genome for novel H/ACA snoRNAs. To achieve this, we introduce some new methods for facilitating the search for noncoding RNAs in genomic sequences which are based on properties of predicted minimum free-energy (MFE) secondary structures. The algorithm has been implemented and can be generalized to enable screening of other eukaryote genomes. We find that use of primary sequence alone is insufficient for identifying novel H/ACA snoRNAs. Only the use of secondary structure filters reduces the number of candidates to a manageable size. From genomic context, we identify three strong H/ACA snoRNA candidates. These together with a further 47 candidates obtained by our analysis are being experimentally screened.

Analysis of energy-based algorithms for RNA secondary structure prediction

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Hajiaghayi Monir

2012-02-01

Full Text Available Abstract Background RNA molecules play critical roles in the cells of organisms, including roles in gene regulation, catalysis, and synthesis of proteins. Since RNA function depends in large part on its folded structures, much effort has been invested in developing accurate methods for prediction of RNA secondary structure from the base sequence. Minimum free energy (MFE predictions are widely used, based on nearest neighbor thermodynamic parameters of Mathews, Turner et al. or those of Andronescu et al. Some recently proposed alternatives that leverage partition function calculations find the structure with maximum expected accuracy (MEA or pseudo-expected accuracy (pseudo-MEA methods. Advances in prediction methods are typically benchmarked using sensitivity, positive predictive value and their harmonic mean, namely F-measure, on datasets of known reference structures. Since such benchmarks document progress in improving accuracy of computational prediction methods, it is important to understand how measures of accuracy vary as a function of the reference datasets and whether advances in algorithms or thermodynamic parameters yield statistically significant improvements. Our work advances such understanding for the MFE and (pseudo-MEA-based methods, with respect to the latest datasets and energy parameters. Results We present three main findings. First, using the bootstrap percentile method, we show that the average F-measure accuracy of the MFE and (pseudo-MEA-based algorithms, as measured on our largest datasets with over 2000 RNAs from diverse families, is a reliable estimate (within a 2% range with high confidence of the accuracy of a population of RNA molecules represented by this set. However, average accuracy on smaller classes of RNAs such as a class of 89 Group I introns used previously in benchmarking algorithm accuracy is not reliable enough to draw meaningful conclusions about the relative merits of the MFE and MEA-based algorithms

Rtools: a web server for various secondary structural analyses on single RNA sequences.

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Hamada, Michiaki; Ono, Yukiteru; Kiryu, Hisanori; Sato, Kengo; Kato, Yuki; Fukunaga, Tsukasa; Mori, Ryota; Asai, Kiyoshi

2016-07-08

The secondary structures, as well as the nucleotide sequences, are the important features of RNA molecules to characterize their functions. According to the thermodynamic model, however, the probability of any secondary structure is very small. As a consequence, any tool to predict the secondary structures of RNAs has limited accuracy. On the other hand, there are a few tools to compensate the imperfect predictions by calculating and visualizing the secondary structural information from RNA sequences. It is desirable to obtain the rich information from those tools through a friendly interface. We implemented a web server of the tools to predict secondary structures and to calculate various structural features based on the energy models of secondary structures. By just giving an RNA sequence to the web server, the user can get the different types of solutions of the secondary structures, the marginal probabilities such as base-paring probabilities, loop probabilities and accessibilities of the local bases, the energy changes by arbitrary base mutations as well as the measures for validations of the predicted secondary structures. The web server is available at http://rtools.cbrc.jp, which integrates software tools, CentroidFold, CentroidHomfold, IPKnot, CapR, Raccess, Rchange and RintD. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more.

Science.gov (United States)

Rivas, Elena; Lang, Raymond; Eddy, Sean R

2012-02-01

The standard approach for single-sequence RNA secondary structure prediction uses a nearest-neighbor thermodynamic model with several thousand experimentally determined energy parameters. An attractive alternative is to use statistical approaches with parameters estimated from growing databases of structural RNAs. Good results have been reported for discriminative statistical methods using complex nearest-neighbor models, including CONTRAfold, Simfold, and ContextFold. Little work has been reported on generative probabilistic models (stochastic context-free grammars [SCFGs]) of comparable complexity, although probabilistic models are generally easier to train and to use. To explore a range of probabilistic models of increasing complexity, and to directly compare probabilistic, thermodynamic, and discriminative approaches, we created TORNADO, a computational tool that can parse a wide spectrum of RNA grammar architectures (including the standard nearest-neighbor model and more) using a generalized super-grammar that can be parameterized with probabilities, energies, or arbitrary scores. By using TORNADO, we find that probabilistic nearest-neighbor models perform comparably to (but not significantly better than) discriminative methods. We find that complex statistical models are prone to overfitting RNA structure and that evaluations should use structurally nonhomologous training and test data sets. Overfitting has affected at least one published method (ContextFold). The most important barrier to improving statistical approaches for RNA secondary structure prediction is the lack of diversity of well-curated single-sequence RNA secondary structures in current RNA databases.

Improved protein structure reconstruction using secondary structures, contacts at higher distance thresholds, and non-contacts.

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Adhikari, Badri; Cheng, Jianlin

2017-08-29

Residue-residue contacts are key features for accurate de novo protein structure prediction. For the optimal utilization of these predicted contacts in folding proteins accurately, it is important to study the challenges of reconstructing protein structures using true contacts. Because contact-guided protein modeling approach is valuable for predicting the folds of proteins that do not have structural templates, it is necessary for reconstruction studies to focus on hard-to-predict protein structures. Using a data set consisting of 496 structural domains released in recent CASP experiments and a dataset of 150 representative protein structures, in this work, we discuss three techniques to improve the reconstruction accuracy using true contacts - adding secondary structures, increasing contact distance thresholds, and adding non-contacts. We find that reconstruction using secondary structures and contacts can deliver accuracy higher than using full contact maps. Similarly, we demonstrate that non-contacts can improve reconstruction accuracy not only when the used non-contacts are true but also when they are predicted. On the dataset consisting of 150 proteins, we find that by simply using low ranked predicted contacts as non-contacts and adding them as additional restraints, can increase the reconstruction accuracy by 5% when the reconstructed models are evaluated using TM-score. Our findings suggest that secondary structures are invaluable companions of contacts for accurate reconstruction. Confirming some earlier findings, we also find that larger distance thresholds are useful for folding many protein structures which cannot be folded using the standard definition of contacts. Our findings also suggest that for more accurate reconstruction using predicted contacts it is useful to predict contacts at higher distance thresholds (beyond 8 Å) and predict non-contacts.

A novel Multi-Agent Ada-Boost algorithm for predicting protein structural class with the information of protein secondary structure.

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Fan, Ming; Zheng, Bin; Li, Lihua

2015-10-01

Knowledge of the structural class of a given protein is important for understanding its folding patterns. Although a lot of efforts have been made, it still remains a challenging problem for prediction of protein structural class solely from protein sequences. The feature extraction and classification of proteins are the main problems in prediction. In this research, we extended our earlier work regarding these two aspects. In protein feature extraction, we proposed a scheme by calculating the word frequency and word position from sequences of amino acid, reduced amino acid, and secondary structure. For an accurate classification of the structural class of protein, we developed a novel Multi-Agent Ada-Boost (MA-Ada) method by integrating the features of Multi-Agent system into Ada-Boost algorithm. Extensive experiments were taken to test and compare the proposed method using four benchmark datasets in low homology. The results showed classification accuracies of 88.5%, 96.0%, 88.4%, and 85.5%, respectively, which are much better compared with the existing methods. The source code and dataset are available on request.

Accurate SHAPE-directed RNA secondary structure modeling, including pseudoknots.

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Hajdin, Christine E; Bellaousov, Stanislav; Huggins, Wayne; Leonard, Christopher W; Mathews, David H; Weeks, Kevin M

2013-04-02

A pseudoknot forms in an RNA when nucleotides in a loop pair with a region outside the helices that close the loop. Pseudoknots occur relatively rarely in RNA but are highly overrepresented in functionally critical motifs in large catalytic RNAs, in riboswitches, and in regulatory elements of viruses. Pseudoknots are usually excluded from RNA structure prediction algorithms. When included, these pairings are difficult to model accurately, especially in large RNAs, because allowing this structure dramatically increases the number of possible incorrect folds and because it is difficult to search the fold space for an optimal structure. We have developed a concise secondary structure modeling approach that combines SHAPE (selective 2'-hydroxyl acylation analyzed by primer extension) experimental chemical probing information and a simple, but robust, energy model for the entropic cost of single pseudoknot formation. Structures are predicted with iterative refinement, using a dynamic programming algorithm. This melded experimental and thermodynamic energy function predicted the secondary structures and the pseudoknots for a set of 21 challenging RNAs of known structure ranging in size from 34 to 530 nt. On average, 93% of known base pairs were predicted, and all pseudoknots in well-folded RNAs were identified.

Critical Features of Fragment Libraries for Protein Structure Prediction.

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Trevizani, Raphael; Custódio, Fábio Lima; Dos Santos, Karina Baptista; Dardenne, Laurent Emmanuel

2017-01-01

The use of fragment libraries is a popular approach among protein structure prediction methods and has proven to substantially improve the quality of predicted structures. However, some vital aspects of a fragment library that influence the accuracy of modeling a native structure remain to be determined. This study investigates some of these features. Particularly, we analyze the effect of using secondary structure prediction guiding fragments selection, different fragments sizes and the effect of structural clustering of fragments within libraries. To have a clearer view of how these factors affect protein structure prediction, we isolated the process of model building by fragment assembly from some common limitations associated with prediction methods, e.g., imprecise energy functions and optimization algorithms, by employing an exact structure-based objective function under a greedy algorithm. Our results indicate that shorter fragments reproduce the native structure more accurately than the longer. Libraries composed of multiple fragment lengths generate even better structures, where longer fragments show to be more useful at the beginning of the simulations. The use of many different fragment sizes shows little improvement when compared to predictions carried out with libraries that comprise only three different fragment sizes. Models obtained from libraries built using only sequence similarity are, on average, better than those built with a secondary structure prediction bias. However, we found that the use of secondary structure prediction allows greater reduction of the search space, which is invaluable for prediction methods. The results of this study can be critical guidelines for the use of fragment libraries in protein structure prediction.

TurboFold: Iterative probabilistic estimation of secondary structures for multiple RNA sequences

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Sharma Gaurav

2011-04-01

Full Text Available Abstract Background The prediction of secondary structure, i.e. the set of canonical base pairs between nucleotides, is a first step in developing an understanding of the function of an RNA sequence. The most accurate computational methods predict conserved structures for a set of homologous RNA sequences. These methods usually suffer from high computational complexity. In this paper, TurboFold, a novel and efficient method for secondary structure prediction for multiple RNA sequences, is presented. Results TurboFold takes, as input, a set of homologous RNA sequences and outputs estimates of the base pairing probabilities for each sequence. The base pairing probabilities for a sequence are estimated by combining intrinsic information, derived from the sequence itself via the nearest neighbor thermodynamic model, with extrinsic information, derived from the other sequences in the input set. For a given sequence, the extrinsic information is computed by using pairwise-sequence-alignment-based probabilities for co-incidence with each of the other sequences, along with estimated base pairing probabilities, from the previous iteration, for the other sequences. The extrinsic information is introduced as free energy modifications for base pairing in a partition function computation based on the nearest neighbor thermodynamic model. This process yields updated estimates of base pairing probability. The updated base pairing probabilities in turn are used to recompute extrinsic information, resulting in the overall iterative estimation procedure that defines TurboFold. TurboFold is benchmarked on a number of ncRNA datasets and compared against alternative secondary structure prediction methods. The iterative procedure in TurboFold is shown to improve estimates of base pairing probability with each iteration, though only small gains are obtained beyond three iterations. Secondary structures composed of base pairs with estimated probabilities higher than a

Predicting protein folding pathways at the mesoscopic level based on native interactions between secondary structure elements

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Sze Sing-Hoi

2008-07-01

Full Text Available Abstract Background Since experimental determination of protein folding pathways remains difficult, computational techniques are often used to simulate protein folding. Most current techniques to predict protein folding pathways are computationally intensive and are suitable only for small proteins. Results By assuming that the native structure of a protein is known and representing each intermediate conformation as a collection of fully folded structures in which each of them contains a set of interacting secondary structure elements, we show that it is possible to significantly reduce the conformation space while still being able to predict the most energetically favorable folding pathway of large proteins with hundreds of residues at the mesoscopic level, including the pig muscle phosphoglycerate kinase with 416 residues. The model is detailed enough to distinguish between different folding pathways of structurally very similar proteins, including the streptococcal protein G and the peptostreptococcal protein L. The model is also able to recognize the differences between the folding pathways of protein G and its two structurally similar variants NuG1 and NuG2, which are even harder to distinguish. We show that this strategy can produce accurate predictions on many other proteins with experimentally determined intermediate folding states. Conclusion Our technique is efficient enough to predict folding pathways for both large and small proteins at the mesoscopic level. Such a strategy is often the only feasible choice for large proteins. A software program implementing this strategy (SSFold is available at http://faculty.cs.tamu.edu/shsze/ssfold.

An efficient method for the prediction of deleterious multiple-point mutations in the secondary structure of RNAs using suboptimal folding solutions

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Barash Danny

2008-04-01

Full Text Available Abstract Background RNAmute is an interactive Java application which, given an RNA sequence, calculates the secondary structure of all single point mutations and organizes them into categories according to their similarity to the predicted structure of the wild type. The secondary structure predictions are performed using the Vienna RNA package. A more efficient implementation of RNAmute is needed, however, to extend from the case of single point mutations to the general case of multiple point mutations, which may often be desired for computational predictions alongside mutagenesis experiments. But analyzing multiple point mutations, a process that requires traversing all possible mutations, becomes highly expensive since the running time is O(nm for a sequence of length n with m-point mutations. Using Vienna's RNAsubopt, we present a method that selects only those mutations, based on stability considerations, which are likely to be conformational rearranging. The approach is best examined using the dot plot representation for RNA secondary structure. Results Using RNAsubopt, the suboptimal solutions for a given wild-type sequence are calculated once. Then, specific mutations are selected that are most likely to cause a conformational rearrangement. For an RNA sequence of about 100 nts and 3-point mutations (n = 100, m = 3, for example, the proposed method reduces the running time from several hours or even days to several minutes, thus enabling the practical application of RNAmute to the analysis of multiple-point mutations. Conclusion A highly efficient addition to RNAmute that is as user friendly as the original application but that facilitates the practical analysis of multiple-point mutations is presented. Such an extension can now be exploited prior to site-directed mutagenesis experiments by virologists, for example, who investigate the change of function in an RNA virus via mutations that disrupt important motifs in its secondary

A method for rapid similarity analysis of RNA secondary structures

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Liu Na

2006-11-01

Full Text Available Abstract Background Owing to the rapid expansion of RNA structure databases in recent years, efficient methods for structure comparison are in demand for function prediction and evolutionary analysis. Usually, the similarity of RNA secondary structures is evaluated based on tree models and dynamic programming algorithms. We present here a new method for the similarity analysis of RNA secondary structures. Results Three sets of real data have been used as input for the example applications. Set I includes the structures from 5S rRNAs. Set II includes the secondary structures from RNase P and RNase MRP. Set III includes the structures from 16S rRNAs. Reasonable phylogenetic trees are derived for these three sets of data by using our method. Moreover, our program runs faster as compared to some existing ones. Conclusion The famous Lempel-Ziv algorithm can efficiently extract the information on repeated patterns encoded in RNA secondary structures and makes our method an alternative to analyze the similarity of RNA secondary structures. This method will also be useful to researchers who are interested in evolutionary analysis.

Use of secondary structural information and Cα-Cα distance ...

Indian Academy of Sciences (India)

PRAKASH KUMAR

2007-06-21

Jun 21, 2007 ... Model evolution; protein modelling; residue contact prediction; secondary structure prediction. Abbreviations used: ... set of sequence data (NR) and calculated conservation index of each ... evaluators (Moult et al 2003) to evaluate these model ... (Siew et al 2000), is a measure aims at identifying the largest.

Secondary structural analyses of ITS1 in Paramecium.

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Hoshina, Ryo

2010-01-01

The nuclear ribosomal RNA gene operon is interrupted by internal transcribed spacer (ITS) 1 and ITS2. Although the secondary structure of ITS2 has been widely investigated, less is known about ITS1 and its structure. In this study, the secondary structure of ITS1 sequences for Paramecium and other ciliates was predicted. Each Paramecium ITS1 forms an open loop with three helices, A through C. Helix B was highly conserved among Paramecium, and similar helices were found in other ciliates. A phylogenetic analysis using the ITS1 sequences showed high-resolution, implying that ITS1 is a good tool for species-level analyses.

Deep recurrent conditional random field network for protein secondary prediction

DEFF Research Database (Denmark)

Johansen, Alexander Rosenberg; Sønderby, Søren Kaae; Sønderby, Casper Kaae

2017-01-01

Deep learning has become the state-of-the-art method for predicting protein secondary structure from only its amino acid residues and sequence profile. Building upon these results, we propose to combine a bi-directional recurrent neural network (biRNN) with a conditional random field (CRF), which...... of the labels for all time-steps. We condition the CRF on the output of biRNN, which learns a distributed representation based on the entire sequence. The biRNN-CRF is therefore close to ideally suited for the secondary structure task because a high degree of cross-talk between neighboring elements can...

An image processing approach to computing distances between RNA secondary structures dot plots

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Sapiro Guillermo

2009-02-01

Full Text Available Abstract Background Computing the distance between two RNA secondary structures can contribute in understanding the functional relationship between them. When used repeatedly, such a procedure may lead to finding a query RNA structure of interest in a database of structures. Several methods are available for computing distances between RNAs represented as strings or graphs, but none utilize the RNA representation with dot plots. Since dot plots are essentially digital images, there is a clear motivation to devise an algorithm for computing the distance between dot plots based on image processing methods. Results We have developed a new metric dubbed 'DoPloCompare', which compares two RNA structures. The method is based on comparing dot plot diagrams that represent the secondary structures. When analyzing two diagrams and motivated by image processing, the distance is based on a combination of histogram correlations and a geometrical distance measure. We introduce, describe, and illustrate the procedure by two applications that utilize this metric on RNA sequences. The first application is the RNA design problem, where the goal is to find the nucleotide sequence for a given secondary structure. Examples where our proposed distance measure outperforms others are given. The second application locates peculiar point mutations that induce significant structural alternations relative to the wild type predicted secondary structure. The approach reported in the past to solve this problem was tested on several RNA sequences with known secondary structures to affirm their prediction, as well as on a data set of ribosomal pieces. These pieces were computationally cut from a ribosome for which an experimentally derived secondary structure is available, and on each piece the prediction conveys similarity to the experimental result. Our newly proposed distance measure shows benefit in this problem as well when compared to standard methods used for assessing

Mathematical and Biological Modelling of RNA Secondary Structure and Its Effects on Gene Expression

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T. A. Hughes

2006-01-01

Full Text Available Secondary structures within the 5′ untranslated regions of messenger RNAs can have profound effects on the efficiency of translation of their messages and thereby on gene expression. Consequently they can act as important regulatory motifs in both physiological and pathological settings. Current approaches to predicting the secondary structure of these RNA sequences find the structure with the global-minimum free energy. However, since RNA folds progressively from the 5′ end when synthesised or released from the translational machinery, this may not be the most probable structure. We discuss secondary structure prediction based on local-minimisation of free energy with thermodynamic fluctuations as nucleotides are added to the 3′ end and show that these can result in different secondary structures. We also discuss approaches for studying the extent of the translational inhibition specified by structures within the 5′ untranslated region.

Prediction of guide strand of microRNAs from its sequence and secondary structure

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Ahmed Firoz

2009-04-01

Full Text Available Abstract Background MicroRNAs (miRNAs are produced by the sequential processing of a long hairpin RNA transcript by Drosha and Dicer, an RNase III enzymes, and form transitory small RNA duplexes. One strand of the duplex, which incorporates into RNA-induced silencing complex (RISC and silences the gene expression is called guide strand, or miRNA; while the other strand of duplex is degraded and called the passenger strand, or miRNA*. Predicting the guide strand of miRNA is important for better understanding the RNA interference pathways. Results This paper describes support vector machine (SVM models developed for predicting the guide strands of miRNAs. All models were trained and tested on a dataset consisting of 329 miRNA and 329 miRNA* pairs using five fold cross validation technique. Firstly, models were developed using mono-, di-, and tri-nucleotide composition of miRNA strands and achieved the highest accuracies of 0.588, 0.638 and 0.596 respectively. Secondly, models were developed using split nucleotide composition and achieved maximum accuracies of 0.553, 0.641 and 0.602 for mono-, di-, and tri-nucleotide respectively. Thirdly, models were developed using binary pattern and achieved the highest accuracy of 0.708. Furthermore, when integrating the secondary structure features with binary pattern, an accuracy of 0.719 was seen. Finally, hybrid models were developed by combining various features and achieved maximum accuracy of 0.799 with sensitivity 0.781 and specificity 0.818. Moreover, the performance of this model was tested on an independent dataset that achieved an accuracy of 0.80. In addition, we also compared the performance of our method with various siRNA-designing methods on miRNA and siRNA datasets. Conclusion In this study, first time a method has been developed to predict guide miRNA strands, of miRNA duplex. This study demonstrates that guide and passenger strand of miRNA precursors can be distinguished using their

MicroRNA prediction using a fixed-order Markov model based on the secondary structure pattern.

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Wei Shen

Full Text Available Predicting miRNAs is an arduous task, due to the diversity of the precursors and complexity of enzyme processes. Although several prediction approaches have reached impressive performances, few of them could achieve a full-function recognition of mature miRNA directly from the candidate hairpins across species. Therefore, researchers continue to seek a more powerful model close to biological recognition to miRNA structure. In this report, we describe a novel miRNA prediction algorithm, known as FOMmiR, using a fixed-order Markov model based on the secondary structural pattern. For a training dataset containing 809 human pre-miRNAs and 6441 human pseudo-miRNA hairpins, the model's parameters were defined and evaluated. The results showed that FOMmiR reached 91% accuracy on the human dataset through 5-fold cross-validation. Moreover, for the independent test datasets, the FOMmiR presented an outstanding prediction in human and other species including vertebrates, Drosophila, worms and viruses, even plants, in contrast to the well-known algorithms and models. Especially, the FOMmiR was not only able to distinguish the miRNA precursors from the hairpins, but also locate the position and strand of the mature miRNA. Therefore, this study provides a new generation of miRNA prediction algorithm, which successfully realizes a full-function recognition of the mature miRNAs directly from the hairpin sequences. And it presents a new understanding of the biological recognition based on the strongest signal's location detected by FOMmiR, which might be closely associated with the enzyme cleavage mechanism during the miRNA maturation.

Correlation of RNA secondary structure statistics with thermodynamic stability and applications to folding.

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Wu, Johnny C; Gardner, David P; Ozer, Stuart; Gutell, Robin R; Ren, Pengyu

2009-08-28

The accurate prediction of the secondary and tertiary structure of an RNA with different folding algorithms is dependent on several factors, including the energy functions. However, an RNA higher-order structure cannot be predicted accurately from its sequence based on a limited set of energy parameters. The inter- and intramolecular forces between this RNA and other small molecules and macromolecules, in addition to other factors in the cell such as pH, ionic strength, and temperature, influence the complex dynamics associated with transition of a single stranded RNA to its secondary and tertiary structure. Since all of the factors that affect the formation of an RNAs 3D structure cannot be determined experimentally, statistically derived potential energy has been used in the prediction of protein structure. In the current work, we evaluate the statistical free energy of various secondary structure motifs, including base-pair stacks, hairpin loops, and internal loops, using their statistical frequency obtained from the comparative analysis of more than 50,000 RNA sequences stored in the RNA Comparative Analysis Database (rCAD) at the Comparative RNA Web (CRW) Site. Statistical energy was computed from the structural statistics for several datasets. While the statistical energy for a base-pair stack correlates with experimentally derived free energy values, suggesting a Boltzmann-like distribution, variation is observed between different molecules and their location on the phylogenetic tree of life. Our statistical energy values calculated for several structural elements were utilized in the Mfold RNA-folding algorithm. The combined statistical energy values for base-pair stacks, hairpins and internal loop flanks result in a significant improvement in the accuracy of secondary structure prediction; the hairpin flanks contribute the most.

Thermodynamic heuristics with case-based reasoning: combined insights for RNA pseudoknot secondary structure.

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Al-Khatib, Ra'ed M; Rashid, Nur'Aini Abdul; Abdullah, Rosni

2011-08-01

The secondary structure of RNA pseudoknots has been extensively inferred and scrutinized by computational approaches. Experimental methods for determining RNA structure are time consuming and tedious; therefore, predictive computational approaches are required. Predicting the most accurate and energy-stable pseudoknot RNA secondary structure has been proven to be an NP-hard problem. In this paper, a new RNA folding approach, termed MSeeker, is presented; it includes KnotSeeker (a heuristic method) and Mfold (a thermodynamic algorithm). The global optimization of this thermodynamic heuristic approach was further enhanced by using a case-based reasoning technique as a local optimization method. MSeeker is a proposed algorithm for predicting RNA pseudoknot structure from individual sequences, especially long ones. This research demonstrates that MSeeker improves the sensitivity and specificity of existing RNA pseudoknot structure predictions. The performance and structural results from this proposed method were evaluated against seven other state-of-the-art pseudoknot prediction methods. The MSeeker method had better sensitivity than the DotKnot, FlexStem, HotKnots, pknotsRG, ILM, NUPACK and pknotsRE methods, with 79% of the predicted pseudoknot base-pairs being correct.

Protein secondary structure appears to be robust under in silico evolution while protein disorder appears not to be.

KAUST Repository

Schaefer, Christian

2010-01-16

MOTIVATION: The mutation of amino acids often impacts protein function and structure. Mutations without negative effect sustain evolutionary pressure. We study a particular aspect of structural robustness with respect to mutations: regular protein secondary structure and natively unstructured (intrinsically disordered) regions. Is the formation of regular secondary structure an intrinsic feature of amino acid sequences, or is it a feature that is lost upon mutation and is maintained by evolution against the odds? Similarly, is disorder an intrinsic sequence feature or is it difficult to maintain? To tackle these questions, we in silico mutated native protein sequences into random sequence-like ensembles and monitored the change in predicted secondary structure and disorder. RESULTS: We established that by our coarse-grained measures for change, predictions and observations were similar, suggesting that our results were not biased by prediction mistakes. Changes in secondary structure and disorder predictions were linearly proportional to the change in sequence. Surprisingly, neither the content nor the length distribution for the predicted secondary structure changed substantially. Regions with long disorder behaved differently in that significantly fewer such regions were predicted after a few mutation steps. Our findings suggest that the formation of regular secondary structure is an intrinsic feature of random amino acid sequences, while the formation of long-disordered regions is not an intrinsic feature of proteins with disordered regions. Put differently, helices and strands appear to be maintained easily by evolution, whereas maintaining disordered regions appears difficult. Neutral mutations with respect to disorder are therefore very unlikely.

Protein secondary structure appears to be robust under in silico evolution while protein disorder appears not to be.

KAUST Repository

Schaefer, Christian; Schlessinger, Avner; Rost, Burkhard

2010-01-01

MOTIVATION: The mutation of amino acids often impacts protein function and structure. Mutations without negative effect sustain evolutionary pressure. We study a particular aspect of structural robustness with respect to mutations: regular protein secondary structure and natively unstructured (intrinsically disordered) regions. Is the formation of regular secondary structure an intrinsic feature of amino acid sequences, or is it a feature that is lost upon mutation and is maintained by evolution against the odds? Similarly, is disorder an intrinsic sequence feature or is it difficult to maintain? To tackle these questions, we in silico mutated native protein sequences into random sequence-like ensembles and monitored the change in predicted secondary structure and disorder. RESULTS: We established that by our coarse-grained measures for change, predictions and observations were similar, suggesting that our results were not biased by prediction mistakes. Changes in secondary structure and disorder predictions were linearly proportional to the change in sequence. Surprisingly, neither the content nor the length distribution for the predicted secondary structure changed substantially. Regions with long disorder behaved differently in that significantly fewer such regions were predicted after a few mutation steps. Our findings suggest that the formation of regular secondary structure is an intrinsic feature of random amino acid sequences, while the formation of long-disordered regions is not an intrinsic feature of proteins with disordered regions. Put differently, helices and strands appear to be maintained easily by evolution, whereas maintaining disordered regions appears difficult. Neutral mutations with respect to disorder are therefore very unlikely.

Computing the Partition Function for Kinetically Trapped RNA Secondary Structures

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Lorenz, William A.; Clote, Peter

2011-01-01

An RNA secondary structure is locally optimal if there is no lower energy structure that can be obtained by the addition or removal of a single base pair, where energy is defined according to the widely accepted Turner nearest neighbor model. Locally optimal structures form kinetic traps, since any evolution away from a locally optimal structure must involve energetically unfavorable folding steps. Here, we present a novel, efficient algorithm to compute the partition function over all locally optimal secondary structures of a given RNA sequence. Our software, RNAlocopt runs in time and space. Additionally, RNAlocopt samples a user-specified number of structures from the Boltzmann subensemble of all locally optimal structures. We apply RNAlocopt to show that (1) the number of locally optimal structures is far fewer than the total number of structures – indeed, the number of locally optimal structures approximately equal to the square root of the number of all structures, (2) the structural diversity of this subensemble may be either similar to or quite different from the structural diversity of the entire Boltzmann ensemble, a situation that depends on the type of input RNA, (3) the (modified) maximum expected accuracy structure, computed by taking into account base pairing frequencies of locally optimal structures, is a more accurate prediction of the native structure than other current thermodynamics-based methods. The software RNAlocopt constitutes a technical breakthrough in our study of the folding landscape for RNA secondary structures. For the first time, locally optimal structures (kinetic traps in the Turner energy model) can be rapidly generated for long RNA sequences, previously impossible with methods that involved exhaustive enumeration. Use of locally optimal structure leads to state-of-the-art secondary structure prediction, as benchmarked against methods involving the computation of minimum free energy and of maximum expected accuracy. Web server

Computing the partition function for kinetically trapped RNA secondary structures.

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William A Lorenz

Full Text Available An RNA secondary structure is locally optimal if there is no lower energy structure that can be obtained by the addition or removal of a single base pair, where energy is defined according to the widely accepted Turner nearest neighbor model. Locally optimal structures form kinetic traps, since any evolution away from a locally optimal structure must involve energetically unfavorable folding steps. Here, we present a novel, efficient algorithm to compute the partition function over all locally optimal secondary structures of a given RNA sequence. Our software, RNAlocopt runs in O(n3 time and O(n2 space. Additionally, RNAlocopt samples a user-specified number of structures from the Boltzmann subensemble of all locally optimal structures. We apply RNAlocopt to show that (1 the number of locally optimal structures is far fewer than the total number of structures--indeed, the number of locally optimal structures approximately equal to the square root of the number of all structures, (2 the structural diversity of this subensemble may be either similar to or quite different from the structural diversity of the entire Boltzmann ensemble, a situation that depends on the type of input RNA, (3 the (modified maximum expected accuracy structure, computed by taking into account base pairing frequencies of locally optimal structures, is a more accurate prediction of the native structure than other current thermodynamics-based methods. The software RNAlocopt constitutes a technical breakthrough in our study of the folding landscape for RNA secondary structures. For the first time, locally optimal structures (kinetic traps in the Turner energy model can be rapidly generated for long RNA sequences, previously impossible with methods that involved exhaustive enumeration. Use of locally optimal structure leads to state-of-the-art secondary structure prediction, as benchmarked against methods involving the computation of minimum free energy and of maximum expected

Algorithms for Protein Structure Prediction

DEFF Research Database (Denmark)

Paluszewski, Martin

-trace. Here we present three different approaches for reconstruction of C-traces from predictable measures. In our first approach [63, 62], the C-trace is positioned on a lattice and a tabu-search algorithm is applied to find minimum energy structures. The energy function is based on half-sphere-exposure (HSE......) is more robust than standard Monte Carlo search. In the second approach for reconstruction of C-traces, an exact branch and bound algorithm has been developed [67, 65]. The model is discrete and makes use of secondary structure predictions, HSE, CN and radius of gyration. We show how to compute good lower...... bounds for partial structures very fast. Using these lower bounds, we are able to find global minimum structures in a huge conformational space in reasonable time. We show that many of these global minimum structures are of good quality compared to the native structure. Our branch and bound algorithm...

Exact calculation of loop formation probability identifies folding motifs in RNA secondary structures

Science.gov (United States)

Sloma, Michael F.; Mathews, David H.

2016-01-01

RNA secondary structure prediction is widely used to analyze RNA sequences. In an RNA partition function calculation, free energy nearest neighbor parameters are used in a dynamic programming algorithm to estimate statistical properties of the secondary structure ensemble. Previously, partition functions have largely been used to estimate the probability that a given pair of nucleotides form a base pair, the conditional stacking probability, the accessibility to binding of a continuous stretch of nucleotides, or a representative sample of RNA structures. Here it is demonstrated that an RNA partition function can also be used to calculate the exact probability of formation of hairpin loops, internal loops, bulge loops, or multibranch loops at a given position. This calculation can also be used to estimate the probability of formation of specific helices. Benchmarking on a set of RNA sequences with known secondary structures indicated that loops that were calculated to be more probable were more likely to be present in the known structure than less probable loops. Furthermore, highly probable loops are more likely to be in the known structure than the set of loops predicted in the lowest free energy structures. PMID:27852924

Protein energetic conformational analysis from NMR chemical shifts (PECAN) and its use in determining secondary structural elements

Energy Technology Data Exchange (ETDEWEB)

Eghbalnia, Hamid R.; Wang Liya; Bahrami, Arash [National Magnetic Resonance Facility at Madison, Biochemistry Department (United States); Assadi, Amir [University of Wisconsin-Madison, Mathematics Department (United States); Markley, John L. [National Magnetic Resonance Facility at Madison, Biochemistry Department (United States)], E-mail: eghbalni@nmrfam.wisc.edu

2005-05-15

We present an energy model that combines information from the amino acid sequence of a protein and available NMR chemical shifts for the purposes of identifying low energy conformations and determining elements of secondary structure. The model ('PECAN', Protein Energetic Conformational Analysis from NMR chemical shifts) optimizes a combination of sequence information and residue-specific statistical energy function to yield energetic descriptions most favorable to predicting secondary structure. Compared to prior methods for secondary structure determination, PECAN provides increased accuracy and range, particularly in regions of extended structure. Moreover, PECAN uses the energetics to identify residues located at the boundaries between regions of predicted secondary structure that may not fit the stringent secondary structure class definitions. The energy model offers insights into the local energetic patterns that underlie conformational preferences. For example, it shows that the information content for defining secondary structure is localized about a residue and reaches a maximum when two residues on either side are considered. The current release of the PECAN software determines the well-defined regions of secondary structure in novel proteins with assigned chemical shifts with an overall accuracy of 90%, which is close to the practical limit of achievable accuracy in classifying the states.

Protein energetic conformational analysis from NMR chemical shifts (PECAN) and its use in determining secondary structural elements

International Nuclear Information System (INIS)

Eghbalnia, Hamid R.; Wang Liya; Bahrami, Arash; Assadi, Amir; Markley, John L.

2005-01-01

We present an energy model that combines information from the amino acid sequence of a protein and available NMR chemical shifts for the purposes of identifying low energy conformations and determining elements of secondary structure. The model ('PECAN', Protein Energetic Conformational Analysis from NMR chemical shifts) optimizes a combination of sequence information and residue-specific statistical energy function to yield energetic descriptions most favorable to predicting secondary structure. Compared to prior methods for secondary structure determination, PECAN provides increased accuracy and range, particularly in regions of extended structure. Moreover, PECAN uses the energetics to identify residues located at the boundaries between regions of predicted secondary structure that may not fit the stringent secondary structure class definitions. The energy model offers insights into the local energetic patterns that underlie conformational preferences. For example, it shows that the information content for defining secondary structure is localized about a residue and reaches a maximum when two residues on either side are considered. The current release of the PECAN software determines the well-defined regions of secondary structure in novel proteins with assigned chemical shifts with an overall accuracy of 90%, which is close to the practical limit of achievable accuracy in classifying the states

Predicting Consensus Structures for RNA Alignments Via Pseudo-Energy Minimization

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Junilda Spirollari

2009-01-01

Full Text Available Thermodynamic processes with free energy parameters are often used in algorithms that solve the free energy minimization problem to predict secondary structures of single RNA sequences. While results from these algorithms are promising, an observation is that single sequence-based methods have moderate accuracy and more information is needed to improve on RNA secondary structure prediction, such as covariance scores obtained from multiple sequence alignments. We present in this paper a new approach to predicting the consensus secondary structure of a set of aligned RNA sequences via pseudo-energy minimization. Our tool, called RSpredict, takes into account sequence covariation and employs effective heuristics for accuracy improvement. RSpredict accepts, as input data, a multiple sequence alignment in FASTA or ClustalW format and outputs the consensus secondary structure of the input sequences in both the Vienna style Dot Bracket format and the Connectivity Table format. Our method was compared with some widely used tools including KNetFold, Pfold and RNAalifold. A comprehensive test on different datasets including Rfam sequence alignments and a multiple sequence alignment obtained from our study on the Drosophila X chromosome reveals that RSpredict is competitive with the existing tools on the tested datasets. RSpredict is freely available online as a web server and also as a jar file for download at http:// datalab.njit.edu/biology/RSpredict.

Secondary Structure of Rat and Human Amylin across Force Fields.

Directory of Open Access Journals (Sweden)

Kyle Quynn Hoffmann

Full Text Available The aggregation of human amylin has been strongly implicated in the progression of Type II diabetes. This 37-residue peptide forms a variety of secondary structures, including random coils, α-helices, and β-hairpins. The balance between these structures depends on the chemical environment, making amylin an ideal candidate to examine inherent biases in force fields. Rat amylin differs from human amylin by only 6 residues; however, it does not form fibrils. Therefore it provides a useful complement to human amylin in studies of the key events along the aggregation pathway. In this work, the free energy of rat and human amylin was determined as a function of α-helix and β-hairpin content for the Gromos96 53a6, OPLS-AA/L, CHARMM22/CMAP, CHARMM22*, Amberff99sb*-ILDN, and Amberff03w force fields using advanced sampling techniques, specifically bias exchange metadynamics. This work represents a first systematic attempt to evaluate the conformations and the corresponding free energy of a large, clinically relevant disordered peptide in solution across force fields. The NMR chemical shifts of rIAPP were calculated for each of the force fields using their respective free energy maps, allowing us to quantitatively assess their predictions. We show that the predicted distribution of secondary structures is sensitive to the choice of force-field: Gromos53a6 is biased towards β-hairpins, while CHARMM22/CMAP predicts structures that are overly α-helical. OPLS-AA/L favors disordered structures. Amberff99sb*-ILDN, AmberFF03w and CHARMM22* provide the balance between secondary structures that is most consistent with available experimental data. In contrast to previous reports, our findings suggest that the equilibrium conformations of human and rat amylin are remarkably similar, but that subtle differences arise in transient alpha-helical and beta-strand containing structures that the human peptide can more readily adopt. We hypothesize that these transient

Predicted Rates of Secondary Malignancies From Proton Versus Photon Radiation Therapy for Stage I Seminoma

Energy Technology Data Exchange (ETDEWEB)

Simone, Charles B., E-mail: csimone@alumni.upenn.edu [Department of Radiation Oncology, Hospital of University of Pennsylvania, Philadelphia, Pennsylvania (United States); Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland (United States); Kramer, Kevin [Henry M. Jackson Foundation for the Advancement of Military Medicine, Rockville, Maryland (United States); O' Meara, William P. [Division of Radiation Oncology, National Naval Medical Center, Bethesda, Maryland (United States); Bekelman, Justin E. [Department of Radiation Oncology, Hospital of University of Pennsylvania, Philadelphia, Pennsylvania (United States); Belard, Arnaud [Henry M. Jackson Foundation for the Advancement of Military Medicine, Rockville, Maryland (United States); McDonough, James [Department of Radiation Oncology, Hospital of University of Pennsylvania, Philadelphia, Pennsylvania (United States); O' Connell, John [Radiation Oncology Service, Walter Reed Army Medical Center, Washington, DC (United States)

2012-01-01

Purpose: Photon radiotherapy has been the standard adjuvant treatment for stage I seminoma. Single-dose carboplatin therapy and observation have emerged as alternative options due to concerns for acute toxicities and secondary malignancies from radiation. In this institutional review board-approved study, we compared photon and proton radiotherapy for stage I seminoma and the predicted rates of excess secondary malignancies for both treatment modalities. Methods and Material: Computed tomography images from 10 consecutive patients with stage I seminoma were used to quantify dosimetric differences between photon and proton therapies. Structures reported to be at increased risk for secondary malignancies and in-field critical structures were contoured. Reported models of organ-specific radiation-induced cancer incidence rates based on organ equivalent dose were used to determine the excess absolute risk of secondary malignancies. Calculated values were compared with tumor registry reports of excess secondary malignancies among testicular cancer survivors. Results: Photon and proton plans provided comparable target volume coverage. Proton plans delivered significantly lower mean doses to all examined normal tissues, except for the kidneys. The greatest absolute reduction in mean dose was observed for the stomach (119 cGy for proton plans vs. 768 cGy for photon plans; p < 0.0001). Significantly more excess secondary cancers per 10,000 patients/year were predicted for photon radiation than for proton radiation to the stomach (4.11; 95% confidence interval [CI], 3.22-5.01), large bowel (0.81; 95% CI, 0.39-1.01), and bladder (0.03; 95% CI, 0.01-0.58), while no difference was demonstrated for radiation to the pancreas (0.02; 95% CI, -0.01-0.06). Conclusions: For patients with stage I seminoma, proton radiation therapy reduced the predicted secondary cancer risk compared with photon therapy. We predict a reduction of one additional secondary cancer for every 50 patients

On the prediction of turbulent secondary flows

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Speziale, C. G.; So, R. M. C.; Younis, B. A.

1992-01-01

The prediction of turbulent secondary flows, with Reynolds stress models, in circular pipes and non-circular ducts is reviewed. Turbulence-driven secondary flows in straight non-circular ducts are considered along with turbulent secondary flows in pipes and ducts that arise from curvature or a system rotation. The physical mechanisms that generate these different kinds of secondary flows are outlined and the level of turbulence closure required to properly compute each type is discussed in detail. Illustrative computations of a variety of different secondary flows obtained from two-equation turbulence models and second-order closures are provided to amplify these points.

Structural protein descriptors in 1-dimension and their sequence-based predictions.

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Kurgan, Lukasz; Disfani, Fatemeh Miri

2011-09-01

The last few decades observed an increasing interest in development and application of 1-dimensional (1D) descriptors of protein structure. These descriptors project 3D structural features onto 1D strings of residue-wise structural assignments. They cover a wide-range of structural aspects including conformation of the backbone, burying depth/solvent exposure and flexibility of residues, and inter-chain residue-residue contacts. We perform first-of-its-kind comprehensive comparative review of the existing 1D structural descriptors. We define, review and categorize ten structural descriptors and we also describe, summarize and contrast over eighty computational models that are used to predict these descriptors from the protein sequences. We show that the majority of the recent sequence-based predictors utilize machine learning models, with the most popular being neural networks, support vector machines, hidden Markov models, and support vector and linear regressions. These methods provide high-throughput predictions and most of them are accessible to a non-expert user via web servers and/or stand-alone software packages. We empirically evaluate several recent sequence-based predictors of secondary structure, disorder, and solvent accessibility descriptors using a benchmark set based on CASP8 targets. Our analysis shows that the secondary structure can be predicted with over 80% accuracy and segment overlap (SOV), disorder with over 0.9 AUC, 0.6 Matthews Correlation Coefficient (MCC), and 75% SOV, and relative solvent accessibility with PCC of 0.7 and MCC of 0.6 (0.86 when homology is used). We demonstrate that the secondary structure predicted from sequence without the use of homology modeling is as good as the structure extracted from the 3D folds predicted by top-performing template-based methods.

Protein Structure Prediction by Protein Threading

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Xu, Ying; Liu, Zhijie; Cai, Liming; Xu, Dong

The seminal work of Bowie, Lüthy, and Eisenberg (Bowie et al., 1991) on "the inverse protein folding problem" laid the foundation of protein structure prediction by protein threading. By using simple measures for fitness of different amino acid types to local structural environments defined in terms of solvent accessibility and protein secondary structure, the authors derived a simple and yet profoundly novel approach to assessing if a protein sequence fits well with a given protein structural fold. Their follow-up work (Elofsson et al., 1996; Fischer and Eisenberg, 1996; Fischer et al., 1996a,b) and the work by Jones, Taylor, and Thornton (Jones et al., 1992) on protein fold recognition led to the development of a new brand of powerful tools for protein structure prediction, which we now term "protein threading." These computational tools have played a key role in extending the utility of all the experimentally solved structures by X-ray crystallography and nuclear magnetic resonance (NMR), providing structural models and functional predictions for many of the proteins encoded in the hundreds of genomes that have been sequenced up to now.

A Reference Database for Circular Dichroism Spectroscopy Covering Fold and Secondary Structure Space

International Nuclear Information System (INIS)

Lees, J.; Miles, A.; Wien, F.; Wallace, B.

2006-01-01

Circular Dichroism (CD) spectroscopy is a long-established technique for studying protein secondary structures in solution. Empirical analyses of CD data rely on the availability of reference datasets comprised of far-UV CD spectra of proteins whose crystal structures have been determined. This article reports on the creation of a new reference dataset which effectively covers both secondary structure and fold space, and uses the higher information content available in synchrotron radiation circular dichroism (SRCD) spectra to more accurately predict secondary structure than has been possible with existing reference datasets. It also examines the effects of wavelength range, structural redundancy and different means of categorizing secondary structures on the accuracy of the analyses. In addition, it describes a novel use of hierarchical cluster analyses to identify protein relatedness based on spectral properties alone. The databases are shown to be applicable in both conventional CD and SRCD spectroscopic analyses of proteins. Hence, by combining new bioinformatics and biophysical methods, a database has been produced that should have wide applicability as a tool for structural molecular biology

Amino acid code of protein secondary structure.

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Shestopalov, B V

2003-01-01

The calculation of protein three-dimensional structure from the amino acid sequence is a fundamental problem to be solved. This paper presents principles of the code theory of protein secondary structure, and their consequence--the amino acid code of protein secondary structure. The doublet code model of protein secondary structure, developed earlier by the author (Shestopalov, 1990), is part of this theory. The theory basis are: 1) the name secondary structure is assigned to the conformation, stabilized only by the nearest (intraresidual) and middle-range (at a distance no more than that between residues i and i + 5) interactions; 2) the secondary structure consists of regular (alpha-helical and beta-structural) and irregular (coil) segments; 3) the alpha-helices, beta-strands and coil segments are encoded, respectively, by residue pairs (i, i + 4), (i, i + 2), (i, i = 1), according to the numbers of residues per period, 3.6, 2, 1; 4) all such pairs in the amino acid sequence are codons for elementary structural elements, or structurons; 5) the codons are divided into 21 types depending on their strength, i.e. their encoding capability; 6) overlappings of structurons of one and the same structure generate the longer segments of this structure; 7) overlapping of structurons of different structures is forbidden, and therefore selection of codons is required, the codon selection is hierarchic; 8) the code theory of protein secondary structure generates six variants of the amino acid code of protein secondary structure. There are two possible kinds of model construction based on the theory: the physical one using physical properties of amino acid residues, and the statistical one using results of statistical analysis of a great body of structural data. Some evident consequences of the theory are: a) the theory can be used for calculating the secondary structure from the amino acid sequence as a partial solution of the problem of calculation of protein three

Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility.

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Heffernan, Rhys; Yang, Yuedong; Paliwal, Kuldip; Zhou, Yaoqi

2017-09-15

The accuracy of predicting protein local and global structural properties such as secondary structure and solvent accessible surface area has been stagnant for many years because of the challenge of accounting for non-local interactions between amino acid residues that are close in three-dimensional structural space but far from each other in their sequence positions. All existing machine-learning techniques relied on a sliding window of 10-20 amino acid residues to capture some 'short to intermediate' non-local interactions. Here, we employed Long Short-Term Memory (LSTM) Bidirectional Recurrent Neural Networks (BRNNs) which are capable of capturing long range interactions without using a window. We showed that the application of LSTM-BRNN to the prediction of protein structural properties makes the most significant improvement for residues with the most long-range contacts (|i-j| >19) over a previous window-based, deep-learning method SPIDER2. Capturing long-range interactions allows the accuracy of three-state secondary structure prediction to reach 84% and the correlation coefficient between predicted and actual solvent accessible surface areas to reach 0.80, plus a reduction of 5%, 10%, 5% and 10% in the mean absolute error for backbone ϕ , ψ , θ and τ angles, respectively, from SPIDER2. More significantly, 27% of 182724 40-residue models directly constructed from predicted C α atom-based θ and τ have similar structures to their corresponding native structures (6Å RMSD or less), which is 3% better than models built by ϕ and ψ angles. We expect the method to be useful for assisting protein structure and function prediction. The method is available as a SPIDER3 server and standalone package at http://sparks-lab.org . yaoqi.zhou@griffith.edu.au or yuedong.yang@griffith.edu.au. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email

Implications of secondary structure prediction and amino acid sequence comparison of class I and class II phosphoribosyl diphosphate synthases on catalysis, regulation, and quaternary structure

DEFF Research Database (Denmark)

Krath, B N; Hove-Jensen, B

2001-01-01

Spinach 5-phospho-D-ribosyl alpha-1-diphosphate (PRPP) synthase isozyme 4 was synthesized in Escherichia coli and purified to near homogeneity. The activity of the enzyme is independent of P(i); it is inhibited by ADP in a competitive manner, indicating a lack of an allosteric site; and it accepts...... is consistent with a homotrimer. Secondary structure prediction shows that spinach PRPP synthase isozyme 4 has a general folding similar to that of Bacillus subtilis class I PRPP synthase, for which the three-dimensional structure has been solved, as the position and extent of helices and beta-sheets of the two...... in the spinach enzyme. In contrast, residues of the active site of B. subtilis PRPP synthase show extensive conservation in spinach PRPP synthase isozyme 4....

Cloud prediction of protein structure and function with PredictProtein for Debian.

Science.gov (United States)

Kaján, László; Yachdav, Guy; Vicedo, Esmeralda; Steinegger, Martin; Mirdita, Milot; Angermüller, Christof; Böhm, Ariane; Domke, Simon; Ertl, Julia; Mertes, Christian; Reisinger, Eva; Staniewski, Cedric; Rost, Burkhard

2013-01-01

We report the release of PredictProtein for the Debian operating system and derivatives, such as Ubuntu, Bio-Linux, and Cloud BioLinux. The PredictProtein suite is available as a standard set of open source Debian packages. The release covers the most popular prediction methods from the Rost Lab, including methods for the prediction of secondary structure and solvent accessibility (profphd), nuclear localization signals (predictnls), and intrinsically disordered regions (norsnet). We also present two case studies that successfully utilize PredictProtein packages for high performance computing in the cloud: the first analyzes protein disorder for whole organisms, and the second analyzes the effect of all possible single sequence variants in protein coding regions of the human genome.

Validation of Molecular Dynamics Simulations for Prediction of Three-Dimensional Structures of Small Proteins.

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Kato, Koichi; Nakayoshi, Tomoki; Fukuyoshi, Shuichi; Kurimoto, Eiji; Oda, Akifumi

2017-10-12

Although various higher-order protein structure prediction methods have been developed, almost all of them were developed based on the three-dimensional (3D) structure information of known proteins. Here we predicted the short protein structures by molecular dynamics (MD) simulations in which only Newton's equations of motion were used and 3D structural information of known proteins was not required. To evaluate the ability of MD simulationto predict protein structures, we calculated seven short test protein (10-46 residues) in the denatured state and compared their predicted and experimental structures. The predicted structure for Trp-cage (20 residues) was close to the experimental structure by 200-ns MD simulation. For proteins shorter or longer than Trp-cage, root-mean square deviation values were larger than those for Trp-cage. However, secondary structures could be reproduced by MD simulations for proteins with 10-34 residues. Simulations by replica exchange MD were performed, but the results were similar to those from normal MD simulations. These results suggest that normal MD simulations can roughly predict short protein structures and 200-ns simulations are frequently sufficient for estimating the secondary structures of protein (approximately 20 residues). Structural prediction method using only fundamental physical laws are useful for investigating non-natural proteins, such as primitive proteins and artificial proteins for peptide-based drug delivery systems.

An Algorithm for Template-Based Prediction of Secondary Structures of Individual RNA Sequences

Czech Academy of Sciences Publication Activity Database

Pánek, Josef; Modrák, Martin; Schwarz, Marek

2017-01-01

Roč. 8, OCT 10 (2017), s. 1-11, č. článku 147. ISSN 1664-8021 R&D Projects: GA ČR GA15-00885S; GA MŠk(CZ) LM2015047 Institutional support: RVO:61388971 Keywords : RNA * secondary structure * homology Subject RIV: EE - Microbiology, Virology OBOR OECD: Microbiology Impact factor: 3.789, year: 2016

Strategies for measuring evolutionary conservation of RNA secondary structures

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Hofacker Ivo L

2008-02-01

Full Text Available Abstract Background Evolutionary conservation of RNA secondary structure is a typical feature of many functional non-coding RNAs. Since almost all of the available methods used for prediction and annotation of non-coding RNA genes rely on this evolutionary signature, accurate measures for structural conservation are essential. Results We systematically assessed the ability of various measures to detect conserved RNA structures in multiple sequence alignments. We tested three existing and eight novel strategies that are based on metrics of folding energies, metrics of single optimal structure predictions, and metrics of structure ensembles. We find that the folding energy based SCI score used in the RNAz program and a simple base-pair distance metric are by far the most accurate. The use of more complex metrics like for example tree editing does not improve performance. A variant of the SCI performed particularly well on highly conserved alignments and is thus a viable alternative when only little evolutionary information is available. Surprisingly, ensemble based methods that, in principle, could benefit from the additional information contained in sub-optimal structures, perform particularly poorly. As a general trend, we observed that methods that include a consensus structure prediction outperformed equivalent methods that only consider pairwise comparisons. Conclusion Structural conservation can be measured accurately with relatively simple and intuitive metrics. They have the potential to form the basis of future RNA gene finders, that face new challenges like finding lineage specific structures or detecting mis-aligned sequences.

Evolution of primary and secondary structures in 5S and 5.8S rRNA

International Nuclear Information System (INIS)

Curtiss, W.C.

1986-01-01

The secondary structure of Bombyx mori 5S rRNA was studied using the sing-strand specific S1 nuclease and the base pair specific cobra venom ribonuclease. The RNA was end-labeled with [ 32 P] at either the 5' or 3' end and sequenced using enzymatic digestion techniques. These enzymatic data coupled with thermodynamic structure prediction were used to generate a secondary structure for 5S rRNA. A computer algorithm has been implemented to aid in the comparison of a large set of homologous RNAs. Eukaryotic 5S rRNA sequences from thirty four diverse species were compared by (1) alignment or the sequences, (2) the positions of substitutions were located with respect to the aligned sequence and secondary structure, and (3) the R-Y model of base stacking was used to study stacking pattern relationships in the structure. Eukaryotic 5S rRNA was found to have significant sequence variation throughout much of the molecule while maintaining a relatively constant secondary structure. A detailed analysis of the sequence and structure variability in each region of the molecule is presented

RNAmutants: a web server to explore the mutational landscape of RNA secondary structures

Science.gov (United States)

Waldispühl, Jerome; Devadas, Srinivas; Berger, Bonnie; Clote, Peter

2009-01-01

The history and mechanism of molecular evolution in DNA have been greatly elucidated by contributions from genetics, probability theory and bioinformatics—indeed, mathematical developments such as Kimura's neutral theory, Kingman's coalescent theory and efficient software such as BLAST, ClustalW, Phylip, etc., provide the foundation for modern population genetics. In contrast to DNA, the function of most noncoding RNA depends on tertiary structure, experimentally known to be largely determined by secondary structure, for which dynamic programming can efficiently compute the minimum free energy secondary structure. For this reason, understanding the effect of pointwise mutations in RNA secondary structure could reveal fundamental properties of structural RNA molecules and improve our understanding of molecular evolution of RNA. The web server RNAmutants provides several efficient tools to compute the ensemble of low-energy secondary structures for all k-mutants of a given RNA sequence, where k is bounded by a user-specified upper bound. As we have previously shown, these tools can be used to predict putative deleterious mutations and to analyze regulatory sequences from the hepatitis C and human immunodeficiency genomes. Web server is available at http://bioinformatics.bc.edu/clotelab/RNAmutants/, and downloadable binaries at http://rnamutants.csail.mit.edu/. PMID:19531740

STUDYING THE SECONDARY STRUCTURE OF ACCESSION NUMBER USING CETD MATRIX

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Anamika Dutta

2016-10-01

Full Text Available This paper, we have tried to analyze about the Secondary Structure of nucleotide sequences of rice. The data have been collected from NCBI (National Centre for Biotechnology Information using Nucleotide as data base. All the programs were developed using R programming language using “sequinr” package. Here, we have used CETD matrix method to study the prediction. The conclusions are drawn accordingly.

Statistically significant dependence of the Xaa-Pro peptide bond conformation on secondary structure and amino acid sequence

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Leitner Dietmar

2005-04-01

Full Text Available Abstract Background A reliable prediction of the Xaa-Pro peptide bond conformation would be a useful tool for many protein structure calculation methods. We have analyzed the Protein Data Bank and show that the combined use of sequential and structural information has a predictive value for the assessment of the cis versus trans peptide bond conformation of Xaa-Pro within proteins. For the analysis of the data sets different statistical methods such as the calculation of the Chou-Fasman parameters and occurrence matrices were used. Furthermore we analyzed the relationship between the relative solvent accessibility and the relative occurrence of prolines in the cis and in the trans conformation. Results One of the main results of the statistical investigations is the ranking of the secondary structure and sequence information with respect to the prediction of the Xaa-Pro peptide bond conformation. We observed a significant impact of secondary structure information on the occurrence of the Xaa-Pro peptide bond conformation, while the sequence information of amino acids neighboring proline is of little predictive value for the conformation of this bond. Conclusion In this work, we present an extensive analysis of the occurrence of the cis and trans proline conformation in proteins. Based on the data set, we derived patterns and rules for a possible prediction of the proline conformation. Upon adoption of the Chou-Fasman parameters, we are able to derive statistically relevant correlations between the secondary structure of amino acid fragments and the Xaa-Pro peptide bond conformation.

PSPP: a protein structure prediction pipeline for computing clusters.

Directory of Open Access Journals (Sweden)

Michael S Lee

2009-07-01

Full Text Available Protein structures are critical for understanding the mechanisms of biological systems and, subsequently, for drug and vaccine design. Unfortunately, protein sequence data exceed structural data by a factor of more than 200 to 1. This gap can be partially filled by using computational protein structure prediction. While structure prediction Web servers are a notable option, they often restrict the number of sequence queries and/or provide a limited set of prediction methodologies. Therefore, we present a standalone protein structure prediction software package suitable for high-throughput structural genomic applications that performs all three classes of prediction methodologies: comparative modeling, fold recognition, and ab initio. This software can be deployed on a user's own high-performance computing cluster.The pipeline consists of a Perl core that integrates more than 20 individual software packages and databases, most of which are freely available from other research laboratories. The query protein sequences are first divided into domains either by domain boundary recognition or Bayesian statistics. The structures of the individual domains are then predicted using template-based modeling or ab initio modeling. The predicted models are scored with a statistical potential and an all-atom force field. The top-scoring ab initio models are annotated by structural comparison against the Structural Classification of Proteins (SCOP fold database. Furthermore, secondary structure, solvent accessibility, transmembrane helices, and structural disorder are predicted. The results are generated in text, tab-delimited, and hypertext markup language (HTML formats. So far, the pipeline has been used to study viral and bacterial proteomes.The standalone pipeline that we introduce here, unlike protein structure prediction Web servers, allows users to devote their own computing assets to process a potentially unlimited number of queries as well as perform

CHSalign: A Web Server That Builds upon Junction-Explorer and RNAJAG for Pairwise Alignment of RNA Secondary Structures with Coaxial Helical Stacking.

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Lei Hua

Full Text Available RNA junctions are important structural elements of RNA molecules. They are formed when three or more helices come together in three-dimensional space. Recent studies have focused on the annotation and prediction of coaxial helical stacking (CHS motifs within junctions. Here we exploit such predictions to develop an efficient alignment tool to handle RNA secondary structures with CHS motifs. Specifically, we build upon our Junction-Explorer software for predicting coaxial stacking and RNAJAG for modelling junction topologies as tree graphs to incorporate constrained tree matching and dynamic programming algorithms into a new method, called CHSalign, for aligning the secondary structures of RNA molecules containing CHS motifs. Thus, CHSalign is intended to be an efficient alignment tool for RNAs containing similar junctions. Experimental results based on thousands of alignments demonstrate that CHSalign can align two RNA secondary structures containing CHS motifs more accurately than other RNA secondary structure alignment tools. CHSalign yields a high score when aligning two RNA secondary structures with similar CHS motifs or helical arrangement patterns, and a low score otherwise. This new method has been implemented in a web server, and the program is also made freely available, at http://bioinformatics.njit.edu/CHSalign/.

Web-Beagle: a web server for the alignment of RNA secondary structures.

Science.gov (United States)

Mattei, Eugenio; Pietrosanto, Marco; Ferrè, Fabrizio; Helmer-Citterich, Manuela

2015-07-01

Web-Beagle (http://beagle.bio.uniroma2.it) is a web server for the pairwise global or local alignment of RNA secondary structures. The server exploits a new encoding for RNA secondary structure and a substitution matrix of RNA structural elements to perform RNA structural alignments. The web server allows the user to compute up to 10 000 alignments in a single run, taking as input sets of RNA sequences and structures or primary sequences alone. In the latter case, the server computes the secondary structure prediction for the RNAs on-the-fly using RNAfold (free energy minimization). The user can also compare a set of input RNAs to one of five pre-compiled RNA datasets including lncRNAs and 3' UTRs. All types of comparison produce in output the pairwise alignments along with structural similarity and statistical significance measures for each resulting alignment. A graphical color-coded representation of the alignments allows the user to easily identify structural similarities between RNAs. Web-Beagle can be used for finding structurally related regions in two or more RNAs, for the identification of homologous regions or for functional annotation. Benchmark tests show that Web-Beagle has lower computational complexity, running time and better performances than other available methods. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Fine-grained parallelism accelerating for RNA secondary structure prediction with pseudoknots based on FPGA.

Science.gov (United States)

Xia, Fei; Jin, Guoqing

2014-06-01

PKNOTS is a most famous benchmark program and has been widely used to predict RNA secondary structure including pseudoknots. It adopts the standard four-dimensional (4D) dynamic programming (DP) method and is the basis of many variants and improved algorithms. Unfortunately, the O(N(6)) computing requirements and complicated data dependency greatly limits the usefulness of PKNOTS package with the explosion in gene database size. In this paper, we present a fine-grained parallel PKNOTS package and prototype system for accelerating RNA folding application based on FPGA chip. We adopted a series of storage optimization strategies to resolve the "Memory Wall" problem. We aggressively exploit parallel computing strategies to improve computational efficiency. We also propose several methods that collectively reduce the storage requirements for FPGA on-chip memory. To the best of our knowledge, our design is the first FPGA implementation for accelerating 4D DP problem for RNA folding application including pseudoknots. The experimental results show a factor of more than 50x average speedup over the PKNOTS-1.08 software running on a PC platform with Intel Core2 Q9400 Quad CPU for input RNA sequences. However, the power consumption of our FPGA accelerator is only about 50% of the general-purpose micro-processors.

Strong eukaryotic IRESs have weak secondary structure.

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Xuhua Xia

Full Text Available BACKGROUND: The objective of this work was to investigate the hypothesis that eukaryotic Internal Ribosome Entry Sites (IRES lack secondary structure and to examine the generality of the hypothesis. METHODOLOGY/PRINCIPAL FINDINGS: IRESs of the yeast and the fruit fly are located in the 5'UTR immediately upstream of the initiation codon. The minimum folding energy (MFE of 60 nt RNA segments immediately upstream of the initiation codons was calculated as a proxy of secondary structure stability. MFE of the reverse complements of these 60 nt segments was also calculated. The relationship between MFE and empirically determined IRES activity was investigated to test the hypothesis that strong IRES activity is associated with weak secondary structure. We show that IRES activity in the yeast and the fruit fly correlates strongly with the structural stability, with highest IRES activity found in RNA segments that exhibit the weakest secondary structure. CONCLUSIONS: We found that a subset of eukaryotic IRESs exhibits very low secondary structure in the 5'-UTR sequences immediately upstream of the initiation codon. The consistency in results between the yeast and the fruit fly suggests a possible shared mechanism of cap-independent translation initiation that relies on an unstructured RNA segment.

RNA 3D modules in genome-wide predictions of RNA 2D structure

DEFF Research Database (Denmark)

Theis, Corinna; Zirbel, Craig L; Zu Siederdissen, Christian Höner

2015-01-01

. These modules can, for example, occur inside structural elements which in RNA 2D predictions appear as internal loops. Hence one question is if the use of such RNA 3D information can improve the prediction accuracy of RNA secondary structure at a genome-wide level. Here, we use RNAz in combination with 3D......Recent experimental and computational progress has revealed a large potential for RNA structure in the genome. This has been driven by computational strategies that exploit multiple genomes of related organisms to identify common sequences and secondary structures. However, these computational...... approaches have two main challenges: they are computationally expensive and they have a relatively high false discovery rate (FDR). Simultaneously, RNA 3D structure analysis has revealed modules composed of non-canonical base pairs which occur in non-homologous positions, apparently by independent evolution...

SeMPI: a genome-based secondary metabolite prediction and identification web server.

Science.gov (United States)

Zierep, Paul F; Padilla, Natàlia; Yonchev, Dimitar G; Telukunta, Kiran K; Klementz, Dennis; Günther, Stefan

2017-07-03

The secondary metabolism of bacteria, fungi and plants yields a vast number of bioactive substances. The constantly increasing amount of published genomic data provides the opportunity for an efficient identification of gene clusters by genome mining. Conversely, for many natural products with resolved structures, the encoding gene clusters have not been identified yet. Even though genome mining tools have become significantly more efficient in the identification of biosynthetic gene clusters, structural elucidation of the actual secondary metabolite is still challenging, especially due to as yet unpredictable post-modifications. Here, we introduce SeMPI, a web server providing a prediction and identification pipeline for natural products synthesized by polyketide synthases of type I modular. In order to limit the possible structures of PKS products and to include putative tailoring reactions, a structural comparison with annotated natural products was introduced. Furthermore, a benchmark was designed based on 40 gene clusters with annotated PKS products. The web server of the pipeline (SeMPI) is freely available at: http://www.pharmaceutical-bioinformatics.de/sempi. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Sensitization predicts asthma development among wheezing toddlers in secondary healthcare.

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Boersma, Nienke A; Meijneke, Ruud W H; Kelder, Johannes C; van der Ent, Cornelis K; Balemans, Walter A F

2017-06-01

Some wheezing toddlers develop asthma later in childhood. Sensitization is known to predict asthma in birth cohorts. However, its predictive value in secondary healthcare is uncertain. This study examines the predictive value of sensitization to inhalant allergens among wheezing toddlers in secondary healthcare for the development of asthma at school age (≥6 years). Preschool children (1-3 years) who presented with wheezing in secondary healthcare were screened on asthma at school age with the International Study of Asthma and Allergies in Childhood questionnaire. The positive and negative predictive value (PPV and NPV) of specific IgE to inhalant allergens (cut-off concentration 0.35 kU/L) and several non-invasive variables from a child's history (such as hospitalization, eczema, and parental atopy) were calculated. The additional predictive value of sensitization when combined with non-invasive predictors was examined in multivariate analysis and by ROC curves. Of 116 included children, 63% developed asthma at school age. Sensitization to inhalant allergens was a strong asthma predictor. The odds ratio (OR), PPV and NPV were 7.4%, 86%, and 55%, respectively. Eczema (OR 3.4) and hospital admission (OR 2.6) were significant non-invasive determinants. Adding sensitization to these non-invasive predictors in multivariate analysis resulted in a significantly better asthma prediction. The area under the ROC curve increased from 0.70 with only non-invasive predictors to 0.79 after adding sensitization. Sensitization to inhalant allergens is a strong predictor of school age asthma in secondary healthcare and has added predictive value when combined with non-invasive determinants. Pediatr Pulmonol. 2017;52:729-736. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Development of methods to predict both the dynamic and the pseudo-static response of secondary structures subjected to seismic excitations

International Nuclear Information System (INIS)

Subudhi, M.; Bezler, P.

1984-01-01

Multiple independent support excitation time history formulations have been used to investigate simplified methods to predict the inertial (or dynamic) component of response as well as the pseudo-static (or static) component of response of secondary structures subjected to seismic excitations. For the dynamic component the independent response spectrum method is used with current industry practice for the modal and direction of excitation combinations being adopted and various procedures for the group combination and sequence being investigated. SRSS combination between support groups is found to yield satisfactory results. For the static component, support grouping by elevation for preliminary design followed by support grouping by attachment point for final design assure overall safety in the design

The trickle-down effect of predictability: Secondary task performance benefits from predictability in the primary task.

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Magdalena Ewa Król

Full Text Available Predictions optimize processing by reducing attentional resources allocation to expected or predictable sensory data. Our study demonstrates that these saved processing resources can be then used on concurrent stimuli, and in consequence improve their processing and encoding. We illustrate this "trickle-down" effect with a dual task, where the primary task varied in terms of predictability. The primary task involved detection of a pre-specified symbol that appeared at some point of a short video of a dot moving along a random, semi-predictable or predictable trajectory. The concurrent secondary task involved memorization of photographs representing either emotionally neutral or non-neutral (social or threatening content. Performance in the secondary task was measured by a memory test. We found that participants allocated more attention to unpredictable (random and semi-predictable stimuli than to predictable stimuli. Additionally, when the stimuli in the primary task were more predictable, participants performed better in the secondary task, as evidenced by higher sensitivity in the memory test. Finally, social or threatening stimuli were allocated more "looking time" and a larger number of saccades than neutral stimuli. This effect was stronger for the threatening stimuli than social stimuli. Thus, predictability of environmental input is used in optimizing the allocation of attentional resources, which trickles-down and benefits the processing of concurrent stimuli.

The trickle-down effect of predictability: Secondary task performance benefits from predictability in the primary task.

Science.gov (United States)

Król, Magdalena Ewa; Król, Michał

2017-01-01

Predictions optimize processing by reducing attentional resources allocation to expected or predictable sensory data. Our study demonstrates that these saved processing resources can be then used on concurrent stimuli, and in consequence improve their processing and encoding. We illustrate this "trickle-down" effect with a dual task, where the primary task varied in terms of predictability. The primary task involved detection of a pre-specified symbol that appeared at some point of a short video of a dot moving along a random, semi-predictable or predictable trajectory. The concurrent secondary task involved memorization of photographs representing either emotionally neutral or non-neutral (social or threatening) content. Performance in the secondary task was measured by a memory test. We found that participants allocated more attention to unpredictable (random and semi-predictable) stimuli than to predictable stimuli. Additionally, when the stimuli in the primary task were more predictable, participants performed better in the secondary task, as evidenced by higher sensitivity in the memory test. Finally, social or threatening stimuli were allocated more "looking time" and a larger number of saccades than neutral stimuli. This effect was stronger for the threatening stimuli than social stimuli. Thus, predictability of environmental input is used in optimizing the allocation of attentional resources, which trickles-down and benefits the processing of concurrent stimuli.

Sensitization predicts asthma development among wheezing toddlers in secondary healthcare

NARCIS (Netherlands)

Boersma, Nienke A.; Meijneke, Ruud W.H.; Kelder, Johannes C.; van der Ent, Cornelis K.; Balemans, Walter A.F.

2017-01-01

Introduction: Some wheezing toddlers develop asthma later in childhood. Sensitization is known to predict asthma in birth cohorts. However, its predictive value in secondary healthcare is uncertain. Aim: This study examines the predictive value of sensitization to inhalant allergens among wheezing

ncRNA consensus secondary structure derivation using grammar strings.

Science.gov (United States)

Achawanantakun, Rujira; Sun, Yanni; Takyar, Seyedeh Shohreh

2011-04-01

Many noncoding RNAs (ncRNAs) function through both their sequences and secondary structures. Thus, secondary structure derivation is an important issue in today's RNA research. The state-of-the-art structure annotation tools are based on comparative analysis, which derives consensus structure of homologous ncRNAs. Despite promising results from existing ncRNA aligning and consensus structure derivation tools, there is a need for more efficient and accurate ncRNA secondary structure modeling and alignment methods. In this work, we introduce a consensus structure derivation approach based on grammar string, a novel ncRNA secondary structure representation that encodes an ncRNA's sequence and secondary structure in the parameter space of a context-free grammar (CFG) and a full RNA grammar including pseudoknots. Being a string defined on a special alphabet constructed from a grammar, grammar string converts ncRNA alignment into sequence alignment. We derive consensus secondary structures from hundreds of ncRNA families from BraliBase 2.1 and 25 families containing pseudoknots using grammar string alignment. Our experiments have shown that grammar string-based structure derivation competes favorably in consensus structure quality with Murlet and RNASampler. Source code and experimental data are available at http://www.cse.msu.edu/~yannisun/grammar-string.

Evidence of pervasive biologically functional secondary structures within the genomes of eukaryotic single-stranded DNA viruses.

Science.gov (United States)

Muhire, Brejnev Muhizi; Golden, Michael; Murrell, Ben; Lefeuvre, Pierre; Lett, Jean-Michel; Gray, Alistair; Poon, Art Y F; Ngandu, Nobubelo Kwanele; Semegni, Yves; Tanov, Emil Pavlov; Monjane, Adérito Luis; Harkins, Gordon William; Varsani, Arvind; Shepherd, Dionne Natalie; Martin, Darren Patrick

2014-02-01

Single-stranded DNA (ssDNA) viruses have genomes that are potentially capable of forming complex secondary structures through Watson-Crick base pairing between their constituent nucleotides. A few of the structural elements formed by such base pairings are, in fact, known to have important functions during the replication of many ssDNA viruses. Unknown, however, are (i) whether numerous additional ssDNA virus genomic structural elements predicted to exist by computational DNA folding methods actually exist and (ii) whether those structures that do exist have any biological relevance. We therefore computationally inferred lists of the most evolutionarily conserved structures within a diverse selection of animal- and plant-infecting ssDNA viruses drawn from the families Circoviridae, Anelloviridae, Parvoviridae, Nanoviridae, and Geminiviridae and analyzed these for evidence of natural selection favoring the maintenance of these structures. While we find evidence that is consistent with purifying selection being stronger at nucleotide sites that are predicted to be base paired than at sites predicted to be unpaired, we also find strong associations between sites that are predicted to pair with one another and site pairs that are apparently coevolving in a complementary fashion. Collectively, these results indicate that natural selection actively preserves much of the pervasive secondary structure that is evident within eukaryote-infecting ssDNA virus genomes and, therefore, that much of this structure is biologically functional. Lastly, we provide examples of various highly conserved but completely uncharacterized structural elements that likely have important functions within some of the ssDNA virus genomes analyzed here.

DNA secondary structures: stability and function of G-quadruplex structures

Science.gov (United States)

Bochman, Matthew L.; Paeschke, Katrin; Zakian, Virginia A.

2013-01-01

In addition to the canonical double helix, DNA can fold into various other inter- and intramolecular secondary structures. Although many such structures were long thought to be in vitro artefacts, bioinformatics demonstrates that DNA sequences capable of forming these structures are conserved throughout evolution, suggesting the existence of non-B-form DNA in vivo. In addition, genes whose products promote formation or resolution of these structures are found in diverse organisms, and a growing body of work suggests that the resolution of DNA secondary structures is critical for genome integrity. This Review focuses on emerging evidence relating to the characteristics of G-quadruplex structures and the possible influence of such structures on genomic stability and cellular processes, such as transcription. PMID:23032257

Phylogenetic Reconstruction of the Calosphaeriales and Togniniales Using Five Genes and Predicted RNA Secondary Structures of ITS, and Flabellascus tenuirostris gen. et sp. nov.

Science.gov (United States)

Réblová, Martina; Jaklitsch, Walter M; Réblová, Kamila; Štěpánek, Václav

2015-01-01

The Calosphaeriales is revisited with new collection data, living cultures, morphological studies of ascoma centrum, secondary structures of the internal transcribed spacer (ITS) rDNA and phylogeny based on novel DNA sequences of five nuclear ribosomal and protein-coding loci. Morphological features, molecular evidence and information from predicted RNA secondary structures of ITS converged upon robust phylogenies of the Calosphaeriales and Togniniales. The current concept of the Calosphaeriales includes the Calosphaeriaceae and Pleurostomataceae encompassing five monophyletic genera, Calosphaeria, Flabellascus gen. nov., Jattaea, Pleurostoma and Togniniella, strongly supported by Bayesian and Maximum Likelihood methods. The structural elements of ITS1 form characteristic patterns that are phylogenetically conserved, corroborate observations based on morphology and have a high predictive value at the generic level. Three major clades containing 44 species of Phaeoacremonium were recovered in the closely related Togniniales based on ITS, actin and β-tubulin sequences. They are newly characterized by sexual and RNA structural characters and ecology. This approach is a first step towards understanding of the molecular systematics of Phaeoacremonium and possibly its new classification. In the Calosphaeriales, Jattaea aphanospora sp. nov. and J. ribicola sp. nov. are introduced, Calosphaeria taediosa is combined in Jattaea and epitypified. The sexual morph of Phaeoacremonium cinereum was encountered for the first time on decaying wood and obtained in vitro. In order to achieve a single nomenclature, the genera of asexual morphs linked with the Calosphaeriales are transferred to synonymy of their sexual morphs following the principle of priority, i.e. Calosphaeriophora to Calosphaeria, Phaeocrella to Togniniella and Pleurostomophora to Pleurostoma. Three new combinations are proposed, i.e. Pleurostoma ochraceum comb. nov., P. repens comb. nov. and P. richardsiae comb

Random generation of RNA secondary structures according to native distributions

Directory of Open Access Journals (Sweden)

Nebel Markus E

2011-10-01

Full Text Available Abstract Background Random biological sequences are a topic of great interest in genome analysis since, according to a powerful paradigm, they represent the background noise from which the actual biological information must differentiate. Accordingly, the generation of random sequences has been investigated for a long time. Similarly, random object of a more complicated structure like RNA molecules or proteins are of interest. Results In this article, we present a new general framework for deriving algorithms for the non-uniform random generation of combinatorial objects according to the encoding and probability distribution implied by a stochastic context-free grammar. Briefly, the framework extends on the well-known recursive method for (uniform random generation and uses the popular framework of admissible specifications of combinatorial classes, introducing weighted combinatorial classes to allow for the non-uniform generation by means of unranking. This framework is used to derive an algorithm for the generation of RNA secondary structures of a given fixed size. We address the random generation of these structures according to a realistic distribution obtained from real-life data by using a very detailed context-free grammar (that models the class of RNA secondary structures by distinguishing between all known motifs in RNA structure. Compared to well-known sampling approaches used in several structure prediction tools (such as SFold ours has two major advantages: Firstly, after a preprocessing step in time O(n2 for the computation of all weighted class sizes needed, with our approach a set of m random secondary structures of a given structure size n can be computed in worst-case time complexity Om⋅n⋅ log(n while other algorithms typically have a runtime in O(m⋅n2. Secondly, our approach works with integer arithmetic only which is faster and saves us from all the discomforting details of using floating point arithmetic with

A possible contribution of mRNA secondary structure to translation initiation efficiency in Lactococcus lactis

NARCIS (Netherlands)

Guchte, Maarten van de; Lende, Ted van der; Kok, Jan; Venema, Gerard

1991-01-01

Gene expression signals derived from Lactococcus lactis were linked to lacZ-fused genes with different 5'-nucleotide sequences. Computer predictions of mRNA secondary structure were combined with lacZ expression studies to direct base-substitutions that could possibly influence gene expression.

Integration of QUARK and I-TASSER for Ab Initio Protein Structure Prediction in CASP11.

Science.gov (United States)

Zhang, Wenxuan; Yang, Jianyi; He, Baoji; Walker, Sara Elizabeth; Zhang, Hongjiu; Govindarajoo, Brandon; Virtanen, Jouko; Xue, Zhidong; Shen, Hong-Bin; Zhang, Yang

2016-09-01

We tested two pipelines developed for template-free protein structure prediction in the CASP11 experiment. First, the QUARK pipeline constructs structure models by reassembling fragments of continuously distributed lengths excised from unrelated proteins. Five free-modeling (FM) targets have the model successfully constructed by QUARK with a TM-score above 0.4, including the first model of T0837-D1, which has a TM-score = 0.736 and RMSD = 2.9 Å to the native. Detailed analysis showed that the success is partly attributed to the high-resolution contact map prediction derived from fragment-based distance-profiles, which are mainly located between regular secondary structure elements and loops/turns and help guide the orientation of secondary structure assembly. In the Zhang-Server pipeline, weakly scoring threading templates are re-ordered by the structural similarity to the ab initio folding models, which are then reassembled by I-TASSER based structure assembly simulations; 60% more domains with length up to 204 residues, compared to the QUARK pipeline, were successfully modeled by the I-TASSER pipeline with a TM-score above 0.4. The robustness of the I-TASSER pipeline can stem from the composite fragment-assembly simulations that combine structures from both ab initio folding and threading template refinements. Despite the promising cases, challenges still exist in long-range beta-strand folding, domain parsing, and the uncertainty of secondary structure prediction; the latter of which was found to affect nearly all aspects of FM structure predictions, from fragment identification, target classification, structure assembly, to final model selection. Significant efforts are needed to solve these problems before real progress on FM could be made. Proteins 2016; 84(Suppl 1):76-86. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Structure of the spin polarization spectrum of secondary electrons emitted from nickel

International Nuclear Information System (INIS)

Helman, J.S.

1985-01-01

The main features of the structure observed in the energy resolved spin polarization of secondary electrons emitted from Ni are interpreted in terms of surface and bulk plasmon assisted emission. The model also predicts a measureable shift of the main polarization peak of about 0.3 eV to lower energies as the temperature is raised from room temperature to closely below the Curie temperature. (Author) [pt

In vivo genome-wide profiling of RNA secondary structure reveals novel regulatory features.

Science.gov (United States)

Ding, Yiliang; Tang, Yin; Kwok, Chun Kit; Zhang, Yu; Bevilacqua, Philip C; Assmann, Sarah M

2014-01-30

RNA structure has critical roles in processes ranging from ligand sensing to the regulation of translation, polyadenylation and splicing. However, a lack of genome-wide in vivo RNA structural data has limited our understanding of how RNA structure regulates gene expression in living cells. Here we present a high-throughput, genome-wide in vivo RNA structure probing method, structure-seq, in which dimethyl sulphate methylation of unprotected adenines and cytosines is identified by next-generation sequencing. Application of this method to Arabidopsis thaliana seedlings yielded the first in vivo genome-wide RNA structure map at nucleotide resolution for any organism, with quantitative structural information across more than 10,000 transcripts. Our analysis reveals a three-nucleotide periodic repeat pattern in the structure of coding regions, as well as a less-structured region immediately upstream of the start codon, and shows that these features are strongly correlated with translation efficiency. We also find patterns of strong and weak secondary structure at sites of alternative polyadenylation, as well as strong secondary structure at 5' splice sites that correlates with unspliced events. Notably, in vivo structures of messenger RNAs annotated for stress responses are poorly predicted in silico, whereas mRNA structures of genes related to cell function maintenance are well predicted. Global comparison of several structural features between these two categories shows that the mRNAs associated with stress responses tend to have more single-strandedness, longer maximal loop length and higher free energy per nucleotide, features that may allow these RNAs to undergo conformational changes in response to environmental conditions. Structure-seq allows the RNA structurome and its biological roles to be interrogated on a genome-wide scale and should be applicable to any organism.

RNA secondary structure image - fRNAdb | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us fRNAdb RNA secondary structure image Data detail Data name RNA secondary structure image DOI... 10.18908/lsdba.nbdc00452-005 Description of data contents RNA secondary structure images - png.zip: RNA secondary structure image...s (PNG) - pdf.zip: RNA secondary structure images (PDF) - thumbnail.zip: Thumbnails of... RNA secondary structure images Data file File name: RNA_secondary_structure_image... File URL: ftp://ftp.biosciencedbc.jp/archive/frnadb/LATEST/RNA_secondary_structure_image File size: 9.6 GB

Prediction of macroscopic and local stress-strain behaviors of perforated plates under primary and secondary creep conditions

International Nuclear Information System (INIS)

Igari, Toshihide; Tokiyoshi, Takumi; Mizokami, Yorikata

2000-01-01

Prediction methods of macroscopic and local creep behaviors of perforated plates are examined in order to apply these methods to the structural design of perforated structures such as heat exchangers used in elevated temperatures. Both primary and secondary creeps are considered for predicting macroscopic and local creep behaviors of perorated plates which are made of actual structural materials. Both uniaxial and multiaxial loading of perforated plates are taken into consideration. The concept of effective stress is applied to the prediction of macroscopic creep behaviors of perforated plates, and the predicted results are compared with the numerical results by FEM for the unit section of perorated plated under creep, in order to confirm the propriety of the proposed method. Based on the idea that stress exponents in creep equations govern the stress distribution of perforated plates, a modified Neuber's rule is used for predicting local stress and strain concentrations. The propriety of this prediction method is shown through a comparison of the prediction with the numerical results by FEM for the unit section of perforated plates under creep, and experimental results by the Moire method. (author)

CSI 3.0: a web server for identifying secondary and super-secondary structure in proteins using NMR chemical shifts.

Science.gov (United States)

Hafsa, Noor E; Arndt, David; Wishart, David S

2015-07-01

The Chemical Shift Index or CSI 3.0 (http://csi3.wishartlab.com) is a web server designed to accurately identify the location of secondary and super-secondary structures in protein chains using only nuclear magnetic resonance (NMR) backbone chemical shifts and their corresponding protein sequence data. Unlike earlier versions of CSI, which only identified three types of secondary structure (helix, β-strand and coil), CSI 3.0 now identifies total of 11 types of secondary and super-secondary structures, including helices, β-strands, coil regions, five common β-turns (type I, II, I', II' and VIII), β hairpins as well as interior and edge β-strands. CSI 3.0 accepts experimental NMR chemical shift data in multiple formats (NMR Star 2.1, NMR Star 3.1 and SHIFTY) and generates colorful CSI plots (bar graphs) and secondary/super-secondary structure assignments. The output can be readily used as constraints for structure determination and refinement or the images may be used for presentations and publications. CSI 3.0 uses a pipeline of several well-tested, previously published programs to identify the secondary and super-secondary structures in protein chains. Comparisons with secondary and super-secondary structure assignments made via standard coordinate analysis programs such as DSSP, STRIDE and VADAR on high-resolution protein structures solved by X-ray and NMR show >90% agreement between those made with CSI 3.0. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Influence of thermodynamically unfavorable secondary structures on DNA hybridization kinetics

Science.gov (United States)

Hata, Hiroaki; Kitajima, Tetsuro

2018-01-01

Abstract Nucleic acid secondary structure plays an important role in nucleic acid–nucleic acid recognition/hybridization processes, and is also a vital consideration in DNA nanotechnology. Although the influence of stable secondary structures on hybridization kinetics has been characterized, unstable secondary structures, which show positive ΔG° with self-folding, can also form, and their effects have not been systematically investigated. Such thermodynamically unfavorable secondary structures should not be ignored in DNA hybridization kinetics, especially under isothermal conditions. Here, we report that positive ΔG° secondary structures can change the hybridization rate by two-orders of magnitude, despite the fact that their hybridization obeyed second-order reaction kinetics. The temperature dependence of hybridization rates showed non-Arrhenius behavior; thus, their hybridization is considered to be nucleation limited. We derived a model describing how ΔG° positive secondary structures affect hybridization kinetics in stopped-flow experiments with 47 pairs of oligonucleotides. The calculated hybridization rates, which were based on the model, quantitatively agreed with the experimental rate constant. PMID:29220504

RaptorX-Property: a web server for protein structure property prediction.

Science.gov (United States)

Wang, Sheng; Li, Wei; Liu, Shiwang; Xu, Jinbo

2016-07-08

RaptorX Property (http://raptorx2.uchicago.edu/StructurePropertyPred/predict/) is a web server predicting structure property of a protein sequence without using any templates. It outperforms other servers, especially for proteins without close homologs in PDB or with very sparse sequence profile (i.e. carries little evolutionary information). This server employs a powerful in-house deep learning model DeepCNF (Deep Convolutional Neural Fields) to predict secondary structure (SS), solvent accessibility (ACC) and disorder regions (DISO). DeepCNF not only models complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent property labels. Our experimental results show that, tested on CASP10, CASP11 and the other benchmarks, this server can obtain ∼84% Q3 accuracy for 3-state SS, ∼72% Q8 accuracy for 8-state SS, ∼66% Q3 accuracy for 3-state solvent accessibility, and ∼0.89 area under the ROC curve (AUC) for disorder prediction. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

VITAL NMR: using chemical shift derived secondary structure information for a limited set of amino acids to assess homology model accuracy

Energy Technology Data Exchange (ETDEWEB)

Brothers, Michael C.; Nesbitt, Anna E.; Hallock, Michael J. [University of Illinois at Urbana-Champaign, Department of Chemistry (United States); Rupasinghe, Sanjeewa G. [University of Illinois at Urbana-Champaign, Department of Cell and Developmental Biology (United States); Tang Ming [University of Illinois at Urbana-Champaign, Department of Chemistry (United States); Harris, Jason; Baudry, Jerome [University of Tennessee, Department of Biochemistry, Cellular and Molecular Biology (United States); Schuler, Mary A. [University of Illinois at Urbana-Champaign, Department of Cell and Developmental Biology (United States); Rienstra, Chad M., E-mail: rienstra@illinois.edu [University of Illinois at Urbana-Champaign, Department of Chemistry (United States)

2012-01-15

Homology modeling is a powerful tool for predicting protein structures, whose success depends on obtaining a reasonable alignment between a given structural template and the protein sequence being analyzed. In order to leverage greater predictive power for proteins with few structural templates, we have developed a method to rank homology models based upon their compliance to secondary structure derived from experimental solid-state NMR (SSNMR) data. Such data is obtainable in a rapid manner by simple SSNMR experiments (e.g., {sup 13}C-{sup 13}C 2D correlation spectra). To test our homology model scoring procedure for various amino acid labeling schemes, we generated a library of 7,474 homology models for 22 protein targets culled from the TALOS+/SPARTA+ training set of protein structures. Using subsets of amino acids that are plausibly assigned by SSNMR, we discovered that pairs of the residues Val, Ile, Thr, Ala and Leu (VITAL) emulate an ideal dataset where all residues are site specifically assigned. Scoring the models with a predicted VITAL site-specific dataset and calculating secondary structure with the Chemical Shift Index resulted in a Pearson correlation coefficient (-0.75) commensurate to the control (-0.77), where secondary structure was scored site specifically for all amino acids (ALL 20) using STRIDE. This method promises to accelerate structure procurement by SSNMR for proteins with unknown folds through guiding the selection of remotely homologous protein templates and assessing model quality.

VITAL NMR: Using Chemical Shift Derived Secondary Structure Information for a Limited Set of Amino Acids to Assess Homology Model Accuracy

Energy Technology Data Exchange (ETDEWEB)

Brothers, Michael C [University of Illinois, Urbana-Champaign; Nesbitt, Anna E [University of Illinois, Urbana-Champaign; Hallock, Michael J [University of Illinois, Urbana-Champaign; Rupasinghe, Sanjeewa [University of Illinois, Urbana-Champaign; Tang, Ming [University of Illinois, Urbana-Champaign; Harris, Jason B [ORNL; Baudry, Jerome Y [ORNL; Schuler, Mary A [University of Illinois, Urbana-Champaign; Rienstra, Chad M [University of Illinois, Urbana-Champaign

2011-01-01

Homology modeling is a powerful tool for predicting protein structures, whose success depends on obtaining a reasonable alignment between a given structural template and the protein sequence being analyzed. In order to leverage greater predictive power for proteins with few structural templates, we have developed a method to rank homology models based upon their compliance to secondary structure derived from experimental solid-state NMR (SSNMR) data. Such data is obtainable in a rapid manner by simple SSNMR experiments (e.g., (13)C-(13)C 2D correlation spectra). To test our homology model scoring procedure for various amino acid labeling schemes, we generated a library of 7,474 homology models for 22 protein targets culled from the TALOS+/SPARTA+ training set of protein structures. Using subsets of amino acids that are plausibly assigned by SSNMR, we discovered that pairs of the residues Val, Ile, Thr, Ala and Leu (VITAL) emulate an ideal dataset where all residues are site specifically assigned. Scoring the models with a predicted VITAL site-specific dataset and calculating secondary structure with the Chemical Shift Index resulted in a Pearson correlation coefficient (-0.75) commensurate to the control (-0.77), where secondary structure was scored site specifically for all amino acids (ALL 20) using STRIDE. This method promises to accelerate structure procurement by SSNMR for proteins with unknown folds through guiding the selection of remotely homologous protein templates and assessing model quality.

Building a better fragment library for de novo protein structure prediction.

Directory of Open Access Journals (Sweden)

Saulo H P de Oliveira

Full Text Available Fragment-based approaches are the current standard for de novo protein structure prediction. These approaches rely on accurate and reliable fragment libraries to generate good structural models. In this work, we describe a novel method for structure fragment library generation and its application in fragment-based de novo protein structure prediction. The importance of correct testing procedures in assessing the quality of fragment libraries is demonstrated. In particular, the exclusion of homologs to the target from the libraries to correctly simulate a de novo protein structure prediction scenario, something which surprisingly is not always done. We demonstrate that fragments presenting different predominant predicted secondary structures should be treated differently during the fragment library generation step and that exhaustive and random search strategies should both be used. This information was used to develop a novel method, Flib. On a validation set of 41 structurally diverse proteins, Flib libraries presents both a higher precision and coverage than two of the state-of-the-art methods, NNMake and HHFrag. Flib also achieves better precision and coverage on the set of 275 protein domains used in the two previous experiments of the the Critical Assessment of Structure Prediction (CASP9 and CASP10. We compared Flib libraries against NNMake libraries in a structure prediction context. Of the 13 cases in which a correct answer was generated, Flib models were more accurate than NNMake models for 10. "Flib is available for download at: http://www.stats.ox.ac.uk/research/proteins/resources".

Building a Better Fragment Library for De Novo Protein Structure Prediction

Science.gov (United States)

de Oliveira, Saulo H. P.; Shi, Jiye; Deane, Charlotte M.

2015-01-01

Fragment-based approaches are the current standard for de novo protein structure prediction. These approaches rely on accurate and reliable fragment libraries to generate good structural models. In this work, we describe a novel method for structure fragment library generation and its application in fragment-based de novo protein structure prediction. The importance of correct testing procedures in assessing the quality of fragment libraries is demonstrated. In particular, the exclusion of homologs to the target from the libraries to correctly simulate a de novo protein structure prediction scenario, something which surprisingly is not always done. We demonstrate that fragments presenting different predominant predicted secondary structures should be treated differently during the fragment library generation step and that exhaustive and random search strategies should both be used. This information was used to develop a novel method, Flib. On a validation set of 41 structurally diverse proteins, Flib libraries presents both a higher precision and coverage than two of the state-of-the-art methods, NNMake and HHFrag. Flib also achieves better precision and coverage on the set of 275 protein domains used in the two previous experiments of the the Critical Assessment of Structure Prediction (CASP9 and CASP10). We compared Flib libraries against NNMake libraries in a structure prediction context. Of the 13 cases in which a correct answer was generated, Flib models were more accurate than NNMake models for 10. “Flib is available for download at: http://www.stats.ox.ac.uk/research/proteins/resources”. PMID:25901595

Structural prediction in aphasia

Directory of Open Access Journals (Sweden)

Tessa Warren

2015-05-01

Full Text Available There is considerable evidence that young healthy comprehenders predict the structure of upcoming material, and that their processing is facilitated when they encounter material matching those predictions (e.g., Staub & Clifton, 2006; Yoshida, Dickey & Sturt, 2013. However, less is known about structural prediction in aphasia. There is evidence that lexical prediction may be spared in aphasia (Dickey et al., 2014; Love & Webb, 1977; cf. Mack et al, 2013. However, predictive mechanisms supporting facilitated lexical access may not necessarily support structural facilitation. Given that many people with aphasia (PWA exhibit syntactic deficits (e.g. Goodglass, 1993, PWA with such impairments may not engage in structural prediction. However, recent evidence suggests that some PWA may indeed predict upcoming structure (Hanne, Burchert, De Bleser, & Vashishth, 2015. Hanne et al. tracked the eyes of PWA (n=8 with sentence-comprehension deficits while they listened to reversible subject-verb-object (SVO and object-verb-subject (OVS sentences in German, in a sentence-picture matching task. Hanne et al. manipulated case and number marking to disambiguate the sentences’ structure. Gazes to an OVS or SVO picture during the unfolding of a sentence were assumed to indicate prediction of the structure congruent with that picture. According to this measure, the PWA’s structural prediction was impaired compared to controls, but they did successfully predict upcoming structure when morphosyntactic cues were strong and unambiguous. Hanne et al.’s visual-world evidence is suggestive, but their forced-choice sentence-picture matching task places tight constraints on possible structural predictions. Clearer evidence of structural prediction would come from paradigms where the content of upcoming material is not as constrained. The current study used self-paced reading study to examine structural prediction among PWA in less constrained contexts. PWA (n=17 who

SimRNA: a coarse-grained method for RNA folding simulations and 3D structure prediction.

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Boniecki, Michal J; Lach, Grzegorz; Dawson, Wayne K; Tomala, Konrad; Lukasz, Pawel; Soltysinski, Tomasz; Rother, Kristian M; Bujnicki, Janusz M

2016-04-20

RNA molecules play fundamental roles in cellular processes. Their function and interactions with other biomolecules are dependent on the ability to form complex three-dimensional (3D) structures. However, experimental determination of RNA 3D structures is laborious and challenging, and therefore, the majority of known RNAs remain structurally uncharacterized. Here, we present SimRNA: a new method for computational RNA 3D structure prediction, which uses a coarse-grained representation, relies on the Monte Carlo method for sampling the conformational space, and employs a statistical potential to approximate the energy and identify conformations that correspond to biologically relevant structures. SimRNA can fold RNA molecules using only sequence information, and, on established test sequences, it recapitulates secondary structure with high accuracy, including correct prediction of pseudoknots. For modeling of complex 3D structures, it can use additional restraints, derived from experimental or computational analyses, including information about secondary structure and/or long-range contacts. SimRNA also can be used to analyze conformational landscapes and identify potential alternative structures. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Constraint Logic Programming approach to protein structure prediction

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Fogolari Federico

2004-11-01

Full Text Available Abstract Background The protein structure prediction problem is one of the most challenging problems in biological sciences. Many approaches have been proposed using database information and/or simplified protein models. The protein structure prediction problem can be cast in the form of an optimization problem. Notwithstanding its importance, the problem has very seldom been tackled by Constraint Logic Programming, a declarative programming paradigm suitable for solving combinatorial optimization problems. Results Constraint Logic Programming techniques have been applied to the protein structure prediction problem on the face-centered cube lattice model. Molecular dynamics techniques, endowed with the notion of constraint, have been also exploited. Even using a very simplified model, Constraint Logic Programming on the face-centered cube lattice model allowed us to obtain acceptable results for a few small proteins. As a test implementation their (known secondary structure and the presence of disulfide bridges are used as constraints. Simplified structures obtained in this way have been converted to all atom models with plausible structure. Results have been compared with a similar approach using a well-established technique as molecular dynamics. Conclusions The results obtained on small proteins show that Constraint Logic Programming techniques can be employed for studying protein simplified models, which can be converted into realistic all atom models. The advantage of Constraint Logic Programming over other, much more explored, methodologies, resides in the rapid software prototyping, in the easy way of encoding heuristics, and in exploiting all the advances made in this research area, e.g. in constraint propagation and its use for pruning the huge search space.

Constraint Logic Programming approach to protein structure prediction.

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Dal Palù, Alessandro; Dovier, Agostino; Fogolari, Federico

2004-11-30

The protein structure prediction problem is one of the most challenging problems in biological sciences. Many approaches have been proposed using database information and/or simplified protein models. The protein structure prediction problem can be cast in the form of an optimization problem. Notwithstanding its importance, the problem has very seldom been tackled by Constraint Logic Programming, a declarative programming paradigm suitable for solving combinatorial optimization problems. Constraint Logic Programming techniques have been applied to the protein structure prediction problem on the face-centered cube lattice model. Molecular dynamics techniques, endowed with the notion of constraint, have been also exploited. Even using a very simplified model, Constraint Logic Programming on the face-centered cube lattice model allowed us to obtain acceptable results for a few small proteins. As a test implementation their (known) secondary structure and the presence of disulfide bridges are used as constraints. Simplified structures obtained in this way have been converted to all atom models with plausible structure. Results have been compared with a similar approach using a well-established technique as molecular dynamics. The results obtained on small proteins show that Constraint Logic Programming techniques can be employed for studying protein simplified models, which can be converted into realistic all atom models. The advantage of Constraint Logic Programming over other, much more explored, methodologies, resides in the rapid software prototyping, in the easy way of encoding heuristics, and in exploiting all the advances made in this research area, e.g. in constraint propagation and its use for pruning the huge search space.

Protein secondary structure and stability determined by combining exoproteolysis and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry.

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Villanueva, Josep; Villegas, Virtudes; Querol, Enrique; Avilés, Francesc X; Serrano, Luis

2002-09-01

In the post-genomic era, several projects focused on the massive experimental resolution of the three-dimensional structures of all the proteins of different organisms have been initiated. Simultaneously, significant progress has been made in the ab initio prediction of protein three-dimensional structure. One of the keys to the success of such a prediction is the use of local information (i.e. secondary structure). Here we describe a new limited proteolysis methodology, based on the use of unspecific exoproteases coupled with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), to map quickly secondary structure elements of a protein from both ends, the N- and C-termini. We show that the proteolytic patterns (mass spectra series) obtained can be interpreted in the light of the conformation and local stability of the analyzed proteins, a direct correlation being observed between the predicted and the experimentally derived protein secondary structure. Further, this methodology can be easily applied to check rapidly the folding state of a protein and characterize mutational effects on protein conformation and stability. Moreover, given global stability information, this methodology allows one to locate the protein regions of increased or decreased conformational stability. All of this can be done with a small fraction of the amount of protein required by most of the other methods for conformational analysis. Thus limited exoproteolysis, together with MALDI-TOF MS, can be a useful tool to achieve quickly the elucidation of protein structure and stability. Copyright 2002 John Wiley & Sons, Ltd.

The secondary structure and the thermal unfolding parameters of the S-layer protein from Lactobacillus salivarius.

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Lighezan, Liliana; Georgieva, Ralitsa; Neagu, Adrian

2016-09-01

Surface layer (S-layer) proteins have been identified in the cell envelope of many organisms, such as bacteria and archaea. They self-assemble, forming monomolecular crystalline arrays. Isolated S-layer proteins are able to recrystallize into regular lattices, which proved useful in biotechnology. Here we investigate the structure and thermal unfolding of the S-layer protein isolated from Lactobacillus salivarius 16 strain of human origin. Using circular dichroism (CD) spectroscopy, and the software CDSSTR from DICHROWEB, CONTINLL from CDPro, as well as CDNN, we assess the fractions of the protein's secondary structural elements at temperatures ranging between 10 and 90 °C, and predict the tertiary class of the protein. To study the thermal unfolding of the protein, we analyze the temperature dependence of the CD signal in the far- and near-UV domains. Fitting the experimental data by two- and three-state models of thermal unfolding, we infer the midpoint temperatures, the temperature dependence of the changes in Gibbs free energy, enthalpy, and entropy of the unfolding transitions in standard conditions, and the temperature dependence of the equilibrium constant. We also estimate the changes in heat capacity at constant pressure in standard conditions. The results indicate that the thermal unfolding of the S-layer protein from L. salivarius is highly cooperative, since changes in the secondary and tertiary structures occur simultaneously. The thermodynamic analysis predicts a "cold" transition, at about -3 °C, of both the secondary and tertiary structures. Our findings may be important for the use of S-layer proteins in biotechnology and in biomedical applications.

DCJ-RNA - double cut and join for RNA secondary structures.

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Badr, Ghada H; Al-Aqel, Haifa A

2017-10-16

Genome rearrangements are essential processes for evolution and are responsible for existing varieties of genome architectures. Many studies have been conducted to obtain an algorithm that identifies the minimum number of inversions that are necessary to transform one genome into another; this allows for genome sequence representation in polynomial time. Studies have not been conducted on the topic of rearranging a genome when it is represented as a secondary structure. Unlike sequences, the secondary structure preserves the functionality of the genome. Sequences can be different, but they all share the same structure and, therefore, the same functionality. This paper proposes a double cut and join for RNA secondary structures (DCJ-RNA) algorithm. This algorithm allows for the description of evolutionary scenarios that are based on secondary structures rather than sequences. The main aim of this paper is to suggest an efficient algorithm that can help researchers compare two ribonucleic acid (RNA) secondary structures based on rearrangement operations. The results, which are based on real datasets, show that the algorithm is able to count the minimum number of rearrangement operations, as well as to report an optimum scenario that can increase the similarity between the two structures. The algorithm calculates the distance between structures and reports a scenario based on the minimum rearrangement operations required to make the given structure similar to the other. DCJ-RNA can also be used to measure the distance between the two structures. This can help identify the common functionalities between different species.

CMD: A Database to Store the Bonding States of Cysteine Motifs with Secondary Structures

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Hamed Bostan

2012-01-01

Full Text Available Computational approaches to the disulphide bonding state and its connectivity pattern prediction are based on various descriptors. One descriptor is the amino acid sequence motifs flanking the cysteine residue motifs. Despite the existence of disulphide bonding information in many databases and applications, there is no complete reference and motif query available at the moment. Cysteine motif database (CMD is the first online resource that stores all cysteine residues, their flanking motifs with their secondary structure, and propensity values assignment derived from the laboratory data. We extracted more than 3 million cysteine motifs from PDB and UniProt data, annotated with secondary structure assignment, propensity value assignment, and frequency of occurrence and coefficiency of their bonding status. Removal of redundancies generated 15875 unique flanking motifs that are always bonded and 41577 unique patterns that are always nonbonded. Queries are based on the protein ID, FASTA sequence, sequence motif, and secondary structure individually or in batch format using the provided APIs that allow remote users to query our database via third party software and/or high throughput screening/querying. The CMD offers extensive information about the bonded, free cysteine residues, and their motifs that allows in-depth characterization of the sequence motif composition.

Deciphering the shape and deformation of secondary structures through local conformation analysis

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Camproux Anne-Claude

2011-02-01

Full Text Available Abstract Background Protein deformation has been extensively analysed through global methods based on RMSD, torsion angles and Principal Components Analysis calculations. Here we use a local approach, able to distinguish among the different backbone conformations within loops, α-helices and β-strands, to address the question of secondary structures' shape variation within proteins and deformation at interface upon complexation. Results Using a structural alphabet, we translated the 3 D structures of large sets of protein-protein complexes into sequences of structural letters. The shape of the secondary structures can be assessed by the structural letters that modeled them in the structural sequences. The distribution analysis of the structural letters in the three protein compartments (surface, core and interface reveals that secondary structures tend to adopt preferential conformations that differ among the compartments. The local description of secondary structures highlights that curved conformations are preferred on the surface while straight ones are preferred in the core. Interfaces display a mixture of local conformations either preferred in core or surface. The analysis of the structural letters transition occurring between protein-bound and unbound conformations shows that the deformation of secondary structure is tightly linked to the compartment preference of the local conformations. Conclusion The conformation of secondary structures can be further analysed and detailed thanks to a structural alphabet which allows a better description of protein surface, core and interface in terms of secondary structures' shape and deformation. Induced-fit modification tendencies described here should be valuable information to identify and characterize regions under strong structural constraints for functional reasons.

Deciphering the shape and deformation of secondary structures through local conformation analysis.

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Baussand, Julie; Camproux, Anne-Claude

2011-02-01

Protein deformation has been extensively analysed through global methods based on RMSD, torsion angles and Principal Components Analysis calculations. Here we use a local approach, able to distinguish among the different backbone conformations within loops, α-helices and β-strands, to address the question of secondary structures' shape variation within proteins and deformation at interface upon complexation. Using a structural alphabet, we translated the 3 D structures of large sets of protein-protein complexes into sequences of structural letters. The shape of the secondary structures can be assessed by the structural letters that modeled them in the structural sequences. The distribution analysis of the structural letters in the three protein compartments (surface, core and interface) reveals that secondary structures tend to adopt preferential conformations that differ among the compartments. The local description of secondary structures highlights that curved conformations are preferred on the surface while straight ones are preferred in the core. Interfaces display a mixture of local conformations either preferred in core or surface. The analysis of the structural letters transition occurring between protein-bound and unbound conformations shows that the deformation of secondary structure is tightly linked to the compartment preference of the local conformations. The conformation of secondary structures can be further analysed and detailed thanks to a structural alphabet which allows a better description of protein surface, core and interface in terms of secondary structures' shape and deformation. Induced-fit modification tendencies described here should be valuable information to identify and characterize regions under strong structural constraints for functional reasons.

Structural properties of MHC class II ligands, implications for the prediction of MHC class II epitopes.

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Kasper Winther Jørgensen

2010-12-01

Full Text Available Major Histocompatibility class II (MHC-II molecules sample peptides from the extracellular space allowing the immune system to detect the presence of foreign microbes from this compartment. Prediction of MHC class II ligands is complicated by the open binding cleft of the MHC class II molecule, allowing binding of peptides extending out of the binding groove. Furthermore, only a few HLA-DR alleles have been characterized with a sufficient number of peptides (100-200 peptides per allele to derive accurate description of their binding motif. Little work has been performed characterizing structural properties of MHC class II ligands. Here, we perform one such large-scale analysis. A large set of SYFPEITHI MHC class II ligands covering more than 20 different HLA-DR molecules was analyzed in terms of their secondary structure and surface exposure characteristics in the context of the native structure of the corresponding source protein. We demonstrated that MHC class II ligands are significantly more exposed and have significantly more coil content than other peptides in the same protein with similar predicted binding affinity. We next exploited this observation to derive an improved prediction method for MHC class II ligands by integrating prediction of MHC- peptide binding with prediction of surface exposure and protein secondary structure. This combined prediction method was shown to significantly outperform the state-of-the-art MHC class II peptide binding prediction method when used to identify MHC class II ligands. We also tried to integrate N- and O-glycosylation in our prediction methods but this additional information was found not to improve prediction performance. In summary, these findings strongly suggest that local structural properties influence antigen processing and/or the accessibility of peptides to the MHC class II molecule.

SFESA: a web server for pairwise alignment refinement by secondary structure shifts.

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Tong, Jing; Pei, Jimin; Grishin, Nick V

2015-09-03

Protein sequence alignment is essential for a variety of tasks such as homology modeling and active site prediction. Alignment errors remain the main cause of low-quality structure models. A bioinformatics tool to refine alignments is needed to make protein alignments more accurate. We developed the SFESA web server to refine pairwise protein sequence alignments. Compared to the previous version of SFESA, which required a set of 3D coordinates for a protein, the new server will search a sequence database for the closest homolog with an available 3D structure to be used as a template. For each alignment block defined by secondary structure elements in the template, SFESA evaluates alignment variants generated by local shifts and selects the best-scoring alignment variant. A scoring function that combines the sequence score of profile-profile comparison and the structure score of template-derived contact energy is used for evaluation of alignments. PROMALS pairwise alignments refined by SFESA are more accurate than those produced by current advanced alignment methods such as HHpred and CNFpred. In addition, SFESA also improves alignments generated by other software. SFESA is a web-based tool for alignment refinement, designed for researchers to compute, refine, and evaluate pairwise alignments with a combined sequence and structure scoring of alignment blocks. To our knowledge, the SFESA web server is the only tool that refines alignments by evaluating local shifts of secondary structure elements. The SFESA web server is available at http://prodata.swmed.edu/sfesa.

Repeat-swap homology modeling of secondary active transporters: updated protocol and prediction of elevator-type mechanisms.

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Vergara-Jaque, Ariela; Fenollar-Ferrer, Cristina; Kaufmann, Desirée; Forrest, Lucy R

2015-01-01

Secondary active transporters are critical for neurotransmitter clearance and recycling during synaptic transmission and uptake of nutrients. These proteins mediate the movement of solutes against their concentration gradients, by using the energy released in the movement of ions down pre-existing concentration gradients. To achieve this, transporters conform to the so-called alternating-access hypothesis, whereby the protein adopts at least two conformations in which the substrate binding sites are exposed to one or other side of the membrane, but not both simultaneously. Structures of a bacterial homolog of neuronal glutamate transporters, GltPh, in several different conformational states have revealed that the protein structure is asymmetric in the outward- and inward-open states, and that the conformational change connecting them involves a elevator-like movement of a substrate binding domain across the membrane. The structural asymmetry is created by inverted-topology repeats, i.e., structural repeats with similar overall folds whose transmembrane topologies are related to each other by two-fold pseudo-symmetry around an axis parallel to the membrane plane. Inverted repeats have been found in around three-quarters of secondary transporter folds. Moreover, the (a)symmetry of these systems has been successfully used as a bioinformatic tool, called "repeat-swap modeling" to predict structural models of a transporter in one conformation using the known structure of the transporter in the complementary conformation as a template. Here, we describe an updated repeat-swap homology modeling protocol, and calibrate the accuracy of the method using GltPh, for which both inward- and outward-facing conformations are known. We then apply this repeat-swap homology modeling procedure to a concentrative nucleoside transporter, VcCNT, which has a three-dimensional arrangement related to that of GltPh. The repeat-swapped model of VcCNT predicts that nucleoside transport also

Repeat-swap homology modeling of secondary active transporters: updated protocol and prediction of elevator-type mechanisms

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Cristina eFenollar Ferrer

2015-09-01

Full Text Available Secondary active transporters are critical for neurotransmitter clearance and recycling during synaptic transmission and uptake of nutrients. These proteins mediate the movement of solutes against their concentration gradients, by using the energy released in the movement of ions down pre-existing concentration gradients. To achieve this, transporters conform to the so-called alternating-access hypothesis, whereby the protein adopts at least two conformations in which the substrate binding sites are exposed to either the outside or inside of the membrane, but not both simultaneously. Structures of a bacterial homolog of neuronal glutamate transporters, GltPh, in several different conformational states have revealed that the protein structure is asymmetric in the outward- and inward-open states, and that the conformational change connecting them involves a elevator-like movement of a substrate binding domain across the membrane. The structural asymmetry is created by inverted-topology repeats, i.e., structural repeats with similar overall folds whose transmembrane topologies are related to each other by two-fold pseudo-symmetry around an axis parallel to the membrane plane. Inverted repeats have been found in around three-quarters of secondary transporter folds. Moreover, the (asymmetry of these systems has been successfully used as a bioinformatic tool, called repeat-swap modeling to predict structural models of a transporter in one conformation using the known structure of the transporter in the complementary conformation as a template. Here, we describe an updated repeat-swap homology modeling protocol, and calibrate the accuracy of the method using GltPh, for which both inward- and outward-facing conformations are known. We then apply this repeat-swap homology modeling procedure to a concentrative nucleoside transporter, VcCNT, which has a three-dimensional arrangement related to that of GltPh. The repeat-swapped model of VcCNT predicts that

3x2 Classroom Goal Structures, Motivational Regulations, Self-Concept, and Affectivity in Secondary School.

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Méndez-Giménez, Antonio; Cecchini-Estrada, José-Antonio; Fernández-Río, Javier; Prieto Saborit, José Antonio; Méndez-Alonso, David

2017-09-20

The main objective was to analyze relationships and predictive patterns between 3x2 classroom goal structures (CGS), and motivational regulations, dimensions of self-concept, and affectivity in the context of secondary education. A sample of 1,347 secondary school students (56.6% young men, 43.4% young women) from 10 different provinces of Spain agreed to participate (M age = 13.43, SD = 1.05). Hierarchical regression analyses indicated the self-approach CGS was the most adaptive within the spectrum of self-determination, followed by the task-approach CGS. The other-approach CGS had an ambivalent influence on motivation. Task-approach and self-approach CGS predicted academic self-concept (p approach CGS (negatively) predicted family self-concept (p approach and other-approach CGS's (p approach-oriented CGS's (p approach (positively) and self-approach (negatively) CGS (p < .001; p < .05, respectively; R 2 = .028). These results expand the 3x2 achievement goal framework to include environmental factors, and reiterate that teachers should focus on raising levels of self- and task-based goals for students in their classes.

Protein secondary structure assignment revisited: a detailed analysis of different assignment methods

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de Brevern Alexandre G

2005-09-01

Full Text Available Abstract Background A number of methods are now available to perform automatic assignment of periodic secondary structures from atomic coordinates, based on different characteristics of the secondary structures. In general these methods exhibit a broad consensus as to the location of most helix and strand core segments in protein structures. However the termini of the segments are often ill-defined and it is difficult to decide unambiguously which residues at the edge of the segments have to be included. In addition, there is a "twilight zone" where secondary structure segments depart significantly from the idealized models of Pauling and Corey. For these segments, one has to decide whether the observed structural variations are merely distorsions or whether they constitute a break in the secondary structure. Methods To address these problems, we have developed a method for secondary structure assignment, called KAKSI. Assignments made by KAKSI are compared with assignments given by DSSP, STRIDE, XTLSSTR, PSEA and SECSTR, as well as secondary structures found in PDB files, on 4 datasets (X-ray structures with different resolution range, NMR structures. Results A detailed comparison of KAKSI assignments with those of STRIDE and PSEA reveals that KAKSI assigns slightly longer helices and strands than STRIDE in case of one-to-one correspondence between the segments. However, KAKSI tends also to favor the assignment of several short helices when STRIDE and PSEA assign longer, kinked, helices. Helices assigned by KAKSI have geometrical characteristics close to those described in the PDB. They are more linear than helices assigned by other methods. The same tendency to split long segments is observed for strands, although less systematically. We present a number of cases of secondary structure assignments that illustrate this behavior. Conclusion Our method provides valuable assignments which favor the regularity of secondary structure segments.

CSSI-PRO: a method for secondary structure type editing, assignment and estimation in proteins using linear combination of backbone chemical shifts

International Nuclear Information System (INIS)

Swain, Monalisa; Atreya, Hanudatta S.

2009-01-01

Estimation of secondary structure in polypeptides is important for studying their structure, folding and dynamics. In NMR spectroscopy, such information is generally obtained after sequence specific resonance assignments are completed. We present here a new methodology for assignment of secondary structure type to spin systems in proteins directly from NMR spectra, without prior knowledge of resonance assignments. The methodology, named Combination of Shifts for Secondary Structure Identification in Proteins (CSSI-PRO), involves detection of specific linear combination of backbone 1 H α and 13 C' chemical shifts in a two-dimensional (2D) NMR experiment based on G-matrix Fourier transform (GFT) NMR spectroscopy. Such linear combinations of shifts facilitate editing of residues belonging to α-helical/β-strand regions into distinct spectral regions nearly independent of the amino acid type, thereby allowing the estimation of overall secondary structure content of the protein. Comparison of the predicted secondary structure content with those estimated based on their respective 3D structures and/or the method of Chemical Shift Index for 237 proteins gives a correlation of more than 90% and an overall rmsd of 7.0%, which is comparable to other biophysical techniques used for structural characterization of proteins. Taken together, this methodology has a wide range of applications in NMR spectroscopy such as rapid protein structure determination, monitoring conformational changes in protein-folding/ligand-binding studies and automated resonance assignment

Linguistic Structure Prediction

CERN Document Server

Smith, Noah A

2011-01-01

A major part of natural language processing now depends on the use of text data to build linguistic analyzers. We consider statistical, computational approaches to modeling linguistic structure. We seek to unify across many approaches and many kinds of linguistic structures. Assuming a basic understanding of natural language processing and/or machine learning, we seek to bridge the gap between the two fields. Approaches to decoding (i.e., carrying out linguistic structure prediction) and supervised and unsupervised learning of models that predict discrete structures as outputs are the focus. W

Structure elucidation of secondary natural products

International Nuclear Information System (INIS)

Seger, C.

2001-06-01

The presented thesis deals with the structure elucidation of secondary natural products. Most of the compounds under investigation were terpenes, especially triterpenes, alkaloids and stilbenoids. Besides characterizing a multitude of already known and also new compounds, it was possible to detect and correct wrongly assigned literature data. The methodological aspect of this thesis lies - beside in the utilization of modern 2D NMR spectroscopy - in the evaluation of computer assisted structure elucidation (CASE) techniques in the course of spectroscopy supported structure elucidation processes. (author)

Factors Predicting Burnout Among Chaplains: Compassion Satisfaction, Organizational Factors, and the Mediators of Mindful Self-Care and Secondary Traumatic Stress.

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Hotchkiss, Jason T; Lesher, Ruth

2018-06-01

This study predicted Burnout from the self-care practices, compassion satisfaction, secondary traumatic stress, and organizational factors among chaplains who participated from all 50 states (N = 534). A hierarchical regression model indicated that the combined effect of compassion satisfaction, secondary traumatic stress, mindful self-care, demographic, and organizational factors explained 83.2% of the variance in Burnout. Chaplains serving in a hospital were slightly more at risk for Burnout than those in hospice or other settings. Organizational factors that most predicted Burnout were feeling bogged down by the "system" (25.7%) and an overwhelming caseload (19.9%). Each self-care category was a statistically significant protective factor against Burnout risk. The strongest protective factors against Burnout in order of strength were self-compassion and purpose, supportive structure, mindful self-awareness, mindful relaxation, supportive relationships, and physical care. For secondary traumatic stress, supportive structure, mindful self-awareness, and self-compassion and purpose were the strongest protective factors. Chaplains who engaged in multiple and frequent self-care strategies experienced higher professional quality of life and low Burnout risk. In the chaplain's journey toward wellness, a reflective practice of feeling good about doing good and mindful self-care are vital. The significance, implications, and limitations of the study were discussed.

How does vegetation structure influence woodpeckers and secondary cavity nesting birds in African cork oak forest?

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Segura, Amalia

2017-08-01

The Great Spotted Woodpecker provides important information about the status of a forest in terms of structure and age. As a primary cavity creator, it provides small-medium size cavities for passerines. However, despite its interest as an ecosystem engineer, studies of this species in Africa are scarce. Here, spatially explicit predictive models were used to investigate how forest structural variables are related to both the Great Spotted Woodpecker and secondary cavity nesting birds in Maamora cork oak forest (northwest Morocco). A positive association between Great Spotted Woodpecker and both dead-tree density and large mature trees (>60 cm dbh) was found. This study area, Maamora, has an old-growth forest structure incorporating a broad range of size and condition of live and dead trees, favouring Great Spotted Woodpecker by providing high availability of foraging and excavating sites. Secondary cavity nesting birds, represented by Great Tit, African Blue Tit, and Hoopoe, were predicted by Great Spotted Woodpecker detections. The findings suggest that the conservation of the Maamora cork oak forest could be key to maintaining these hole-nesting birds. However, this forest is threatened by forestry practises and livestock overgrazing and the challenge is therefore to find sustainable management strategies that ensure conservation while allowing its exploitation.

Detection of non-coding RNAs on the basis of predicted secondary structure formation free energy change

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Uzilov Andrew V

2006-03-01

Full Text Available Abstract Background Non-coding RNAs (ncRNAs have a multitude of roles in the cell, many of which remain to be discovered. However, it is difficult to detect novel ncRNAs in biochemical screens. To advance biological knowledge, computational methods that can accurately detect ncRNAs in sequenced genomes are therefore desirable. The increasing number of genomic sequences provides a rich dataset for computational comparative sequence analysis and detection of novel ncRNAs. Results Here, Dynalign, a program for predicting secondary structures common to two RNA sequences on the basis of minimizing folding free energy change, is utilized as a computational ncRNA detection tool. The Dynalign-computed optimal total free energy change, which scores the structural alignment and the free energy change of folding into a common structure for two RNA sequences, is shown to be an effective measure for distinguishing ncRNA from randomized sequences. To make the classification as a ncRNA, the total free energy change of an input sequence pair can either be compared with the total free energy changes of a set of control sequence pairs, or be used in combination with sequence length and nucleotide frequencies as input to a classification support vector machine. The latter method is much faster, but slightly less sensitive at a given specificity. Additionally, the classification support vector machine method is shown to be sensitive and specific on genomic ncRNA screens of two different Escherichia coli and Salmonella typhi genome alignments, in which many ncRNAs are known. The Dynalign computational experiments are also compared with two other ncRNA detection programs, RNAz and QRNA. Conclusion The Dynalign-based support vector machine method is more sensitive for known ncRNAs in the test genomic screens than RNAz and QRNA. Additionally, both Dynalign-based methods are more sensitive than RNAz and QRNA at low sequence pair identities. Dynalign can be used as a

Approaches to link RNA secondary structures with splicing regulation

DEFF Research Database (Denmark)

Plass, Mireya; Eyras, Eduardo

2014-01-01

In higher eukaryotes, alternative splicing is usually regulated by protein factors, which bind to the pre-mRNA and affect the recognition of splicing signals. There is recent evidence that the secondary structure of the pre-mRNA may also play an important role in this process, either by facilitat...... describes the steps in the analysis of the secondary structure of the pre-mRNA and its possible relation to splicing. As a working example, we use the case of yeast and the problem of the recognition of the 3' splice site (3'ss).......In higher eukaryotes, alternative splicing is usually regulated by protein factors, which bind to the pre-mRNA and affect the recognition of splicing signals. There is recent evidence that the secondary structure of the pre-mRNA may also play an important role in this process, either...

Non-B DNA Secondary Structures and Their Resolution by RecQ Helicases

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Sudha Sharma

2011-01-01

Full Text Available In addition to the canonical B-form structure first described by Watson and Crick, DNA can adopt a number of alternative structures. These non-B-form DNA secondary structures form spontaneously on tracts of repeat sequences that are abundant in genomes. In addition, structured forms of DNA with intrastrand pairing may arise on single-stranded DNA produced transiently during various cellular processes. Such secondary structures have a range of biological functions but also induce genetic instability. Increasing evidence suggests that genomic instabilities induced by non-B DNA secondary structures result in predisposition to diseases. Secondary DNA structures also represent a new class of molecular targets for DNA-interactive compounds that might be useful for targeting telomeres and transcriptional control. The equilibrium between the duplex DNA and formation of multistranded non-B-form structures is partly dependent upon the helicases that unwind (resolve these alternate DNA structures. With special focus on tetraplex, triplex, and cruciform, this paper summarizes the incidence of non-B DNA structures and their association with genomic instability and emphasizes the roles of RecQ-like DNA helicases in genome maintenance by resolution of DNA secondary structures. In future, RecQ helicases are anticipated to be additional molecular targets for cancer chemotherapeutics.

Practical use of chemical shift databases for protein solid-state NMR: 2D chemical shift maps and amino-acid assignment with secondary-structure information

International Nuclear Information System (INIS)

Fritzsching, K. J.; Yang, Y.; Schmidt-Rohr, K.; Hong Mei

2013-01-01

We introduce a Python-based program that utilizes the large database of 13 C and 15 N chemical shifts in the Biological Magnetic Resonance Bank to rapidly predict the amino acid type and secondary structure from correlated chemical shifts. The program, called PACSYlite Unified Query (PLUQ), is designed to help assign peaks obtained from 2D 13 C– 13 C, 15 N– 13 C, or 3D 15 N– 13 C– 13 C magic-angle-spinning correlation spectra. We show secondary-structure specific 2D 13 C– 13 C correlation maps of all twenty amino acids, constructed from a chemical shift database of 262,209 residues. The maps reveal interesting conformation-dependent chemical shift distributions and facilitate searching of correlation peaks during amino-acid type assignment. Based on these correlations, PLUQ outputs the most likely amino acid types and the associated secondary structures from inputs of experimental chemical shifts. We test the assignment accuracy using four high-quality protein structures. Based on only the Cα and Cβ chemical shifts, the highest-ranked PLUQ assignments were 40–60 % correct in both the amino-acid type and the secondary structure. For three input chemical shifts (CO–Cα–Cβ or N–Cα–Cβ), the first-ranked assignments were correct for 60 % of the residues, while within the top three predictions, the correct assignments were found for 80 % of the residues. PLUQ and the chemical shift maps are expected to be useful at the first stage of sequential assignment, for combination with automated sequential assignment programs, and for highly disordered proteins for which secondary structure analysis is the main goal of structure determination.

Density functional study of molecular interactions in secondary structures of proteins.

Science.gov (United States)

Takano, Yu; Kusaka, Ayumi; Nakamura, Haruki

2016-01-01

Proteins play diverse and vital roles in biology, which are dominated by their three-dimensional structures. The three-dimensional structure of a protein determines its functions and chemical properties. Protein secondary structures, including α-helices and β-sheets, are key components of the protein architecture. Molecular interactions, in particular hydrogen bonds, play significant roles in the formation of protein secondary structures. Precise and quantitative estimations of these interactions are required to understand the principles underlying the formation of three-dimensional protein structures. In the present study, we have investigated the molecular interactions in α-helices and β-sheets, using ab initio wave function-based methods, the Hartree-Fock method (HF) and the second-order Møller-Plesset perturbation theory (MP2), density functional theory, and molecular mechanics. The characteristic interactions essential for forming the secondary structures are discussed quantitatively.

Detection of secondary structure elements in proteins by hydrophobic cluster analysis.

Science.gov (United States)

Woodcock, S; Mornon, J P; Henrissat, B

1992-10-01

Hydrophobic cluster analysis (HCA) is a protein sequence comparison method based on alpha-helical representations of the sequences where the size, shape and orientation of the clusters of hydrophobic residues are primarily compared. The effectiveness of HCA has been suggested to originate from its potential ability to focus on the residues forming the hydrophobic core of globular proteins. We have addressed the robustness of the bidimensional representation used for HCA in its ability to detect the regular secondary structure elements of proteins. Various parameters have been studied such as those governing cluster size and limits, the hydrophobic residues constituting the clusters as well as the potential shift of the cluster positions with respect to the position of the regular secondary structure elements. The following results have been found to support the alpha-helical bidimensional representation used in HCA: (i) there is a positive correlation (clearly above background noise) between the hydrophobic clusters and the regular secondary structure elements in proteins; (ii) the hydrophobic clusters are centred on the regular secondary structure elements; (iii) the pitch of the helical representation which gives the best correspondence is that of an alpha-helix. The correspondence between hydrophobic clusters and regular secondary structure elements suggests a way to implement variable gap penalties during the automatic alignment of protein sequences.

Including RNA secondary structures improves accuracy and robustness in reconstruction of phylogenetic trees.

Science.gov (United States)

Keller, Alexander; Förster, Frank; Müller, Tobias; Dandekar, Thomas; Schultz, Jörg; Wolf, Matthias

2010-01-15

In several studies, secondary structures of ribosomal genes have been used to improve the quality of phylogenetic reconstructions. An extensive evaluation of the benefits of secondary structure, however, is lacking. This is the first study to counter this deficiency. We inspected the accuracy and robustness of phylogenetics with individual secondary structures by simulation experiments for artificial tree topologies with up to 18 taxa and for divergency levels in the range of typical phylogenetic studies. We chose the internal transcribed spacer 2 of the ribosomal cistron as an exemplary marker region. Simulation integrated the coevolution process of sequences with secondary structures. Additionally, the phylogenetic power of marker size duplication was investigated and compared with sequence and sequence-structure reconstruction methods. The results clearly show that accuracy and robustness of Neighbor Joining trees are largely improved by structural information in contrast to sequence only data, whereas a doubled marker size only accounts for robustness. Individual secondary structures of ribosomal RNA sequences provide a valuable gain of information content that is useful for phylogenetics. Thus, the usage of ITS2 sequence together with secondary structure for taxonomic inferences is recommended. Other reconstruction methods as maximum likelihood, bayesian inference or maximum parsimony may equally profit from secondary structure inclusion. This article was reviewed by Shamil Sunyaev, Andrea Tanzer (nominated by Frank Eisenhaber) and Eugene V. Koonin. Reviewed by Shamil Sunyaev, Andrea Tanzer (nominated by Frank Eisenhaber) and Eugene V. Koonin. For the full reviews, please go to the Reviewers' comments section.

Sparse RNA folding revisited: space-efficient minimum free energy structure prediction.

Science.gov (United States)

Will, Sebastian; Jabbari, Hosna

2016-01-01

RNA secondary structure prediction by energy minimization is the central computational tool for the analysis of structural non-coding RNAs and their interactions. Sparsification has been successfully applied to improve the time efficiency of various structure prediction algorithms while guaranteeing the same result; however, for many such folding problems, space efficiency is of even greater concern, particularly for long RNA sequences. So far, space-efficient sparsified RNA folding with fold reconstruction was solved only for simple base-pair-based pseudo-energy models. Here, we revisit the problem of space-efficient free energy minimization. Whereas the space-efficient minimization of the free energy has been sketched before, the reconstruction of the optimum structure has not even been discussed. We show that this reconstruction is not possible in trivial extension of the method for simple energy models. Then, we present the time- and space-efficient sparsified free energy minimization algorithm SparseMFEFold that guarantees MFE structure prediction. In particular, this novel algorithm provides efficient fold reconstruction based on dynamically garbage-collected trace arrows. The complexity of our algorithm depends on two parameters, the number of candidates Z and the number of trace arrows T; both are bounded by [Formula: see text], but are typically much smaller. The time complexity of RNA folding is reduced from [Formula: see text] to [Formula: see text]; the space complexity, from [Formula: see text] to [Formula: see text]. Our empirical results show more than 80 % space savings over RNAfold [Vienna RNA package] on the long RNAs from the RNA STRAND database (≥2500 bases). The presented technique is intentionally generalizable to complex prediction algorithms; due to their high space demands, algorithms like pseudoknot prediction and RNA-RNA-interaction prediction are expected to profit even stronger than "standard" MFE folding. SparseMFEFold is free

Distance matrix-based approach to protein structure prediction.

Science.gov (United States)

Kloczkowski, Andrzej; Jernigan, Robert L; Wu, Zhijun; Song, Guang; Yang, Lei; Kolinski, Andrzej; Pokarowski, Piotr

2009-03-01

Much structural information is encoded in the internal distances; a distance matrix-based approach can be used to predict protein structure and dynamics, and for structural refinement. Our approach is based on the square distance matrix D = [r(ij)(2)] containing all square distances between residues in proteins. This distance matrix contains more information than the contact matrix C, that has elements of either 0 or 1 depending on whether the distance r (ij) is greater or less than a cutoff value r (cutoff). We have performed spectral decomposition of the distance matrices D = sigma lambda(k)V(k)V(kT), in terms of eigenvalues lambda kappa and the corresponding eigenvectors v kappa and found that it contains at most five nonzero terms. A dominant eigenvector is proportional to r (2)--the square distance of points from the center of mass, with the next three being the principal components of the system of points. By predicting r (2) from the sequence we can approximate a distance matrix of a protein with an expected RMSD value of about 7.3 A, and by combining it with the prediction of the first principal component we can improve this approximation to 4.0 A. We can also explain the role of hydrophobic interactions for the protein structure, because r is highly correlated with the hydrophobic profile of the sequence. Moreover, r is highly correlated with several sequence profiles which are useful in protein structure prediction, such as contact number, the residue-wise contact order (RWCO) or mean square fluctuations (i.e. crystallographic temperature factors). We have also shown that the next three components are related to spatial directionality of the secondary structure elements, and they may be also predicted from the sequence, improving overall structure prediction. We have also shown that the large number of available HIV-1 protease structures provides a remarkable sampling of conformations, which can be viewed as direct structural information about the

Practical use of chemical shift databases for protein solid-state NMR: 2D chemical shift maps and amino-acid assignment with secondary-structure information

Energy Technology Data Exchange (ETDEWEB)

Fritzsching, K. J.; Yang, Y.; Schmidt-Rohr, K.; Hong Mei, E-mail: mhong@iastate.edu [Iowa State University, Department of Chemistry (United States)

2013-06-15

We introduce a Python-based program that utilizes the large database of {sup 13}C and {sup 15}N chemical shifts in the Biological Magnetic Resonance Bank to rapidly predict the amino acid type and secondary structure from correlated chemical shifts. The program, called PACSYlite Unified Query (PLUQ), is designed to help assign peaks obtained from 2D {sup 13}C-{sup 13}C, {sup 15}N-{sup 13}C, or 3D {sup 15}N-{sup 13}C-{sup 13}C magic-angle-spinning correlation spectra. We show secondary-structure specific 2D {sup 13}C-{sup 13}C correlation maps of all twenty amino acids, constructed from a chemical shift database of 262,209 residues. The maps reveal interesting conformation-dependent chemical shift distributions and facilitate searching of correlation peaks during amino-acid type assignment. Based on these correlations, PLUQ outputs the most likely amino acid types and the associated secondary structures from inputs of experimental chemical shifts. We test the assignment accuracy using four high-quality protein structures. Based on only the C{alpha} and C{beta} chemical shifts, the highest-ranked PLUQ assignments were 40-60 % correct in both the amino-acid type and the secondary structure. For three input chemical shifts (CO-C{alpha}-C{beta} or N-C{alpha}-C{beta}), the first-ranked assignments were correct for 60 % of the residues, while within the top three predictions, the correct assignments were found for 80 % of the residues. PLUQ and the chemical shift maps are expected to be useful at the first stage of sequential assignment, for combination with automated sequential assignment programs, and for highly disordered proteins for which secondary structure analysis is the main goal of structure determination.

Prediction of molecular crystal structures

International Nuclear Information System (INIS)

Beyer, Theresa

2001-01-01

The ab initio prediction of molecular crystal structures is a scientific challenge. Reliability of first-principle prediction calculations would show a fundamental understanding of crystallisation. Crystal structure prediction is also of considerable practical importance as different crystalline arrangements of the same molecule in the solid state (polymorphs)are likely to have different physical properties. A method of crystal structure prediction based on lattice energy minimisation has been developed in this work. The choice of the intermolecular potential and of the molecular model is crucial for the results of such studies and both of these criteria have been investigated. An empirical atom-atom repulsion-dispersion potential for carboxylic acids has been derived and applied in a crystal structure prediction study of formic, benzoic and the polymorphic system of tetrolic acid. As many experimental crystal structure determinations at different temperatures are available for the polymorphic system of paracetamol (acetaminophen), the influence of the variations of the molecular model on the crystal structure lattice energy minima, has also been studied. The general problem of prediction methods based on the assumption that the experimental thermodynamically stable polymorph corresponds to the global lattice energy minimum, is that more hypothetical low lattice energy structures are found within a few kJ mol -1 of the global minimum than are likely to be experimentally observed polymorphs. This is illustrated by the results for molecule I, 3-oxabicyclo(3.2.0)hepta-1,4-diene, studied for the first international blindtest for small organic crystal structures organised by the Cambridge Crystallographic Data Centre (CCDC) in May 1999. To reduce the number of predicted polymorphs, additional factors to thermodynamic criteria have to be considered. Therefore the elastic constants and vapour growth morphologies have been calculated for the lowest lattice energy

Prediction of molecular crystal structures

Energy Technology Data Exchange (ETDEWEB)

Beyer, Theresa

2001-07-01

The ab initio prediction of molecular crystal structures is a scientific challenge. Reliability of first-principle prediction calculations would show a fundamental understanding of crystallisation. Crystal structure prediction is also of considerable practical importance as different crystalline arrangements of the same molecule in the solid state (polymorphs)are likely to have different physical properties. A method of crystal structure prediction based on lattice energy minimisation has been developed in this work. The choice of the intermolecular potential and of the molecular model is crucial for the results of such studies and both of these criteria have been investigated. An empirical atom-atom repulsion-dispersion potential for carboxylic acids has been derived and applied in a crystal structure prediction study of formic, benzoic and the polymorphic system of tetrolic acid. As many experimental crystal structure determinations at different temperatures are available for the polymorphic system of paracetamol (acetaminophen), the influence of the variations of the molecular model on the crystal structure lattice energy minima, has also been studied. The general problem of prediction methods based on the assumption that the experimental thermodynamically stable polymorph corresponds to the global lattice energy minimum, is that more hypothetical low lattice energy structures are found within a few kJ mol{sup -1} of the global minimum than are likely to be experimentally observed polymorphs. This is illustrated by the results for molecule I, 3-oxabicyclo(3.2.0)hepta-1,4-diene, studied for the first international blindtest for small organic crystal structures organised by the Cambridge Crystallographic Data Centre (CCDC) in May 1999. To reduce the number of predicted polymorphs, additional factors to thermodynamic criteria have to be considered. Therefore the elastic constants and vapour growth morphologies have been calculated for the lowest lattice energy

Original Paper Floristic and structural changes in secondary forests ...

African Journals Online (AJOL)

Data from the first inventory in secondary and old-growth forests were ... Structural changes in secondary forests are less known in West Africa, and ... temporal succession from one time spatial ..... s = number of species sampled per hectare; S = species richness of the whole forest; NF = the number of taxonomic families,.

Including RNA secondary structures improves accuracy and robustness in reconstruction of phylogenetic trees

Directory of Open Access Journals (Sweden)

Dandekar Thomas

2010-01-01

Full Text Available Abstract Background In several studies, secondary structures of ribosomal genes have been used to improve the quality of phylogenetic reconstructions. An extensive evaluation of the benefits of secondary structure, however, is lacking. Results This is the first study to counter this deficiency. We inspected the accuracy and robustness of phylogenetics with individual secondary structures by simulation experiments for artificial tree topologies with up to 18 taxa and for divergency levels in the range of typical phylogenetic studies. We chose the internal transcribed spacer 2 of the ribosomal cistron as an exemplary marker region. Simulation integrated the coevolution process of sequences with secondary structures. Additionally, the phylogenetic power of marker size duplication was investigated and compared with sequence and sequence-structure reconstruction methods. The results clearly show that accuracy and robustness of Neighbor Joining trees are largely improved by structural information in contrast to sequence only data, whereas a doubled marker size only accounts for robustness. Conclusions Individual secondary structures of ribosomal RNA sequences provide a valuable gain of information content that is useful for phylogenetics. Thus, the usage of ITS2 sequence together with secondary structure for taxonomic inferences is recommended. Other reconstruction methods as maximum likelihood, bayesian inference or maximum parsimony may equally profit from secondary structure inclusion. Reviewers This article was reviewed by Shamil Sunyaev, Andrea Tanzer (nominated by Frank Eisenhaber and Eugene V. Koonin. Open peer review Reviewed by Shamil Sunyaev, Andrea Tanzer (nominated by Frank Eisenhaber and Eugene V. Koonin. For the full reviews, please go to the Reviewers' comments section.

Distill: a suite of web servers for the prediction of one-, two- and three-dimensional structural features of proteins

Directory of Open Access Journals (Sweden)

Walsh Ian

2006-09-01

Full Text Available Abstract Background We describe Distill, a suite of servers for the prediction of protein structural features: secondary structure; relative solvent accessibility; contact density; backbone structural motifs; residue contact maps at 6, 8 and 12 Angstrom; coarse protein topology. The servers are based on large-scale ensembles of recursive neural networks and trained on large, up-to-date, non-redundant subsets of the Protein Data Bank. Together with structural feature predictions, Distill includes a server for prediction of Cα traces for short proteins (up to 200 amino acids. Results The servers are state-of-the-art, with secondary structure predicted correctly for nearly 80% of residues (currently the top performance on EVA, 2-class solvent accessibility nearly 80% correct, and contact maps exceeding 50% precision on the top non-diagonal contacts. A preliminary implementation of the predictor of protein Cα traces featured among the top 20 Novel Fold predictors at the last CASP6 experiment as group Distill (ID 0348. The majority of the servers, including the Cα trace predictor, now take into account homology information from the PDB, when available, resulting in greatly improved reliability. Conclusion All predictions are freely available through a simple joint web interface and the results are returned by email. In a single submission the user can send protein sequences for a total of up to 32k residues to all or a selection of the servers. Distill is accessible at the address: http://distill.ucd.ie/distill/.

Life prediction of simple structures subject to cyclic primary and secondary loading resulting in creep and platicity

International Nuclear Information System (INIS)

Otter, N.R.; Jones, R.T.

1979-01-01

High temperature reactors are subject to cyclic mechanical and thermal loadings resulting from start up and shut down operations. The design must therefore guard against structural failure resulting from excessive deformation and creep-fatigue damage. Before any simplified inelastic analysis techniques can be applied, their validity needs to be examined under situations representative of the reactor. For this to be carried out it is necessary to determine the behaviour of components, initially geometrically simple, subject to loadings, cyclic primary and secondary in nature, which result in creep and plasticity. Beam-like structures have been investigated on a finite element basis with the aim of determining how cyclic plasticity, creep enhancement and plastic ratchetting vary in relationship with modified shakedown criteria, magnitude of loading and hold time. (orig.)

Class Anxiety in Secondary Education: Exploring Structural Relations with Perceived Control, Engagement, Disaffection, and Performance.

Science.gov (United States)

González, Antonio; Faílde Garrido, José María; Rodríguez Castro, Yolanda; Carrera Rodríguez, María Victoria

2015-09-14

The aim of this study was to assess the relationships between class-related anxiety with perceived control, teacher-reported behavioral engagement, behavioral disaffection, and academic performance. Participants were 355 compulsory secondary students (9th and 10th grades; Mean age = 15.2 years; SD = 1.8 years). Structural equation models revealed performance was predicted by perceived control, anxiety, disaffection, and engagement. Perceived control predicted anxiety, disaffection, and engagement. Anxiety predicted disaffection and engagement, and partially mediated the effects from control on disaffection (β = -.277, p anxiety and performance was mediated by engagement and disaffection (β = -.295, p Anxiety, engagement, and disaffection mediated the effects of control on performance (β = .352, p < .003; CI = .279, .440). The implications of these results are discussed in the light of current theory and educational interventions.

Evaluating the effect of disturbed ensemble distributions on SCFG based statistical sampling of RNA secondary structures

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Scheid Anika

2012-07-01

Full Text Available Abstract Background Over the past years, statistical and Bayesian approaches have become increasingly appreciated to address the long-standing problem of computational RNA structure prediction. Recently, a novel probabilistic method for the prediction of RNA secondary structures from a single sequence has been studied which is based on generating statistically representative and reproducible samples of the entire ensemble of feasible structures for a particular input sequence. This method samples the possible foldings from a distribution implied by a sophisticated (traditional or length-dependent stochastic context-free grammar (SCFG that mirrors the standard thermodynamic model applied in modern physics-based prediction algorithms. Specifically, that grammar represents an exact probabilistic counterpart to the energy model underlying the Sfold software, which employs a sampling extension of the partition function (PF approach to produce statistically representative subsets of the Boltzmann-weighted ensemble. Although both sampling approaches have the same worst-case time and space complexities, it has been indicated that they differ in performance (both with respect to prediction accuracy and quality of generated samples, where neither of these two competing approaches generally outperforms the other. Results In this work, we will consider the SCFG based approach in order to perform an analysis on how the quality of generated sample sets and the corresponding prediction accuracy changes when different degrees of disturbances are incorporated into the needed sampling probabilities. This is motivated by the fact that if the results prove to be resistant to large errors on the distinct sampling probabilities (compared to the exact ones, then it will be an indication that these probabilities do not need to be computed exactly, but it may be sufficient and more efficient to approximate them. Thus, it might then be possible to decrease the worst

Aromatic claw: A new fold with high aromatic content that evades structural prediction: Aromatic Claw

Energy Technology Data Exchange (ETDEWEB)

Sachleben, Joseph R. [Biomolecular NMR Core Facility, University of Chicago, Chicago Illinois; Adhikari, Aashish N. [Department of Chemistry, University of Chicago, Chicago Illinois; Gawlak, Grzegorz [Department of Biochemistry and Molecular Biology, University of Chicago, Chicago Illinois; Hoey, Robert J. [Department of Biochemistry and Molecular Biology, University of Chicago, Chicago Illinois; Liu, Gaohua [Northeast Structural Genomics Consortium (NESG), Department of Molecular Biology and Biochemistry, School of Arts and Sciences, and Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, and Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway New Jersey; Joachimiak, Andrzej [Department of Biochemistry and Molecular Biology, University of Chicago, Chicago Illinois; Biological Sciences Division, Argonne National Laboratory, Argonne Illinois; Montelione, Gaetano T. [Northeast Structural Genomics Consortium (NESG), Department of Molecular Biology and Biochemistry, School of Arts and Sciences, and Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, and Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway New Jersey; Sosnick, Tobin R. [Department of Biochemistry and Molecular Biology, University of Chicago, Chicago Illinois; Koide, Shohei [Department of Biochemistry and Molecular Biology, University of Chicago, Chicago Illinois; Department of Biochemistry and Molecular Pharmacology and the Perlmutter Cancer Center, New York University School of Medicine, New York New York

2016-11-10

We determined the NMR structure of a highly aromatic (13%) protein of unknown function, Aq1974 from Aquifex aeolicus (PDB ID: 5SYQ). The unusual sequence of this protein has a tryptophan content five times the normal (six tryptophan residues of 114 or 5.2% while the average tryptophan content is 1.0%) with the tryptophans occurring in a WXW motif. It has no detectable sequence homology with known protein structures. Although its NMR spectrum suggested that the protein was rich in β-sheet, upon resonance assignment and solution structure determination, the protein was found to be primarily α-helical with a small two-stranded β-sheet with a novel fold that we have termed an Aromatic Claw. As this fold was previously unknown and the sequence unique, we submitted the sequence to CASP10 as a target for blind structural prediction. At the end of the competition, the sequence was classified a hard template based model; the structural relationship between the template and the experimental structure was small and the predictions all failed to predict the structure. CSRosetta was found to predict the secondary structure and its packing; however, it was found that there was little correlation between CSRosetta score and the RMSD between the CSRosetta structure and the NMR determined one. This work demonstrates that even in relatively small proteins, we do not yet have the capacity to accurately predict the fold for all primary sequences. The experimental discovery of new folds helps guide the improvement of structural prediction methods.

Multi-Scale Modeling for Predicting the Stiffness and Strength of Hollow-Structured Metal Foams with Structural Hierarchy

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Yong Yi

2018-03-01

Full Text Available This work was inspired by previous experiments which managed to establish an optimal template-dealloying route to prepare ultralow density metal foams. In this study, we propose a new analytical–numerical model of hollow-structured metal foams with structural hierarchy to predict its stiffness and strength. The two-level model comprises a main backbone and a secondary nanoporous structure. The main backbone is composed of hollow sphere-packing architecture, while the secondary one is constructed of a bicontinuous nanoporous network proposed to describe the nanoscale interactions in the shell. Firstly, two nanoporous models with different geometries are generated by Voronoi tessellation, then the scaling laws of the mechanical properties are determined as a function of relative density by finite volume simulation. Furthermore, the scaling laws are applied to identify the uniaxial compression behavior of metal foams. It is shown that the thickness and relative density highly influence the Young’s modulus and yield strength, and vacancy defect determines the foams being self-supported. The present study provides not only new insights into the mechanical behaviors of both nanoporous metals and metal foams, but also a practical guide for their fabrication and application.

Nuclear fuel assembly incorporating primary and secondary structural support members

International Nuclear Information System (INIS)

Carlson, W.R.; Gjertsen, R.K.; Miller, J.V.

1987-01-01

A nuclear fuel assembly, comprising: (a) an upper end structure; (b) a lower end structure; (c) elongated primary structural members extending longitudinally between and rigidly interconnecting the upper and lower end structures, the upper and lower end structures and primary structural members together forming a rigid structural skeleton of the fuel assembly; (d) transverse grids supported on the primary structural members at axially spaced locations therealong between the upper and lower end structures; (e) fuel rods extending through and supported by the grids between the upper and lower end structures so as to extend in generally side-by-side spaced relation to one another and to the primary structural members; and (f) elongated secondary structural members extending longitudinally between but unconnected with the upper and lower end structures, the secondary structural members extending through and rigidly interconnected with the grids to extend in generally side-by-side spaced relation to one another, to the fuel rods and to the primary structural members so as to bolster the stiffness of the structural skeleton of the fuel assembly

Global Analysis of RNA Secondary Structure in Two Metazoans

Directory of Open Access Journals (Sweden)

Fan Li

2012-01-01

Full Text Available The secondary structure of RNA is necessary for its maturation, regulation, processing, and function. However, the global influence of RNA folding in eukaryotes is still unclear. Here, we use a high-throughput, sequencing-based, structure-mapping approach to identify the paired (double-stranded RNA [dsRNA] and unpaired (single-stranded RNA [ssRNA] components of the Drosophila melanogaster and Caenorhabditis elegans transcriptomes, which allows us to identify conserved features of RNA secondary structure in metazoans. From this analysis, we find that ssRNAs and dsRNAs are significantly correlated with specific epigenetic modifications. Additionally, we find key structural patterns across protein-coding transcripts that indicate that RNA folding demarcates regions of protein translation and likely affects microRNA-mediated regulation of mRNAs in animals. Finally, we identify and characterize 546 mRNAs whose folding pattern is significantly correlated between these metazoans, suggesting that their structure has some function. Overall, our findings provide a global assessment of RNA folding in animals.

GC content around splice sites affects splicing through pre-mRNA secondary structures

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Chen Liang

2011-01-01

Full Text Available Abstract Background Alternative splicing increases protein diversity by generating multiple transcript isoforms from a single gene through different combinations of exons or through different selections of splice sites. It has been reported that RNA secondary structures are involved in alternative splicing. Here we perform a genomic study of RNA secondary structures around splice sites in humans (Homo sapiens, mice (Mus musculus, fruit flies (Drosophila melanogaster, and nematodes (Caenorhabditis elegans to further investigate this phenomenon. Results We observe that GC content around splice sites is closely associated with the splice site usage in multiple species. RNA secondary structure is the possible explanation, because the structural stability difference among alternative splice sites, constitutive splice sites, and skipped splice sites can be explained by the GC content difference. Alternative splice sites tend to be GC-enriched and exhibit more stable RNA secondary structures in all of the considered species. In humans and mice, splice sites of first exons and long exons tend to be GC-enriched and hence form more stable structures, indicating the special role of RNA secondary structures in promoter proximal splicing events and the splicing of long exons. In addition, GC-enriched exon-intron junctions tend to be overrepresented in tissue-specific alternative splice sites, indicating the functional consequence of the GC effect. Compared with regions far from splice sites and decoy splice sites, real splice sites are GC-enriched. We also found that the GC-content effect is much stronger than the nucleotide-order effect to form stable secondary structures. Conclusion All of these results indicate that GC content is related to splice site usage and it may mediate the splicing process through RNA secondary structures.

RNACompress: Grammar-based compression and informational complexity measurement of RNA secondary structure

Directory of Open Access Journals (Sweden)

Chen Chun

2008-03-01

Full Text Available Abstract Background With the rapid emergence of RNA databases and newly identified non-coding RNAs, an efficient compression algorithm for RNA sequence and structural information is needed for the storage and analysis of such data. Although several algorithms for compressing DNA sequences have been proposed, none of them are suitable for the compression of RNA sequences with their secondary structures simultaneously. This kind of compression not only facilitates the maintenance of RNA data, but also supplies a novel way to measure the informational complexity of RNA structural data, raising the possibility of studying the relationship between the functional activities of RNA structures and their complexities, as well as various structural properties of RNA based on compression. Results RNACompress employs an efficient grammar-based model to compress RNA sequences and their secondary structures. The main goals of this algorithm are two fold: (1 present a robust and effective way for RNA structural data compression; (2 design a suitable model to represent RNA secondary structure as well as derive the informational complexity of the structural data based on compression. Our extensive tests have shown that RNACompress achieves a universally better compression ratio compared with other sequence-specific or common text-specific compression algorithms, such as Gencompress, winrar and gzip. Moreover, a test of the activities of distinct GTP-binding RNAs (aptamers compared with their structural complexity shows that our defined informational complexity can be used to describe how complexity varies with activity. These results lead to an objective means of comparing the functional properties of heteropolymers from the information perspective. Conclusion A universal algorithm for the compression of RNA secondary structure as well as the evaluation of its informational complexity is discussed in this paper. We have developed RNACompress, as a useful tool

Secondary Education in the European Union: Structures, Organisation and Administration.

Science.gov (United States)

EURYDICE European Unit, Brussels (Belgium).

This study examines the existing secondary education structures of the European Union member nations, the organization of education, teacher training, and the way in which secondary education is managed in Europe today. The three European Free Trade Association/European Economic Area (EFTA/EEC) countries (Iceland, Liechtenstein, and Norway) also…

RNA-TVcurve: a Web server for RNA secondary structure comparison based on a multi-scale similarity of its triple vector curve representation.

Science.gov (United States)

Li, Ying; Shi, Xiaohu; Liang, Yanchun; Xie, Juan; Zhang, Yu; Ma, Qin

2017-01-21

RNAs have been found to carry diverse functionalities in nature. Inferring the similarity between two given RNAs is a fundamental step to understand and interpret their functional relationship. The majority of functional RNAs show conserved secondary structures, rather than sequence conservation. Those algorithms relying on sequence-based features usually have limitations in their prediction performance. Hence, integrating RNA structure features is very critical for RNA analysis. Existing algorithms mainly fall into two categories: alignment-based and alignment-free. The alignment-free algorithms of RNA comparison usually have lower time complexity than alignment-based algorithms. An alignment-free RNA comparison algorithm was proposed, in which novel numerical representations RNA-TVcurve (triple vector curve representation) of RNA sequence and corresponding secondary structure features are provided. Then a multi-scale similarity score of two given RNAs was designed based on wavelet decomposition of their numerical representation. In support of RNA mutation and phylogenetic analysis, a web server (RNA-TVcurve) was designed based on this alignment-free RNA comparison algorithm. It provides three functional modules: 1) visualization of numerical representation of RNA secondary structure; 2) detection of single-point mutation based on secondary structure; and 3) comparison of pairwise and multiple RNA secondary structures. The inputs of the web server require RNA primary sequences, while corresponding secondary structures are optional. For the primary sequences alone, the web server can compute the secondary structures using free energy minimization algorithm in terms of RNAfold tool from Vienna RNA package. RNA-TVcurve is the first integrated web server, based on an alignment-free method, to deliver a suite of RNA analysis functions, including visualization, mutation analysis and multiple RNAs structure comparison. The comparison results with two popular RNA

Prediction of Protein Structural Classes for Low-Similarity Sequences Based on Consensus Sequence and Segmented PSSM

Directory of Open Access Journals (Sweden)

Yunyun Liang

2015-01-01

Full Text Available Prediction of protein structural classes for low-similarity sequences is useful for understanding fold patterns, regulation, functions, and interactions of proteins. It is well known that feature extraction is significant to prediction of protein structural class and it mainly uses protein primary sequence, predicted secondary structure sequence, and position-specific scoring matrix (PSSM. Currently, prediction solely based on the PSSM has played a key role in improving the prediction accuracy. In this paper, we propose a novel method called CSP-SegPseP-SegACP by fusing consensus sequence (CS, segmented PsePSSM, and segmented autocovariance transformation (ACT based on PSSM. Three widely used low-similarity datasets (1189, 25PDB, and 640 are adopted in this paper. Then a 700-dimensional (700D feature vector is constructed and the dimension is decreased to 224D by using principal component analysis (PCA. To verify the performance of our method, rigorous jackknife cross-validation tests are performed on 1189, 25PDB, and 640 datasets. Comparison of our results with the existing PSSM-based methods demonstrates that our method achieves the favorable and competitive performance. This will offer an important complementary to other PSSM-based methods for prediction of protein structural classes for low-similarity sequences.

RNA secondary structure diagrams for very large molecules: RNAfdl

DEFF Research Database (Denmark)

Hecker, Nikolai; Wiegels, Tim; Torda, Andrew E.

2013-01-01

There are many programs that can read the secondary structure of an RNA molecule and draw a diagram, but hardly any that can cope with 10 3 bases. RNAfdl is slow but capable of producing intersection-free diagrams for ribosome-sized structures, has a graphical user interface for adjustments...

THE PECULIARITIES OF NICKNAME STRUCTURE IN THE VICINITY OF VELIUONA: SECONDARY NICKNAMES

Directory of Open Access Journals (Sweden)

Ilona Mickienė

2014-10-01

Full Text Available The paper analyses 782 nicknames that were recorded at Veliuona vicinity during the project of the Institute of the Lithuanian Language “Modern Research of Geolinguistics in Lithuania: Optimisation of Network of Points and Interactive Spread of Dialectal Information”. The paper aims to identify the characteristic attributes of nickname structure. The analysis of the relations in derivation, i. e., tentatively distinguishing the derivation base and formant is the only way to talk about common word derivation. While researching the nicknames it is difficult to find such a universal criterion in derivation which would enable the distribution of nicknames into the primary and the secondary ones due to the fact that when a nickname and its appellative derivation motivation coincides the confusion arises. Thus, the paper invokes the structural analysis of nicknames to find universal criteria that would enable the distinction of nicknames into the primary and the secondary. The article eliminates the primary nicknames that do not differ from the motivational word, 241 secondary nickname is being researched ant structurally analysed. The structural analysis discloses a proper structure and common words being selected for nickname creation. Structurally analysing the secondary nicknames, the nicknames with suffix, inflection, mixed structure, compound, composite and phrasal nicknames were distinguished. It was determined that in vacinity of Veliuona the nicknames with suffix and inflection are mostly used.

Characterization and visualization of RNA secondary structure Boltzmann ensemble via information theory.

Science.gov (United States)

Lin, Luan; McKerrow, Wilson H; Richards, Bryce; Phonsom, Chukiat; Lawrence, Charles E

2018-03-05

The nearest neighbor model and associated dynamic programming algorithms allow for the efficient estimation of the RNA secondary structure Boltzmann ensemble. However because a given RNA secondary structure only contains a fraction of the possible helices that could form from a given sequence, the Boltzmann ensemble is multimodal. Several methods exist for clustering structures and finding those modes. However less focus is given to exploring the underlying reasons for this multimodality: the presence of conflicting basepairs. Information theory, or more specifically mutual information, provides a method to identify those basepairs that are key to the secondary structure. To this end we find most informative basepairs and visualize the effect of these basepairs on the secondary structure. Knowing whether a most informative basepair is present tells us not only the status of the particular pair but also provides a large amount of information about which other pairs are present or not present. We find that a few basepairs account for a large amount of the structural uncertainty. The identification of these pairs indicates small changes to sequence or stability that will have a large effect on structure. We provide a novel algorithm that uses mutual information to identify the key basepairs that lead to a multimodal Boltzmann distribution. We then visualize the effect of these pairs on the overall Boltzmann ensemble.

Irradiation effects on secondary structure of protein induced by keV ions

International Nuclear Information System (INIS)

Cui, F.Z.; Lin, Y.B.; Zhang, D.M.; Tian, M.B.

2001-01-01

Protein secondary structure changes by low-energy ion irradiation are reported for the first time. The selected system is 30 keV N + irradiation on bovine serum albumin (BSA). After irradiation at increasing fluences from 1.0x10 15 to 2.5x10 16 ion/cm 2 , Fourier transform infrared spectra analysis was conducted. It was found that the secondary structures of BSA molecules were very sensitive to ion irradiation. Secondary conformations showed different trends of change during irradiation. With the increase of ion fluence from 0 to 2.5x10 16 ion/cm 2 , the fraction of α-helix and β-turns decreased from 17 to 12%, and from 40 to 31%, respectively, while that of random coil and β-sheet structure increased from 18 to 27%, and from 25 to 30%, respectively. Possible explanations for the secondary conformational changes of protein are proposed. (author)

Visualizing RNA Secondary Structure Base Pair Binding Probabilities using Nested Concave Hulls

OpenAIRE

Sansen , Joris; Bourqui , Romain; Thebault , Patricia; Allali , Julien; Auber , David

2015-01-01

International audience; The challenge 1 of the BIOVIS 2015 design contest consists in designing an intuitive visual depiction of base pairs binding probabilities for secondary structure of ncRNA. Our representation depicts the potential nucleotide pairs binding using nested concave hulls over the computed MFE ncRNA secondary structure. Thus, it allows to identify regions with a high level of uncertainty in the MFE computation and the structures which seem to match to reality.

A comparative method for finding and folding RNA secondary structures within protein-coding regions

DEFF Research Database (Denmark)

Pedersen, Jakob Skou; Meyer, Irmtraud Margret; Forsberg, Roald

2004-01-01

that RNA-DECODER's parameters can be automatically trained to successfully fold known secondary structures within the HCV genome. We scan the genomes of HCV and polio virus for conserved secondary-structure elements, and analyze performance as a function of available evolutionary information. On known...... secondary structures, RNA-DECODER shows a sensitivity similar to the programs MFOLD, PFOLD and RNAALIFOLD. When scanning the entire genomes of HCV and polio virus for structure elements, RNA-DECODER's results indicate a markedly higher specificity than MFOLD, PFOLD and RNAALIFOLD....

Ab-initio conformational epitope structure prediction using genetic algorithm and SVM for vaccine design.

Science.gov (United States)

Moghram, Basem Ameen; Nabil, Emad; Badr, Amr

2018-01-01

T-cell epitope structure identification is a significant challenging immunoinformatic problem within epitope-based vaccine design. Epitopes or antigenic peptides are a set of amino acids that bind with the Major Histocompatibility Complex (MHC) molecules. The aim of this process is presented by Antigen Presenting Cells to be inspected by T-cells. MHC-molecule-binding epitopes are responsible for triggering the immune response to antigens. The epitope's three-dimensional (3D) molecular structure (i.e., tertiary structure) reflects its proper function. Therefore, the identification of MHC class-II epitopes structure is a significant step towards epitope-based vaccine design and understanding of the immune system. In this paper, we propose a new technique using a Genetic Algorithm for Predicting the Epitope Structure (GAPES), to predict the structure of MHC class-II epitopes based on their sequence. The proposed Elitist-based genetic algorithm for predicting the epitope's tertiary structure is based on Ab-Initio Empirical Conformational Energy Program for Peptides (ECEPP) Force Field Model. The developed secondary structure prediction technique relies on Ramachandran Plot. We used two alignment algorithms: the ROSS alignment and TM-Score alignment. We applied four different alignment approaches to calculate the similarity scores of the dataset under test. We utilized the support vector machine (SVM) classifier as an evaluation of the prediction performance. The prediction accuracy and the Area Under Receiver Operating Characteristic (ROC) Curve (AUC) were calculated as measures of performance. The calculations are performed on twelve similarity-reduced datasets of the Immune Epitope Data Base (IEDB) and a large dataset of peptide-binding affinities to HLA-DRB1*0101. The results showed that GAPES was reliable and very accurate. We achieved an average prediction accuracy of 93.50% and an average AUC of 0.974 in the IEDB dataset. Also, we achieved an accuracy of 95

FTIR study of secondary structure of bovine serum albumin and ovalbumin

International Nuclear Information System (INIS)

Abrosimova, K V; Shulenina, O V; Paston, S V

2016-01-01

Proteins structure is the critical factor for their functioning. Fourier transform infrared spectroscopy provides a possibility to obtain information about secondary structure of proteins in different states and also in a whole biological samples. Infrared spectra of egg white from the untreated and hard-boiled hen's egg, and also of chicken ovalbumin and bovine serum albumin in lyophilic form and in aqueous solution were studied. Lyophilization of investigated globular proteins is accompanied by the decrease of a-helix structures and the increase in amount of intermolecular β-sheets. Analysis of infrared spectrum of egg white allowed to make an estimation of OVA secondary structure and to observe α-to-β structural transformation as a result of the heat denaturation. (paper)

Secondary structures of rRNAs from all three domains of life.

Directory of Open Access Journals (Sweden)

Anton S Petrov

Full Text Available Accurate secondary structures are important for understanding ribosomes, which are extremely large and highly complex. Using 3D structures of ribosomes as input, we have revised and corrected traditional secondary (2° structures of rRNAs. We identify helices by specific geometric and molecular interaction criteria, not by co-variation. The structural approach allows us to incorporate non-canonical base pairs on parity with Watson-Crick base pairs. The resulting rRNA 2° structures are up-to-date and consistent with three-dimensional structures, and are information-rich. These 2° structures are relatively simple to understand and are amenable to reproduction and modification by end-users. The 2° structures made available here broadly sample the phylogenetic tree and are mapped with a variety of data related to molecular interactions and geometry, phylogeny and evolution. We have generated 2° structures for both large subunit (LSU 23S/28S and small subunit (SSU 16S/18S rRNAs of Escherichia coli, Thermus thermophilus, Haloarcula marismortui (LSU rRNA only, Saccharomyces cerevisiae, Drosophila melanogaster, and Homo sapiens. We provide high-resolution editable versions of the 2° structures in several file formats. For the SSU rRNA, the 2° structures use an intuitive representation of the central pseudoknot where base triples are presented as pairs of base pairs. Both LSU and SSU secondary maps are available (http://apollo.chemistry.gatech.edu/RibosomeGallery. Mapping of data onto 2° structures was performed on the RiboVision server (http://apollo.chemistry.gatech.edu/RiboVision.

UNRES server for physics-based coarse-grained simulations and prediction of protein structure, dynamics and thermodynamics.

Science.gov (United States)

Czaplewski, Cezary; Karczynska, Agnieszka; Sieradzan, Adam K; Liwo, Adam

2018-04-30

A server implementation of the UNRES package (http://www.unres.pl) for coarse-grained simulations of protein structures with the physics-based UNRES model, coined a name UNRES server, is presented. In contrast to most of the protein coarse-grained models, owing to its physics-based origin, the UNRES force field can be used in simulations, including those aimed at protein-structure prediction, without ancillary information from structural databases; however, the implementation includes the possibility of using restraints. Local energy minimization, canonical molecular dynamics simulations, replica exchange and multiplexed replica exchange molecular dynamics simulations can be run with the current UNRES server; the latter are suitable for protein-structure prediction. The user-supplied input includes protein sequence and, optionally, restraints from secondary-structure prediction or small x-ray scattering data, and simulation type and parameters which are selected or typed in. Oligomeric proteins, as well as those containing D-amino-acid residues and disulfide links can be treated. The output is displayed graphically (minimized structures, trajectories, final models, analysis of trajectory/ensembles); however, all output files can be downloaded by the user. The UNRES server can be freely accessed at http://unres-server.chem.ug.edu.pl.

A combinatorial enumeration problem of RNA secondary structures

African Journals Online (AJOL)

use

2011-12-21

Dec 21, 2011 ... connection between Discrete Mathematics and Compu- tational Molecular Biology (Chen et al, 2005; Hofacker et ... in Computational Molecular Biology. An RNA molecule is described by its sequences of bases ... Here, a mathematical definition of secondary structure is given (Stein and Waterman 1978).

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.

Prediction of protein hydration sites from sequence by modular neural networks

DEFF Research Database (Denmark)

Ehrlich, L.; Reczko, M.; Bohr, Henrik

1998-01-01

The hydration properties of a protein are important determinants of its structure and function. Here, modular neural networks are employed to predict ordered hydration sites using protein sequence information. First, secondary structure and solvent accessibility are predicted from sequence with two...... separate neural networks. These predictions are used as input together with protein sequences for networks predicting hydration of residues, backbone atoms and sidechains. These networks are teined with protein crystal structures. The prediction of hydration is improved by adding information on secondary...... structure and solvent accessibility and, using actual values of these properties, redidue hydration can be predicted to 77% accuracy with a Metthews coefficient of 0.43. However, predicted property data with an accuracy of 60-70% result in less than half the improvement in predictive performance observed...

Secondary Structure Adopted by the Gly-Gly-X Repetitive Regions of Dragline Spider Silk

Directory of Open Access Journals (Sweden)

Geoffrey M. Gray

2016-12-01

Full Text Available Solid-state NMR and molecular dynamics (MD simulations are presented to help elucidate the molecular secondary structure of poly(Gly-Gly-X, which is one of the most common structural repetitive motifs found in orb-weaving dragline spider silk proteins. The combination of NMR and computational experiments provides insight into the molecular secondary structure of poly(Gly-Gly-X segments and provides further support that these regions are disordered and primarily non-β-sheet. Furthermore, the combination of NMR and MD simulations illustrate the possibility for several secondary structural elements in the poly(Gly-Gly-X regions of dragline silks, including β-turns, 310-helicies, and coil structures with a negligible population of α-helix observed.

A combinatorial enumeration problem of RNA secondary structures

African Journals Online (AJOL)

use

2011-12-21

Dec 21, 2011 ... interesting combinatorial questions (Chen et al., 2005;. Liu, 2006; Schmitt and Waterman 1994; Stein and. Waterman 1978). The research on the enumeration of. RNA secondary structures becomes one of the hot topics in Computational Molecular Biology. An RNA molecule is described by its sequences of.

GARN2: coarse-grained prediction of 3D structure of large RNA molecules by regret minimization.

Science.gov (United States)

Boudard, Mélanie; Barth, Dominique; Bernauer, Julie; Denise, Alain; Cohen, Johanne

2017-08-15

Predicting the 3D structure of RNA molecules is a key feature towards predicting their functions. Methods which work at atomic or nucleotide level are not suitable for large molecules. In these cases, coarse-grained prediction methods aim to predict a shape which could be refined later by using more precise methods on smaller parts of the molecule. We developed a complete method for sampling 3D RNA structure at a coarse-grained model, taking a secondary structure as input. One of the novelties of our method is that a second step extracts two best possible structures close to the native, from a set of possible structures. Although our method benefits from the first version of GARN, some of the main features on GARN2 are very different. GARN2 is much faster than the previous version and than the well-known methods of the state-of-art. Our experiments show that GARN2 can also provide better structures than the other state-of-the-art methods. GARN2 is written in Java. It is freely distributed and available at http://garn.lri.fr/. melanie.boudard@lri.fr or johanne.cohen@lri.fr. 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

Applications of contact predictions to structural biology

Directory of Open Access Journals (Sweden)

Felix Simkovic

2017-05-01

Full Text Available Evolutionary pressure on residue interactions, intramolecular or intermolecular, that are important for protein structure or function can lead to covariance between the two positions. Recent methodological advances allow much more accurate contact predictions to be derived from this evolutionary covariance signal. The practical application of contact predictions has largely been confined to structural bioinformatics, yet, as this work seeks to demonstrate, the data can be of enormous value to the structural biologist working in X-ray crystallography, cryo-EM or NMR. Integrative structural bioinformatics packages such as Rosetta can already exploit contact predictions in a variety of ways. The contribution of contact predictions begins at construct design, where structural domains may need to be expressed separately and contact predictions can help to predict domain limits. Structure solution by molecular replacement (MR benefits from contact predictions in diverse ways: in difficult cases, more accurate search models can be constructed using ab initio modelling when predictions are available, while intermolecular contact predictions can allow the construction of larger, oligomeric search models. Furthermore, MR using supersecondary motifs or large-scale screens against the PDB can exploit information, such as the parallel or antiparallel nature of any β-strand pairing in the target, that can be inferred from contact predictions. Contact information will be particularly valuable in the determination of lower resolution structures by helping to assign sequence register. In large complexes, contact information may allow the identity of a protein responsible for a certain region of density to be determined and then assist in the orientation of an available model within that density. In NMR, predicted contacts can provide long-range information to extend the upper size limit of the technique in a manner analogous but complementary to experimental

Predicting turns in proteins with a unified model.

Directory of Open Access Journals (Sweden)

Qi Song

Full Text Available MOTIVATION: Turns are a critical element of the structure of a protein; turns play a crucial role in loops, folds, and interactions. Current prediction methods are well developed for the prediction of individual turn types, including α-turn, β-turn, and γ-turn, etc. However, for further protein structure and function prediction it is necessary to develop a uniform model that can accurately predict all types of turns simultaneously. RESULTS: In this study, we present a novel approach, TurnP, which offers the ability to investigate all the turns in a protein based on a unified model. The main characteristics of TurnP are: (i using newly exploited features of structural evolution information (secondary structure and shape string of protein based on structure homologies, (ii considering all types of turns in a unified model, and (iii practical capability of accurate prediction of all turns simultaneously for a query. TurnP utilizes predicted secondary structures and predicted shape strings, both of which have greater accuracy, based on innovative technologies which were both developed by our group. Then, sequence and structural evolution features, which are profile of sequence, profile of secondary structures and profile of shape strings are generated by sequence and structure alignment. When TurnP was validated on a non-redundant dataset (4,107 entries by five-fold cross-validation, we achieved an accuracy of 88.8% and a sensitivity of 71.8%, which exceeded the most state-of-the-art predictors of certain type of turn. Newly determined sequences, the EVA and CASP9 datasets were used as independent tests and the results we achieved were outstanding for turn predictions and confirmed the good performance of TurnP for practical applications.

A quantitative prediction model of SCC rate for nuclear structure materials in high temperature water based on crack tip creep strain rate

International Nuclear Information System (INIS)

Yang, F.Q.; Xue, H.; Zhao, L.Y.; Fang, X.R.

2014-01-01

Highlights: • Creep is considered to be the primary mechanical factor of crack tip film degradation. • The prediction model of SCC rate is based on crack tip creep strain rate. • The SCC rate calculated at the secondary stage of creep is recommended. • The effect of stress intensity factor on SCC growth rate is discussed. - Abstract: The quantitative prediction of stress corrosion cracking (SCC) of structure materials is essential in safety assessment of nuclear power plants. A new quantitative prediction model is proposed by combining the Ford–Andresen model, a crack tip creep model and an elastic–plastic finite element method. The creep at the crack tip is considered to be the primary mechanical factor of protective film degradation, and the creep strain rate at the crack tip is suggested as primary mechanical factor in predicting the SCC rate. The SCC rates at secondary stage of creep are recommended when using the approach introduced in this study to predict the SCC rates of materials in high temperature water. The proposed approach can be used to understand the SCC crack growth in structural materials of light water reactors

Predicting Homework Time Management at the Secondary School Level: A Multilevel Analysis

Science.gov (United States)

Xu, Jianzhong

2010-01-01

The purpose of this study is to test empirical models of variables posited to predict homework time management at the secondary school level. Student- and class-level predictors of homework time management were analyzed in a survey of 1895 students from 111 classes. Most of the variance in homework time management occurred at the student level,…

General enumeration of RNA secondary structures based on new ...

African Journals Online (AJOL)

Crick base pairs between AU and GC. Based on the new representation, this paper also computes the number of various types of constrained secondary structures taking the minimum stack length 1 and minimum size m for each bonding loop as ...

[Changes in the secondary and tertiary structure of serum albumin in interactions with ligands of various structures].

Science.gov (United States)

Trinus, F P; Braver-Chernobul'skaia, B S; Luĭk, A I; Boldeskul, A E; Velichko, A N

1984-01-01

High affinity interactions between blood serum albumin and five substances of various chemical structure, exhibiting distinct physiological activity, were accompanied by alterations in the protein tertiary structure, while the albumin secondary structure was involved in conformational transformation after less effective affinity binding.

Primary School Text Comprehension Predicts Mathematical Word Problem-Solving Skills in Secondary School

Science.gov (United States)

Björn, Piia Maria; Aunola, Kaisa; Nurmi, Jari-Erik

2016-01-01

This longitudinal study aimed to investigate the extent to which primary school text comprehension predicts mathematical word problem-solving skills in secondary school among Finnish students. The participants were 224 fourth graders (9-10 years old at the baseline). The children's text-reading fluency, text comprehension and basic calculation…

Significance of uncertainties derived from settling tank model structure and parameters on predicting WWTP performance - A global sensitivity analysis study

DEFF Research Database (Denmark)

Ramin, Elham; Sin, Gürkan; Mikkelsen, Peter Steen

2011-01-01

Uncertainty derived from one of the process models – such as one-dimensional secondary settling tank (SST) models – can impact the output of the other process models, e.g., biokinetic (ASM1), as well as the integrated wastewater treatment plant (WWTP) models. The model structure and parameter...... and from the last aerobic bioreactor upstream to the SST (Garrett/hydraulic method). For model structure uncertainty, two one-dimensional secondary settling tank (1-D SST) models are assessed, including a first-order model (the widely used Takács-model), in which the feasibility of using measured...... uncertainty of settler models can therefore propagate, and add to the uncertainties in prediction of any plant performance criteria. Here we present an assessment of the relative significance of secondary settling model performance in WWTP simulations. We perform a global sensitivity analysis (GSA) based...

Predicting Academic Success and Technological Literacy in Secondary Education: A Learning Styles Perspective

Science.gov (United States)

Avsec, Stanislav; Szewczyk-Zakrzewska, Agnieszka

2017-01-01

This paper aims to investigate the predictive validity of learning styles on academic achievement and technological literacy (TL). For this purpose, secondary school students were recruited (n = 150). An empirical research design was followed where the TL test was used with a learning style inventory measuring learning orientation, processing…

A phase transition in energy-filtered RNA secondary structures

DEFF Research Database (Denmark)

Han, Hillary Siwei; reidys, Christian

2012-01-01

In this paper we study the effect of energy parameters on minimum free energy (mfe) RNA secondary structures. Employing a simplified combinatorial energy model, that is only dependent on the diagram representation and that is not sequence specific, we prove the following dichotomy result. Mfe...... this phase transition from a discrete limit to a central limit distribution and subsequently put our result into the context of quantifying the effect of sparsification of the folding of these respective mfe-structures. We show that the sparsification of realistic mfe-structures leads to a constant time...

The Globular State of the Single-Stranded RNA: Effect of the Secondary Structure Rearrangements

Science.gov (United States)

Grigoryan, Zareh A.; Karapetian, Armen T.

2015-01-01

The mutual influence of the slow rearrangements of secondary structure and fast collapse of the long single-stranded RNA (ssRNA) in approximation of coarse-grained model is studied with analytic calculations. It is assumed that the characteristic time of the secondary structure rearrangement is much longer than that for the formation of the tertiary structure. A nonequilibrium phase transition of the 2nd order has been observed. PMID:26345143

The Globular State of the Single-Stranded RNA: Effect of the Secondary Structure Rearrangements

Directory of Open Access Journals (Sweden)

Zareh A. Grigoryan

2015-01-01

Full Text Available The mutual influence of the slow rearrangements of secondary structure and fast collapse of the long single-stranded RNA (ssRNA in approximation of coarse-grained model is studied with analytic calculations. It is assumed that the characteristic time of the secondary structure rearrangement is much longer than that for the formation of the tertiary structure. A nonequilibrium phase transition of the 2nd order has been observed.

Translation of the flavivirus kunjin NS3 gene in cis but not its RNA sequence or secondary structure is essential for efficient RNA packaging.

Science.gov (United States)

Pijlman, Gorben P; Kondratieva, Natasha; Khromykh, Alexander A

2006-11-01

Our previous studies using trans-complementation analysis of Kunjin virus (KUN) full-length cDNA clones harboring in-frame deletions in the NS3 gene demonstrated the inability of these defective complemented RNAs to be packaged into virus particles (W. J. Liu, P. L. Sedlak, N. Kondratieva, and A. A. Khromykh, J. Virol. 76:10766-10775). In this study we aimed to establish whether this requirement for NS3 in RNA packaging is determined by the secondary RNA structure of the NS3 gene or by the essential role of the translated NS3 gene product. Multiple silent mutations of three computer-predicted stable RNA structures in the NS3 coding region of KUN replicon RNA aimed at disrupting RNA secondary structure without affecting amino acid sequence did not affect RNA replication and packaging into virus-like particles in the packaging cell line, thus demonstrating that the predicted conserved RNA structures in the NS3 gene do not play a role in RNA replication and/or packaging. In contrast, double frameshift mutations in the NS3 coding region of full-length KUN RNA, producing scrambled NS3 protein but retaining secondary RNA structure, resulted in the loss of ability of these defective RNAs to be packaged into virus particles in complementation experiments in KUN replicon-expressing cells. Furthermore, the more robust complementation-packaging system based on established stable cell lines producing large amounts of complemented replicating NS3-deficient replicon RNAs and infection with KUN virus to provide structural proteins also failed to detect any secreted virus-like particles containing packaged NS3-deficient replicon RNAs. These results have now firmly established the requirement of KUN NS3 protein translated in cis for genome packaging into virus particles.

Predictive validity of examinations at the Secondary Education Certificate (SEC) level

OpenAIRE

Farrugia, Josette; Ventura, Frank

2007-01-01

This paper presents the predictive validity of results obtained by 16-year-old Maltese students in the May 2004 Secondary Education Certificate (SEC) examinations in Biology, Chemistry, Physics, Mathematics, Computing, English and Maltese for the Advanced level examinations in these subjects taken by the same students two years later. The study checks whether the SEC level is a good foundation for the higher level, the likelihood of obtaining a high grade at A-level from particular SEC result...

Statistical mechanical approach to secondary processes and structural relaxation in glasses and glass formers: a leading model to describe the onset of Johari-Goldstein processes and their relationship with fully cooperative processes.

Science.gov (United States)

Crisanti, A; Leuzzi, L; Paoluzzi, M

2011-09-01

The interrelation of dynamic processes active on separated time-scales in glasses and viscous liquids is investigated using a model displaying two time-scale bifurcations both between fast and secondary relaxation and between secondary and structural relaxation. The study of the dynamics allows for predictions on the system relaxation above the temperature of dynamic arrest in the mean-field approximation, that are compared with the outcomes of the equations of motion directly derived within the Mode Coupling Theory (MCT) for under-cooled viscous liquids. By varying the external thermodynamic parameters, a wide range of phenomenology can be represented, from a very clear separation of structural and secondary peak in the susceptibility loss to excess wing structures.

Quantitative DMS mapping for automated RNA secondary structure inference

OpenAIRE

Cordero, Pablo; Kladwang, Wipapat; VanLang, Christopher C.; Das, Rhiju

2012-01-01

For decades, dimethyl sulfate (DMS) mapping has informed manual modeling of RNA structure in vitro and in vivo. Here, we incorporate DMS data into automated secondary structure inference using a pseudo-energy framework developed for 2'-OH acylation (SHAPE) mapping. On six non-coding RNAs with crystallographic models, DMS- guided modeling achieves overall false negative and false discovery rates of 9.5% and 11.6%, comparable or better than SHAPE-guided modeling; and non-parametric bootstrappin...

Two-dimensional dynamics of a free molecular chain with a secondary structure

DEFF Research Database (Denmark)

Zolotaryuk, Alexander; Christiansen, Peter Leth; Savin, A.V.

1996-01-01

A simple two-dimensional (2D) model of an isolated (free) molecular chain with primary and secondary structures has been suggested and investigated both analytically and numerically. This model can be considered as the simplest generalization of the well-known Fermi-Pasta-Ulam model of an anharmo......A simple two-dimensional (2D) model of an isolated (free) molecular chain with primary and secondary structures has been suggested and investigated both analytically and numerically. This model can be considered as the simplest generalization of the well-known Fermi-Pasta-Ulam model...

Mechanical properties of amyloid-like fibrils defined by secondary structures

Science.gov (United States)

Bortolini, C.; Jones, N. C.; Hoffmann, S. V.; Wang, C.; Besenbacher, F.; Dong, M.

2015-04-01

Amyloid and amyloid-like fibrils represent a generic class of highly ordered nanostructures that are implicated in some of the most fatal neurodegenerative diseases. On the other hand, amyloids, by possessing outstanding mechanical robustness, have also been successfully employed as functional biomaterials. For these reasons, physical and chemical factors driving fibril self-assembly and morphology are extensively studied - among these parameters, the secondary structures and the pH have been revealed to be crucial, since a variation in pH changes the fibril morphology and net chirality during protein aggregation. It is important to quantify the mechanical properties of these fibrils in order to help the design of effective strategies for treating diseases related to the presence of amyloid fibrils. In this work, we show that by changing pH the mechanical properties of amyloid-like fibrils vary as well. In particular, we reveal that these mechanical properties are strongly related to the content of secondary structures. We analysed and estimated the Young's modulus (E) by comparing the persistence length (Lp) - measured from the observation of TEM images by using statistical mechanics arguments - with the mechanical information provided by peak force quantitative nanomechanical property mapping (PF-QNM). The secondary structure content and the chirality are investigated by means of synchrotron radiation circular dichroism (SR-CD). Results arising from this study could be fruitfully used as a protocol to investigate other medical or engineering relevant peptide fibrils.Amyloid and amyloid-like fibrils represent a generic class of highly ordered nanostructures that are implicated in some of the most fatal neurodegenerative diseases. On the other hand, amyloids, by possessing outstanding mechanical robustness, have also been successfully employed as functional biomaterials. For these reasons, physical and chemical factors driving fibril self-assembly and morphology

Framework for Structural Online Health Monitoring of Aging and Degradation of Secondary Systems due to some Aspects of Erosion

Energy Technology Data Exchange (ETDEWEB)

Gribok, Andrei [Idaho National Lab. (INL), Idaho Falls, ID (United States); Patnaik, Sobhan [Idaho National Lab. (INL), Idaho Falls, ID (United States); Williams, Christian [Idaho National Lab. (INL), Idaho Falls, ID (United States); Pattanaik, Marut [Idaho National Lab. (INL), Idaho Falls, ID (United States); Kanakala, Raghunath [Idaho National Lab. (INL), Idaho Falls, ID (United States)

2016-09-01

This report describes the current state of research related to critical aspects of erosion and selected aspects of degradation of secondary components in nuclear power plants. The report also proposes a framework for online health monitoring of aging and degradation of secondary components. The framework consists of an integrated multi-sensor modality system which can be used to monitor different piping configurations under different degradation conditions. The report analyses the currently known degradation mechanisms and available predictive models. Based on this analysis, the structural health monitoring framework is proposed. The Light Water Reactor Sustainability Program began to evaluate technologies that could be used to perform online monitoring of piping and other secondary system structural components in commercial NPPs. These online monitoring systems have the potential to identify when a more detailed inspection is needed using real-time measurements, rather than at a pre-determined inspection interval. This transition to condition-based, risk informed automated maintenance will contribute to a significant reduction of operations and maintenance costs that account for the majority of nuclear power generation costs. There is unanimous agreement between industry experts and academic researchers that identifying and prioritizing inspection locations in secondary piping systems (for example, in raw water piping or diesel piping) would eliminate many excessive in-service inspections. The proposed structural health monitoring framework takes aim at answering this challenge by combining long-range guided wave technologies with other monitoring techniques, which can significantly increase the inspection length and pinpoint the locations that degraded the most. More widely, the report suggests research efforts aimed at developing, validating, and deploying online corrosion monitoring techniques for complex geometries, which are pervasive in NPPs.

Framework for Structural Online Health Monitoring of Aging and Degradation of Secondary Systems due to some Aspects of Erosion

International Nuclear Information System (INIS)

Gribok, Andrei; Patnaik, Sobhan; Williams, Christian; Pattanaik, Marut; Kanakala, Raghunath

2016-01-01

This report describes the current state of research related to critical aspects of erosion and selected aspects of degradation of secondary components in nuclear power plants. The report also proposes a framework for online health monitoring of aging and degradation of secondary components. The framework consists of an integrated multi-sensor modality system which can be used to monitor different piping configurations under different degradation conditions. The report analyses the currently known degradation mechanisms and available predictive models. Based on this analysis, the structural health monitoring framework is proposed. The Light Water Reactor Sustainability Program began to evaluate technologies that could be used to perform online monitoring of piping and other secondary system structural components in commercial NPPs. These online monitoring systems have the potential to identify when a more detailed inspection is needed using real-time measurements, rather than at a pre-determined inspection interval. This transition to condition-based, risk informed automated maintenance will contribute to a significant reduction of operations and maintenance costs that account for the majority of nuclear power generation costs. There is unanimous agreement between industry experts and academic researchers that identifying and prioritizing inspection locations in secondary piping systems (for example, in raw water piping or diesel piping) would eliminate many excessive in-service inspections. The proposed structural health monitoring framework takes aim at answering this challenge by combining long-range guided wave technologies with other monitoring techniques, which can significantly increase the inspection length and pinpoint the locations that degraded the most. More widely, the report suggests research efforts aimed at developing, validating, and deploying online corrosion monitoring techniques for complex geometries, which are pervasive in NPPs.

VMD-SS: A graphical user interface plug-in to calculate the protein secondary structure in VMD program.

Science.gov (United States)

Yahyavi, Masoumeh; Falsafi-Zadeh, Sajad; Karimi, Zahra; Kalatarian, Giti; Galehdari, Hamid

2014-01-01

The investigation on the types of secondary structure (SS) of a protein is important. The evolution of secondary structures during molecular dynamics simulations is a useful parameter to analyze protein structures. Therefore, it is of interest to describe VMD-SS (a software program) for the identification of secondary structure elements and its trajectories during simulation for known structures available at the Protein Data Bank (PDB). The program helps to calculate (1) percentage SS, (2) SS occurrence in each residue, (3) percentage SS during simulation, and (4) percentage residues in all SS types during simulation. The VMD-SS plug-in was designed using TCL script and stride to calculate secondary structure features. The database is available for free at http://science.scu.ac.ir/HomePage.aspx?TabID=13755.

Antibody structural modeling with prediction of immunoglobulin structure (PIGS)

DEFF Research Database (Denmark)

Marcatili, Paolo; Olimpieri, Pier Paolo; Chailyan, Anna

2014-01-01

Antibodies (or immunoglobulins) are crucial for defending organisms from pathogens, but they are also key players in many medical, diagnostic and biotechnological applications. The ability to predict their structure and the specific residues involved in antigen recognition has several useful...... applications in all of these areas. Over the years, we have developed or collaborated in developing a strategy that enables researchers to predict the 3D structure of antibodies with a very satisfactory accuracy. The strategy is completely automated and extremely fast, requiring only a few minutes (∼10 min...... on average) to build a structural model of an antibody. It is based on the concept of canonical structures of antibody loops and on our understanding of the way light and heavy chains pack together....

GTfold: Enabling parallel RNA secondary structure prediction on multi-core desktops

DEFF Research Database (Denmark)

Swenson, M Shel; Anderson, Joshua; Ash, Andrew

2012-01-01

achieved significant improvements in runtime, but their implementations were not portable from niche high-performance computers or easily accessible to most RNA researchers. With the increasing prevalence of multi-core desktop machines, a new parallel prediction program is needed to take full advantage...

Landscape and variation of RNA secondary structure across the human transcriptome.

OpenAIRE

Wan, Y; Qu, K; Zhang, QC; Flynn, RA; Manor, O; Ouyang, Z; Zhang, J; Spitale, RC; Snyder, MP; Segal, E; Chang, HY

2014-01-01

In parallel to the genetic code for protein synthesis, a second layer of information is embedded in all RNA transcripts in the form of RNA structure. RNA structure influences practically every step in the gene expression program. However, the nature of most RNA structures or effects of sequence variation on structure are not known. Here we report the initial landscape and variation of RNA secondary structures (RSSs) in a human family trio (mother, father and their child). This provides a comp...

Examination of factors predicting secondary students' interest in tertiary STEM education

Science.gov (United States)

Chachashvili-Bolotin, Svetlana; Milner-Bolotin, Marina; Lissitsa, Sabina

2016-02-01

Based on the Social Cognitive Career Theory (SCCT), the study aims to investigate factors that predict students' interest in pursuing science, technology, engineering, and mathematics (STEM) fields in tertiary education both in general and in relation to their gender and socio-economic background. The results of the analysis of survey responses of 2458 secondary public school students in the fifth-largest Israeli city indicate that STEM learning experience positively associates with students' interest in pursuing STEM fields in tertiary education as opposed to non-STEM fields. Moreover, studying advanced science courses at the secondary school level decreases (but does not eliminate) the gender gap and eliminates the effect of family background on students' interest in pursuing STEM fields in the future. Findings regarding outcome expectations and self-efficacy beliefs only partially support the SCCT model. Outcome expectations and self-efficacy beliefs positively correlate with students' entering tertiary education but did not differentiate between their interests in the fields of study.

Analysis of the secondary structure of ITS transcripts in peritrich ciliates (Ciliophora, Oligohymenophorea): implications for structural evolution and phylogenetic reconstruction.

Science.gov (United States)

Sun, Ping; Clamp, John C; Xu, Dapeng

2010-07-01

Despite extensive previous morphological work, little agreement has been reached about phylogenetic relationships among peritrich ciliates, making it difficult to study the evolution of the group in a phylogenetic framework. In this study, the nucleotide characteristics and secondary structures of internal transcribed spacers 1 and 2 (ITS1 and ITS2) of 26 peritrich ciliates in 12 genera were analyzed. Information from secondary structures of ITS1 and ITS2 then was used to perform the first systematic study of ITS regions in peritrich ciliates, including one species of Rhabdostyla for which no sequence has been reported previously. Lengths of ITS1 and ITS2 sequences varied relatively little among taxa studied, but their G+C content was highly variable. General secondary structure models of ITS1 and ITS2 were proposed for peritrich ciliates and their reliability was assessed by compensatory base changes. The secondary structure of ITS1 contains three major helices in peritrich ciliates and deviations from this basic structure were found in all taxa examined. The core structure of peritrich ITS2 includes four helices, with helix III as the longest and containing a motif 5'-MAC versus GUK-3' at its apex as well as a YU-UY mismatch in helix II. In addition, the structural motifs of both ITS secondary structures were identified. Phylogenetic analyses using ITS data were performed by means of Bayesian inference, maximum likelihood and neighbor joining methods. Trees had a consistent branching pattern that included the following features: (1) Rhabdostyla always clustered with members of the family Vorticellidae, instead of members of the family Epistylididae, in which it is now classified on the basis of morphology. (2) The systematically questionable genus Ophrydium closely associated with Carchesium, forming a clearly defined, monophyletic group within the Vorticellidae. This supported the hypothesis derived from previous study based on small subunit rRNA gene sequences

Antibody structural modeling with prediction of immunoglobulin structure (PIGS)

KAUST Repository

Marcatili, Paolo

2014-11-06

© 2014 Nature America, Inc. All rights reserved. Antibodies (or immunoglobulins) are crucial for defending organisms from pathogens, but they are also key players in many medical, diagnostic and biotechnological applications. The ability to predict their structure and the specific residues involved in antigen recognition has several useful applications in all of these areas. Over the years, we have developed or collaborated in developing a strategy that enables researchers to predict the 3D structure of antibodies with a very satisfactory accuracy. The strategy is completely automated and extremely fast, requiring only a few minutes (~10 min on average) to build a structural model of an antibody. It is based on the concept of canonical structures of antibody loops and on our understanding of the way light and heavy chains pack together.

Prediction of primary vs secondary hypertension in children.

Science.gov (United States)

Baracco, Rossana; Kapur, Gaurav; Mattoo, Tej; Jain, Amrish; Valentini, Rudolph; Ahmed, Maheen; Thomas, Ronald

2012-05-01

Despite current guidelines, variability exists in the workup of hypertensive children due to physician preferences. The study evaluates primary vs secondary hypertension diagnosis from investigations routinely performed in hypertensive children. This retrospective study included children 5 to 19 years with primary and secondary hypertension. The proportions of abnormal laboratory and imaging tests were compared between primary and secondary hypertension groups. Risk factors for primary vs secondary hypertension were evaluated by logistic regression and likelihood function analysis. Patients with secondary hypertension were younger (5-12 years) and had a higher proportion of abnormal creatinine, renal ultrasound, and echocardiogram findings. There was no significant difference in abnormal results of thyroid function, urine catecholamines, plasma renin, and aldosterone. Abnormal renal ultrasound findings and age were predictors of secondary hypertension by regression and likelihood function analysis. Children aged 5 to 12 years with abnormal renal ultrasound findings and high diastolic blood pressures are at higher risk for secondary hypertension that requires detailed evaluation. © 2012 Wiley Periodicals, Inc.

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

Directory of Open Access Journals (Sweden)

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.

Testing Mediation Using Multiple Regression and Structural Equation Modeling Analyses in Secondary Data

Science.gov (United States)

Li, Spencer D.

2011-01-01

Mediation analysis in child and adolescent development research is possible using large secondary data sets. This article provides an overview of two statistical methods commonly used to test mediated effects in secondary analysis: multiple regression and structural equation modeling (SEM). Two empirical studies are presented to illustrate the…

Alignment-free comparative genomic screen for structured RNAs using coarse-grained secondary structure dot plots

DEFF Research Database (Denmark)

Kato, Yuki; Gorodkin, Jan; Havgaard, Jakob Hull

2017-01-01

. Methods: Here we present a fast and efficient method, DotcodeR, for detecting structurally similar RNAs in genomic sequences by comparing their corresponding coarse-grained secondary structure dot plots at string level. This allows us to perform an all-against-all scan of all window pairs from two genomes...... without alignment. Results: Our computational experiments with simulated data and real chromosomes demonstrate that the presented method has good sensitivity. Conclusions: DotcodeR can be useful as a pre-filter in a genomic comparative scan for structured RNAs....

A comprehensive comparison of comparative RNA structure prediction approaches

DEFF Research Database (Denmark)

Gardner, P. P.; Giegerich, R.

2004-01-01

-finding and multiple-sequence-alignment algorithms. Results Here we evaluate a number of RNA folding algorithms using reliable RNA data-sets and compare their relative performance. Conclusions We conclude that comparative data can enhance structure prediction but structure-prediction-algorithms vary widely in terms......Background An increasing number of researchers have released novel RNA structure analysis and prediction algorithms for comparative approaches to structure prediction. Yet, independent benchmarking of these algorithms is rarely performed as is now common practice for protein-folding, gene...

Prediction of the location and type of beta-turns in proteins using neural networks.

OpenAIRE

Shepherd, A. J.; Gorse, D.; Thornton, J. M.

1999-01-01

A neural network has been used to predict both the location and the type of beta-turns in a set of 300 nonhomologous protein domains. A substantial improvement in prediction accuracy compared with previous methods has been achieved by incorporating secondary structure information in the input data. The total percentage of residues correctly classified as beta-turn or not-beta-turn is around 75% with predicted secondary structure information. More significantly, the method gives a Matthews cor...

Predicting beta-turns in proteins using support vector machines with fractional polynomials.

Science.gov (United States)

Elbashir, Murtada; Wang, Jianxin; Wu, Fang-Xiang; Wang, Lusheng

2013-11-07

β-turns are secondary structure type that have essential role in molecular recognition, protein folding, and stability. They are found to be the most common type of non-repetitive structures since 25% of amino acids in protein structures are situated on them. Their prediction is considered to be one of the crucial problems in bioinformatics and molecular biology, which can provide valuable insights and inputs for the fold recognition and drug design. We propose an approach that combines support vector machines (SVMs) and logistic regression (LR) in a hybrid prediction method, which we call (H-SVM-LR) to predict β-turns in proteins. Fractional polynomials are used for LR modeling. We utilize position specific scoring matrices (PSSMs) and predicted secondary structure (PSS) as features. Our simulation studies show that H-SVM-LR achieves Qtotal of 82.87%, 82.84%, and 82.32% on the BT426, BT547, and BT823 datasets respectively. These values are the highest among other β-turns prediction methods that are based on PSSMs and secondary structure information. H-SVM-LR also achieves favorable performance in predicting β-turns as measured by the Matthew's correlation coefficient (MCC) on these datasets. Furthermore, H-SVM-LR shows good performance when considering shape strings as additional features. In this paper, we present a comprehensive approach for β-turns prediction. Experiments show that our proposed approach achieves better performance compared to other competing prediction methods.

Psychosis prediction in secondary mental health services. A broad, comprehensive approach to the "at risk mental state" syndrome.

Science.gov (United States)

Francesconi, M; Minichino, A; Carrión, R E; Delle Chiaie, R; Bevilacqua, A; Parisi, M; Rullo, S; Bersani, F Saverio; Biondi, M; Cadenhead, K

2017-02-01

Accuracy of risk algorithms for psychosis prediction in "at risk mental state" (ARMS) samples may differ according to the recruitment setting. Standardized criteria used to detect ARMS individuals may lack specificity if the recruitment setting is a secondary mental health service. The authors tested a modified strategy to predict psychosis conversion in this setting by using a systematic selection of trait-markers of the psychosis prodrome in a sample with a heterogeneous ARMS status. 138 non-psychotic outpatients (aged 17-31) were consecutively recruited in secondary mental health services and followed-up for up to 3 years (mean follow-up time, 2.2 years; SD=0.9). Baseline ARMS status, clinical, demographic, cognitive, and neurological soft signs measures were collected. Cox regression was used to derive a risk index. 48% individuals met ARMS criteria (ARMS-Positive, ARMS+). Conversion rate to psychosis was 21% for the overall sample, 34% for ARMS+, and 9% for ARMS-Negative (ARMS-). The final predictor model with a positive predictive validity of 80% consisted of four variables: Disorder of Thought Content, visuospatial/constructional deficits, sensory-integration, and theory-of-mind abnormalities. Removing Disorder of Thought Content from the model only slightly modified the predictive accuracy (-6.2%), but increased the sensitivity (+9.5%). These results suggest that in a secondary mental health setting the use of trait-markers of the psychosis prodrome may predict psychosis conversion with great accuracy despite the heterogeneity of the ARMS status. The use of the proposed predictive algorithm may enable a selective recruitment, potentially reducing duration of untreated psychosis and improving prognostic outcomes. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

CATHARE-2 prediction of large primary to secondary leakage (PRISE) at PSB-VVER experimental facility

Energy Technology Data Exchange (ETDEWEB)

Sabotinov, L.; Chevrier, P. [Institut de Radioprotection et de Surete Nucleaire, 92 - Fontenay aux Roses (France)

2007-07-01

The large primary to secondary leakage (PRISE) is a specific loss-of-coolant accident in VVER reactors, related to the break of the steam generator collector cover, leading to loss of primary mass inventory and possible direct radioactive release to atmosphere. The best estimate thermal-hydraulic computer code CATHARE-2 Version 2.5-1 was used for post-test analysis of a PRISE experiment, conducted at the large scale test facility PSB-VVER in Russia. The PSB rig is 1:300 scaled model of VVER-1000 NPP. The accident is calculated with a 1.4% break size, which corresponds to 100 mm leak from primary to secondary side in the real NPP. A computer model has been developed for CATHARE-2 V2.5-1, taking into account all important components of the PSB facility: reactor model (lower plenum, core, bypass, upper plenum, downcomer), 4 separate loops, pressurizer, horizontal multi-tube steam generators, break section. The secondary side is presented by recirculation model. A large number of sensitivity calculations has been performed regarding break modeling, reactor pressure vessel modeling, counter current flow modeling, hydraulic losses, heat losses, steam generator level regulation. Comparison between calculated and experimental results shows good prediction of the basic thermal-hydraulic phenomena and parameters such as primary and secondary pressures, temperatures, loop flows, etc. Some discrepancies were observed in the calculations of primary mass inventory and loop seal clearance. Nevertheless the final core heat up, which is one of the most important safety criteria, was correctly predicted. (authors)

An HMM posterior decoder for sequence feature prediction that includes homology information

DEFF Research Database (Denmark)

Käll, Lukas; Krogh, Anders Stærmose; Sonnhammer, Erik L. L.

2005-01-01

Motivation: When predicting sequence features like transmembrane topology, signal peptides, coil-coil structures, protein secondary structure or genes, extra support can be gained from homologs. Results: We present here a general hidden Markov model (HMM) decoding algorithm that combines probabil......Motivation: When predicting sequence features like transmembrane topology, signal peptides, coil-coil structures, protein secondary structure or genes, extra support can be gained from homologs. Results: We present here a general hidden Markov model (HMM) decoding algorithm that combines......://phobius.cgb.ki.se/poly.html . An implementation of the algorithm is available on request from the authors....

Computational predictions of zinc oxide hollow structures

Science.gov (United States)

Tuoc, Vu Ngoc; Huan, Tran Doan; Thao, Nguyen Thi

2018-03-01

Nanoporous materials are emerging as potential candidates for a wide range of technological applications in environment, electronic, and optoelectronics, to name just a few. Within this active research area, experimental works are predominant while theoretical/computational prediction and study of these materials face some intrinsic challenges, one of them is how to predict porous structures. We propose a computationally and technically feasible approach for predicting zinc oxide structures with hollows at the nano scale. The designed zinc oxide hollow structures are studied with computations using the density functional tight binding and conventional density functional theory methods, revealing a variety of promising mechanical and electronic properties, which can potentially find future realistic applications.

Instruction in text-structure as a determinant of senior secondary ...

African Journals Online (AJOL)

The study determined the effectiveness of instruction in text-structure on achievement of students in English narrative text. The pretest-posttest control group quasi experimental design was adopted for the study. The participants were 120 students in intact classes from four purposively selected senior secondary schools in ...

Fisher: a program for the detection of H/ACA snoRNAs using MFE secondary structure prediction and comparative genomics - assessment and update.

Science.gov (United States)

Freyhult, Eva; Edvardsson, Sverker; Tamas, Ivica; Moulton, Vincent; Poole, Anthony M

2008-07-21

The H/ACA family of small nucleolar RNAs (snoRNAs) plays a central role in guiding the pseudouridylation of ribosomal RNA (rRNA). In an effort to systematically identify the complete set of rRNA-modifying H/ACA snoRNAs from the genome sequence of the budding yeast, Saccharomyces cerevisiae, we developed a program - Fisher - and previously presented several candidate snoRNAs based on our analysis 1. In this report, we provide a brief update of this work, which was aborted after the publication of experimentally-identified snoRNAs 2 identical to candidates we had identified bioinformatically using Fisher. Our motivation for revisiting this work is to report on the status of the candidate snoRNAs described in 1, and secondly, to report that a modified version of Fisher together with the available multiple yeast genome sequences was able to correctly identify several H/ACA snoRNAs for modification sites not identified by the snoGPS program 3. While we are no longer developing Fisher, we briefly consider the merits of the Fisher algorithm relative to snoGPS, which may be of use for workers considering pursuing a similar search strategy for the identification of small RNAs. The modified source code for Fisher is made available as supplementary material. Our results confirm the validity of using minimum free energy (MFE) secondary structure prediction to guide comparative genomic screening for RNA families with few sequence constraints.

Effects prediction guidelines for structures subjected to ground motion

International Nuclear Information System (INIS)

1975-07-01

Part of the planning for an underground nuclear explosion (UNE) is determining the effects of expected ground motion on exposed structures. Because of the many types of structures and the wide variation in ground motion intensity typically encountered, no single prediction method is both adequate and feasible for a complete evaluation. Furthermore, the nature and variability of ground motion and structure damage prescribe effects predictions that are made probabilistically. Initially, prediction for a UNE involves a preliminary assessment of damage to establish overall project feasibility. Subsequent efforts require more detailed damage evaluations, based on structure inventories and analyses of specific structures, so that safety problems can be identified and safety and remedial measures can be recommended. To cover this broad range of effects prediction needs for a typical UNE project, three distinct but interrelated methods have been developed and are described. First, the fundamental practical and theoretical aspects of predicting the effects of dynamic ground motion on structures are summarized. Next, experimentally derived and theoretically determined observations of the behavior of typical structures subjected to ground motion are presented. Then, based on these fundamental considerations and on the observed behavior of structures, the formulation of the three effects prediction procedures is described, along with guidelines regarding their applicability. Example damage predictions for hypothetical UNEs demonstrate these procedures. To aid in identifying the vibration properties of complex structures, one chapter discusses alternatives in vibration testing, instrumentation, and data analysis. Finally, operational guidelines regarding data acquisition procedures, safety criteria, and remedial measures involved in conducting structure effects evaluations are discussed. (U.S.)

Feature-Based and String-Based Models for Predicting RNA-Protein Interaction

Directory of Open Access Journals (Sweden)

Donald Adjeroh

2018-03-01

Full Text Available In this work, we study two approaches for the problem of RNA-Protein Interaction (RPI. In the first approach, we use a feature-based technique by combining extracted features from both sequences and secondary structures. The feature-based approach enhanced the prediction accuracy as it included much more available information about the RNA-protein pairs. In the second approach, we apply search algorithms and data structures to extract effective string patterns for prediction of RPI, using both sequence information (protein and RNA sequences, and structure information (protein and RNA secondary structures. This led to different string-based models for predicting interacting RNA-protein pairs. We show results that demonstrate the effectiveness of the proposed approaches, including comparative results against leading state-of-the-art methods.

Prediction of P53 mutants (multiple sites transcriptional activity based on structural (2D&3D properties.

Directory of Open Access Journals (Sweden)

R Geetha Ramani

Full Text Available Prediction of secondary site mutations that reinstate mutated p53 to normalcy has been the focus of intense research in the recent past owing to the fact that p53 mutants have been implicated in more than half of all human cancers and restoration of p53 causes tumor regression. However laboratory investigations are more often laborious and resource intensive but computational techniques could well surmount these drawbacks. In view of this, we formulated a novel approach utilizing computational techniques to predict the transcriptional activity of multiple site (one-site to five-site p53 mutants. The optimal MCC obtained by the proposed approach on prediction of one-site, two-site, three-site, four-site and five-site mutants were 0.775,0.341,0.784,0.916 and 0.655 respectively, the highest reported thus far in literature. We have also demonstrated that 2D and 3D features generate higher prediction accuracy of p53 activity and our findings revealed the optimal results for prediction of p53 status, reported till date. We believe detection of the secondary site mutations that suppress tumor growth may facilitate better understanding of the relationship between p53 structure and function and further knowledge on the molecular mechanisms and biological activity of p53, a targeted source for cancer therapy. We expect that our prediction methods and reported results may provide useful insights on p53 functional mechanisms and generate more avenues for utilizing computational techniques in biological data analysis.

Prediction of the secondary structure of the mitochondrial tRNASer (UCN) of Lutzomyia hartmanni (Diptera: Psychodidae)

International Nuclear Information System (INIS)

Perez Doria, Alveiro; Bejarano, Eduar E

2011-01-01

Lutzomyia (Helcocyrtomyia) hartmanni is a sand fly that has been implicated in the transmission of Leishmania (Viannia) colombiensis, an etiologic agent of cutaneous Leishmaniasis in Colombia. The objective of this work was to explore the potential usefulness of the mitochondrial serine transfer RNA (UCN) (tRNASer) in the taxonomic determination of L. hartmanni. Mitochondrial DNA was extracted, amplified and sequenced from entomological material collected in Envigado, Antioquia, Colombia. The tRNASer gene length was 68 nucleotide pairs, with an average adenine-thymine content of 80.9%. The studied tRNASer differs from other sand fly tRNASer known to date, on the basis of its primary and secondary structure. The observed number of intrachain base pairing was 7 in the acceptor arm, 3 in the dihydrouridine (DHU) arm, 5 in the anticodon arm, and 5 in the ribothymidine-pseudouridine-cytosine (TC) arm. The size of the DHU, anticodon, variable and TC loops was estimated to be 5, 7, 4, and 8 nucleotides, respectively. The notorious absence of non-Watson-Crick base pairs in the four arms of the tRNASer distinguishes that of L. hartmanni from others Lutzomyia spp.

Prediction of Antibody Epitopes

DEFF Research Database (Denmark)

Nielsen, Morten; Marcatili, Paolo

2015-01-01

Antibodies recognize their cognate antigens in a precise and effective way. In order to do so, they target regions of the antigenic molecules that have specific features such as large exposed areas, presence of charged or polar atoms, specific secondary structure elements, and lack of similarity...... to self-proteins. Given the sequence or the structure of a protein of interest, several methods exploit such features to predict the residues that are more likely to be recognized by an immunoglobulin.Here, we present two methods (BepiPred and DiscoTope) to predict linear and discontinuous antibody...

An O(n(5)) algorithm for MFE prediction of kissing hairpins and 4-chains in nucleic acids.

Science.gov (United States)

Chen, Ho-Lin; Condon, Anne; Jabbari, Hosna

2009-06-01

Efficient methods for prediction of minimum free energy (MFE) nucleic secondary structures are widely used, both to better understand structure and function of biological RNAs and to design novel nano-structures. Here, we present a new algorithm for MFE secondary structure prediction, which significantly expands the class of structures that can be handled in O(n(5)) time. Our algorithm can handle H-type pseudoknotted structures, kissing hairpins, and chains of four overlapping stems, as well as nested substructures of these types.

Secondary α-deuterium isotope effects as a probe to the relationship between structure and mechanism of pyrolysis of secondary azoalkanes

International Nuclear Information System (INIS)

Grizzle, P.L.

1975-01-01

This study was carried out to investigate the mechanism of azoalkane thermolysis and the effect of molecular structure on the potential-energy hypersurface for pyrolysis utilizing secondary α-deuterium isotope effects. Since the magnitude of the α-effect for 1,1'-diphenylazoethane is of singular importance in the interpretation of those for related compounds, it has been redetermined. To investigate the effect of molecular structure on the potential-energy hypersurface for thermolysis, α-effects have been determined for 2,2,2',2'-tetramethyl-1,1'-diphenylazoethane and (2,2-dimethyl-1-phenylpropyl)azomethane; the inability to prepare these compounds by conventional methods necessitated the development of a new method for synthesis of secondary azoalkanes. A convenient synthesis of secondary azo compounds is reported. Secondary α-deuterium isotope effects were obtained for the thermal decomposition of 1,1'-diphenylazoethane (III) and 1,1'-diphenylazoethane-1,1'-d 2 (III-d 2 ). The isotope effect is entirely consistent with a simultaneous one-step thermolysis mechanism. Secondary α-deuterium isotope effects and activation parameters were obtained in the thermolysis of 2,2,2',2'-tetramethyl-1,1'-diphenylazopropane (VIII) and (2,2-dimethyl-1-phenylpropyl)azomethane (IX). The data for VIII is considered in terms of both a one- and two-step thermolysis mechanism. The α-effect and activation energy for VIII are not obviously reconcilable with a one-step mechanism. The α-effects, activation energies, and rates of thermolysis for VIII, IX, and (1-phenylethyl)azomethane are most easily rationalized by a two-step mechanism

Stochastic Extreme Load Predictions for Marine Structures

DEFF Research Database (Denmark)

Jensen, Jørgen Juncher

1999-01-01

Development of rational design criteria for marine structures requires reliable estimates for the maximum wave-induced loads the structure may encounter during its operational lifetime. The paper discusses various methods for extreme value predictions taking into account the non-linearity of the ......Development of rational design criteria for marine structures requires reliable estimates for the maximum wave-induced loads the structure may encounter during its operational lifetime. The paper discusses various methods for extreme value predictions taking into account the non......-linearity of the waves and the response. As example the wave-induced bending moment in the ship hull girder is considered....

Identifying secondary structures in proteins using NMR chemical shift 3D correlation maps

Science.gov (United States)

Kumari, Amrita; Dorai, Kavita

2013-06-01

NMR chemical shifts are accurate indicators of molecular environment and have been extensively used as aids in protein structure determination. This work focuses on creating empirical 3D correlation maps of backbone chemical shift nuclei for use as identifiers of secondary structure elements in proteins. A correlated database of backbone nuclei chemical shifts was constructed from experimental structural data gathered from entries in the Protein Data Bank (PDB) as well as isotropic chemical shift values from the RefDB database. Rigorous statistical analysis of the maps led to the conclusion that specific correlations between triplets of backbone chemical shifts are best able to differentiate between different secondary structures such as α-helices, β-strands and turns. The method is compared with similar techniques that use NMR chemical shift information as aids in biomolecular structure determination and performs well in tests done on experimental data determined for different types of proteins, including large multi-domain proteins and membrane proteins.

Changes in secondary structure of poliovirus ribonucleic acid

International Nuclear Information System (INIS)

Koza, J.

1975-01-01

Infectious single-stranded RNA isolated from mature purified poliovirus was separated into three fractions by means of chromatography on an ''evaporated'' calcium phosphate column. RNA molecules with a higher degree of secondary structure were detected in two of the fractions as a result of the chromatography. These RNA molecules (1) were resistant to hydrolysis by pancreatic ribonuclease A, (2) retained unchanged the original infectivity for actinomycin D-pretreated cells, (3) were resistant to ultraviolet-light inactivation and (4) were partially resistant to formaldehyde inactivation

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

Directory of Open Access Journals (Sweden)

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.

Secbase: database module to retrieve secondary structure elements with ligand binding motifs.

Science.gov (United States)

Koch, Oliver; Cole, Jason; Block, Peter; Klebe, Gerhard

2009-10-01

Secbase is presented as a novel extension module of Relibase. It integrates the information about secondary structure elements into the retrieval facilities of Relibase. The data are accessible via the extended Relibase user interface, and integrated retrieval queries can be addressed using an extended version of Reliscript. The primary information about alpha-helices and beta-sheets is used as provided by the PDB. Furthermore, a uniform classification of all turn families, based on recent clustering methods, and a new helix assignment that is based on this turn classification has been included. Algorithms to analyze the geometric features of helices and beta-strands were also implemented. To demonstrate the performance of the Secbase implementation, some application examples are given. They provide new insights into the involvement of secondary structure elements in ligand binding. A survey of water molecules detected next to the N-terminus of helices is analyzed to show their involvement in ligand binding. Additionally, the parallel oriented NH groups at the alpha-helix N-termini provide special binding motifs to bind particular ligand functional groups with two adjacent oxygen atoms, e.g., as found in negatively charged carboxylate or phosphate groups, respectively. The present study also shows that the specific structure of the first turn of alpha-helices provides a suitable explanation for stabilizing charged structures. The magnitude of the overall helix macrodipole seems to have no or only a minor influence on binding. Furthermore, an overview of the involvement of secondary structure elements with the recognition of some important endogenous ligands such as cofactors shows some distinct preference for particular binding motifs and amino acids.

Determination of the secondary structure content of proteins in aqueous solutions from their amide I and amide II infrared bands. Comparison between classical and partial least-squares methods

International Nuclear Information System (INIS)

Dousseau, F.; Pezolet, M.

1990-01-01

A method for estimating protein secondary structure from infrared spectra has been developed. The infrared spectra of H 2 O solutions of 13 proteins of known crystal structure have been recorded and corrected for the spectral contribution of water in the amide I and II region by using the algorithm of Dousseau et al. This calibration set of proteins has been analyzed by using either a classical least-squares (CLS) method or the partial least-squares (PLS) method. The pure-structure spectra calculated by the classical least-squares method are in good agreement with spectra of poly(L-lysine) in the α-helix, β-sheet, and undefined conformations. The results show that the best agreement between the secondary structure determined by X-ray crystal-lography and that predicted by infrared spectroscopy is obtained when both the amide I and II bands are used to generate the calibration set, when the PLS method is used, and when it is assumed that the secondary structure of proteins is composed of only four types of structure: ordered and disordered α-helices, β-sheet, and undefined conformation. Attempts to include turns in the secondary structure estimation have led to a loss of accuracy. The spectra of the calibration proteins were also recorded in 2 H 2 O solution. After correction for the contribution of the combination band of 2 H 2 O in the amide I' band region, the spectra were analyzed with PLS, but the results were not as good as for the spectra obtained in H 2 O, especially for the α-helical conformation

Protein Secondary Structures (α-helix and β-sheet) at a Cellular Level and Protein Fractions in Relation to Rumen Degradation Behaviours of Protein: A New Approach

International Nuclear Information System (INIS)

Yu, P.

2007-01-01

percentage of β-sheets (from 37.2% to 49.8%: S-FTIR absorption intensity) and reduced the α-helix to β-sheet ratio (from 0.3 to 0.7) in the golden flaxseeds, which indicated a negative effect of the roasting on protein values, utilisation and bioavailability. These results were proved by the Cornell Net Carbohydrate Protein System in situ animal trial, which also revealed that roasting increased the amount of protein bound to lignin, and well as of the Maillard reaction protein (both of which are poorly used by ruminants), and increased the level of indigestible and undegradable protein in ruminants. The present results demonstrate the potential of highly spatially resolved synchrotron-based infrared microspectroscopy to locate 'pure' protein in feed tissues, and reveal protein secondary structures and digestive behaviour, making a significant step forward in and an important contribution to protein nutritional research. Further study is needed to determine the sensitivities of protein secondary structures to various heat-processing conditions, and to quantify the relationship between protein secondary structures and the nutrient availability and digestive behaviour of various protein sources. Information from the present study arising from the synchrotron-based IR probing of the protein secondary structures of protein sources at the cellular level will be valuable as a guide to maintaining protein quality and predicting digestive behaviours

Protein Secondary Structures (alpha-helix and beta-sheet) at a Cellular Levle and Protein Fractions in Relation to Rumen Degradation Behaviours of Protein: A New Approach

Energy Technology Data Exchange (ETDEWEB)

Yu,P.

2007-01-01

-FTIR absorption intensity), increased the percentage of {beta}-sheets (from 37.2% to 49.8%: S-FTIR absorption intensity) and reduced the {alpha}-helix to {beta}-sheet ratio (from 0.3 to 0.7) in the golden flaxseeds, which indicated a negative effect of the roasting on protein values, utilisation and bioavailability. These results were proved by the Cornell Net Carbohydrate Protein System in situ animal trial, which also revealed that roasting increased the amount of protein bound to lignin, and well as of the Maillard reaction protein (both of which are poorly used by ruminants), and increased the level of indigestible and undegradable protein in ruminants. The present results demonstrate the potential of highly spatially resolved synchrotron-based infrared microspectroscopy to locate 'pure' protein in feed tissues, and reveal protein secondary structures and digestive behaviour, making a significant step forward in and an important contribution to protein nutritional research. Further study is needed to determine the sensitivities of protein secondary structures to various heat-processing conditions, and to quantify the relationship between protein secondary structures and the nutrient availability and digestive behaviour of various protein sources. Information from the present study arising from the synchrotron-based IR probing of the protein secondary structures of protein sources at the cellular level will be valuable as a guide to maintaining protein quality and predicting digestive behaviours.

Secondary flow structures in a 180∘ elastic curved vessel with torsion under steady and pulsatile inflow conditions

Science.gov (United States)

Najjari, Mohammad Reza; Plesniak, Michael W.

2017-11-01

Secondary flow vortical structures were investigated in an elastic 180° curved pipe with and without torsion under steady and pulsatile flow using particle image velocimetry (PIV). The elastic thin-walled curved pipes were constructed using Sylgard 184, and inserted into a bath of refractive index matched fluid to perform PIV. A vortex identification method was employed to identify various vortical structures in the flow. The secondary flow structures in the planar compliant model with dilatation of 0.61%-3.23% under pulsatile flow rate were compared with the rigid vessel model results, and it was found that local vessel compliance has a negligible effect on secondary flow morphology. The secondary flow structures were found to be more sensitive to out of plane curvature (torsion) than to vessel compliance. Torsion distorts the symmetry of secondary flow and results in more complex vortical structures in both steady and pulsatile flows. In high Re number steady flow with torsion, a single dominant vortical structure can be detected at the middle of the 90° cross section. In pulsatile flow with torsion, the split-Dean and Lyne-type vortices with same rotation direction originating from opposite sides of the cross section tend to merge together. supported by GW Center for Biomimetics and Bioinspired Engineering.

Prediction of adolescents doing physical activity after completing secondary education.

Science.gov (United States)

Moreno-Murcia, Juan Antonio; Huéscar, Elisa; Cervelló, Eduardo

2012-03-01

The purpose of this study, based on the self-determination theory (Ryan & Deci, 2000) was to test the prediction power of student's responsibility, psychological mediators, intrinsic motivation and the importance attached to physical education in the intention to continue to practice some form of physical activity and/or sport, and the possible relationships that exist between these variables. We used a sample of 482 adolescent students in physical education classes, with a mean age of 14.3 years, which were measured for responsibility, psychological mediators, sports motivation, the importance of physical education and intention to be physically active. We completed an analysis of structural equations modelling. The results showed that the responsibility positively predicted psychological mediators, and this predicted intrinsic motivation, which positively predicted the importance students attach to physical education, and this, finally, positively predicted the intention of the student to continue doing sport. Results are discussed in relation to the promotion of student's responsibility towards a greater commitment to the practice of physical exercise.

EVA: continuous automatic evaluation of protein structure prediction servers.

Science.gov (United States)

Eyrich, V A; Martí-Renom, M A; Przybylski, D; Madhusudhan, M S; Fiser, A; Pazos, F; Valencia, A; Sali, A; Rost, B

2001-12-01

Evaluation of protein structure prediction methods is difficult and time-consuming. Here, we describe EVA, a web server for assessing protein structure prediction methods, in an automated, continuous and large-scale fashion. Currently, EVA evaluates the performance of a variety of prediction methods available through the internet. Every week, the sequences of the latest experimentally determined protein structures are sent to prediction servers, results are collected, performance is evaluated, and a summary is published on the web. EVA has so far collected data for more than 3000 protein chains. These results may provide valuable insight to both developers and users of prediction methods. http://cubic.bioc.columbia.edu/eva. eva@cubic.bioc.columbia.edu

Personality traits measured at baseline can predict academic performance in upper secondary school three years late.

Science.gov (United States)

Rosander, Pia; Bäckström, Martin

2014-12-01

The aim of the present study was to explore the ability of personality to predict academic performance in a longitudinal study of a Swedish upper secondary school sample. Academic performance was assessed throughout a three-year period via final grades from the compulsory school and upper secondary school. The Big Five personality factors (Costa & McCrae, ) - particularly Conscientiousness and Neuroticism - were found to predict overall academic performance, after controlling for general intelligence. Results suggest that Conscientiousness, as measured at the age of 16, can explain change in academic performance at the age of 19. The effect of Neuroticism on Conscientiousness indicates that, as regarding getting good grades, it is better to be a bit neurotic than to be stable. The study extends previous work by assessing the relationship between the Big Five and academic performance over a three-year period. The results offer educators avenues for improving educational achievement. © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd.

Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction.

Science.gov (United States)

Boiteau, Rene M; Hoyt, David W; Nicora, Carrie D; Kinmonth-Schultz, Hannah A; Ward, Joy K; Bingol, Kerem

2018-01-17

We introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS²), and NMR into a single analysis platform to accurately identify unknown metabolites in untargeted studies. The approach starts with an unknown LC-MS feature, and then combines the experimental MS/MS and NMR information of the unknown to effectively filter out the false positive candidate structures based on their predicted MS/MS and NMR spectra. We demonstrate the approach on a model mixture, and then we identify an uncatalogued secondary metabolite in Arabidopsis thaliana . The NMR/MS² approach is well suited to the discovery of new metabolites in plant extracts, microbes, soils, dissolved organic matter, food extracts, biofuels, and biomedical samples, facilitating the identification of metabolites that are not present in experimental NMR and MS metabolomics databases.

A computer graphics program system for protein structure representation.

Science.gov (United States)

Ross, A M; Golub, E E

1988-01-01

We have developed a computer graphics program system for the schematic representation of several protein secondary structure analysis algorithms. The programs calculate the probability of occurrence of alpha-helix, beta-sheet and beta-turns by the method of Chou and Fasman and assign unique predicted structure to each residue using a novel conflict resolution algorithm based on maximum likelihood. A detailed structure map containing secondary structure, hydrophobicity, sequence identity, sequence numbering and the location of putative N-linked glycosylation sites is then produced. In addition, helical wheel diagrams and hydrophobic moment calculations can be performed to further analyze the properties of selected regions of the sequence. As they require only structure specification as input, the graphics programs can easily be adapted for use with other secondary structure prediction schemes. The use of these programs to analyze protein structure-function relationships is described and evaluated. PMID:2832829

Fisher: a program for the detection of H/ACA snoRNAs using MFE secondary structure prediction and comparative genomics – assessment and update

Directory of Open Access Journals (Sweden)

Tamas Ivica

2008-07-01

Full Text Available Abstract Background The H/ACA family of small nucleolar RNAs (snoRNAs plays a central role in guiding the pseudouridylation of ribosomal RNA (rRNA. In an effort to systematically identify the complete set of rRNA-modifying H/ACA snoRNAs from the genome sequence of the budding yeast, Saccharomyces cerevisiae, we developed a program – Fisher – and previously presented several candidate snoRNAs based on our analysis 1. Findings In this report, we provide a brief update of this work, which was aborted after the publication of experimentally-identified snoRNAs 2 identical to candidates we had identified bioinformatically using Fisher. Our motivation for revisiting this work is to report on the status of the candidate snoRNAs described in 1, and secondly, to report that a modified version of Fisher together with the available multiple yeast genome sequences was able to correctly identify several H/ACA snoRNAs for modification sites not identified by the snoGPS program 3. While we are no longer developing Fisher, we briefly consider the merits of the Fisher algorithm relative to snoGPS, which may be of use for workers considering pursuing a similar search strategy for the identification of small RNAs. The modified source code for Fisher is made available as supplementary material. Conclusion Our results confirm the validity of using minimum free energy (MFE secondary structure prediction to guide comparative genomic screening for RNA families with few sequence constraints.

Secondary flow structures under stent-induced perturbations for cardiovascular flow in a curved artery model

International Nuclear Information System (INIS)

Glenn, Autumn L.; Bulusu, Kartik V.; Shu Fangjun; Plesniak, Michael W.

2012-01-01

Secondary flows within curved arteries with unsteady forcing result from amplified centrifugal instabilities and are expected to be driven by the rapid accelerations and decelerations inherent in physiological waveforms. These secondary flows may also affect the function of curved arteries through pro-atherogenic wall shear stresses, platelet residence time and other vascular response mechanisms. Planar PIV measurements were performed under multi-harmonic non-zero-mean and physiological carotid artery waveforms at various locations in a rigid bent-pipe curved artery model. Results revealed symmetric counter-rotating vortex pairs that developed during the acceleration phases of both multi-harmonic and physiological waveforms. An idealized stent model was placed upstream of the bend, which initiated flow perturbations under physiological inflow conditions. Changes in the secondary flow structures were observed during the systolic deceleration phase (t/T ≈ 0.20–0.50). Proper Orthogonal Decomposition (POD) analysis of the flow morphologies under unsteady conditions indicated similarities in the coherent secondary-flow structures and correlation with phase-averaged velocity fields. A regime map was created that characterizes the kaleidoscope of vortical secondary flows with multiple vortex pairs and interesting secondary flow morphologies. This regime map in the curved artery model was created by plotting the secondary Reynolds number against another dimensionless acceleration-based parameter marking numbered regions of vortex pairs.

Secondary flow structures under stent-induced perturbations for cardiovascular flow in a curved artery model

Energy Technology Data Exchange (ETDEWEB)

Glenn, Autumn L.; Bulusu, Kartik V. [Department of Mechanical and Aerospace Engineering, George Washington University, 801 22nd Street, NW., Washington, DC 20052 (United States); Shu Fangjun [Department of Mechanical and Aerospace Engineering, New Mexico State University, MSC 3450, P.O. Box 30001, Las Cruces, NM 88003-8001 (United States); Plesniak, Michael W., E-mail: plesniak@gwu.edu [Department of Mechanical and Aerospace Engineering, George Washington University, 801 22nd Street, NW., Washington, DC 20052 (United States)

2012-06-15

Secondary flows within curved arteries with unsteady forcing result from amplified centrifugal instabilities and are expected to be driven by the rapid accelerations and decelerations inherent in physiological waveforms. These secondary flows may also affect the function of curved arteries through pro-atherogenic wall shear stresses, platelet residence time and other vascular response mechanisms. Planar PIV measurements were performed under multi-harmonic non-zero-mean and physiological carotid artery waveforms at various locations in a rigid bent-pipe curved artery model. Results revealed symmetric counter-rotating vortex pairs that developed during the acceleration phases of both multi-harmonic and physiological waveforms. An idealized stent model was placed upstream of the bend, which initiated flow perturbations under physiological inflow conditions. Changes in the secondary flow structures were observed during the systolic deceleration phase (t/T Almost-Equal-To 0.20-0.50). Proper Orthogonal Decomposition (POD) analysis of the flow morphologies under unsteady conditions indicated similarities in the coherent secondary-flow structures and correlation with phase-averaged velocity fields. A regime map was created that characterizes the kaleidoscope of vortical secondary flows with multiple vortex pairs and interesting secondary flow morphologies. This regime map in the curved artery model was created by plotting the secondary Reynolds number against another dimensionless acceleration-based parameter marking numbered regions of vortex pairs.

Blind Test of Physics-Based Prediction of Protein Structures

Science.gov (United States)

Shell, M. Scott; Ozkan, S. Banu; Voelz, Vincent; Wu, Guohong Albert; Dill, Ken A.

2009-01-01

We report here a multiprotein blind test of a computer method to predict native protein structures based solely on an all-atom physics-based force field. We use the AMBER 96 potential function with an implicit (GB/SA) model of solvation, combined with replica-exchange molecular-dynamics simulations. Coarse conformational sampling is performed using the zipping and assembly method (ZAM), an approach that is designed to mimic the putative physical routes of protein folding. ZAM was applied to the folding of six proteins, from 76 to 112 monomers in length, in CASP7, a community-wide blind test of protein structure prediction. Because these predictions have about the same level of accuracy as typical bioinformatics methods, and do not utilize information from databases of known native structures, this work opens up the possibility of predicting the structures of membrane proteins, synthetic peptides, or other foldable polymers, for which there is little prior knowledge of native structures. This approach may also be useful for predicting physical protein folding routes, non-native conformations, and other physical properties from amino acid sequences. PMID:19186130

Protein structure prediction using bee colony optimization metaheuristic

DEFF Research Database (Denmark)

Fonseca, Rasmus; Paluszewski, Martin; Winter, Pawel

2010-01-01

of the proteins structure, an energy potential and some optimization algorithm that ¿nds the structure with minimal energy. Bee Colony Optimization (BCO) is a relatively new approach to solving opti- mization problems based on the foraging behaviour of bees. Several variants of BCO have been suggested......Predicting the native structure of proteins is one of the most challenging problems in molecular biology. The goal is to determine the three-dimensional struc- ture from the one-dimensional amino acid sequence. De novo prediction algorithms seek to do this by developing a representation...... our BCO method to generate good solutions to the protein structure prediction problem. The results show that BCO generally ¿nds better solutions than simulated annealing which so far has been the metaheuristic of choice for this problem....

Secondary structure classification of amino-acid sequences using state-space modeling

OpenAIRE

Brunnert, Marcus; Krahnke, Tillmann; Urfer, Wolfgang

2001-01-01

The secondary structure classification of amino acid sequences can be carried out by a statistical analysis of sequence and structure data using state-space models. Aiming at this classification, a modified filter algorithm programmed in S is applied to data of three proteins. The application leads to correct classifications of two proteins even when using relatively simple estimation methods for the parameters of the state-space models. Furthermore, it has been shown that the assumed initial...

Structural profiles of human miRNA families from pairwise clustering

DEFF Research Database (Denmark)

Kaczkowski, Bogumil; Þórarinsson, Elfar; Reiche, Kristin

2009-01-01

secondary structure already predicted, little is known about the patterns of structural conservation among pre-miRNAs. We address this issue by clustering the human pre-miRNA sequences based on pairwise, sequence and secondary structure alignment using FOLDALIGN, followed by global multiple alignment...... of obtained clusters by WAR. As a result, the common secondary structure was successfully determined for four FOLDALIGN clusters: the RF00027 structural family of the Rfam database and three clusters with previously undescribed consensus structures. Availability: http://genome.ku.dk/resources/mirclust...

On the importance of cotranscriptional RNA structure formation

Science.gov (United States)

Lai, Daniel; Proctor, Jeff R.; Meyer, Irmtraud M.

2013-01-01

The expression of genes, both coding and noncoding, can be significantly influenced by RNA structural features of their corresponding transcripts. There is by now mounting experimental and some theoretical evidence that structure formation in vivo starts during transcription and that this cotranscriptional folding determines the functional RNA structural features that are being formed. Several decades of research in bioinformatics have resulted in a wide range of computational methods for predicting RNA secondary structures. Almost all state-of-the-art methods in terms of prediction accuracy, however, completely ignore the process of structure formation and focus exclusively on the final RNA structure. This review hopes to bridge this gap. We summarize the existing evidence for cotranscriptional folding and then review the different, currently used strategies for RNA secondary-structure prediction. Finally, we propose a range of ideas on how state-of-the-art methods could be potentially improved by explicitly capturing the process of cotranscriptional structure formation. PMID:24131802

Glassy transition in a disordered model for the RNA secondary structure

International Nuclear Information System (INIS)

Pagnani, A.; Parisi, G.; Ricci-Tersenghi, F.

2000-04-01

We numerically study a disordered model for the RNA secondary structure and we find that it undergoes a phase transition, with a breaking of the replica symmetry in the low temperature region (like in spin glasses). Our results are based on the exact evaluation of the partition function. (author)

The conserved structures of the 5' nontranslated region of Citrus tristeza virus are involved in replication and virion assembly

International Nuclear Information System (INIS)

Gowda, Siddarame; Satyanarayana, Tatineni; Ayllon, Maria A.; Moreno, Pedro; Flores, Ricardo; Dawson, William O.

2003-01-01

The genomic RNA of different isolates of Citrus tristeza virus (CTV) reveals an unusual pattern of sequence diversity: the 3' halves are highly conserved (homology >90%), while the 5' halves show much more dissimilarity, with the 5' nontranslated region (NTR) containing the highest diversity (homology as low as 42%). Yet, positive-sense sequences of the 5' NTR were predicted to fold into nearly identical structures consisting of two stem-loops (SL1 and SL2) separated by a short spacer region. The predicted most stable secondary structures of the negative-sense sequences were more variable. We introduced mutations into the 5' NTR of a CTV replicon to alter the sequence and/or the predicted secondary structures with or without additional compensatory changes designed to restore predicted secondary structures, and examined their effect on replication in transfected protoplasts. The results suggested that the predicted secondary structures of the 5' NTR were more important for replication than the primary structure. Most mutations that were predicted to disrupt the secondary structures fail to replicate, while compensatory mutations were allowed replication to resume. The 5' NTR mutations that were tolerated by the CTV replicon were examined in the full-length virus for effects on replication and production of the multiple subgenomic RNAs. Additionally, the ability of these mutants to produce virions was monitored by electron microscopy and by passaging the progeny nucleocapsids to another batch of protoplasts. Some of the mutants with compensatory sequence alterations predicted to rebuild similar secondary structures allowed replication at near wild-type levels but failed to passage, suggesting that the 5' NTR contains sequences required for both replication and virion assembly

Molecular systematics of Barbatosphaeria (Sordariomycetes): multigene phylogeny and secondary ITS structure

Czech Academy of Sciences Publication Activity Database

Réblová, Martina; Réblová, K.; Štěpánek, Václav

2015-01-01

Roč. 35, December 2015 (2015), s. 21-38 ISSN 0031-5850 R&D Projects: GA ČR GAP506/12/0038 Institutional support: RVO:67985939 ; RVO:61388971 Keywords : Barbatosphaeria * molecular systematic * ITS secondary structures Subject RIV: EF - Botanics; EE - Microbiology, Virology (MBU-M) Impact factor: 5.725, year: 2015

Study on the Effect of Secondary Banded Structure on the Fatigue Property of Non-Quenched and Tempered Micro Alloyed Steel

Science.gov (United States)

Yajie, Cheng; Qingliang, Liao; Yue, Zhang

Due to composition segregation and cooling speed, streamline or banded structure were often obtained in the thermal forming parts along the direction of parts forming. Generally speaking, banded structure doesn't decrease the longitudinal mechanical properties, so the secondary banded structure can't get enough attention. The effect of secondary banded structure on the fatigue properties of micro alloyed DG20Mn and 35CrMo steel was investigated using the axial tensile fatigue test of stress ratio of 0.1. The result shows that secondary banded structure was obtained in the center of the steel parts, because of the composition segregation and the lower cooling rate in center part of steel. Secondary banded structure has no significant effect on axial tensile properties of both DG20Mn and 35CrMo, but decreases the axial tensile fatigue performance of DG20Mn steel. This study suggests that under the high cyclic tensile stress, multi-source damage cracks in steel initiated by large strain of pearlite of secondary banded structure, which is larger than damage strain, is the major factor of the decrease of fatigue life of steel.

De novo prediction of structured RNAs from genomic sequences

DEFF Research Database (Denmark)

Gorodkin, Jan; Hofacker, Ivo L.; Þórarinsson, Elfar

2010-01-01

currently available, because evolutionary conservation highlights functionally important regions. Conserved secondary structure, rather than primary sequence, is the hallmark of many functionally important RNAs, because compensatory substitutions in base-paired regions preserve structure. Unfortunately...

Understanding Quality of Life in Adults with Spinal Cord Injury Via SCI-Related Needs and Secondary Complications.

Science.gov (United States)

Sweet, Shane N; Noreau, Luc; Leblond, Jean; Dumont, Frédéric S

2014-01-01

Understanding the factors that can predict greater quality of life (QoL) is important for adults with spinal cord injury (SCI), given that they report lower levels of QoL than the general population. To build a conceptual model linking SCI-related needs, secondary complications, and QoL in adults with SCI. Prior to testing the conceptual model, we aimed to develop and evaluate the factor structure for both SCI-related needs and secondary complications. Individuals with a traumatic SCI (N = 1,137) responded to an online survey measuring 13 SCI-related needs, 13 secondary complications, and the Life Satisfaction Questionnaire to assess QoL. The SCI-related needs and secondary complications were conceptualized into factors, tested with a confirmatory factor analysis, and subsequently evaluated in a structural equation model to predict QoL. The confirmatory factor analysis supported a 2-factor model for SCI related needs, χ(2)(61, N = 1,137) = 250.40, P SCI-related needs (β = -.22 and -.20, P SCI-related needs of individuals with SCI and preventing or managing secondary complications are essential to their QoL.

[Peculiarities of secondary structure of serum albumin of some representatives of the animal kingdom].

Science.gov (United States)

Pekhymenko, G V; Kuchmerovskaia, T M

2011-01-01

Methods of infrared (IR) spectroscopy and circular dichroism (CD) are suitable techniques for detection of proteins structural changes. These methods were used for determinating peculiarities of the secondary structure of serum albumins in some representatives of two classes of reptiles: Horsfield's tortoise (Testudo horsfieldi), water snake (Natrix tessellata) and grass snake (Natrix natrix) and birds: domestic goose (Anser anser), domestic chicken (Gallus domesticus), domestic duck (Anas platyrhyncha) and dove colored (Columba livia). An analysis of IR spectra and spectra obtained by the method of CD of serum albumins of both classes representatives revealed that beta-folding structure and alpha-helical sections that form the alpha-conformation play an important role in conformational structure formation of polypeptide chain and also disordered sites of molecules of these proteins. It was observed that certain redistribution depending on animals species exists, in the formation of secondary structure of serum albumins of the investigated representatives of reptiles and birds classes between the content of beta-folding structure, alpha-helical sections and disordered sites in molecules of these proteins.

FRAMEWORK FOR STRUCTURAL ONLINE HEALTH MONITORING OF AGING AND DEGRADATION OF SECONDARY PIPING SYSTEMS DUE TO SOME ASPECTS OF EROSION

Energy Technology Data Exchange (ETDEWEB)

Gribok, Andrei V.; Agarwal, Vivek

2017-06-01

This paper describes the current state of research related to critical aspects of erosion and selected aspects of degradation of secondary components in nuclear power plants (NPPs). The paper also proposes a framework for online health monitoring of aging and degradation of secondary components. The framework consists of an integrated multi-sensor modality system, which can be used to monitor different piping configurations under different degradation conditions. The report analyses the currently known degradation mechanisms and available predictive models. Based on this analysis, the structural health monitoring framework is proposed. The Light Water Reactor Sustainability Program began to evaluate technologies that could be used to perform online monitoring of piping and other secondary system structural components in commercial NPPs. These online monitoring systems have the potential to identify when a more detailed inspection is needed using real time measurements, rather than at a pre-determined inspection interval. This transition to condition-based, risk-informed automated maintenance will contribute to a significant reduction of operations and maintenance costs that account for the majority of nuclear power generation costs. Furthermore, of the operations and maintenance costs in U.S. plants, approximately 80% are labor costs. To address the issue of rising operating costs and economic viability, in 2017, companies that operate the national nuclear energy fleet started the Delivering the Nuclear Promise Initiative, which is a 3 year program aimed at maintaining operational focus, increasing value, and improving efficiency. There is unanimous agreement between industry experts and academic researchers that identifying and prioritizing inspection locations in secondary piping systems (for example, in raw water piping or diesel piping) would eliminate many excessive in-service inspections. The proposed structural health monitoring framework takes aim at

Low pressure-induced secondary structure transitions of regenerated silk fibroin in its wet film studied by time-resolved infrared spectroscopy.

Science.gov (United States)

He, Zhipeng; Liu, Zhao; Zhou, Xiaofeng; Huang, He

2018-06-01

The secondary structure transitions of regenerated silk fibroin (RSF) under different external perturbations have been studied extensively, except for pressure. In this work, time-resolved infrared spectroscopy with the attenuated total reflectance (ATR) accessory was employed to follow the secondary structure transitions of RSF in its wet film under low pressure. It has been found that pressure alone is favorable only to the formation of β-sheet structure. Under constant pressure there is an optimum amount of D 2 O in the wet film (D 2 O : film = 2:1) so as to provide the optimal condition for the reorganization of the secondary structure and to have the largest formation of β-sheet structure. Under constant amount of D 2 O and constant pressure, the secondary structure transitions of RSF in its wet film can be divided into three stages along with time. In the first stage, random coil, α-helix, and β-turn were quickly transformed into β-sheet. In the second stage, random coil and β-turn were relatively slowly transformed into β-sheet and α-helix, and the content of α-helix was recovered to the value prior to the application of pressure. In the third and final stage, no measurable changes can be found for each secondary structure. This study may be helpful to understand the secondary structure changes of silk fibroin in silkworm's glands under hydrostatic pressure. © 2018 Wiley Periodicals, Inc.

Secondary Structure Preferences of Mn2+ Binding Sites in Bacterial Proteins

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Tatyana Aleksandrovna Khrustaleva

2014-01-01

Full Text Available 3D structures of proteins with coordinated Mn2+ ions from bacteria with low, average, and high genomic GC-content have been analyzed (149 PDB files were used. Major Mn2+ binders are aspartic acid (6.82% of Asp residues, histidine (14.76% of His residues, and glutamic acid (3.51% of Glu residues. We found out that the motif of secondary structure “beta strand-major binder-random coil” is overrepresented around all the three major Mn2+ binders. That motif may be followed by either alpha helix or beta strand. Beta strands near Mn2+ binding residues should be stable because they are enriched by such beta formers as valine and isoleucine, as well as by specific combinations of hydrophobic and hydrophilic amino acid residues characteristic to beta sheet. In the group of proteins from GC-rich bacteria glutamic acid residues situated in alpha helices frequently coordinate Mn2+ ions, probably, because of the decrease of Lys usage under the influence of mutational GC-pressure. On the other hand, the percentage of Mn2+ sites with at least one amino acid in the “beta strand-major binder-random coil” motif of secondary structure (77.88% does not depend on genomic GC-content.

Environmental changes during secondary succession in a tropical dry forest in Mexico

NARCIS (Netherlands)

Lebrija Trejos, E.E.; Pérez-Garcia, E.A.; Meave, J.; Poorter, L.; Bongers, F.

2011-01-01

Vegetation and environment change mutually during secondary succession, yet the idiosyncrasies of the vegetation effect on the understorey environment are poorly understood. To test whether the successional understorey environment changes predictably and is shaped by the structure and seasonality of

Relationship between mRNA secondary structure and sequence variability in Chloroplast genes: possible life history implications.

Science.gov (United States)

Krishnan, Neeraja M; Seligmann, Hervé; Rao, Basuthkar J

2008-01-28

Synonymous sites are freer to vary because of redundancy in genetic code. Messenger RNA secondary structure restricts this freedom, as revealed by previous findings in mitochondrial genes that mutations at third codon position nucleotides in helices are more selected against than those in loops. This motivated us to explore the constraints imposed by mRNA secondary structure on evolutionary variability at all codon positions in general, in chloroplast systems. We found that the evolutionary variability and intrinsic secondary structure stability of these sequences share an inverse relationship. Simulations of most likely single nucleotide evolution in Psilotum nudum and Nephroselmis olivacea mRNAs, indicate that helix-forming propensities of mutated mRNAs are greater than those of the natural mRNAs for short sequences and vice-versa for long sequences. Moreover, helix-forming propensity estimated by the percentage of total mRNA in helices increases gradually with mRNA length, saturating beyond 1000 nucleotides. Protection levels of functionally important sites vary across plants and proteins: r-strategists minimize mutation costs in large genes; K-strategists do the opposite. Mrna length presumably predisposes shorter mRNAs to evolve under different constraints than longer mRNAs. The positive correlation between secondary structure protection and functional importance of sites suggests that some sites might be conserved due to packing-protection constraints at the nucleic acid level in addition to protein level constraints. Consequently, nucleic acid secondary structure a priori biases mutations. The converse (exposure of conserved sites) apparently occurs in a smaller number of cases, indicating a different evolutionary adaptive strategy in these plants. The differences between the protection levels of functionally important sites for r- and K-strategists reflect their respective molecular adaptive strategies. These converge with increasing domestication levels of

Influence of secondary structure on in-source decay of protein in matrix-assisted laser desorption/ionization mass spectrometry.

Science.gov (United States)

Takayama, Mitsuo; Osaka, Issey; Sakakura, Motoshi

2012-01-01

The susceptibility of the N-Cα bond of the peptide backbone to specific cleavage by in-source decay (ISD) in matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) was studied from the standpoint of the secondary structure of three proteins. A naphthalene derivative, 5-amino-1-naphtol (5,1-ANL), was used as the matrix. The resulting c'-ions, which originate from the cleavage at N-Cα bonds in flexible secondary structures such as turn and bend, and are free from intra-molecular hydrogen-bonded α-helix structure, gave relatively intense peaks. Furthermore, ISD spectra of the proteins showed that the N-Cα bonds of specific amino acid residues, namely Gly-Xxx, Xxx-Asp, and Xxx-Asn, were more susceptible to MALDI-ISD than other amino acid residues. This is in agreement with the observation that Gly, Asp and Asn residues usually located in turns, rather than α-helix. The results obtained indicate that protein molecules embedded into the matrix crystal in the MALDI experiments maintain their secondary structures as determined by X-ray crystallography, and that MALDI-ISD has the capability for providing information concerning the secondary structure of protein.

QCD dipole predictions for DIS and diffractive structure functions

International Nuclear Information System (INIS)

Royon, C.

1997-01-01

The proton structure function F 2 , the gluon density F G , and the longitudinal structure function F L are derived in the QCD dipole picture of BFKL dynamics. We use a three parameter fit to describe the 1994 H1 proton structure function F 2 data in the low x, moderate Q 2 range. Without any additional parameter, the gluon density and the longitudinal structure functions are predicted. The diffractive dissociation processes are also discussed within the same framework, and a new prediction for the proton diffractive structure function is obtained

A quantitative analysis of secondary RNA structure using domination based parameters on trees

Directory of Open Access Journals (Sweden)

Zou Yue

2006-03-01

Full Text Available Abstract Background It has become increasingly apparent that a comprehensive database of RNA motifs is essential in order to achieve new goals in genomic and proteomic research. Secondary RNA structures have frequently been represented by various modeling methods as graph-theoretic trees. Using graph theory as a modeling tool allows the vast resources of graphical invariants to be utilized to numerically identify secondary RNA motifs. The domination number of a graph is a graphical invariant that is sensitive to even a slight change in the structure of a tree. The invariants selected in this study are variations of the domination number of a graph. These graphical invariants are partitioned into two classes, and we define two parameters based on each of these classes. These parameters are calculated for all small order trees and a statistical analysis of the resulting data is conducted to determine if the values of these parameters can be utilized to identify which trees of orders seven and eight are RNA-like in structure. Results The statistical analysis shows that the domination based parameters correctly distinguish between the trees that represent native structures and those that are not likely candidates to represent RNA. Some of the trees previously identified as candidate structures are found to be "very" RNA like, while others are not, thereby refining the space of structures likely to be found as representing secondary RNA structure. Conclusion Search algorithms are available that mine nucleotide sequence databases. However, the number of motifs identified can be quite large, making a further search for similar motif computationally difficult. Much of the work in the bioinformatics arena is toward the development of better algorithms to address the computational problem. This work, on the other hand, uses mathematical descriptors to more clearly characterize the RNA motifs and thereby reduce the corresponding search space. These

RNA secondary structures of the bacteriophage phi6 packaging regions.

Science.gov (United States)

Pirttimaa, M J; Bamford, D H

2000-06-01

Bacteriophage phi6 genome consists of three segments of double-stranded RNA. During maturation, single-stranded copies of these segments are packaged into preformed polymerase complex particles. Only phi6 RNA is packaged, and each particle contains only one copy of each segment. An in vitro packaging and replication assay has been developed for phi6, and the packaging signals (pac sites) have been mapped to the 5' ends of the RNA segments. In this study, we propose secondary structure models for the pac sites of phi6 single-stranded RNA segments. Our models accommodate data from structure-specific chemical modifications, free energy minimizations, and phylogenetic comparisons. Previously reported pac site deletion studies are also discussed. Each pac site possesses a unique architecture, that, however, contains common structural elements.

Improved Model for Predicting the Free Energy Contribution of Dinucleotide Bulges to RNA Duplex Stability.

Science.gov (United States)

Tomcho, Jeremy C; Tillman, Magdalena R; Znosko, Brent M

2015-09-01

Predicting the secondary structure of RNA is an intermediate in predicting RNA three-dimensional structure. Commonly, determining RNA secondary structure from sequence uses free energy minimization and nearest neighbor parameters. Current algorithms utilize a sequence-independent model to predict free energy contributions of dinucleotide bulges. To determine if a sequence-dependent model would be more accurate, short RNA duplexes containing dinucleotide bulges with different sequences and nearest neighbor combinations were optically melted to derive thermodynamic parameters. These data suggested energy contributions of dinucleotide bulges were sequence-dependent, and a sequence-dependent model was derived. This model assigns free energy penalties based on the identity of nucleotides in the bulge (3.06 kcal/mol for two purines, 2.93 kcal/mol for two pyrimidines, 2.71 kcal/mol for 5'-purine-pyrimidine-3', and 2.41 kcal/mol for 5'-pyrimidine-purine-3'). The predictive model also includes a 0.45 kcal/mol penalty for an A-U pair adjacent to the bulge and a -0.28 kcal/mol bonus for a G-U pair adjacent to the bulge. The new sequence-dependent model results in predicted values within, on average, 0.17 kcal/mol of experimental values, a significant improvement over the sequence-independent model. This model and new experimental values can be incorporated into algorithms that predict RNA stability and secondary structure from sequence.

QCD dipole prediction for dis and diffractive structure functions

International Nuclear Information System (INIS)

Royon, CH.

1996-01-01

The F 2 , F G , R = F L /F T proton structure functions are derived in the QCD dipole picture of BFKL dynamics. We get a three parameter fit describing the 1994 H1 proton structure function F 2 data in the low x, moderate Q 2 range. Without any additional parameter, the gluon density and the longitudinal structure functions are predicted. The diffractive dissociation processes are also discussed, and a new prediction for the proton diffractive structure function is obtained. (author)

I-TASSER server for protein 3D structure prediction

Directory of Open Access Journals (Sweden)

Zhang Yang

2008-01-01

Full Text Available Abstract Background Prediction of 3-dimensional protein structures from amino acid sequences represents one of the most important problems in computational structural biology. The community-wide Critical Assessment of Structure Prediction (CASP experiments have been designed to obtain an objective assessment of the state-of-the-art of the field, where I-TASSER was ranked as the best method in the server section of the recent 7th CASP experiment. Our laboratory has since then received numerous requests about the public availability of the I-TASSER algorithm and the usage of the I-TASSER predictions. Results An on-line version of I-TASSER is developed at the KU Center for Bioinformatics which has generated protein structure predictions for thousands of modeling requests from more than 35 countries. A scoring function (C-score based on the relative clustering structural density and the consensus significance score of multiple threading templates is introduced to estimate the accuracy of the I-TASSER predictions. A large-scale benchmark test demonstrates a strong correlation between the C-score and the TM-score (a structural similarity measurement with values in [0, 1] of the first models with a correlation coefficient of 0.91. Using a C-score cutoff > -1.5 for the models of correct topology, both false positive and false negative rates are below 0.1. Combining C-score and protein length, the accuracy of the I-TASSER models can be predicted with an average error of 0.08 for TM-score and 2 Å for RMSD. Conclusion The I-TASSER server has been developed to generate automated full-length 3D protein structural predictions where the benchmarked scoring system helps users to obtain quantitative assessments of the I-TASSER models. The output of the I-TASSER server for each query includes up to five full-length models, the confidence score, the estimated TM-score and RMSD, and the standard deviation of the estimations. The I-TASSER server is freely available

Imaging the 3D structure of secondary osteons in human cortical bone using phase-retrieval tomography

Energy Technology Data Exchange (ETDEWEB)

Arhatari, B D; Peele, A G [Department of Physics, La Trobe University, Victoria 3086 (Australia); Cooper, D M L [Department of Anatomy and Cell Biology, University of Saskatchewan, Saskatoon (Canada); Thomas, C D L; Clement, J G [Melbourne Dental School, University of Melbourne, Victoria 3010 (Australia)

2011-08-21

By applying a phase-retrieval step before carrying out standard filtered back-projection reconstructions in tomographic imaging, we were able to resolve structures with small differences in density within a densely absorbing sample. This phase-retrieval tomography is particularly suited for the three-dimensional segmentation of secondary osteons (roughly cylindrical structures) which are superimposed upon an existing cortical bone structure through the process of turnover known as remodelling. The resulting images make possible the analysis of the secondary osteon structure and the relationship between an osteon and the surrounding tissue. Our observations have revealed many different and complex 3D structures of osteons that could not be studied using previous methods. This work was carried out using a laboratory-based x-ray source, which makes obtaining these sorts of images readily accessible.

Protein Phosphorylation and Mineral Binding Affect the Secondary Structure of the Leucine-Rich Amelogenin Peptide

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Hajime Yamazaki

2017-06-01

Full Text Available Previously, we have shown that serine-16 phosphorylation in native full-length porcine amelogenin (P173 and the Leucine-Rich Amelogenin Peptide (LRAP(+P, an alternative amelogenin splice product, affects protein assembly and mineralization in vitro. Notably, P173 and LRAP(+P stabilize amorphous calcium phosphate (ACP and inhibit hydroxyapatite (HA formation, while non-phosphorylated counterparts (rP172, LRAP(−P guide the growth of ordered bundles of HA crystals. Based on these findings, we hypothesize that the phosphorylation of full-length amelogenin and LRAP induces conformational changes that critically affect its capacity to interact with forming calcium phosphate mineral phases. To test this hypothesis, we have utilized Fourier transform infrared spectroscopy (FTIR to determine the secondary structure of LRAP(−P and LRAP(+P in the absence/presence of calcium and selected mineral phases relevant to amelogenesis; i.e., hydroxyapatite (HA: an enamel crystal prototype and (ACP: an enamel crystal precursor phase. Aqueous solutions of LRAP(−P or LRAP(+P were prepared with or without 7.5 mM of CaCl2 at pH 7.4. FTIR spectra of each solution were obtained using attenuated total reflectance, and amide-I peaks were analyzed to provide secondary structure information. Secondary structures of LRAP(+P and LRAP(−P were similarly assessed following incubation with suspensions of HA and pyrophosphate-stabilized ACP. Amide I spectra of LRAP(−P and LRAP(+P were found to be distinct from each other in all cases. Spectra analyses showed that LRAP(−P is comprised mostly of random coil and β-sheet, while LRAP(+P exhibits more β-sheet and α-helix with little random coil. With added Ca, the random coil content increased in LRAP(−P, while LRAP(+P exhibited a decrease in α-helix components. Incubation of LRAP(−P with HA or ACP resulted in comparable increases in β-sheet structure. Notably, however, LRAP(+P secondary structure was more affected by

Correlating Structural Order with Structural Rearrangement in Dusty Plasma Liquids: Can Structural Rearrangement be Predicted by Static Structural Information?

Science.gov (United States)

Su, Yen-Shuo; Liu, Yu-Hsuan; I, Lin

2012-11-01

Whether the static microstructural order information is strongly correlated with the subsequent structural rearrangement (SR) and their predicting power for SR are investigated experimentally in the quenched dusty plasma liquid with microheterogeneities. The poor local structural order is found to be a good alarm to identify the soft spot and predict the short term SR. For the site with good structural order, the persistent time for sustaining the structural memory until SR has a large mean value but a broad distribution. The deviation of the local structural order from that averaged over nearest neighbors serves as a good second alarm to further sort out the short time SR sites. It has the similar sorting power to that using the temporal fluctuation of the local structural order over a small time interval.

Secondary flow vortical structures in a 180∘ elastic curved vessel with torsion under steady and pulsatile inflow conditions

Science.gov (United States)

Najjari, Mohammad Reza; Plesniak, Michael W.

2018-01-01

Secondary flow structures in a 180∘ curved pipe model of an artery are studied using particle image velocimetry. Both steady and pulsatile inflow conditions are investigated. In planar curved pipes with steady flow, multiple (two, four, six) vortices are detected. For pulsatile flow, various pairs of vortices, i.e., Dean, deformed-Dean, Lyne-type, and split-Dean, are present in the cross section of the pipe at 90∘ into the bend. The effects of nonplanar curvature (torsion) and vessel dilatation on these vortical structures are studied. Torsion distorts the symmetric secondary flows (which exist in planar curvatures) and can result in formation of more complex vortical structures. For example, the split-Dean and Lyne-type vortices with same rotation direction originating from opposite sides of the cross section tend to merge together in pulsatile flow. The vortical structures in elastic vessels with dilatation (0.61%-3.23%) are also investigated and the results are compared with rigid model results. It was found that the secondary flow structures in rigid and elastic models are similar, and hence the local compliance of the vessel does not affect the morphology of secondary flow structures.

ConStruct: Improved construction of RNA consensus structures

Directory of Open Access Journals (Sweden)

Steger Gerhard

2008-04-01

Full Text Available Abstract Background Aligning homologous non-coding RNAs (ncRNAs correctly in terms of sequence and structure is an unresolved problem, due to both mathematical complexity and imperfect scoring functions. High quality alignments, however, are a prerequisite for most consensus structure prediction approaches, homology searches, and tools for phylogeny inference. Automatically created ncRNA alignments often need manual corrections, yet this manual refinement is tedious and error-prone. Results We present an extended version of CONSTRUCT, a semi-automatic, graphical tool suitable for creating RNA alignments correct in terms of both consensus sequence and consensus structure. To this purpose CONSTRUCT combines sequence alignment, thermodynamic data and various measures of covariation. One important feature is that the user is guided during the alignment correction step by a consensus dotplot, which displays all thermodynamically optimal base pairs and the corresponding covariation. Once the initial alignment is corrected, optimal and suboptimal secondary structures as well as tertiary interaction can be predicted. We demonstrate CONSTRUCT's ability to guide the user in correcting an initial alignment, and show an example for optimal secondary consensus structure prediction on very hard to align SECIS elements. Moreover we use CONSTRUCT to predict tertiary interactions from sequences of the internal ribosome entry site of CrP-like viruses. In addition we show that alignments specifically designed for benchmarking can be easily be optimized using CONSTRUCT, although they share very little sequence identity. Conclusion CONSTRUCT's graphical interface allows for an easy alignment correction based on and guided by predicted and known structural constraints. It combines several algorithms for prediction of secondary consensus structure and even tertiary interactions. The CONSTRUCT package can be downloaded from the URL listed in the Availability and

Prediction of degradation and fracture of structural materials

International Nuclear Information System (INIS)

Tomkins, B.

1992-01-01

Prediction of materials performance in an engineering integrity context requires the underpinning of predictive modelling tuned by inputs from design, fabrication, operating experience, and laboratory testing. In this regard, in addition to fracture resistance four important areas of time dependent degradation are considered - mechanical, environmental, irradiation and thermal. The status of prediction of materials performance is discussed in relation to a number of important components such as LWR reactor pressure vessels and steam generators, and Fast Reactor high temperature structures. In each case the role of materials modelling is examined and the balance of factors which contribute to the overall prediction of component integrity/reliability noted. Structural integrity arguments must follow a clear strategy if the required level of confidence is to be established. Various strategies and their evolution are discussed. (author)

New tips for structure prediction by comparative modeling

Science.gov (United States)

Rayan, Anwar

2009-01-01

Comparative modelling is utilized to predict the 3-dimensional conformation of a given protein (target) based on its sequence alignment to experimentally determined protein structure (template). The use of such technique is already rewarding and increasingly widespread in biological research and drug development. The accuracy of the predictions as commonly accepted depends on the score of sequence identity of the target protein to the template. To assess the relationship between sequence identity and model quality, we carried out an analysis of a set of 4753 sequence and structure alignments. Throughout this research, the model accuracy was measured by root mean square deviations of Cα atoms of the target-template structures. Surprisingly, the results show that sequence identity of the target protein to the template is not a good descriptor to predict the accuracy of the 3-D structure model. However, in a large number of cases, comparative modelling with lower sequence identity of target to template proteins led to more accurate 3-D structure model. As a consequence of this study, we suggest new tips for improving the quality of omparative models, particularly for models whose target-template sequence identity is below 50%. PMID:19255646

Vfold: a web server for RNA structure and folding thermodynamics prediction.

Science.gov (United States)

Xu, Xiaojun; Zhao, Peinan; Chen, Shi-Jie

2014-01-01

The ever increasing discovery of non-coding RNAs leads to unprecedented demand for the accurate modeling of RNA folding, including the predictions of two-dimensional (base pair) and three-dimensional all-atom structures and folding stabilities. Accurate modeling of RNA structure and stability has far-reaching impact on our understanding of RNA functions in human health and our ability to design RNA-based therapeutic strategies. The Vfold server offers a web interface to predict (a) RNA two-dimensional structure from the nucleotide sequence, (b) three-dimensional structure from the two-dimensional structure and the sequence, and (c) folding thermodynamics (heat capacity melting curve) from the sequence. To predict the two-dimensional structure (base pairs), the server generates an ensemble of structures, including loop structures with the different intra-loop mismatches, and evaluates the free energies using the experimental parameters for the base stacks and the loop entropy parameters given by a coarse-grained RNA folding model (the Vfold model) for the loops. To predict the three-dimensional structure, the server assembles the motif scaffolds using structure templates extracted from the known PDB structures and refines the structure using all-atom energy minimization. The Vfold-based web server provides a user friendly tool for the prediction of RNA structure and stability. The web server and the source codes are freely accessible for public use at "http://rna.physics.missouri.edu".

Inelastic analysis of prestressed concrete secondary containments

International Nuclear Information System (INIS)

Murray, D.W.; Chitnuyanondh, L.; Wong, C.; Rijub-Agha, K.Y.

1978-07-01

An elastic-plastic constitutive model for the simulation of stress-strain response of concrete under any biaxial combination of compressive and/or tensile stresses is developed. An effective tensile stress-strain curve is obtained indirectly from experimental results of a test on a large scale prestressed concrete wall segment. These concrete properties are then utilized in predicting the response of a second test and the results compared with the experiment. Modificications to the BOSOR5 program, in order to incorporate the new constitutive relation into it, are described. Techniques of modelling structures in order to perform inelastic analysis of thin shell axisymmetric prestressed concrete secondary containments are investigated. The results of inelastic BOSOR5 analyses of two different models of the University of Alberta Test Structure are presented. The predicted deterioration of the structure and the limit states associated with its behaviour are determined and discussed. It is concluded that the technique is a practical one which can be used for the inelastic analysis of Gentilly-type containment structures. (author)

Nucleotide sequence of Phaseolus vulgaris L. alcohol dehydrogenase encoding cDNA and three-dimensional structure prediction of the deduced protein.

Science.gov (United States)

Amelia, Kassim; Khor, Chin Yin; Shah, Farida Habib; Bhore, Subhash J

2015-01-01

Common beans (Phaseolus vulgaris L.) are widely consumed as a source of proteins and natural products. However, its yield needs to be increased. In line with the agenda of Phaseomics (an international consortium), work of expressed sequence tags (ESTs) generation from bean pods was initiated. Altogether, 5972 ESTs have been isolated. Alcohol dehydrogenase (AD) encoding gene cDNA was a noticeable transcript among the generated ESTs. This AD is an important enzyme; therefore, to understand more about it this study was undertaken. The objective of this study was to elucidate P. vulgaris L. AD (PvAD) gene cDNA sequence and to predict the three-dimensional (3D) structure of deduced protein. positive and negative strands of the PvAD cDNA clone were sequenced using M13 forward and M13 reverse primers to elucidate the nucleotide sequence. Deduced PvAD cDNA and protein sequence was analyzed for their basic features using online bioinformatics tools. Sequence comparison was carried out using bl2seq program, and tree-view program was used to construct a phylogenetic tree. The secondary structures and 3D structure of PvAD protein were predicted by using the PHYRE automatic fold recognition server. The sequencing results analysis showed that PvAD cDNA is 1294 bp in length. It's open reading frame encodes for a protein that contains 371 amino acids. Deduced protein sequence analysis showed the presence of putative substrate binding, catalytic Zn binding, and NAD binding sites. Results indicate that the predicted 3D structure of PvAD protein is analogous to the experimentally determined crystal structure of s-nitrosoglutathione reductase from an Arabidopsis species. The 1294 bp long PvAD cDNA encodes for 371 amino acid long protein that contains conserved domains required for biological functions of AD. The predicted deduced PvAD protein's 3D structure reflects the analogy with the crystal structure of Arabidopsis thaliana s-nitrosoglutathione reductase. Further study is required

Structural features that predict real-value fluctuations of globular proteins.

Science.gov (United States)

Jamroz, Michal; Kolinski, Andrzej; Kihara, Daisuke

2012-05-01

It is crucial to consider dynamics for understanding the biological function of proteins. We used a large number of molecular dynamics (MD) trajectories of nonhomologous proteins as references and examined static structural features of proteins that are most relevant to fluctuations. We examined correlation of individual structural features with fluctuations and further investigated effective combinations of features for predicting the real value of residue fluctuations using the support vector regression (SVR). It was found that some structural features have higher correlation than crystallographic B-factors with fluctuations observed in MD trajectories. Moreover, SVR that uses combinations of static structural features showed accurate prediction of fluctuations with an average Pearson's correlation coefficient of 0.669 and a root mean square error of 1.04 Å. This correlation coefficient is higher than the one observed in predictions by the Gaussian network model (GNM). An advantage of the developed method over the GNMs is that the former predicts the real value of fluctuation. The results help improve our understanding of relationships between protein structure and fluctuation. Furthermore, the developed method provides a convienient practial way to predict fluctuations of proteins using easily computed static structural features of proteins. Copyright © 2012 Wiley Periodicals, Inc.

“Pinning strategy”: a novel approach for predicting the backbone ...

Indian Academy of Sciences (India)

Prakash

Related to this observation, we propose, in the present work, a novel prediction method called “pinning strategy”. This ... on both ends until a predictability limit is reached. The principle therefore consists in assembling ...... J-F 2005 Protein secondary structure assignment revisited: a detailed analysis of different assignment ...

Predicting Thermal Behavior of Secondary Organic Aerosols

Data.gov (United States)

U.S. Environmental Protection Agency — Volume concentrations of secondary organic aerosol (SOA) are measured in 139 steady-state, single precursor hydrocarbon oxidation experiments after passing through a...

Secondary structural entropy in RNA switch (Riboswitch) identification.

Science.gov (United States)

Manzourolajdad, Amirhossein; Arnold, Jonathan

2015-04-28

RNA regulatory elements play a significant role in gene regulation. Riboswitches, a widespread group of regulatory RNAs, are vital components of many bacterial genomes. These regulatory elements generally function by forming a ligand-induced alternative fold that controls access to ribosome binding sites or other regulatory sites in RNA. Riboswitch-mediated mechanisms are ubiquitous across bacterial genomes. A typical class of riboswitch has its own unique structural and biological complexity, making de novo riboswitch identification a formidable task. Traditionally, riboswitches have been identified through comparative genomics based on sequence and structural homology. The limitations of structural-homology-based approaches, coupled with the assumption that there is a great diversity of undiscovered riboswitches, suggests the need for alternative methods for riboswitch identification, possibly based on features intrinsic to their structure. As of yet, no such reliable method has been proposed. We used structural entropy of riboswitch sequences as a measure of their secondary structural dynamics. Entropy values of a diverse set of riboswitches were compared to that of their mutants, their dinucleotide shuffles, and their reverse complement sequences under different stochastic context-free grammar folding models. Significance of our results was evaluated by comparison to other approaches, such as the base-pairing entropy and energy landscapes dynamics. Classifiers based on structural entropy optimized via sequence and structural features were devised as riboswitch identifiers and tested on Bacillus subtilis, Escherichia coli, and Synechococcus elongatus as an exploration of structural entropy based approaches. The unusually long untranslated region of the cotH in Bacillus subtilis, as well as upstream regions of certain genes, such as the sucC genes were associated with significant structural entropy values in genome-wide examinations. Various tests show that there

On infrared spectroscopic analysis of transfer RNA secondary structure

Energy Technology Data Exchange (ETDEWEB)

Semenov, M A; Starikov, E B

1987-07-14

Various techniques of IR spectroscopy in the 1550-1750 cm/sup -1/ region employed to analyse the tRNA secondary structure are discussed and a novel improved method is proposed. The main novel features of this method are the approximation of tRNA helical region spectra by catalogue carbonyl absorption bands and approximation of tRNA nonhelical region spectra by those of homopolyribonucleotides. The IR spectra of tRNA/sub yeast//sup phe/ and tRNA/sub E.coli//sup fmet/ in the carbonyl vibration region are explained on the basis of calculated transition moment coupling.

Secondary structure of 5S RNA: NMR experiments on RNA molecules partially labeled with Nitrogen-15

International Nuclear Information System (INIS)

Gewirth, D.T.; Abo, S.R.; Leontis, N.B.; Moore, P.B.

1987-01-01

A method has been found for reassembling fragment 1 of Escherichia coli 5S RNA from mixtures containing strand III (bases 69-87) and the complex consisting of strand II (bases 89-120) and strand IV (bases 1-11). The reassembled molecule is identical with unreconstituted fragment 1. With this technique, fragment 1 molecules have been constructed 15 N-labeled either in strand III or in the strand II-strand IV complex. Spectroscopic data obtained with these partially labeled molecules show that the terminal helix of 5S RNA includes the GU and GC base pairs at positions 9 and 10 which the standard model for 5S secondary structure predicts but that these base pairs are unstable both in the fragment and in native 5S RNA. The data also assign three resonances to the helix V region of the molecule (bases 70-77 and 99-106). None of these resonances has a normal chemical shift even though two of them correspond to AU or GU base pairs in the standard model. The implications of these findings for the authors understanding of the structure of 5S RNA and its complex with ribosomal protein L25 are discussed

BLAST-based structural annotation of protein residues using Protein Data Bank.

Science.gov (United States)

Singh, Harinder; Raghava, Gajendra P S

2016-01-25

In the era of next-generation sequencing where thousands of genomes have been already sequenced; size of protein databases is growing with exponential rate. Structural annotation of these proteins is one of the biggest challenges for the computational biologist. Although, it is easy to perform BLAST search against Protein Data Bank (PDB) but it is difficult for a biologist to annotate protein residues from BLAST search. A web-server StarPDB has been developed for structural annotation of a protein based on its similarity with known protein structures. It uses standard BLAST software for performing similarity search of a query protein against protein structures in PDB. This server integrates wide range modules for assigning different types of annotation that includes, Secondary-structure, Accessible surface area, Tight-turns, DNA-RNA and Ligand modules. Secondary structure module allows users to predict regular secondary structure states to each residue in a protein. Accessible surface area predict the exposed or buried residues in a protein. Tight-turns module is designed to predict tight turns like beta-turns in a protein. DNA-RNA module developed for predicting DNA and RNA interacting residues in a protein. Similarly, Ligand module of server allows one to predicted ligands, metal and nucleotides ligand interacting residues in a protein. In summary, this manuscript presents a web server for comprehensive annotation of a protein based on similarity search. It integrates number of visualization tools that facilitate users to understand structure and function of protein residues. This web server is available freely for scientific community from URL http://crdd.osdd.net/raghava/starpdb .

RNA secondary structures of the bacteriophage phi6 packaging regions.

OpenAIRE

Pirttimaa, M J; Bamford, D H

2000-01-01

Bacteriophage phi6 genome consists of three segments of double-stranded RNA. During maturation, single-stranded copies of these segments are packaged into preformed polymerase complex particles. Only phi6 RNA is packaged, and each particle contains only one copy of each segment. An in vitro packaging and replication assay has been developed for phi6, and the packaging signals (pac sites) have been mapped to the 5' ends of the RNA segments. In this study, we propose secondary structure models ...

Workplace Deviance: A Predictive study of Occupational Stress and Emotional Intelligence among Secondary School teachers

OpenAIRE

Oguegbe Tochukwu Matthew; Uzoh Bonaventure Chigozie; Anyikwa Kosiso

2014-01-01

The study investigated workplace deviance:A predictive study of Occupational stress and Emotional intelligence among secondary school teachers. A total number of 198 teachers from Nigeria served as participants with the mean age of 32.98,standarddeviation 0f 9.26 and age range of 22 to 54years. Three instruments were used in the study: Workplace deviance scale, Occupational stress inventory and Emotional intelligence scale. The study adopted a correlational design with Pearson Product Moment ...

Secondary reconstruction of maxillofacial trauma.

Science.gov (United States)

Castro-Núñez, Jaime; Van Sickels, Joseph E

2017-08-01

Craniomaxillofacial trauma is one of the most complex clinical conditions in contemporary maxillofacial surgery. Vital structures and possible functional and esthetic sequelae are important considerations following this type of trauma and intervention. Despite the best efforts of the primary surgery, there are a group of patients that will have poor outcomes requiring secondary reconstruction to restore form and function. The purpose of this study is to review current concepts on secondary reconstruction to the maxillofacial complex. The evaluation of a posttraumatic patient for a secondary reconstruction must include an assessment of the different subunits of the upper face, middle face, and lower face. Virtual surgical planning and surgical guides represent the most important innovations in secondary reconstruction over the past few years. Intraoperative navigational surgery/computed-assisted navigation is used in complex cases. Facial asymmetry can be corrected or significantly improved by segmentation of the computerized tomography dataset and mirroring of the unaffected side by means of virtual surgical planning. Navigational surgery/computed-assisted navigation allows for a more precise surgical correction when secondary reconstruction involves the replacement of extensive anatomical areas. The use of technology can result in custom-made replacements and prebent plates, which are more stable and resistant to fracture because of metal fatigue. Careful perioperative evaluation is the key to positive outcomes of secondary reconstruction after trauma. The advent of technological tools has played a capital role in helping the surgical team perform a given treatment plan in a more precise and predictable manner.

Structure Prediction and Analysis of Neuraminidase Sequence Variants

Science.gov (United States)

Thayer, Kelly M.

2016-01-01

Analyzing protein structure has become an integral aspect of understanding systems of biochemical import. The laboratory experiment endeavors to introduce protein folding to ascertain structures of proteins for which the structure is unavailable, as well as to critically evaluate the quality of the prediction obtained. The model system used is the…

Predictive models for deposit accumulation and corrosion on secondary side of steam generators

International Nuclear Information System (INIS)

Choi, Samuel; Moroney, Velvet; Marks, Chuck; Kreider, Marc

2012-09-01

Experience demonstrates that deposit accumulation in steam generators (SGs) can lead to corrosion of tubes. To minimize the probability of this corrosion, utilities employ a variety of deposit control strategies. However, these processes can involve significant costs and potentially affect outage critical paths. Since there has been no model that quantifies tube degradation as a function of deposit accumulation, utilities have had to make decisions regarding deposit control strategies without a reliable quantitative basis. The objective of this study is to develop methods that utilities can use to quantify benefits of SG deposit control strategies with regard to rates of secondary-side tube corrosion. Two different methodologies are employed in this work. The first methodology is empirical and is involved an attempt to correlate degradation rates with deposit accumulation as indicated by sludge pile height. Because there has been relatively little tube degradation in currently operating steam generators, this correlation is developed using data for Alloy 600MA SG tubes. To increase the number of units that could be used for defect/deposit correlations, a method to relate the sludge pile deposit mass and the number of tubes with non-zero sludge height is developed. The second methodology is theoretical and is based on the use of calculated differences in temperature and chemistry to predict the effect of deposits on corrosion rates. Computational fluid dynamics (CFD) models are developed to simulate thermal-hydraulic conditions representative of conditions that are present within porous deposits formed at the top of tube sheet. This paper will discuss the development and application of the predictive models for deposit accumulation and corrosion on the secondary side of steam generators. (authors)

Interfacial ordering of thermotropic liquid crystals triggered by the secondary structures of oligopeptides.

Science.gov (United States)

Wang, Xiaoguang; Yang, Pei; Mondiot, Frederic; Li, Yaoxin; Miller, Daniel S; Chen, Zhan; Abbott, Nicholas L

2015-12-07

We report that assemblies formed by eight oligopeptides at phospholipid-decorated interfaces of thermotropic liquid crystals (LCs) trigger changes in ordering of the LCs that are dependent on the secondary structures of the oligopeptides (as characterized in situ using infrared-visible sum-frequency spectroscopy).

Tropical rain-forest matrix quality affects bat assemblage structure in secondary forest patches

NARCIS (Netherlands)

Vleut, I.; Levy-Tacher, I.; Galindo-Gonzalez, J.; Boer, de W.F.; Ramirez-Marcial, N.

2012-01-01

We studied Phyllostomidae bat assemblage structure in patches of secondary forest dominated by the pioneer tree Ochroma pyramidale, largely (.85%) or partially (,35%) surrounded by a matrix of tropical rain forest, to test 3 hypotheses: the highest bat diversity and richness is observed in the

Models for predicting turnover of residential aged care nurses: a structural equation modelling analysis of secondary data.

Science.gov (United States)

Gao, Fengsong; Newcombe, Peter; Tilse, Cheryl; Wilson, Jill; Tuckett, Anthony

2014-09-01

Nurse turnover in the residential aged care industry is a pressing issue. Researchers have shown ongoing interest in exploring how the factors that are amendable to change in aged care policy, regulation and funding and in organizational procedures (e.g. job demands, coping resources and psychological health of nurses) impact on turnover. However, the findings are mixed. This study tested two theoretical models of turnover to examine the structural relationships among job demands, coping resources, psychological health and turnover of residential aged care nurses. Although many previous studies operationalized turnover as intention to leave, the present study investigated actual turnover by following up with the same individuals over time, and thus provided more accurate predictive models of turnover behaviour. The sample, 239 Australian residential aged care nurses, came from the Nurses and Midwives e-cohort Study. Job demands, coping resources, and psychological health were measured using standardized instruments. Structural equation modelling was used to test the measurement and structural models. Controlling for a number of workforce and individual characteristics, coping resources (measured by job control, supervisor support, and co-worker support) were negatively and directly associated with turnover. Additionally, the findings supported the Job Demand-Control-Support model in that higher coping resources and lower job demands (indicated by psychological demands, physical demands, and effort) were related to better psychological health (measured by vitality, social functioning, role emotional, and mental health), and higher job demands were related to lower coping resources. Findings suggest that aged care policy makers and service providers might consider increasing coping resources available to nurses and minimizing job demands of care work to reduce turnover and improve nurses' psychological health. Moreover, findings from this Australian study may provide

Influence of Secondary Cooling Mode on Solidification Structure and Macro-segregation Behavior for High-carbon Continuous Casting Bloom

Science.gov (United States)

Dou, Kun; Yang, Zhenguo; Liu, Qing; Huang, Yunhua; Dong, Hongbiao

2017-07-01

A cellular automaton-finite element coupling model for high-carbon continuously cast bloom of GCr15 steel is established to simulate the solidification structure and to investigate the influence of different secondary cooling modes on characteristic parameters such as equiaxed crystal ratio, grain size and secondary dendrite arm spacing, in which the effect of phase transformation and electromagnetic stirring is taken into consideration. On this basis, evolution of carbon macro-segregation for GCr15 steel bloom is researched correspondingly via industrial tests. Based on above analysis, the relationship among secondary cooling modes, characteristic parameters for solidification structure as well as carbon macro-segregation is illustrated to obtain optimum secondary cooling strategy and alleviate carbon macro-segregation degree for GCr15 steel bloom in continuous casting process. The evaluating method for element macro-segregation is applicable in various steel types.

Update on protein structure prediction: results of the 1995 IRBM workshop

DEFF Research Database (Denmark)

Hubbard, Tim; Tramontano, Anna; Hansen, Jan

1996-01-01

Computational tools for protein structure prediction are of great interest to molecular, structural and theoretical biologists due to a rapidly increasing number of protein sequences with no known structure. In October 1995, a workshop was held at IRBM to predict as much as possible about a numbe...

Update on protein structure prediction: results of the 1995 IRBM workshop

DEFF Research Database (Denmark)

Hubbard, Tim; Tramontano, Anna; Hansen, Jan

1996-01-01

Computational tools for protein structure prediction are of great interest to molecular, structural and theoretical biologists due to a rapidly increasing number of protein sequences with no known structure. In October 1995, a workshop was held at IRBM to predict as much as possible about a number...

AFM observation of silk fibroin on mica substrates: morphologies reflecting the secondary structures

Energy Technology Data Exchange (ETDEWEB)

Yamada, Kazushi; Tsuboi, Yasuyuki; Itaya, Akira

2003-09-01

Bombyx mori silk fibroin was fixed on mica substrates by cast of aqueous fibroin solutions, and the microscopic morphologies of the samples were revealed by means of atomic force microscopy. By adjusting the method used to prepare the solution, we succeeded in forming quasi-2-dimensional thin films in which a network of fibroin molecules developed over the substrate. The film network consisted of fibroin in a random coil structure. The morphology of the network changed after thermal or methanol treatments, which are known to convert the secondary structure of fibroin from the random coil to the {beta}-sheet type. In both of these cases, the network morphology disappeared and characteristic island-like morphologies appeared. On the other hand, temporally evolving gelation occurred in a fibroin solution due to the formation of {beta}-sheet crystals. Such islands were also observable in a specimen prepared by the cast of the gel-containing solution. Based on these results, it was concluded that the islands consist of {beta}-sheet crystals. Of particular interest is the observation that all of the islands had a common thickness value of 1.3 nm. These morphologies are discussed in terms of the secondary structure of fibroin.

Predicting nucleic acid binding interfaces from structural models of proteins.

Science.gov (United States)

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.

Secondary structure of bovine albumin as studied by polarization-sensitive multiplex CARS spectroscopy

NARCIS (Netherlands)

Voroshilov, A.; Voroshilov, Artemy; Otto, Cornelis; Greve, Jan

1996-01-01

The first application of polarization-sensitive multiplex coherent anti-Stokes Raman spectroscopy (MCARS) in the absence of resonance enhancement to the resolution of the secondary structure of a protein in solution is reported. Polarization MCARS spectra of bovine albumin in D2O were obtained in

Contribution of long-range interactions to the secondary structure of an unfolded globin.

Science.gov (United States)

Fedyukina, Daria V; Rajagopalan, Senapathy; Sekhar, Ashok; Fulmer, Eric C; Eun, Ye-Jin; Cavagnero, Silvia

2010-09-08

This work explores the effect of long-range tertiary contacts on the distribution of residual secondary structure in the unfolded state of an alpha-helical protein. N-terminal fragments of increasing length, in conjunction with multidimensional nuclear magnetic resonance, were employed. A protein representative of the ubiquitous globin fold was chosen as the model system. We found that, while most of the detectable alpha-helical population in the unfolded ensemble does not depend on the presence of the C-terminal region (corresponding to the native G and H helices), specific N-to-C long-range contacts between the H and A-B-C regions enhance the helical secondary structure content of the N terminus (A-B-C regions). The simple approach introduced here, based on the evaluation of N-terminal polypeptide fragments of increasing length, is of general applicability to identify the influence of long-range interactions in unfolded proteins. Copyright 2010 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Predicting Thermal Behavior of Secondary Organic Aerosols

Science.gov (United States)

Volume concentrations of steady-state secondary organic aerosol (SOA) were measured in 139 steadystate single precursor hydrocarbon oxidation experiments after passing through a temperature controlled inlet tube. Higher temperatures resulted in greater loss of particle volume, wi...

Correlation between protein secondary structure, backbone bond angles, and side-chain orientations

Science.gov (United States)

Lundgren, Martin; Niemi, Antti J.

2012-08-01

We investigate the fine structure of the sp3 hybridized covalent bond geometry that governs the tetrahedral architecture around the central Cα carbon of a protein backbone, and for this we develop new visualization techniques to analyze high-resolution x-ray structures in the Protein Data Bank. We observe that there is a correlation between the deformations of the ideal tetrahedral symmetry and the local secondary structure of the protein. We propose a universal coarse-grained energy function to describe the ensuing side-chain geometry in terms of the Cβ carbon orientations. The energy function can model the side-chain geometry with a subatomic precision. As an example we construct the Cα-Cβ structure of HP35 chicken villin headpiece. We obtain a configuration that deviates less than 0.4 Å in root-mean-square distance from the experimental x-ray structure.

A study on prediction of metal loss by flow-accelerated corrosion in the CANDU NPP secondary piping systems

International Nuclear Information System (INIS)

Shim, S. H.; Song, J. S.; Yoon, K. B.; Hwang, K. M.; Jin, T. E.; Lee, S. H.; Kim, W. S.

2001-01-01

Flow-Accelerated Corrosion(FAC) is a phenomenon that results in metal loss from piping, vessels, and equipment made of carbon steel. FAC occurs only under certain conditions of flow, chemistry, geometry, and material. Unfortunately, those conditions are in much of the high-energy piping in nuclear and fossil-fueled power plants. Also, for domestic NPP secondary pipings whose operating time become longer, more evidences of FAC have been reported. The authors are studying on FAC management using CHECWORKS, computer code developed by EPRI. This paper is on the prediction results of metal loss by FAC in the one of CANDU type NPP secondary piping systems

QCD predictions for weak neutral current structure functions

International Nuclear Information System (INIS)

Wu Jimin

1987-01-01

Employing the analytic expression (to the next leading order) for non-singlet component of structure function which the author got from QCD theory and putting recent experiment result of neutral current structure function at Q 2 = 11 (GeV/C) 2 as input, the QCD prediction for neutral current structure function of their scaling violation behaviours was given

Halide salts and their structural properties in presence of secondary amine based molecule: A combined experimental and theoretical analysis

Science.gov (United States)

Ghosh, Pritam; Hazra, Abhijit; Ghosh, Meenakshi; Chandra Murmu, Naresh; Banerjee, Priyabrata

2018-04-01

Biologically relevant halide salts and its solution state structural properties are always been significant. In general, exposure of halide salts into polar solution medium results in solvation which in turn separates the cationic and anionic part of the salt. However, the conventional behaviour of salts might alter in presence of any secondary amine based compound, i.e.; moderately strong Lewis acid. In its consequence, to investigate the effect of secondary amine based compound in the salt solution, novel (E)-2-(4-bromobenzylidene)-1-(perfluorophenyl) hydrazine has been synthesized and used as secondary amine source. The secondary amine compound interestingly shows a drastic color change upon exposure to fluoride salts owing to hydrogen bonding interaction. Several experimental methods, e.g.; SCXRD, UV-Vis, FT-IR, ESI-MS and DLS together with modern DFT (i.e.; DFT-D3) have been performed to explore the structural properties of the halide salts upon exposure to secondary amine based compound. The effect of counter cation of the fluoride salt in binding with secondary amine source has also been investigated.

Predicting protein structures with a multiplayer online game.

Science.gov (United States)

Cooper, Seth; Khatib, Firas; Treuille, Adrien; Barbero, Janos; Lee, Jeehyung; Beenen, Michael; Leaver-Fay, Andrew; Baker, David; Popović, Zoran; Players, Foldit

2010-08-05

People exert large amounts of problem-solving effort playing computer games. Simple image- and text-recognition tasks have been successfully 'crowd-sourced' through games, but it is not clear if more complex scientific problems can be solved with human-directed computing. Protein structure prediction is one such problem: locating the biologically relevant native conformation of a protein is a formidable computational challenge given the very large size of the search space. Here we describe Foldit, a multiplayer online game that engages non-scientists in solving hard prediction problems. Foldit players interact with protein structures using direct manipulation tools and user-friendly versions of algorithms from the Rosetta structure prediction methodology, while they compete and collaborate to optimize the computed energy. We show that top-ranked Foldit players excel at solving challenging structure refinement problems in which substantial backbone rearrangements are necessary to achieve the burial of hydrophobic residues. Players working collaboratively develop a rich assortment of new strategies and algorithms; unlike computational approaches, they explore not only the conformational space but also the space of possible search strategies. The integration of human visual problem-solving and strategy development capabilities with traditional computational algorithms through interactive multiplayer games is a powerful new approach to solving computationally-limited scientific problems.

(PS)2: protein structure prediction server version 3.0.

Science.gov (United States)

Huang, Tsun-Tsao; Hwang, Jenn-Kang; Chen, Chu-Huang; Chu, Chih-Sheng; Lee, Chi-Wen; Chen, Chih-Chieh

2015-07-01

Protein complexes are involved in many biological processes. Examining coupling between subunits of a complex would be useful to understand the molecular basis of protein function. Here, our updated (PS)(2) web server predicts the three-dimensional structures of protein complexes based on comparative modeling; furthermore, this server examines the coupling between subunits of the predicted complex by combining structural and evolutionary considerations. The predicted complex structure could be indicated and visualized by Java-based 3D graphics viewers and the structural and evolutionary profiles are shown and compared chain-by-chain. For each subunit, considerations with or without the packing contribution of other subunits cause the differences in similarities between structural and evolutionary profiles, and these differences imply which form, complex or monomeric, is preferred in the biological condition for the subunit. We believe that the (PS)(2) server would be a useful tool for biologists who are interested not only in the structures of protein complexes but also in the coupling between subunits of the complexes. The (PS)(2) is freely available at http://ps2v3.life.nctu.edu.tw/. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Compound Structure-Independent Activity Prediction in High-Dimensional Target Space.