In silico modeling of the yeast protein and protein family interaction network

Science.gov (United States)

Goh, K.-I.; Kahng, B.; Kim, D.

2004-03-01

Understanding of how protein interaction networks of living organisms have evolved or are organized can be the first stepping stone in unveiling how life works on a fundamental ground. Here we introduce an in silico ``coevolutionary'' model for the protein interaction network and the protein family network. The essential ingredient of the model includes the protein family identity and its robustness under evolution, as well as the three previously proposed: gene duplication, divergence, and mutation. This model produces a prototypical feature of complex networks in a wide range of parameter space, following the generalized Pareto distribution in connectivity. Moreover, we investigate other structural properties of our model in detail with some specific values of parameters relevant to the yeast Saccharomyces cerevisiae, showing excellent agreement with the empirical data. Our model indicates that the physical constraints encoded via the domain structure of proteins play a crucial role in protein interactions.

Quantitative measurement of cell membrane receptor internalization by the nanoluciferase reporter: Using the G protein-coupled receptor RXFP3 as a model.

Science.gov (United States)

Liu, Yu; Song, Ge; Shao, Xiao-Xia; Liu, Ya-Li; Guo, Zhan-Yun

2015-02-01

Nanoluciferase (NanoLuc) is a newly developed small luciferase reporter with the brightest bioluminescence to date. In the present work, we developed NanoLuc as a sensitive bioluminescent reporter to measure quantitatively the internalization of cell membrane receptors, based on the pH dependence of the reporter activity. The G protein-coupled receptor RXFP3, the cognate receptor of relaxin-3/INSL7, was used as a model receptor. We first generated stable HEK293T cells that inducibly coexpressed a C-terminally NanoLuc-tagged human RXFP3 and a C-terminally enhanced green fluorescent protein (EGFP)-tagged human RXFP3. The C-terminal EGFP-tag and NanoLuc-tag had no detrimental effects on the ligand-binding potency and intracellular trafficking of RXFP3. Based on the fluorescence of the tagged EGFP reporter, the ligand-induced RXFP3 internalization was visualized directly under a fluorescence microscope. Based on the bioluminescence of the tagged NanoLuc reporter, the ligand-induced RXFP3 internalization was measured quantitatively by a convenient bioluminescent assay. Coexpression of an EGFP-tagged inactive [E141R]RXFP3 had no detrimental effect on the ligand-binding potency and ligand-induced internalization of the NanoLuc-tagged wild-type RXFP3, suggesting that the mutant RXFP3 and wild-type RXFP3 worked independently. The present bioluminescent internalization assay could be extended to other G protein-coupled receptors and other cell membrane receptors to study ligand-receptor and receptor-receptor interactions. Copyright © 2014 Elsevier B.V. All rights reserved.

A Mesoscopic Model for Protein-Protein Interactions in Solution

OpenAIRE

Lund, Mikael; Jönsson, Bo

2003-01-01

Protein self-association may be detrimental in biological systems, but can be utilized in a controlled fashion for protein crystallization. It is hence of considerable interest to understand how factors like solution conditions prevent or promote aggregation. Here we present a computational model describing interactions between protein molecules in solution. The calculations are based on a molecular description capturing the detailed structure of the protein molecule using x-ray or nuclear ma...

Prediction of protein-protein interactions between viruses and human by an SVM model

Directory of Open Access Journals (Sweden)

Cui Guangyu

2012-05-01

Full Text Available Abstract Background Several computational methods have been developed to predict protein-protein interactions from amino acid sequences, but most of those methods are intended for the interactions within a species rather than for interactions across different species. Methods for predicting interactions between homogeneous proteins are not appropriate for finding those between heterogeneous proteins since they do not distinguish the interactions between proteins of the same species from those of different species. Results We developed a new method for representing a protein sequence of variable length in a frequency vector of fixed length, which encodes the relative frequency of three consecutive amino acids of a sequence. We built a support vector machine (SVM model to predict human proteins that interact with virus proteins. In two types of viruses, human papillomaviruses (HPV and hepatitis C virus (HCV, our SVM model achieved an average accuracy above 80%, which is higher than that of another SVM model with a different representation scheme. Using the SVM model and Gene Ontology (GO annotations of proteins, we predicted new interactions between virus proteins and human proteins. Conclusions Encoding the relative frequency of amino acid triplets of a protein sequence is a simple yet powerful representation method for predicting protein-protein interactions across different species. The representation method has several advantages: (1 it enables a prediction model to achieve a better performance than other representations, (2 it generates feature vectors of fixed length regardless of the sequence length, and (3 the same representation is applicable to different types of proteins.

On the characterization and software implementation of general protein lattice models.

Directory of Open Access Journals (Sweden)

Alessio Bechini

Full Text Available models of proteins have been widely used as a practical means to computationally investigate general properties of the system. In lattice models any sterically feasible conformation is represented as a self-avoiding walk on a lattice, and residue types are limited in number. So far, only two- or three-dimensional lattices have been used. The inspection of the neighborhood of alpha carbons in the core of real proteins reveals that also lattices with higher coordination numbers, possibly in higher dimensional spaces, can be adopted. In this paper, a new general parametric lattice model for simplified protein conformations is proposed and investigated. It is shown how the supporting software can be consistently designed to let algorithms that operate on protein structures be implemented in a lattice-agnostic way. The necessary theoretical foundations are developed and organically presented, pinpointing the role of the concept of main directions in lattice-agnostic model handling. Subsequently, the model features across dimensions and lattice types are explored in tests performed on benchmark protein sequences, using a Python implementation. Simulations give insights on the use of square and triangular lattices in a range of dimensions. The trend of potential minimum for sequences of different lengths, varying the lattice dimension, is uncovered. Moreover, an extensive quantitative characterization of the usage of the so-called "move types" is reported for the first time. The proposed general framework for the development of lattice models is simple yet complete, and an object-oriented architecture can be proficiently employed for the supporting software, by designing ad-hoc classes. The proposed framework represents a new general viewpoint that potentially subsumes a number of solutions previously studied. The adoption of the described model pushes to look at protein structure issues from a more general and essential perspective, making

A KINETIC MODEL FOR MONO-LAYER GLOBULAR PROTEIN ADSORPTION ON SOLID/LIQUID INTERFACES

Directory of Open Access Journals (Sweden)

Kamal I. M. Al-Malah

2012-12-01

Full Text Available A kinetic model was derived for globular protein adsorption. The model takes into account the three possible scenarios of a protein molecule in solution, being exposed to an interface: adsorption step from the solution to the interface; the possible desorption back into the solution; and the surface-induced unfolding or spreading of the protein unto the substrate surface. A globular protein molecule is visualized as a sphere with radius D. In addition to the general case of protein adsorption, which portrays either the surface coverage (Theta or surface concentration (� as a function of the adsorption time, special cases, like equilibrium condition, lowsurface coverage, irreversible, and Langmuirian were also presented and treated in light of the derived model. The general model was simplified for each of the subset cases. The irreversibility versus reversibility of protein adsorption was discussed. The substrate surface energetics or effects are accounted for via the proposition of the percent relative change in D/V ratio for the adsorbing protein, called (D/VPRC parameter. (D/VPRC is calculated with respect to the monolayer surface concentration of protein, where the latter is given by D/Vratio. This can be used as a landmark to protein adsorption isotherms or even kinetics. This is visualized as an indicator for solid substrate effects on the adsorbing proteins. (D/VPRC can be zero (fresh monolayer, negative (aged monolayer, or positive (multi-layer. The reference surface concentration is reported for some selected proteins.

Electronic transport on the spatial structure of the protein: Three-dimensional lattice model

International Nuclear Information System (INIS)

Sarmento, R.G.; Frazão, N.F.; Macedo-Filho, A.

2017-01-01

Highlights: • The electronic transport on the structure of the three-dimensional lattice model of the protein is studied. • The signing of the current–voltage is directly affected by permutations of the weak bonds in the structure. • Semiconductor behave of the proteins suggest a potential application in the development of novel biosensors. - Abstract: We report a numerical analysis of the electronic transport in protein chain consisting of thirty-six standard amino acids. The protein chains studied have three-dimensional structure, which can present itself in three distinct conformations and the difference consist in the presence or absence of thirteen hydrogen-bondings. Our theoretical method uses an electronic tight-binding Hamiltonian model, appropriate to describe the protein segments modeled by the amino acid chain. We note that the presence and the permutations between weak bonds in the structure of proteins are directly related to the signing of the current–voltage. Furthermore, the electronic transport depends on the effect of temperature. In addition, we have found a semiconductor behave in the models investigated and it suggest a potential application in the development of novel biosensors for molecular diagnostics.

Electronic transport on the spatial structure of the protein: Three-dimensional lattice model

Energy Technology Data Exchange (ETDEWEB)

Sarmento, R.G. [Departamento de Ciências Biológicas, Universidade Federal do Piauí, 64800-000 Floriano, PI (Brazil); Frazão, N.F. [Centro de Educação e Saúde, Universidade Federal de Campina Grande, 581750-000 Cuité, PB (Brazil); Macedo-Filho, A., E-mail: amfilho@gmail.com [Campus Prof. Antonio Geovanne Alves de Sousa, Universidade Estadual do Piauí, 64260-000 Piripiri, PI (Brazil)

2017-01-30

Highlights: • The electronic transport on the structure of the three-dimensional lattice model of the protein is studied. • The signing of the current–voltage is directly affected by permutations of the weak bonds in the structure. • Semiconductor behave of the proteins suggest a potential application in the development of novel biosensors. - Abstract: We report a numerical analysis of the electronic transport in protein chain consisting of thirty-six standard amino acids. The protein chains studied have three-dimensional structure, which can present itself in three distinct conformations and the difference consist in the presence or absence of thirteen hydrogen-bondings. Our theoretical method uses an electronic tight-binding Hamiltonian model, appropriate to describe the protein segments modeled by the amino acid chain. We note that the presence and the permutations between weak bonds in the structure of proteins are directly related to the signing of the current–voltage. Furthermore, the electronic transport depends on the effect of temperature. In addition, we have found a semiconductor behave in the models investigated and it suggest a potential application in the development of novel biosensors for molecular diagnostics.

Protein folding simulations: from coarse-grained model to all-atom model.

Science.gov (United States)

Zhang, Jian; Li, Wenfei; Wang, Jun; Qin, Meng; Wu, Lei; Yan, Zhiqiang; Xu, Weixin; Zuo, Guanghong; Wang, Wei

2009-06-01

Protein folding is an important and challenging problem in molecular biology. During the last two decades, molecular dynamics (MD) simulation has proved to be a paramount tool and was widely used to study protein structures, folding kinetics and thermodynamics, and structure-stability-function relationship. It was also used to help engineering and designing new proteins, and to answer even more general questions such as the minimal number of amino acid or the evolution principle of protein families. Nowadays, the MD simulation is still undergoing rapid developments. The first trend is to toward developing new coarse-grained models and studying larger and more complex molecular systems such as protein-protein complex and their assembling process, amyloid related aggregations, and structure and motion of chaperons, motors, channels and virus capsides; the second trend is toward building high resolution models and explore more detailed and accurate pictures of protein folding and the associated processes, such as the coordination bond or disulfide bond involved folding, the polarization, charge transfer and protonate/deprotonate process involved in metal coupled folding, and the ion permeation and its coupling with the kinetics of channels. On these new territories, MD simulations have given many promising results and will continue to offer exciting views. Here, we review several new subjects investigated by using MD simulations as well as the corresponding developments of appropriate protein models. These include but are not limited to the attempt to go beyond the topology based Gō-like model and characterize the energetic factors in protein structures and dynamics, the study of the thermodynamics and kinetics of disulfide bond involved protein folding, the modeling of the interactions between chaperonin and the encapsulated protein and the protein folding under this circumstance, the effort to clarify the important yet still elusive folding mechanism of protein BBL

DockQ: A Quality Measure for Protein-Protein Docking Models.

Directory of Open Access Journals (Sweden)

Sankar Basu

Full Text Available The state-of-the-art to assess the structural quality of docking models is currently based on three related yet independent quality measures: Fnat, LRMS, and iRMS as proposed and standardized by CAPRI. These quality measures quantify different aspects of the quality of a particular docking model and need to be viewed together to reveal the true quality, e.g. a model with relatively poor LRMS (>10Å might still qualify as 'acceptable' with a descent Fnat (>0.50 and iRMS (<3.0Å. This is also the reason why the so called CAPRI criteria for assessing the quality of docking models is defined by applying various ad-hoc cutoffs on these measures to classify a docking model into the four classes: Incorrect, Acceptable, Medium, or High quality. This classification has been useful in CAPRI, but since models are grouped in only four bins it is also rather limiting, making it difficult to rank models, correlate with scoring functions or use it as target function in machine learning algorithms. Here, we present DockQ, a continuous protein-protein docking model quality measure derived by combining Fnat, LRMS, and iRMS to a single score in the range [0, 1] that can be used to assess the quality of protein docking models. By using DockQ on CAPRI models it is possible to almost completely reproduce the original CAPRI classification into Incorrect, Acceptable, Medium and High quality. An average PPV of 94% at 90% Recall demonstrating that there is no need to apply predefined ad-hoc cutoffs to classify docking models. Since DockQ recapitulates the CAPRI classification almost perfectly, it can be viewed as a higher resolution version of the CAPRI classification, making it possible to estimate model quality in a more quantitative way using Z-scores or sum of top ranked models, which has been so valuable for the CASP community. The possibility to directly correlate a quality measure to a scoring function has been crucial for the development of scoring functions for

Modelling of proteins in membranes

DEFF Research Database (Denmark)

Sperotto, Maria Maddalena; May, S.; Baumgaertner, A.

2006-01-01

This review describes some recent theories and simulations of mesoscopic and microscopic models of lipid membranes with embedded or attached proteins. We summarize results supporting our understanding of phenomena for which the activities of proteins in membranes are expected to be significantly ...

Interpretive Reporting of Protein Electrophoresis Data by Microcomputer

Science.gov (United States)

Talamo, Thomas S.; Losos, Frank J.; Kessler, G. Frederick

1982-01-01

A microcomputer based system for interpretive reporting of protein electrophoretic data has been developed. Data for serum, urine and cerebrospinal fluid protein electrophoreses as well as immunoelectrophoresis can be entered. Patient demographic information is entered through the keyboard followed by manual entry of total and fractionated protein levels obtained after densitometer scanning of the electrophoretic strip. The patterns are then coded, interpreted, and final reports generated. In most cases interpretation time is less than one second. Misinterpretation by computer is uncommon and can be corrected by edit functions within the system. These discrepancies between computer and pathologist interpretation are automatically stored in a data file for later review and possible program modification. Any or all previous tests on a patient may be reviewed with graphic display of the electrophoretic pattern. The system has been in use for several months and is presently well accepted by both laboratory and clinical staff. It also allows rapid storage, retrieval and analysis of protein electrophoretic datab.

Combining random gene fission and rational gene fusion to discover near-infrared fluorescent protein fragments that report on protein-protein interactions.

Science.gov (United States)

Pandey, Naresh; Nobles, Christopher L; Zechiedrich, Lynn; Maresso, Anthony W; Silberg, Jonathan J

2015-05-15

Gene fission can convert monomeric proteins into two-piece catalysts, reporters, and transcription factors for systems and synthetic biology. However, some proteins can be challenging to fragment without disrupting function, such as near-infrared fluorescent protein (IFP). We describe a directed evolution strategy that can overcome this challenge by randomly fragmenting proteins and concomitantly fusing the protein fragments to pairs of proteins or peptides that associate. We used this method to create libraries that express fragmented IFP as fusions to a pair of associating peptides (IAAL-E3 and IAAL-K3) and proteins (CheA and CheY) and screened for fragmented IFP with detectable near-infrared fluorescence. Thirteen novel fragmented IFPs were identified, all of which arose from backbone fission proximal to the interdomain linker. Either the IAAL-E3 and IAAL-K3 peptides or CheA and CheY proteins could assist with IFP fragment complementation, although the IAAL-E3 and IAAL-K3 peptides consistently yielded higher fluorescence. These results demonstrate how random gene fission can be coupled to rational gene fusion to create libraries enriched in fragmented proteins with AND gate logic that is dependent upon a protein-protein interaction, and they suggest that these near-infrared fluorescent protein fragments will be suitable as reporters for pairs of promoters and protein-protein interactions within whole animals.

Protein adsorption on nanoparticles: model development using computer simulation

International Nuclear Information System (INIS)

Shao, Qing; Hall, Carol K

2016-01-01

The adsorption of proteins on nanoparticles results in the formation of the protein corona, the composition of which determines how nanoparticles influence their biological surroundings. We seek to better understand corona formation by developing models that describe protein adsorption on nanoparticles using computer simulation results as data. Using a coarse-grained protein model, discontinuous molecular dynamics simulations are conducted to investigate the adsorption of two small proteins (Trp-cage and WW domain) on a model nanoparticle of diameter 10.0 nm at protein concentrations ranging from 0.5 to 5 mM. The resulting adsorption isotherms are well described by the Langmuir, Freundlich, Temkin and Kiselev models, but not by the Elovich, Fowler–Guggenheim and Hill–de Boer models. We also try to develop a generalized model that can describe protein adsorption equilibrium on nanoparticles of different diameters in terms of dimensionless size parameters. The simulation results for three proteins (Trp-cage, WW domain, and GB3) on four nanoparticles (diameter  =  5.0, 10.0, 15.0, and 20.0 nm) illustrate both the promise and the challenge associated with developing generalized models of protein adsorption on nanoparticles. (paper)

Incorporating water-release and lateral protein interactions in modeling equilibrium adsorption for ion-exchange chromatography.

Science.gov (United States)

Thrash, Marvin E; Pinto, Neville G

2006-09-08

The equilibrium adsorption of two albumin proteins on a commercial ion exchanger has been studied using a colloidal model. The model accounts for electrostatic and van der Waals forces between proteins and the ion exchanger surface, the energy of interaction between adsorbed proteins, and the contribution of entropy from water-release accompanying protein adsorption. Protein-surface interactions were calculated using methods previously reported in the literature. Lateral interactions between adsorbed proteins were experimentally measured with microcalorimetry. Water-release was estimated by applying the preferential interaction approach to chromatographic retention data. The adsorption of ovalbumin and bovine serum albumin on an anion exchanger at solution pH>pI of protein was measured. The experimental isotherms have been modeled from the linear region to saturation, and the influence of three modulating alkali chlorides on capacity has been evaluated. The heat of adsorption is endothermic for all cases studied, despite the fact that the net charge on the protein is opposite that of the adsorbing surface. Strong repulsive forces between adsorbed proteins underlie the endothermic heat of adsorption, and these forces intensify with protein loading. It was found that the driving force for adsorption is the entropy increase due to the release of water from the protein and adsorbent surfaces. It is shown that the colloidal model predicts protein adsorption capacity in both the linear and non-linear isotherm regions, and can account for the effects of modulating salt.

Quantitative chemogenomics: machine-learning models of protein-ligand interaction.

Science.gov (United States)

Andersson, Claes R; Gustafsson, Mats G; Strömbergsson, Helena

2011-01-01

Chemogenomics is an emerging interdisciplinary field that lies in the interface of biology, chemistry, and informatics. Most of the currently used drugs are small molecules that interact with proteins. Understanding protein-ligand interaction is therefore central to drug discovery and design. In the subfield of chemogenomics known as proteochemometrics, protein-ligand-interaction models are induced from data matrices that consist of both protein and ligand information along with some experimentally measured variable. The two general aims of this quantitative multi-structure-property-relationship modeling (QMSPR) approach are to exploit sparse/incomplete information sources and to obtain more general models covering larger parts of the protein-ligand space, than traditional approaches that focuses mainly on specific targets or ligands. The data matrices, usually obtained from multiple sparse/incomplete sources, typically contain series of proteins and ligands together with quantitative information about their interactions. A useful model should ideally be easy to interpret and generalize well to new unseen protein-ligand combinations. Resolving this requires sophisticated machine-learning methods for model induction, combined with adequate validation. This review is intended to provide a guide to methods and data sources suitable for this kind of protein-ligand-interaction modeling. An overview of the modeling process is presented including data collection, protein and ligand descriptor computation, data preprocessing, machine-learning-model induction and validation. Concerns and issues specific for each step in this kind of data-driven modeling will be discussed. © 2011 Bentham Science Publishers

InXy and SeXy, compact heterologous reporter proteins for mammalian cells.

Science.gov (United States)

Fluri, David A; Kelm, Jens M; Lesage, Guillaume; Baba, Marie Daoud-El; Fussenegger, Martin

2007-10-15

Mammalian reporter proteins are essential for gene-function analysis, drugscreening initiatives and as model product proteins for biopharmaceutical manufacturing. Bacillus subtilis can maintain its metabolism by secreting Xylanase A (XynA), which converts xylan into shorter xylose oligosaccharides. XynA is a family 11 xylanase monospecific for D-xylose containing substrates. Mammalian cells transgenic for constitutive expression of wild-type xynA showed substantial secretion of this prokaryotic enzyme. Deletion analysis confirmed that a prokaryotic signal sequence encoded within the first 81 nucleotides was compatible with the secretory pathway of mammalian cells. Codon optimization combined with elimination of the prokaryotic signal sequence resulted in an exclusively intracellular mammalian Xylanase A variant (InXy) while replacement by an immunoglobulin-derived secretion signal created an optimal secreted Xylanase A derivative (SeXy). A variety of chromogenic and fluorescence-based assays adapted for use with mammalian cells detected InXy and SeXy with high sensitivity and showed that both reporter proteins resisted repeated freeze/thaw cycles, remained active over wide temperature and pH ranges, were extremely stable in human serum stored at room temperature and could independently be quantified in samples also containing other prominent reporter proteins such as the human placental alkaline phosphatase (SEAP) and the Bacillus stearothermophilus-derived secreted alpha-amylase (SAMY). Glycoprofiling revealed that SeXy produced in mammalian cells was N- glycosylated at four different sites, mutation of which resulted in impaired secretion. SeXy was successfully expressed in a variety of mammalian cell lines and primary cells following transient transfection and transduction with adeno-associated virus particles (AAV) engineered for constitutive SeXy expression. Intramuscular injection of transgenic AAVs into mice showed significant SeXy levels in the bloodstream

Physiologically Based Pharmacokinetic Modeling of Therapeutic Proteins.

Science.gov (United States)

Wong, Harvey; Chow, Timothy W

2017-09-01

Biologics or therapeutic proteins are becoming increasingly important as treatments for disease. The most common class of biologics are monoclonal antibodies (mAbs). Recently, there has been an increase in the use of physiologically based pharmacokinetic (PBPK) modeling in the pharmaceutical industry in drug development. We review PBPK models for therapeutic proteins with an emphasis on mAbs. Due to their size and similarity to endogenous antibodies, there are distinct differences between PBPK models for small molecules and mAbs. The high-level organization of a typical mAb PBPK model consists of a whole-body PBPK model with organ compartments interconnected by both blood and lymph flows. The whole-body PBPK model is coupled with tissue-level submodels used to describe key mechanisms governing mAb disposition including tissue efflux via the lymphatic system, elimination by catabolism, protection from catabolism binding to the neonatal Fc (FcRn) receptor, and nonlinear binding to specific pharmacological targets of interest. The use of PBPK modeling in the development of therapeutic proteins is still in its infancy. Further application of PBPK modeling for therapeutic proteins will help to define its developing role in drug discovery and development. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

A hidden markov model derived structural alphabet for proteins.

Science.gov (United States)

Camproux, A C; Gautier, R; Tufféry, P

2004-06-04

Understanding and predicting protein structures depends on the complexity and the accuracy of the models used to represent them. We have set up a hidden Markov model that discretizes protein backbone conformation as series of overlapping fragments (states) of four residues length. This approach learns simultaneously the geometry of the states and their connections. We obtain, using a statistical criterion, an optimal systematic decomposition of the conformational variability of the protein peptidic chain in 27 states with strong connection logic. This result is stable over different protein sets. Our model fits well the previous knowledge related to protein architecture organisation and seems able to grab some subtle details of protein organisation, such as helix sub-level organisation schemes. Taking into account the dependence between the states results in a description of local protein structure of low complexity. On an average, the model makes use of only 8.3 states among 27 to describe each position of a protein structure. Although we use short fragments, the learning process on entire protein conformations captures the logic of the assembly on a larger scale. Using such a model, the structure of proteins can be reconstructed with an average accuracy close to 1.1A root-mean-square deviation and for a complexity of only 3. Finally, we also observe that sequence specificity increases with the number of states of the structural alphabet. Such models can constitute a very relevant approach to the analysis of protein architecture in particular for protein structure prediction.

DockQ: A Quality Measure for Protein-Protein Docking Models

Science.gov (United States)

Basu, Sankar

2016-01-01

The state-of-the-art to assess the structural quality of docking models is currently based on three related yet independent quality measures: Fnat, LRMS, and iRMS as proposed and standardized by CAPRI. These quality measures quantify different aspects of the quality of a particular docking model and need to be viewed together to reveal the true quality, e.g. a model with relatively poor LRMS (>10Å) might still qualify as 'acceptable' with a descent Fnat (>0.50) and iRMS (iRMS to a single score in the range [0, 1] that can be used to assess the quality of protein docking models. By using DockQ on CAPRI models it is possible to almost completely reproduce the original CAPRI classification into Incorrect, Acceptable, Medium and High quality. An average PPV of 94% at 90% Recall demonstrating that there is no need to apply predefined ad-hoc cutoffs to classify docking models. Since DockQ recapitulates the CAPRI classification almost perfectly, it can be viewed as a higher resolution version of the CAPRI classification, making it possible to estimate model quality in a more quantitative way using Z-scores or sum of top ranked models, which has been so valuable for the CASP community. The possibility to directly correlate a quality measure to a scoring function has been crucial for the development of scoring functions for protein structure prediction, and DockQ should be useful in a similar development in the protein docking field. DockQ is available at http://github.com/bjornwallner/DockQ/ PMID:27560519

Models of protein-ligand crystal structures: trust, but verify.

Science.gov (United States)

Deller, Marc C; Rupp, Bernhard

2015-09-01

X-ray crystallography provides the most accurate models of protein-ligand structures. These models serve as the foundation of many computational methods including structure prediction, molecular modelling, and structure-based drug design. The success of these computational methods ultimately depends on the quality of the underlying protein-ligand models. X-ray crystallography offers the unparalleled advantage of a clear mathematical formalism relating the experimental data to the protein-ligand model. In the case of X-ray crystallography, the primary experimental evidence is the electron density of the molecules forming the crystal. The first step in the generation of an accurate and precise crystallographic model is the interpretation of the electron density of the crystal, typically carried out by construction of an atomic model. The atomic model must then be validated for fit to the experimental electron density and also for agreement with prior expectations of stereochemistry. Stringent validation of protein-ligand models has become possible as a result of the mandatory deposition of primary diffraction data, and many computational tools are now available to aid in the validation process. Validation of protein-ligand complexes has revealed some instances of overenthusiastic interpretation of ligand density. Fundamental concepts and metrics of protein-ligand quality validation are discussed and we highlight software tools to assist in this process. It is essential that end users select high quality protein-ligand models for their computational and biological studies, and we provide an overview of how this can be achieved.

Markov dynamic models for long-timescale protein motion.

KAUST Repository

Chiang, Tsung-Han

2010-06-01

Molecular dynamics (MD) simulation is a well-established method for studying protein motion at the atomic scale. However, it is computationally intensive and generates massive amounts of data. One way of addressing the dual challenges of computation efficiency and data analysis is to construct simplified models of long-timescale protein motion from MD simulation data. In this direction, we propose to use Markov models with hidden states, in which the Markovian states represent potentially overlapping probabilistic distributions over protein conformations. We also propose a principled criterion for evaluating the quality of a model by its ability to predict long-timescale protein motions. Our method was tested on 2D synthetic energy landscapes and two extensively studied peptides, alanine dipeptide and the villin headpiece subdomain (HP-35 NleNle). One interesting finding is that although a widely accepted model of alanine dipeptide contains six states, a simpler model with only three states is equally good for predicting long-timescale motions. We also used the constructed Markov models to estimate important kinetic and dynamic quantities for protein folding, in particular, mean first-passage time. The results are consistent with available experimental measurements.

Markov dynamic models for long-timescale protein motion.

KAUST Repository

Chiang, Tsung-Han; Hsu, David; Latombe, Jean-Claude

2010-01-01

Molecular dynamics (MD) simulation is a well-established method for studying protein motion at the atomic scale. However, it is computationally intensive and generates massive amounts of data. One way of addressing the dual challenges of computation efficiency and data analysis is to construct simplified models of long-timescale protein motion from MD simulation data. In this direction, we propose to use Markov models with hidden states, in which the Markovian states represent potentially overlapping probabilistic distributions over protein conformations. We also propose a principled criterion for evaluating the quality of a model by its ability to predict long-timescale protein motions. Our method was tested on 2D synthetic energy landscapes and two extensively studied peptides, alanine dipeptide and the villin headpiece subdomain (HP-35 NleNle). One interesting finding is that although a widely accepted model of alanine dipeptide contains six states, a simpler model with only three states is equally good for predicting long-timescale motions. We also used the constructed Markov models to estimate important kinetic and dynamic quantities for protein folding, in particular, mean first-passage time. The results are consistent with available experimental measurements.

Molecular cloning, sequence analysis and homology modeling of the first caudata amphibian antifreeze-like protein in axolotl (Ambystoma mexicanum).

Science.gov (United States)

Zhang, Songyan; Gao, Jiuxiang; Lu, Yiling; Cai, Shasha; Qiao, Xue; Wang, Yipeng; Yu, Haining

2013-08-01

Antifreeze proteins (AFPs) refer to a class of polypeptides that are produced by certain vertebrates, plants, fungi, and bacteria and which permit their survival in subzero environments. In this study, we report the molecular cloning, sequence analysis and three-dimensional structure of the axolotl antifreeze-like protein (AFLP) by homology modeling of the first caudate amphibian AFLP. We constructed a full-length spleen cDNA library of axolotl (Ambystoma mexicanum). An EST having highest similarity (∼42%) with freeze-responsive liver protein Li16 from Rana sylvatica was identified, and the full-length cDNA was subsequently obtained by RACE-PCR. The axolotl antifreeze-like protein sequence represents an open reading frame for a putative signal peptide and the mature protein composed of 93 amino acids. The calculated molecular mass and the theoretical isoelectric point (pl) of this mature protein were 10128.6 Da and 8.97, respectively. The molecular characterization of this gene and its deduced protein were further performed by detailed bioinformatics analysis. The three-dimensional structure of current AFLP was predicted by homology modeling, and the conserved residues required for functionality were identified. The homology model constructed could be of use for effective drug design. This is the first report of an antifreeze-like protein identified from a caudate amphibian.

A 3D model of the membrane protein complex formed by the white spot syndrome virus structural proteins.

Directory of Open Access Journals (Sweden)

Yun-Shiang Chang

Full Text Available BACKGROUND: Outbreaks of white spot disease have had a large negative economic impact on cultured shrimp worldwide. However, the pathogenesis of the causative virus, WSSV (whit spot syndrome virus, is not yet well understood. WSSV is a large enveloped virus. The WSSV virion has three structural layers surrounding its core DNA: an outer envelope, a tegument and a nucleocapsid. In this study, we investigated the protein-protein interactions of the major WSSV structural proteins, including several envelope and tegument proteins that are known to be involved in the infection process. PRINCIPAL FINDINGS: In the present report, we used coimmunoprecipitation and yeast two-hybrid assays to elucidate and/or confirm all the interactions that occur among the WSSV structural (envelope and tegument proteins VP51A, VP19, VP24, VP26 and VP28. We found that VP51A interacted directly not only with VP26 but also with VP19 and VP24. VP51A, VP19 and VP24 were also shown to have an affinity for self-interaction. Chemical cross-linking assays showed that these three self-interacting proteins could occur as dimers. CONCLUSIONS: From our present results in conjunction with other previously established interactions we construct a 3D model in which VP24 acts as a core protein that directly associates with VP26, VP28, VP38A, VP51A and WSV010 to form a membrane-associated protein complex. VP19 and VP37 are attached to this complex via association with VP51A and VP28, respectively. Through the VP26-VP51C interaction this envelope complex is anchored to the nucleocapsid, which is made of layers of rings formed by VP664. A 3D model of the nucleocapsid and the surrounding outer membrane is presented.

Binding free energy analysis of protein-protein docking model structures by evERdock.

Science.gov (United States)

Takemura, Kazuhiro; Matubayasi, Nobuyuki; Kitao, Akio

2018-03-14

To aid the evaluation of protein-protein complex model structures generated by protein docking prediction (decoys), we previously developed a method to calculate the binding free energies for complexes. The method combines a short (2 ns) all-atom molecular dynamics simulation with explicit solvent and solution theory in the energy representation (ER). We showed that this method successfully selected structures similar to the native complex structure (near-native decoys) as the lowest binding free energy structures. In our current work, we applied this method (evERdock) to 100 or 300 model structures of four protein-protein complexes. The crystal structures and the near-native decoys showed the lowest binding free energy of all the examined structures, indicating that evERdock can successfully evaluate decoys. Several decoys that show low interface root-mean-square distance but relatively high binding free energy were also identified. Analysis of the fraction of native contacts, hydrogen bonds, and salt bridges at the protein-protein interface indicated that these decoys were insufficiently optimized at the interface. After optimizing the interactions around the interface by including interfacial water molecules, the binding free energies of these decoys were improved. We also investigated the effect of solute entropy on binding free energy and found that consideration of the entropy term does not necessarily improve the evaluations of decoys using the normal model analysis for entropy calculation.

Dynamical modeling of microRNA action on the protein translation process.

Science.gov (United States)

Zinovyev, Andrei; Morozova, Nadya; Nonne, Nora; Barillot, Emmanuel; Harel-Bellan, Annick; Gorban, Alexander N

2010-02-24

Protein translation is a multistep process which can be represented as a cascade of biochemical reactions (initiation, ribosome assembly, elongation, etc.), the rate of which can be regulated by small non-coding microRNAs through multiple mechanisms. It remains unclear what mechanisms of microRNA action are the most dominant: moreover, many experimental reports deliver controversial messages on what is the concrete mechanism actually observed in the experiment. Nissan and Parker have recently demonstrated that it might be impossible to distinguish alternative biological hypotheses using the steady state data on the rate of protein synthesis. For their analysis they used two simple kinetic models of protein translation. In contrary to the study by Nissan and Parker, we show that dynamical data allow discriminating some of the mechanisms of microRNA action. We demonstrate this using the same models as developed by Nissan and Parker for the sake of comparison but the methods developed (asymptotology of biochemical networks) can be used for other models. We formulate a hypothesis that the effect of microRNA action is measurable and observable only if it affects the dominant system (generalization of the limiting step notion for complex networks) of the protein translation machinery. The dominant system can vary in different experimental conditions that can partially explain the existing controversy of some of the experimental data. Our analysis of the transient protein translation dynamics shows that it gives enough information to verify or reject a hypothesis about a particular molecular mechanism of microRNA action on protein translation. For multiscale systems only that action of microRNA is distinguishable which affects the parameters of dominant system (critical parameters), or changes the dominant system itself. Dominant systems generalize and further develop the old and very popular idea of limiting step. Algorithms for identifying dominant systems in multiscale

Dynamical modeling of microRNA action on the protein translation process

Directory of Open Access Journals (Sweden)

Barillot Emmanuel

2010-02-01

Full Text Available Abstract Background Protein translation is a multistep process which can be represented as a cascade of biochemical reactions (initiation, ribosome assembly, elongation, etc., the rate of which can be regulated by small non-coding microRNAs through multiple mechanisms. It remains unclear what mechanisms of microRNA action are the most dominant: moreover, many experimental reports deliver controversial messages on what is the concrete mechanism actually observed in the experiment. Nissan and Parker have recently demonstrated that it might be impossible to distinguish alternative biological hypotheses using the steady state data on the rate of protein synthesis. For their analysis they used two simple kinetic models of protein translation. Results In contrary to the study by Nissan and Parker, we show that dynamical data allow discriminating some of the mechanisms of microRNA action. We demonstrate this using the same models as developed by Nissan and Parker for the sake of comparison but the methods developed (asymptotology of biochemical networks can be used for other models. We formulate a hypothesis that the effect of microRNA action is measurable and observable only if it affects the dominant system (generalization of the limiting step notion for complex networks of the protein translation machinery. The dominant system can vary in different experimental conditions that can partially explain the existing controversy of some of the experimental data. Conclusions Our analysis of the transient protein translation dynamics shows that it gives enough information to verify or reject a hypothesis about a particular molecular mechanism of microRNA action on protein translation. For multiscale systems only that action of microRNA is distinguishable which affects the parameters of dominant system (critical parameters, or changes the dominant system itself. Dominant systems generalize and further develop the old and very popular idea of limiting step

A Minimalistic Resource Allocation Model to Explain Ubiquitous Increase in Protein Expression with Growth Rate.

Directory of Open Access Journals (Sweden)

Uri Barenholz

Full Text Available Most proteins show changes in level across growth conditions. Many of these changes seem to be coordinated with the specific growth rate rather than the growth environment or the protein function. Although cellular growth rates, gene expression levels and gene regulation have been at the center of biological research for decades, there are only a few models giving a base line prediction of the dependence of the proteome fraction occupied by a gene with the specific growth rate. We present a simple model that predicts a widely coordinated increase in the fraction of many proteins out of the proteome, proportionally with the growth rate. The model reveals how passive redistribution of resources, due to active regulation of only a few proteins, can have proteome wide effects that are quantitatively predictable. Our model provides a potential explanation for why and how such a coordinated response of a large fraction of the proteome to the specific growth rate arises under different environmental conditions. The simplicity of our model can also be useful by serving as a baseline null hypothesis in the search for active regulation. We exemplify the usage of the model by analyzing the relationship between growth rate and proteome composition for the model microorganism E.coli as reflected in recent proteomics data sets spanning various growth conditions. We find that the fraction out of the proteome of a large number of proteins, and from different cellular processes, increases proportionally with the growth rate. Notably, ribosomal proteins, which have been previously reported to increase in fraction with growth rate, are only a small part of this group of proteins. We suggest that, although the fractions of many proteins change with the growth rate, such changes may be partially driven by a global effect, not necessarily requiring specific cellular control mechanisms.

Case report: a novel KERA mutation associated with cornea plana and its predicted effect on protein function

DEFF Research Database (Denmark)

Roos, Laura; Bertelsen, Birgitte; Harris, Pernille

2015-01-01

individuals, hypotrichosis was found. KERA was screened for mutations using Sanger sequencing. We detected a novel KERA variant, p.(Ile225Thr), that segregates with the disease in the homozygous form. The three-dimensional structure of keratocan protein was modelled, and we showed that this missense variation...... of the keratocan gene (KERA) on chromosome 12q22. To date, only nine different disease-associated KERA mutations, including four missense mutations, have been described. Case presentation: In this report, we present clinical data from a Turkish family with autosomal recessive cornea plana. In some of the affected...... are predicted to result in destabilization of the protein. Conclusion: We present the 10th pathogenic KERA mutation identified so far. Protein modelling is a useful tool in predicting the effect of missense mutations. This case underline the importance of the leucin rich repeat domain for the protein function...

Model for calculation of electrostatic contribution into protein stability

Science.gov (United States)

Kundrotas, Petras; Karshikoff, Andrey

2003-03-01

Existing models of the denatured state of proteins consider only one possible spatial distribution of protein charges and therefore are applicable to a limited number of cases. In this presentation a more general framework for the modeling of the denatured state is proposed. It is based on the assumption that the titratable groups of an unfolded protein can adopt a quasi-random distribution, restricted by the protein sequence. The model was tested on two proteins, barnase and N-terminal domain of the ribosomal protein L9. The calculated free energy of denaturation, Δ G( pH), reproduces the experimental data essentially better than the commonly used null approximation (NA). It was demonstrated that the seemingly good agreement with experimental data obtained by NA originates from the compensatory effect between the pair-wise electrostatic interactions and the desolvation energy of the individual sites. It was also found that the ionization properties of denatured proteins are influenced by the protein sequence.

Discrete persistent-chain model for protein binding on DNA.

Science.gov (United States)

Lam, Pui-Man; Zhen, Yi

2011-04-01

We describe and solve a discrete persistent-chain model of protein binding on DNA, involving an extra σ(i) at a site i of the DNA. This variable takes the value 1 or 0, depending on whether or not the site is occupied by a protein. In addition, if the site is occupied by a protein, there is an extra energy cost ɛ. For a small force, we obtain analytic expressions for the force-extension curve and the fraction of bound protein on the DNA. For higher forces, the model can be solved numerically to obtain force-extension curves and the average fraction of bound proteins as a function of applied force. Our model can be used to analyze experimental force-extension curves of protein binding on DNA, and hence deduce the number of bound proteins in the case of nonspecific binding. ©2011 American Physical Society

Modeling Mercury in Proteins

Energy Technology Data Exchange (ETDEWEB)

Smith, Jeremy C [ORNL; Parks, Jerry M [ORNL

2016-01-01

Mercury (Hg) is a naturally occurring element that is released into the biosphere both by natural processes and anthropogenic activities. Although its reduced, elemental form Hg(0) is relatively non-toxic, other forms such as Hg2+ and, in particular, its methylated form, methylmercury, are toxic, with deleterious effects on both ecosystems and humans. Microorganisms play important roles in the transformation of mercury in the environment. Inorganic Hg2+ can be methylated by certain bacteria and archaea to form methylmercury. Conversely, bacteria also demethylate methylmercury and reduce Hg2+ to relatively inert Hg(0). Transformations and toxicity occur as a result of mercury interacting with various proteins. Clearly, then, understanding the toxic effects of mercury and its cycling in the environment requires characterization of these interactions. Computational approaches are ideally suited to studies of mercury in proteins because they can provide a detailed picture and circumvent issues associated with toxicity. Here we describe computational methods for investigating and characterizing how mercury binds to proteins, how inter- and intra-protein transfer of mercury is orchestrated in biological systems, and how chemical reactions in proteins transform the metal. We describe quantum chemical analyses of aqueous Hg(II), which reveal critical factors that determine ligand binding propensities. We then provide a perspective on how we used chemical reasoning to discover how microorganisms methylate mercury. We also highlight our combined computational and experimental studies of the proteins and enzymes of the mer operon, a suite of genes that confers mercury resistance in many bacteria. Lastly, we place work on mercury in proteins in the context of what is needed for a comprehensive multi-scale model of environmental mercury cycling.

Cyclin B1 Destruction Box-Mediated Protein Instability: The Enhanced Sensitivity of Fluorescent-Protein-Based Reporter Gene System

Directory of Open Access Journals (Sweden)

Chao-Hsun Yang

2013-01-01

Full Text Available The periodic expression and destruction of several cyclins are the most important steps for the exact regulation of cell cycle. Cyclins are degraded by the ubiquitin-proteasome system during cell cycle. Besides, a short sequence near the N-terminal of cyclin B called the destruction box (D-box; CDB is also required. Fluorescent-protein-based reporter gene system is insensitive to analysis because of the overly stable fluorescent proteins. Therefore, in this study, we use human CDB fused with both enhanced green fluorescent protein (EGFP at C-terminus and red fluorescent protein (RFP, DsRed at N-terminus in the transfected human melanoma cells to examine the effects of CDB on different fluorescent proteins. Our results indicated that CDB-fused fluorescent protein can be used to examine the slight gene regulations in the reporter gene system and have the potential to be the system for screening of functional compounds in the future.

Phosphate sensing by fluorecent reporter proteins embedded in poly-acrylamide nanoparticles

DEFF Research Database (Denmark)

Sun, Honghao; Scharff-Poulsen, Anne Marie; Gu, Hong

2008-01-01

Phosphate sensors were developed by embedding fluorescent reporter proteins (FLIPPi) in polyacrylamide nanoparticles; with diameters from 40 to 120 nm. The sensor activity and protein loading efficiency varied according to nanoparticle composition, that is, the total monomer content (% T) and the......, in nanoparticles for, for example, sensing, biological catalysis, and gene delivery.......Phosphate sensors were developed by embedding fluorescent reporter proteins (FLIPPi) in polyacrylamide nanoparticles; with diameters from 40 to 120 nm. The sensor activity and protein loading efficiency varied according to nanoparticle composition, that is, the total monomer content (% T......) and the cross-linker content (% C). Nanoparticles with 28% T and 20% C were considered optimal as a result of relatively high loading efficiency (50.6%) as well as high protein activity (50%). The experimental results prove that the cross-linked polyacrylamide matrix could protect FLIPPi from degradation...

Protein Folding: Search for Basic Physical Models

Directory of Open Access Journals (Sweden)

Ivan Y. Torshin

2003-01-01

Full Text Available How a unique three-dimensional structure is rapidly formed from the linear sequence of a polypeptide is one of the important questions in contemporary science. Apart from biological context of in vivo protein folding (which has been studied only for a few proteins, the roles of the fundamental physical forces in the in vitro folding remain largely unstudied. Despite a degree of success in using descriptions based on statistical and/or thermodynamic approaches, few of the current models explicitly include more basic physical forces (such as electrostatics and Van Der Waals forces. Moreover, the present-day models rarely take into account that the protein folding is, essentially, a rapid process that produces a highly specific architecture. This review considers several physical models that may provide more direct links between sequence and tertiary structure in terms of the physical forces. In particular, elaboration of such simple models is likely to produce extremely effective computational techniques with value for modern genomics.

Models of crk adaptor proteins in cancer.

Science.gov (United States)

Bell, Emily S; Park, Morag

2012-05-01

The Crk family of adaptor proteins (CrkI, CrkII, and CrkL), originally discovered as the oncogene fusion product, v-Crk, of the CT10 chicken retrovirus, lacks catalytic activity but engages with multiple signaling pathways through their SH2 and SH3 domains. Crk proteins link upstream tyrosine kinase and integrin-dependent signals to downstream effectors, acting as adaptors in diverse signaling pathways and cellular processes. Crk proteins are now recognized to play a role in the malignancy of many human cancers, stimulating renewed interest in their mechanism of action in cancer progression. The contribution of Crk signaling to malignancy has been predominantly studied in fibroblasts and in hematopoietic models and more recently in epithelial models. A mechanistic understanding of Crk proteins in cancer progression in vivo is still poorly understood in part due to the highly pleiotropic nature of Crk signaling. Recent advances in the structural organization of Crk domains, new roles in kinase regulation, and increased knowledge of the mechanisms and frequency of Crk overexpression in human cancers have provided an incentive for further study in in vivo models. An understanding of the mechanisms through which Crk proteins act as oncogenic drivers could have important implications in therapeutic targeting.

Modelling Protein Dynamics on the Microsecond Time Scale

DEFF Research Database (Denmark)

Siuda, Iwona Anna

Recent years have shown an increase in coarse-grained (CG) molecular dynamics simulations, providing structural and dynamic details of large proteins and enabling studies of self-assembly of biological materials. It is not easy to acquire such data experimentally, and access is also still limited...... in atomistic simulations. During her PhD studies, Iwona Siuda used MARTINI CG models to study the dynamics of different globular and membrane proteins. In several cases, the MARTINI model was sufficient to study conformational changes of small, purely alpha-helical proteins. However, in studies of larger......ELNEDIN was therefore proposed as part of the work. Iwona Siuda’s results from the CG simulations had biological implications that provide insights into possible mechanisms of the periplasmic leucine-binding protein, the sarco(endo)plasmic reticulum calcium pump, and several proteins from the saposin-like proteins...

Immunogenicity of therapeutic proteins: the use of animal models.

Science.gov (United States)

Brinks, Vera; Jiskoot, Wim; Schellekens, Huub

2011-10-01

Immunogenicity of therapeutic proteins lowers patient well-being and drastically increases therapeutic costs. Preventing immunogenicity is an important issue to consider when developing novel therapeutic proteins and applying them in the clinic. Animal models are increasingly used to study immunogenicity of therapeutic proteins. They are employed as predictive tools to assess different aspects of immunogenicity during drug development and have become vital in studying the mechanisms underlying immunogenicity of therapeutic proteins. However, the use of animal models needs critical evaluation. Because of species differences, predictive value of such models is limited, and mechanistic studies can be restricted. This review addresses the suitability of animal models for immunogenicity prediction and summarizes the insights in immunogenicity that they have given so far.

Protein-protein interaction networks identify targets which rescue the MPP+ cellular model of Parkinson’s disease

Science.gov (United States)

Keane, Harriet; Ryan, Brent J.; Jackson, Brendan; Whitmore, Alan; Wade-Martins, Richard

2015-11-01

Neurodegenerative diseases are complex multifactorial disorders characterised by the interplay of many dysregulated physiological processes. As an exemplar, Parkinson’s disease (PD) involves multiple perturbed cellular functions, including mitochondrial dysfunction and autophagic dysregulation in preferentially-sensitive dopamine neurons, a selective pathophysiology recapitulated in vitro using the neurotoxin MPP+. Here we explore a network science approach for the selection of therapeutic protein targets in the cellular MPP+ model. We hypothesised that analysis of protein-protein interaction networks modelling MPP+ toxicity could identify proteins critical for mediating MPP+ toxicity. Analysis of protein-protein interaction networks constructed to model the interplay of mitochondrial dysfunction and autophagic dysregulation (key aspects of MPP+ toxicity) enabled us to identify four proteins predicted to be key for MPP+ toxicity (P62, GABARAP, GBRL1 and GBRL2). Combined, but not individual, knockdown of these proteins increased cellular susceptibility to MPP+ toxicity. Conversely, combined, but not individual, over-expression of the network targets provided rescue of MPP+ toxicity associated with the formation of autophagosome-like structures. We also found that modulation of two distinct proteins in the protein-protein interaction network was necessary and sufficient to mitigate neurotoxicity. Together, these findings validate our network science approach to multi-target identification in complex neurological diseases.

Scoring predictive models using a reduced representation of proteins: model and energy definition.

Science.gov (United States)

Fogolari, Federico; Pieri, Lidia; Dovier, Agostino; Bortolussi, Luca; Giugliarelli, Gilberto; Corazza, Alessandra; Esposito, Gennaro; Viglino, Paolo

2007-03-23

Reduced representations of proteins have been playing a keyrole in the study of protein folding. Many such models are available, with different representation detail. Although the usefulness of many such models for structural bioinformatics applications has been demonstrated in recent years, there are few intermediate resolution models endowed with an energy model capable, for instance, of detecting native or native-like structures among decoy sets. The aim of the present work is to provide a discrete empirical potential for a reduced protein model termed here PC2CA, because it employs a PseudoCovalent structure with only 2 Centers of interactions per Amino acid, suitable for protein model quality assessment. All protein structures in the set top500H have been converted in reduced form. The distribution of pseudobonds, pseudoangle, pseudodihedrals and distances between centers of interactions have been converted into potentials of mean force. A suitable reference distribution has been defined for non-bonded interactions which takes into account excluded volume effects and protein finite size. The correlation between adjacent main chain pseudodihedrals has been converted in an additional energetic term which is able to account for cooperative effects in secondary structure elements. Local energy surface exploration is performed in order to increase the robustness of the energy function. The model and the energy definition proposed have been tested on all the multiple decoys' sets in the Decoys'R'us database. The energetic model is able to recognize, for almost all sets, native-like structures (RMSD less than 2.0 A). These results and those obtained in the blind CASP7 quality assessment experiment suggest that the model compares well with scoring potentials with finer granularity and could be useful for fast exploration of conformational space. Parameters are available at the url: http://www.dstb.uniud.it/~ffogolari/download/.

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.

Fast loop modeling for protein structures

Science.gov (United States)

Zhang, Jiong; Nguyen, Son; Shang, Yi; Xu, Dong; Kosztin, Ioan

2015-03-01

X-ray crystallography is the main method for determining 3D protein structures. In many cases, however, flexible loop regions of proteins cannot be resolved by this approach. This leads to incomplete structures in the protein data bank, preventing further computational study and analysis of these proteins. For instance, all-atom molecular dynamics (MD) simulation studies of structure-function relationship require complete protein structures. To address this shortcoming, we have developed and implemented an efficient computational method for building missing protein loops. The method is database driven and uses deep learning and multi-dimensional scaling algorithms. We have implemented the method as a simple stand-alone program, which can also be used as a plugin in existing molecular modeling software, e.g., VMD. The quality and stability of the generated structures are assessed and tested via energy scoring functions and by equilibrium MD simulations. The proposed method can also be used in template-based protein structure prediction. Work supported by the National Institutes of Health [R01 GM100701]. Computer time was provided by the University of Missouri Bioinformatics Consortium.

Modeling protein structures: construction and their applications.

Science.gov (United States)

Ring, C S; Cohen, F E

1993-06-01

Although no general solution to the protein folding problem exists, the three-dimensional structures of proteins are being successfully predicted when experimentally derived constraints are used in conjunction with heuristic methods. In the case of interleukin-4, mutagenesis data and CD spectroscopy were instrumental in the accurate assignment of secondary structure. In addition, the tertiary structure was highly constrained by six cysteines separated by many residues that formed three disulfide bridges. Although the correct structure was a member of a short list of plausible structures, the "best" structure was the topological enantiomer of the experimentally determined conformation. For many proteases, other experimentally derived structures can be used as templates to identify the secondary structure elements. In a procedure called modeling by homology, the structure of a known protein is used as a scaffold to predict the structure of another related protein. This method has been used to model a serine and a cysteine protease that are important in the schistosome and malarial life cycles, respectively. The model structures were then used to identify putative small molecule enzyme inhibitors computationally. Experiments confirm that some of these nonpeptidic compounds are active at concentrations of less than 10 microM.

A computational model of the LGI1 protein suggests a common binding site for ADAM proteins.

Directory of Open Access Journals (Sweden)

Emanuela Leonardi

Full Text Available Mutations of human leucine-rich glioma inactivated (LGI1 gene encoding the epitempin protein cause autosomal dominant temporal lateral epilepsy (ADTLE, a rare familial partial epileptic syndrome. The LGI1 gene seems to have a role on the transmission of neuronal messages but the exact molecular mechanism remains unclear. In contrast to other genes involved in epileptic disorders, epitempin shows no homology with known ion channel genes but contains two domains, composed of repeated structural units, known to mediate protein-protein interactions.A three dimensional in silico model of the two epitempin domains was built to predict the structure-function relationship and propose a functional model integrating previous experimental findings. Conserved and electrostatic charged regions of the model surface suggest a possible arrangement between the two domains and identifies a possible ADAM protein binding site in the β-propeller domain and another protein binding site in the leucine-rich repeat domain. The functional model indicates that epitempin could mediate the interaction between proteins localized to different synaptic sides in a static way, by forming a dimer, or in a dynamic way, by binding proteins at different times.The model was also used to predict effects of known disease-causing missense mutations. Most of the variants are predicted to alter protein folding while several other map to functional surface regions. In agreement with experimental evidence, this suggests that non-secreted LGI1 mutants could be retained within the cell by quality control mechanisms or by altering interactions required for the secretion process.

Scalable rule-based modelling of allosteric proteins and biochemical networks.

Directory of Open Access Journals (Sweden)

Julien F Ollivier

2010-11-01

Full Text Available Much of the complexity of biochemical networks comes from the information-processing abilities of allosteric proteins, be they receptors, ion-channels, signalling molecules or transcription factors. An allosteric protein can be uniquely regulated by each combination of input molecules that it binds. This "regulatory complexity" causes a combinatorial increase in the number of parameters required to fit experimental data as the number of protein interactions increases. It therefore challenges the creation, updating, and re-use of biochemical models. Here, we propose a rule-based modelling framework that exploits the intrinsic modularity of protein structure to address regulatory complexity. Rather than treating proteins as "black boxes", we model their hierarchical structure and, as conformational changes, internal dynamics. By modelling the regulation of allosteric proteins through these conformational changes, we often decrease the number of parameters required to fit data, and so reduce over-fitting and improve the predictive power of a model. Our method is thermodynamically grounded, imposes detailed balance, and also includes molecular cross-talk and the background activity of enzymes. We use our Allosteric Network Compiler to examine how allostery can facilitate macromolecular assembly and how competitive ligands can change the observed cooperativity of an allosteric protein. We also develop a parsimonious model of G protein-coupled receptors that explains functional selectivity and can predict the rank order of potency of agonists acting through a receptor. Our methodology should provide a basis for scalable, modular and executable modelling of biochemical networks in systems and synthetic biology.

Folding 19 proteins to their native state and stability of large proteins from a coarse-grained model.

Science.gov (United States)

Kapoor, Abhijeet; Travesset, Alex

2014-03-01

We develop an intermediate resolution model, where the backbone is modeled with atomic resolution but the side chain with a single bead, by extending our previous model (Proteins (2013) DOI: 10.1002/prot.24269) to properly include proline, preproline residues and backbone rigidity. Starting from random configurations, the model properly folds 19 proteins (including a mutant 2A3D sequence) into native states containing β sheet, α helix, and mixed α/β. As a further test, the stability of H-RAS (a 169 residue protein, critical in many signaling pathways) is investigated: The protein is stable, with excellent agreement with experimental B-factors. Despite that proteins containing only α helices fold to their native state at lower backbone rigidity, and other limitations, which we discuss thoroughly, the model provides a reliable description of the dynamics as compared with all atom simulations, but does not constrain secondary structures as it is typically the case in more coarse-grained models. Further implications are described. Copyright © 2013 Wiley Periodicals, Inc.

Trade-off between positive and negative design of protein stability: from lattice models to real proteins.

Directory of Open Access Journals (Sweden)

Orly Noivirt-Brik

2009-12-01

Full Text Available Two different strategies for stabilizing proteins are (i positive design in which the native state is stabilized and (ii negative design in which competing non-native conformations are destabilized. Here, the circumstances under which one strategy might be favored over the other are explored in the case of lattice models of proteins and then generalized and discussed with regard to real proteins. The balance between positive and negative design of proteins is found to be determined by their average "contact-frequency", a property that corresponds to the fraction of states in the conformational ensemble of the sequence in which a pair of residues is in contact. Lattice model proteins with a high average contact-frequency are found to use negative design more than model proteins with a low average contact-frequency. A mathematical derivation of this result indicates that it is general and likely to hold also for real proteins. Comparison of the results of correlated mutation analysis for real proteins with typical contact-frequencies to those of proteins likely to have high contact-frequencies (such as disordered proteins and proteins that are dependent on chaperonins for their folding indicates that the latter tend to have stronger interactions between residues that are not in contact in their native conformation. Hence, our work indicates that negative design is employed when insufficient stabilization is achieved via positive design owing to high contact-frequencies.

Roles of beta-turns in protein folding: from peptide models to protein engineering.

Science.gov (United States)

Marcelino, Anna Marie C; Gierasch, Lila M

2008-05-01

Reverse turns are a major class of protein secondary structure; they represent sites of chain reversal and thus sites where the globular character of a protein is created. It has been speculated for many years that turns may nucleate the formation of structure in protein folding, as their propensity to occur will favor the approximation of their flanking regions and their general tendency to be hydrophilic will favor their disposition at the solvent-accessible surface. Reverse turns are local features, and it is therefore not surprising that their structural properties have been extensively studied using peptide models. In this article, we review research on peptide models of turns to test the hypothesis that the propensities of turns to form in short peptides will relate to the roles of corresponding sequences in protein folding. Turns with significant stability as isolated entities should actively promote the folding of a protein, and by contrast, turn sequences that merely allow the chain to adopt conformations required for chain reversal are predicted to be passive in the folding mechanism. We discuss results of protein engineering studies of the roles of turn residues in folding mechanisms. Factors that correlate with the importance of turns in folding indeed include their intrinsic stability, as well as their topological context and their participation in hydrophobic networks within the protein's structure.

An Integrated Framework Advancing Membrane Protein Modeling and Design.

Directory of Open Access Journals (Sweden)

Rebecca F Alford

2015-09-01

Full Text Available Membrane proteins are critical functional molecules in the human body, constituting more than 30% of open reading frames in the human genome. Unfortunately, a myriad of difficulties in overexpression and reconstitution into membrane mimetics severely limit our ability to determine their structures. Computational tools are therefore instrumental to membrane protein structure prediction, consequently increasing our understanding of membrane protein function and their role in disease. Here, we describe a general framework facilitating membrane protein modeling and design that combines the scientific principles for membrane protein modeling with the flexible software architecture of Rosetta3. This new framework, called RosettaMP, provides a general membrane representation that interfaces with scoring, conformational sampling, and mutation routines that can be easily combined to create new protocols. To demonstrate the capabilities of this implementation, we developed four proof-of-concept applications for (1 prediction of free energy changes upon mutation; (2 high-resolution structural refinement; (3 protein-protein docking; and (4 assembly of symmetric protein complexes, all in the membrane environment. Preliminary data show that these algorithms can produce meaningful scores and structures. The data also suggest needed improvements to both sampling routines and score functions. Importantly, the applications collectively demonstrate the potential of combining the flexible nature of RosettaMP with the power of Rosetta algorithms to facilitate membrane protein modeling and design.

Comparative assessment of fluorescent proteins for in vivo imaging in an animal model system.

Science.gov (United States)

Heppert, Jennifer K; Dickinson, Daniel J; Pani, Ariel M; Higgins, Christopher D; Steward, Annette; Ahringer, Julie; Kuhn, Jeffrey R; Goldstein, Bob

2016-11-07

Fluorescent protein tags are fundamental tools used to visualize gene products and analyze their dynamics in vivo. Recent advances in genome editing have expedited the precise insertion of fluorescent protein tags into the genomes of diverse organisms. These advances expand the potential of in vivo imaging experiments and facilitate experimentation with new, bright, photostable fluorescent proteins. Most quantitative comparisons of the brightness and photostability of different fluorescent proteins have been made in vitro, removed from biological variables that govern their performance in cells or organisms. To address the gap, we quantitatively assessed fluorescent protein properties in vivo in an animal model system. We generated transgenic Caenorhabditis elegans strains expressing green, yellow, or red fluorescent proteins in embryos and imaged embryos expressing different fluorescent proteins under the same conditions for direct comparison. We found that mNeonGreen was not as bright in vivo as predicted based on in vitro data but is a better tag than GFP for specific kinds of experiments, and we report on optimal red fluorescent proteins. These results identify ideal fluorescent proteins for imaging in vivo in C. elegans embryos and suggest good candidate fluorescent proteins to test in other animal model systems for in vivo imaging experiments. © 2016 Heppert et al. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Modeling of human factor Va inactivation by activated protein C

Directory of Open Access Journals (Sweden)

Bravo Maria

2012-05-01

Full Text Available Abstract Background Because understanding of the inventory, connectivity and dynamics of the components characterizing the process of coagulation is relatively mature, it has become an attractive target for physiochemical modeling. Such models can potentially improve the design of therapeutics. The prothrombinase complex (composed of the protease factor (FXa and its cofactor FVa plays a central role in this network as the main producer of thrombin, which catalyses both the activation of platelets and the conversion of fibrinogen to fibrin, the main substances of a clot. A key negative feedback loop that prevents clot propagation beyond the site of injury is the thrombin-dependent generation of activated protein C (APC, an enzyme that inactivates FVa, thus neutralizing the prothrombinase complex. APC inactivation of FVa is complex, involving the production of partially active intermediates and “protection” of FVa from APC by both FXa and prothrombin. An empirically validated mathematical model of this process would be useful in advancing the predictive capacity of comprehensive models of coagulation. Results A model of human APC inactivation of prothrombinase was constructed in a stepwise fashion by analyzing time courses of FVa inactivation in empirical reaction systems with increasing number of interacting components and generating corresponding model constructs of each reaction system. Reaction mechanisms, rate constants and equilibrium constants informing these model constructs were initially derived from various research groups reporting on APC inactivation of FVa in isolation, or in the presence of FXa or prothrombin. Model predictions were assessed against empirical data measuring the appearance and disappearance of multiple FVa degradation intermediates as well as prothrombinase activity changes, with plasma proteins derived from multiple preparations. Our work integrates previously published findings and through the cooperative

Mechanical Modeling and Computer Simulation of Protein Folding

Science.gov (United States)

Prigozhin, Maxim B.; Scott, Gregory E.; Denos, Sharlene

2014-01-01

In this activity, science education and modern technology are bridged to teach students at the high school and undergraduate levels about protein folding and to strengthen their model building skills. Students are guided from a textbook picture of a protein as a rigid crystal structure to a more realistic view: proteins are highly dynamic…

MASCOT HTML and XML parser: an implementation of a novel object model for protein identification data.

Science.gov (United States)

Yang, Chunguang G; Granite, Stephen J; Van Eyk, Jennifer E; Winslow, Raimond L

2006-11-01

Protein identification using MS is an important technique in proteomics as well as a major generator of proteomics data. We have designed the protein identification data object model (PDOM) and developed a parser based on this model to facilitate the analysis and storage of these data. The parser works with HTML or XML files saved or exported from MASCOT MS/MS ions search in peptide summary report or MASCOT PMF search in protein summary report. The program creates PDOM objects, eliminates redundancy in the input file, and has the capability to output any PDOM object to a relational database. This program facilitates additional analysis of MASCOT search results and aids the storage of protein identification information. The implementation is extensible and can serve as a template to develop parsers for other search engines. The parser can be used as a stand-alone application or can be driven by other Java programs. It is currently being used as the front end for a system that loads HTML and XML result files of MASCOT searches into a relational database. The source code is freely available at http://www.ccbm.jhu.edu and the program uses only free and open-source Java libraries.

Insight into the intermolecular recognition mechanism between Keap1 and IKKβ combining homology modelling, protein-protein docking, molecular dynamics simulations and virtual alanine mutation.

Directory of Open Access Journals (Sweden)

Zheng-Yu Jiang

Full Text Available Degradation of certain proteins through the ubiquitin-proteasome pathway is a common strategy taken by the key modulators responsible for stress responses. Kelch-like ECH-associated protein-1(Keap1, a substrate adaptor component of the Cullin3 (Cul3-based ubiquitin E3 ligase complex, mediates the ubiquitination of two key modulators, NF-E2-related factor 2 (Nrf2 and IκB kinase β (IKKβ, which are involved in the redox control of gene transcription. However, compared to the Keap1-Nrf2 protein-protein interaction (PPI, the intermolecular recognition mechanism of Keap1 and IKKβ has been poorly investigated. In order to explore the binding pattern between Keap1 and IKKβ, the PPI model of Keap1 and IKKβ was investigated. The structure of human IKKβ was constructed by means of the homology modeling method and using reported crystal structure of Xenopus laevis IKKβ as the template. A protein-protein docking method was applied to develop the Keap1-IKKβ complex model. After the refinement and visual analysis of docked proteins, the chosen pose was further optimized through molecular dynamics simulations. The resulting structure was utilized to conduct the virtual alanine mutation for the exploration of hot-spots significant for the intermolecular interaction. Overall, our results provided structural insights into the PPI model of Keap1-IKKβ and suggest that the substrate specificity of Keap1 depend on the interaction with the key tyrosines, namely Tyr525, Tyr574 and Tyr334. The study presented in the current project may be useful to design molecules that selectively modulate Keap1. The selective recognition mechanism of Keap1 with IKKβ or Nrf2 will be helpful to further know the crosstalk between NF-κB and Nrf2 signaling.

In silico modelling and validation of differential expressed proteins in lung cancer

Directory of Open Access Journals (Sweden)

Bhagavathi S

2012-05-01

Full Text Available Objective: The present study aims predict the three dimensional structure of three major proteins responsible for causing Lung cancer. Methods: These are the differentially expressed proteins in lung cancer dataset. Initially, the structural template for these proteins is identified from structural database using homology search and perform homology modelling approach to predict its native 3D structure. Three-dimensional model obtained was validated using Ramachandran plot analysis to find the reliability of the model. Results: Four proteins were differentially expressed and were significant proteins in causing lung cancer. Among the four proteins, Matrixmetallo proteinase (P39900 had a known 3D structure and hence was not considered for modelling. The remaining proteins Polo like kinase I Q58A51, Trophinin B1AKF1, Thrombomodulin P07204 were modelled and validated. Conclusions: The three dimensional structure of proteins provides insights about the functional aspect and regulatory aspect of the protein. Thus, this study will be a breakthrough for further lung cancer related studies.

Protein single-model quality assessment by feature-based probability density functions.

Science.gov (United States)

Cao, Renzhi; Cheng, Jianlin

2016-04-04

Protein quality assessment (QA) has played an important role in protein structure prediction. We developed a novel single-model quality assessment method-Qprob. Qprob calculates the absolute error for each protein feature value against the true quality scores (i.e. GDT-TS scores) of protein structural models, and uses them to estimate its probability density distribution for quality assessment. Qprob has been blindly tested on the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM-NOVEL server. The official CASP result shows that Qprob ranks as one of the top single-model QA methods. In addition, Qprob makes contributions to our protein tertiary structure predictor MULTICOM, which is officially ranked 3rd out of 143 predictors. The good performance shows that Qprob is good at assessing the quality of models of hard targets. These results demonstrate that this new probability density distribution based method is effective for protein single-model quality assessment and is useful for protein structure prediction. The webserver of Qprob is available at: http://calla.rnet.missouri.edu/qprob/. The software is now freely available in the web server of Qprob.

Reef-coral proteins as visual, non-destructive reporters for plant transformation.

Science.gov (United States)

Wenck, A; Pugieux, C; Turner, M; Dunn, M; Stacy, C; Tiozzo, A; Dunder, E; van Grinsven, E; Khan, R; Sigareva, M; Wang, W C; Reed, J; Drayton, P; Oliver, D; Trafford, H; Legris, G; Rushton, H; Tayab, S; Launis, K; Chang, Y-F; Chen, D-F; Melchers, L

2003-11-01

Recently, five novel fluorescent proteins have been isolated from non-bioluminescent species of reef-coral organisms and have been made available through ClonTech. They are AmCyan, AsRed, DsRed, ZsGreen and ZsYellow. These proteins are valuable as reporters for transformation because they do not require a substrate or external co-factor to emit fluorescence and can be tested in vivo without destruction of the tissue under study. We have evaluated them in a large range of plants, both monocots and dicots, and our results indicate that they are valuable reporting tools for transformation in a wide variety of crops. We report here their successful expression in wheat, maize, barley, rice, banana, onion, soybean, cotton, tobacco, potato and tomato. Transient expression could be observed as early as 24 h after DNA delivery in some cases, allowing for very clear visualization of individually transformed cells. Stable transgenic events were generated, using mannose, kanamycin or hygromycin selection. Transgenic plants were phenotypically normal, showing a wide range of fluorescence levels, and were fertile. Expression of AmCyan, ZsGreen and AsRed was visible in maize T1 seeds, allowing visual segregation to more than 99% accuracy. The excitation and emission wavelengths of some of these proteins are significantly different; the difference is enough for the simultaneous visualization of cells transformed with more than one of the fluorescent proteins. These proteins will become useful tools for transformation optimization and other studies. The wide variety of plants successfully tested demonstrates that these proteins will potentially find broad use in plant biology.

Scoring functions for protein-protein interactions.

Science.gov (United States)

Moal, Iain H; Moretti, Rocco; Baker, David; Fernández-Recio, Juan

2013-12-01

The computational evaluation of protein-protein interactions will play an important role in organising the wealth of data being generated by high-throughput initiatives. Here we discuss future applications, report recent developments and identify areas requiring further investigation. Many functions have been developed to quantify the structural and energetic properties of interacting proteins, finding use in interrelated challenges revolving around the relationship between sequence, structure and binding free energy. These include loop modelling, side-chain refinement, docking, multimer assembly, affinity prediction, affinity change upon mutation, hotspots location and interface design. Information derived from models optimised for one of these challenges can be used to benefit the others, and can be unified within the theoretical frameworks of multi-task learning and Pareto-optimal multi-objective learning. Copyright © 2013 Elsevier Ltd. All rights reserved.

Quality assessment of protein model-structures based on structural and functional similarities.

Science.gov (United States)

Konopka, Bogumil M; Nebel, Jean-Christophe; Kotulska, Malgorzata

2012-09-21

Experimental determination of protein 3D structures is expensive, time consuming and sometimes impossible. A gap between number of protein structures deposited in the World Wide Protein Data Bank and the number of sequenced proteins constantly broadens. Computational modeling is deemed to be one of the ways to deal with the problem. Although protein 3D structure prediction is a difficult task, many tools are available. These tools can model it from a sequence or partial structural information, e.g. contact maps. Consequently, biologists have the ability to generate automatically a putative 3D structure model of any protein. However, the main issue becomes evaluation of the model quality, which is one of the most important challenges of structural biology. GOBA--Gene Ontology-Based Assessment is a novel Protein Model Quality Assessment Program. It estimates the compatibility between a model-structure and its expected function. GOBA is based on the assumption that a high quality model is expected to be structurally similar to proteins functionally similar to the prediction target. Whereas DALI is used to measure structure similarity, protein functional similarity is quantified using standardized and hierarchical description of proteins provided by Gene Ontology combined with Wang's algorithm for calculating semantic similarity. Two approaches are proposed to express the quality of protein model-structures. One is a single model quality assessment method, the other is its modification, which provides a relative measure of model quality. Exhaustive evaluation is performed on data sets of model-structures submitted to the CASP8 and CASP9 contests. The validation shows that the method is able to discriminate between good and bad model-structures. The best of tested GOBA scores achieved 0.74 and 0.8 as a mean Pearson correlation to the observed quality of models in our CASP8 and CASP9-based validation sets. GOBA also obtained the best result for two targets of CASP8, and

Simulation studies of protein-induced bilayer deformations, and lipid-induced protein tilting, on a mesoscopic model for lipid bilayers with embedded proteins

DEFF Research Database (Denmark)

Venturoli, M.; Smit, B.; Sperotto, Maria Maddalena

2005-01-01

membranes. Here we present a mesoscopic model for lipid bilayers with embedded proteins, which we have studied with the help of the dissipative particle dynamics simulation technique. Because hydrophobic matching is believed to be one of the main physical mechanisms regulating lipid-protein interactions......-induced protein tilt, with the hydrophobic mismatch ( positive and negative) between the protein hydrophobic length and the pure lipid bilayer hydrophobic thickness. The protein-induced bilayer perturbation was quantified in terms of a coherence length, xi(P), of the lipid bilayer hydrophobic thickness pro. le...... for positive values of mismatch; a dependence on the protein size appears as well. In the case of large model proteins experiencing extreme mismatch conditions, in the region next to the so-called lipid annulus, there appears an undershooting ( or overshooting) region where the bilayer hydrophobic thickness...

An approach to creating a more realistic working model from a protein data bank entry.

Science.gov (United States)

Brandon, Christopher J; Martin, Benjamin P; McGee, Kelly J; Stewart, James J P; Braun-Sand, Sonja B

2015-01-01

An accurate model of three-dimensional protein structure is important in a variety of fields such as structure-based drug design and mechanistic studies of enzymatic reactions. While the entries in the Protein Data Bank ( http://www.pdb.org ) provide valuable information about protein structures, a small fraction of the PDB structures were found to contain anomalies not reported in the PDB file. The semiempirical PM7 method in MOPAC2012 was used for identifying anomalously short hydrogen bonds, C-H⋯O/C-H⋯N interactions, non-bonding close contacts, and unrealistic covalent bond lengths in recently published Protein Data Bank files. It was also used to generate new structures with these faults removed. When the semiempirical models were compared to those of PDB_REDO (http://www.cmbi.ru.nl/pdb_redo/), the clashscores, as defined by MolProbity ( http://molprobity.biochem.duke.edu/), were better in about 50% of the structures. The semiempirical models also had a lower root-mean-square-deviation value in nearly all cases than those from PDB_REDO, indicative of a better conservation of the tertiary structure. Finally, the semiempirical models were found to have lower clashscores than the initial PDB file in all but one case. Because this approach maintains as much of the original tertiary structure as possible while improving anomalous interactions, it should be useful to theoreticians, experimentalists, and crystallographers investigating the structure and function of proteins.

Hydration dynamics near a model protein surface

International Nuclear Information System (INIS)

Russo, Daniela; Hura, Greg; Head-Gordon, Teresa

2003-01-01

The evolution of water dynamics from dilute to very high concentration solutions of a prototypical hydrophobic amino acid with its polar backbone, N-acetyl-leucine-methylamide (NALMA), is studied by quasi-elastic neutron scattering and molecular dynamics simulation for both the completely deuterated and completely hydrogenated leucine monomer. We observe several unexpected features in the dynamics of these biological solutions under ambient conditions. The NALMA dynamics shows evidence of de Gennes narrowing, an indication of coherent long timescale structural relaxation dynamics. The translational water dynamics are analyzed in a first approximation with a jump diffusion model. At the highest solute concentrations, the hydration water dynamics is significantly suppressed and characterized by a long residential time and a slow diffusion coefficient. The analysis of the more dilute concentration solutions takes into account the results of the 2.0M solution as a model of the first hydration shell. Subtracting the first hydration layer based on the 2.0M spectra, the translational diffusion dynamics is still suppressed, although the rotational relaxation time and residential time are converged to bulk-water values. Molecular dynamics analysis shows spatially heterogeneous dynamics at high concentration that becomes homogeneous at more dilute concentrations. We discuss the hydration dynamics results of this model protein system in the context of glassy systems, protein function, and protein-protein interfaces

Spatial separation and bidirectional trafficking of proteins using a multi-functional reporter

Directory of Open Access Journals (Sweden)

Klaubert Dieter H

2008-04-01

Full Text Available Abstract Background The ability to specifically label proteins within living cells can provide information about their dynamics and function. To study a membrane protein, we fused a multi-functional reporter protein, HaloTag®, to the extracellular domain of a truncated integrin. Results Using the HaloTag technology, we could study the localization, trafficking and processing of an integrin-HaloTag fusion, which we showed had cellular dynamics consistent with native integrins. By labeling live cells with different fluorescent impermeable and permeable ligands, we showed spatial separation of plasma membrane and internal pools of the integrin-HaloTag fusion, and followed these protein pools over time to study bi-directional trafficking. In addition to combining the HaloTag reporter protein with different fluorophores, we also employed an affinity tag to achieve cell capture. Conclusion The HaloTag technology was used successfully to study expression, trafficking, spatial separation and real-time translocation of an integrin-HaloTag fusion, thereby demonstrating that this technology can be a powerful tool to investigate membrane protein biology in live cells.

In silico modeling and experimental evidence of coagulant protein interaction with precursors for nanoparticle functionalization.

Science.gov (United States)

Okoli, Chuka; Sengottaiyan, Selvaraj; Arul Murugan, N; Pavankumar, Asalapuram R; Agren, Hans; Kuttuva Rajarao, Gunaratna

2013-10-01

The design of novel protein-nanoparticle hybrid systems has applications in many fields of science ranging from biomedicine, catalysis, water treatment, etc. The main barrier in devising such tool is lack of adequate information or poor understanding of protein-ligand chemistry. Here, we establish a new strategy based on computational modeling for protein and precursor linkers that can decorate the nanoparticles. Moringa oleifera (MO2.1) seed protein that has coagulation and antimicrobial properties was used. Superparamagnetic nanoparticles (SPION) with precursor ligands were used for the protein-ligand interaction studies. The molecular docking studies reveal that there are two binding sites, one is located at the core binding site; tetraethoxysilane (TEOS) or 3-aminopropyl trimethoxysilane (APTES) binds to this site while the other one is located at the side chain residues where trisodium citrate (TSC) or Si60 binds to this site. The protein-ligand distance profile analysis explains the differences in functional activity of the decorated SPION. Experimentally, TSC-coated nanoparticles showed higher coagulation activity as compared to TEOS- and APTES-coated SPION. To our knowledge, this is the first report on in vitro experimental data, which endorses the computational modeling studies as a powerful tool to design novel precursors for functionalization of nanomaterials; and develop interface hybrid systems for various applications.

Statistical-mechanical lattice models for protein-DNA binding in chromatin

International Nuclear Information System (INIS)

Teif, Vladimir B; Rippe, Karsten

2010-01-01

Statistical-mechanical lattice models for protein-DNA binding are well established as a method to describe complex ligand binding equilibria measured in vitro with purified DNA and protein components. Recently, a new field of applications has opened up for this approach since it has become possible to experimentally quantify genome-wide protein occupancies in relation to the DNA sequence. In particular, the organization of the eukaryotic genome by histone proteins into a nucleoprotein complex termed chromatin has been recognized as a key parameter that controls the access of transcription factors to the DNA sequence. New approaches have to be developed to derive statistical-mechanical lattice descriptions of chromatin-associated protein-DNA interactions. Here, we present the theoretical framework for lattice models of histone-DNA interactions in chromatin and investigate the (competitive) DNA binding of other chromosomal proteins and transcription factors. The results have a number of applications for quantitative models for the regulation of gene expression.

Exploiting conformational ensembles in modeling protein-protein interactions on the proteome scale

Science.gov (United States)

Kuzu, Guray; Gursoy, Attila; Nussinov, Ruth; Keskin, Ozlem

2013-01-01

Cellular functions are performed through protein-protein interactions; therefore, identification of these interactions is crucial for understanding biological processes. Recent studies suggest that knowledge-based approaches are more useful than ‘blind’ docking for modeling at large scales. However, a caveat of knowledge-based approaches is that they treat molecules as rigid structures. The Protein Data Bank (PDB) offers a wealth of conformations. Here, we exploited ensemble of the conformations in predictions by a knowledge-based method, PRISM. We tested ‘difficult’ cases in a docking-benchmark dataset, where the unbound and bound protein forms are structurally different. Considering alternative conformations for each protein, the percentage of successfully predicted interactions increased from ~26% to 66%, and 57% of the interactions were successfully predicted in an ‘unbiased’ scenario, in which data related to the bound forms were not utilized. If the appropriate conformation, or relevant template interface, is unavailable in the PDB, PRISM could not predict the interaction successfully. The pace of the growth of the PDB promises a rapid increase of ensemble conformations emphasizing the merit of such knowledge-based ensemble strategies for higher success rates in protein-protein interaction predictions on an interactome-scale. We constructed the structural network of ERK interacting proteins as a case study. PMID:23590674

Stochastic lattice model of synaptic membrane protein domains.

Science.gov (United States)

Li, Yiwei; Kahraman, Osman; Haselwandter, Christoph A

2017-05-01

Neurotransmitter receptor molecules, concentrated in synaptic membrane domains along with scaffolds and other kinds of proteins, are crucial for signal transmission across chemical synapses. In common with other membrane protein domains, synaptic domains are characterized by low protein copy numbers and protein crowding, with rapid stochastic turnover of individual molecules. We study here in detail a stochastic lattice model of the receptor-scaffold reaction-diffusion dynamics at synaptic domains that was found previously to capture, at the mean-field level, the self-assembly, stability, and characteristic size of synaptic domains observed in experiments. We show that our stochastic lattice model yields quantitative agreement with mean-field models of nonlinear diffusion in crowded membranes. Through a combination of analytic and numerical solutions of the master equation governing the reaction dynamics at synaptic domains, together with kinetic Monte Carlo simulations, we find substantial discrepancies between mean-field and stochastic models for the reaction dynamics at synaptic domains. Based on the reaction and diffusion properties of synaptic receptors and scaffolds suggested by previous experiments and mean-field calculations, we show that the stochastic reaction-diffusion dynamics of synaptic receptors and scaffolds provide a simple physical mechanism for collective fluctuations in synaptic domains, the molecular turnover observed at synaptic domains, key features of the observed single-molecule trajectories, and spatial heterogeneity in the effective rates at which receptors and scaffolds are recycled at the cell membrane. Our work sheds light on the physical mechanisms and principles linking the collective properties of membrane protein domains to the stochastic dynamics that rule their molecular components.

Reconstruction of the yeast protein-protein interaction network involved in nutrient sensing and global metabolic regulation

DEFF Research Database (Denmark)

Nandy, Subir Kumar; Jouhten, Paula; Nielsen, Jens

2010-01-01

proteins. Despite the value of BioGRID for studying protein-protein interactions, there is a need for manual curation of these interactions in order to remove false positives. RESULTS: Here we describe an annotated reconstruction of the protein-protein interactions around four key nutrient......) and for all the interactions between them (edges). The annotated information is readily available utilizing the functionalities of network modelling tools such as Cytoscape and CellDesigner. CONCLUSIONS: The reported fully annotated interaction model serves as a platform for integrated systems biology studies...

CONFOLD2: improved contact-driven ab initio protein structure modeling.

Science.gov (United States)

Adhikari, Badri; Cheng, Jianlin

2018-01-25

Contact-guided protein structure prediction methods are becoming more and more successful because of the latest advances in residue-residue contact prediction. To support contact-driven structure prediction, effective tools that can quickly build tertiary structural models of good quality from predicted contacts need to be developed. We develop an improved contact-driven protein modelling method, CONFOLD2, and study how it may be effectively used for ab initio protein structure prediction with predicted contacts as input. It builds models using various subsets of input contacts to explore the fold space under the guidance of a soft square energy function, and then clusters the models to obtain the top five models. CONFOLD2 obtains an average reconstruction accuracy of 0.57 TM-score for the 150 proteins in the PSICOV contact prediction dataset. When benchmarked on the CASP11 contacts predicted using CONSIP2 and CASP12 contacts predicted using Raptor-X, CONFOLD2 achieves a mean TM-score of 0.41 on both datasets. CONFOLD2 allows to quickly generate top five structural models for a protein sequence when its secondary structures and contacts predictions at hand. The source code of CONFOLD2 is publicly available at https://github.com/multicom-toolbox/CONFOLD2/ .

Experimental determination and thermodynamic modeling of phase equilibrium and protein partitioning in aqueous two-phase systems containing biodegradable salts

International Nuclear Information System (INIS)

Perez, Brenda; Malpiedi, Luciana Pellegrini; Tubío, Gisela; Nerli, Bibiana; Alcântara Pessôa Filho, Pedro de

2013-01-01

Highlights: ► Binodal data of systems (water + polyethyleneglycol + sodium) succinate are reported. ► Pitzer model describes the phase equilibrium of systems formed by polyethyleneglycol and biodegradable salts satisfactorily. ► This simple thermodynamic framework was able to predict the partitioning behaviour of model proteins acceptably well. - Abstract: Phase diagrams of sustainable aqueous two-phase systems (ATPSs) formed by polyethyleneglycols (PEGs) of different average molar masses (4000, 6000, and 8000) and sodium succinate are reported in this work. Partition coefficients (Kps) of seven model proteins: bovine serum albumin, catalase, beta-lactoglobulin, alpha-amylase, lysozyme, pepsin, urease and trypsin were experimentally determined in these systems and in ATPSs formed by the former PEGs and other biodegradable sodium salts: citrate and tartrate. An extension of Pitzer model comprising long and short-range term contributions to the excess Gibbs free energy was used to describe the (liquid + liquid) equilibrium. Comparison between experimental and calculated tie line data showed mean deviations always lower than 3%, thus indicating a good correlation. The partition coefficients were modeled by using the same thermodynamic approach. Predicted and experimental partition coefficients correlated quite successfully. Mean deviations were found to be lower than the experimental uncertainty for most of the assayed proteins.

Are animal models predictive for human postmortem muscle protein degradation?

Science.gov (United States)

Ehrenfellner, Bianca; Zissler, Angela; Steinbacher, Peter; Monticelli, Fabio C; Pittner, Stefan

2017-11-01

A most precise determination of the postmortem interval (PMI) is a crucial aspect in forensic casework. Although there are diverse approaches available to date, the high heterogeneity of cases together with the respective postmortal changes often limit the validity and sufficiency of many methods. Recently, a novel approach for time since death estimation by the analysis of postmortal changes of muscle proteins was proposed. It is however necessary to improve the reliability and accuracy, especially by analysis of possible influencing factors on protein degradation. This is ideally investigated on standardized animal models that, however, require legitimization by a comparison of human and animal tissue, and in this specific case of protein degradation profiles. Only if protein degradation events occur in comparable fashion within different species, respective findings can sufficiently be transferred from the animal model to application in humans. Therefor samples from two frequently used animal models (mouse and pig), as well as forensic cases with representative protein profiles of highly differing PMIs were analyzed. Despite physical and physiological differences between species, western blot analysis revealed similar patterns in most of the investigated proteins. Even most degradation events occurred in comparable fashion. In some other aspects, however, human and animal profiles depicted distinct differences. The results of this experimental series clearly indicate the huge importance of comparative studies, whenever animal models are considered. Although animal models could be shown to reflect the basic principles of protein degradation processes in humans, we also gained insight in the difficulties and limitations of the applicability of the developed methodology in different mammalian species regarding protein specificity and methodic functionality.

Using the 2A Protein Coexpression System: Multicistronic 2A Vectors Expressing Gene(s) of Interest and Reporter Proteins.

Science.gov (United States)

Luke, Garry A; Ryan, Martin D

2018-01-01

To date, a huge range of different proteins-many with cotranslational and posttranslational subcellular localization signals-have been coexpressed together with various reporter proteins in vitro and in vivo using 2A peptides. The pros and cons of 2A co-expression technology are considered below, followed by a simple example of a "how to" protocol to concatenate multiple genes of interest, together with a reporter gene, into a single gene linked via 2As for easy identification or selection of transduced cells.

Hidden markov model for the prediction of transmembrane proteins using MATLAB.

Science.gov (United States)

Chaturvedi, Navaneet; Shanker, Sudhanshu; Singh, Vinay Kumar; Sinha, Dhiraj; Pandey, Paras Nath

2011-01-01

Since membranous proteins play a key role in drug targeting therefore transmembrane proteins prediction is active and challenging area of biological sciences. Location based prediction of transmembrane proteins are significant for functional annotation of protein sequences. Hidden markov model based method was widely applied for transmembrane topology prediction. Here we have presented a revised and a better understanding model than an existing one for transmembrane protein prediction. Scripting on MATLAB was built and compiled for parameter estimation of model and applied this model on amino acid sequence to know the transmembrane and its adjacent locations. Estimated model of transmembrane topology was based on TMHMM model architecture. Only 7 super states are defined in the given dataset, which were converted to 96 states on the basis of their length in sequence. Accuracy of the prediction of model was observed about 74 %, is a good enough in the area of transmembrane topology prediction. Therefore we have concluded the hidden markov model plays crucial role in transmembrane helices prediction on MATLAB platform and it could also be useful for drug discovery strategy. The database is available for free at bioinfonavneet@gmail.comvinaysingh@bhu.ac.in.

Modeling of axonal endoplasmic reticulum network by spastic paraplegia proteins.

Science.gov (United States)

Yalçın, Belgin; Zhao, Lu; Stofanko, Martin; O'Sullivan, Niamh C; Kang, Zi Han; Roost, Annika; Thomas, Matthew R; Zaessinger, Sophie; Blard, Olivier; Patto, Alex L; Sohail, Anood; Baena, Valentina; Terasaki, Mark; O'Kane, Cahir J

2017-07-25

Axons contain a smooth tubular endoplasmic reticulum (ER) network that is thought to be continuous with ER throughout the neuron; the mechanisms that form this axonal network are unknown. Mutations affecting reticulon or REEP proteins, with intramembrane hairpin domains that model ER membranes, cause an axon degenerative disease, hereditary spastic paraplegia (HSP). We show that Drosophila axons have a dynamic axonal ER network, which these proteins help to model. Loss of HSP hairpin proteins causes ER sheet expansion, partial loss of ER from distal motor axons, and occasional discontinuities in axonal ER. Ultrastructural analysis reveals an extensive ER network in axons, which shows larger and fewer tubules in larvae that lack reticulon and REEP proteins, consistent with loss of membrane curvature. Therefore HSP hairpin-containing proteins are required for shaping and continuity of axonal ER, thus suggesting roles for ER modeling in axon maintenance and function.

Proteins in growth regulation during early development. Comprehensive three year report, 1974--1977

Energy Technology Data Exchange (ETDEWEB)

Klein, N.W.

1977-08-01

Progress is reported on the following research projects: response of embryo regions to nutrition; synthesis of serum proteins by the yolk-sac; serum protein synthesis in relation to protein nutrition, protease secretion, teratogenic agents, genetic abnormalities, yolk-sac cell cultures, and cell free systems; and effects of serum proteins on rat embryos, chick embryos without yolk-sacs, and isolated brains. (HLW)

A model in which heat shock protein 90 targets protein-folding clefts: rationale for a new approach to neuroprotective treatment of protein folding diseases.

Science.gov (United States)

Pratt, William B; Morishima, Yoshihiro; Gestwicki, Jason E; Lieberman, Andrew P; Osawa, Yoichi

2014-11-01

In an EBM Minireview published in 2010, we proposed that the heat shock protein (Hsp)90/Hsp70-based chaperone machinery played a major role in determining the selection of proteins that have undergone oxidative or other toxic damage for ubiquitination and proteasomal degradation. The proposal was based on a model in which the Hsp90 chaperone machinery regulates signaling by modulating ligand-binding clefts. The model provides a framework for thinking about the development of neuroprotective therapies for protein-folding diseases like Alzheimer's disease (AD), Parkinson's disease (PD), and the polyglutamine expansion disorders, such as Huntington's disease (HD) and spinal and bulbar muscular atrophy (SBMA). Major aberrant proteins that misfold and accumulate in these diseases are "client" proteins of the abundant and ubiquitous stress chaperone Hsp90. These Hsp90 client proteins include tau (AD), α-synuclein (PD), huntingtin (HD), and the expanded glutamine androgen receptor (polyQ AR) (SBMA). In this Minireview, we update our model in which Hsp90 acts on protein-folding clefts and show how it forms a rational basis for developing drugs that promote the targeted elimination of these aberrant proteins. © 2014 by the Society for Experimental Biology and Medicine.

Models of protein and amino acid requirements for cattle

Directory of Open Access Journals (Sweden)

Luis Orlindo Tedeschi

2015-03-01

Full Text Available Protein supply and requirements by ruminants have been studied for more than a century. These studies led to the accumulation of lots of scientific information about digestion and metabolism of protein by ruminants as well as the characterization of the dietary protein in order to maximize animal performance. During the 1980s and 1990s, when computers became more accessible and powerful, scientists began to conceptualize and develop mathematical nutrition models, and to program them into computers to assist with ration balancing and formulation for domesticated ruminants, specifically dairy and beef cattle. The most commonly known nutrition models developed during this period were the National Research Council (NRC in the United States, Agricultural Research Council (ARC in the United Kingdom, Institut National de la Recherche Agronomique (INRA in France, and the Commonwealth Scientific and Industrial Research Organization (CSIRO in Australia. Others were derivative works from these models with different degrees of modifications in the supply or requirement calculations, and the modeling nature (e.g., static or dynamic, mechanistic, or deterministic. Circa 1990s, most models adopted the metabolizable protein (MP system over the crude protein (CP and digestible CP systems to estimate supply of MP and the factorial system to calculate MP required by the animal. The MP system included two portions of protein (i.e., the rumen-undegraded dietary CP - RUP - and the contributions of microbial CP - MCP as the main sources of MP for the animal. Some models would explicitly account for the impact of dry matter intake (DMI on the MP required for maintenance (MPm; e.g., Cornell Net Carbohydrate and Protein System - CNCPS, the Dutch system - DVE/OEB, while others would simply account for scurf, urinary, metabolic fecal, and endogenous contributions independently of DMI. All models included milk yield and its components in estimating MP required for lactation

A resource for benchmarking the usefulness of protein structure models.

Science.gov (United States)

Carbajo, Daniel; Tramontano, Anna

2012-08-02

Increasingly, biologists and biochemists use computational tools to design experiments to probe the function of proteins and/or to engineer them for a variety of different purposes. The most effective strategies rely on the knowledge of the three-dimensional structure of the protein of interest. However it is often the case that an experimental structure is not available and that models of different quality are used instead. On the other hand, the relationship between the quality of a model and its appropriate use is not easy to derive in general, and so far it has been analyzed in detail only for specific application. This paper describes a database and related software tools that allow testing of a given structure based method on models of a protein representing different levels of accuracy. The comparison of the results of a computational experiment on the experimental structure and on a set of its decoy models will allow developers and users to assess which is the specific threshold of accuracy required to perform the task effectively. The ModelDB server automatically builds decoy models of different accuracy for a given protein of known structure and provides a set of useful tools for their analysis. Pre-computed data for a non-redundant set of deposited protein structures are available for analysis and download in the ModelDB database. IMPLEMENTATION, AVAILABILITY AND REQUIREMENTS: Project name: A resource for benchmarking the usefulness of protein structure models. Project home page: http://bl210.caspur.it/MODEL-DB/MODEL-DB_web/MODindex.php.Operating system(s): Platform independent. Programming language: Perl-BioPerl (program); mySQL, Perl DBI and DBD modules (database); php, JavaScript, Jmol scripting (web server). Other requirements: Java Runtime Environment v1.4 or later, Perl, BioPerl, CPAN modules, HHsearch, Modeller, LGA, NCBI Blast package, DSSP, Speedfill (Surfnet) and PSAIA. License: Free. Any restrictions to use by non-academics: No.

A resource for benchmarking the usefulness of protein structure models

Directory of Open Access Journals (Sweden)

Carbajo Daniel

2012-08-01

Full Text Available Abstract Background Increasingly, biologists and biochemists use computational tools to design experiments to probe the function of proteins and/or to engineer them for a variety of different purposes. The most effective strategies rely on the knowledge of the three-dimensional structure of the protein of interest. However it is often the case that an experimental structure is not available and that models of different quality are used instead. On the other hand, the relationship between the quality of a model and its appropriate use is not easy to derive in general, and so far it has been analyzed in detail only for specific application. Results This paper describes a database and related software tools that allow testing of a given structure based method on models of a protein representing different levels of accuracy. The comparison of the results of a computational experiment on the experimental structure and on a set of its decoy models will allow developers and users to assess which is the specific threshold of accuracy required to perform the task effectively. Conclusions The ModelDB server automatically builds decoy models of different accuracy for a given protein of known structure and provides a set of useful tools for their analysis. Pre-computed data for a non-redundant set of deposited protein structures are available for analysis and download in the ModelDB database. Implementation, availability and requirements Project name: A resource for benchmarking the usefulness of protein structure models. Project home page: http://bl210.caspur.it/MODEL-DB/MODEL-DB_web/MODindex.php. Operating system(s: Platform independent. Programming language: Perl-BioPerl (program; mySQL, Perl DBI and DBD modules (database; php, JavaScript, Jmol scripting (web server. Other requirements: Java Runtime Environment v1.4 or later, Perl, BioPerl, CPAN modules, HHsearch, Modeller, LGA, NCBI Blast package, DSSP, Speedfill (Surfnet and PSAIA. License: Free. Any

A resource for benchmarking the usefulness of protein structure models.

KAUST Repository

Carbajo, Daniel

2012-08-02

BACKGROUND: Increasingly, biologists and biochemists use computational tools to design experiments to probe the function of proteins and/or to engineer them for a variety of different purposes. The most effective strategies rely on the knowledge of the three-dimensional structure of the protein of interest. However it is often the case that an experimental structure is not available and that models of different quality are used instead. On the other hand, the relationship between the quality of a model and its appropriate use is not easy to derive in general, and so far it has been analyzed in detail only for specific application. RESULTS: This paper describes a database and related software tools that allow testing of a given structure based method on models of a protein representing different levels of accuracy. The comparison of the results of a computational experiment on the experimental structure and on a set of its decoy models will allow developers and users to assess which is the specific threshold of accuracy required to perform the task effectively. CONCLUSIONS: The ModelDB server automatically builds decoy models of different accuracy for a given protein of known structure and provides a set of useful tools for their analysis. Pre-computed data for a non-redundant set of deposited protein structures are available for analysis and download in the ModelDB database. IMPLEMENTATION, AVAILABILITY AND REQUIREMENTS: Project name: A resource for benchmarking the usefulness of protein structure models. Project home page: http://bl210.caspur.it/MODEL-DB/MODEL-DB_web/MODindex.php.Operating system(s): Platform independent. Programming language: Perl-BioPerl (program); mySQL, Perl DBI and DBD modules (database); php, JavaScript, Jmol scripting (web server). Other requirements: Java Runtime Environment v1.4 or later, Perl, BioPerl, CPAN modules, HHsearch, Modeller, LGA, NCBI Blast package, DSSP, Speedfill (Surfnet) and PSAIA. License: Free. Any restrictions to use by

A resource for benchmarking the usefulness of protein structure models.

KAUST Repository

Carbajo, Daniel; Tramontano, Anna

2012-01-01

BACKGROUND: Increasingly, biologists and biochemists use computational tools to design experiments to probe the function of proteins and/or to engineer them for a variety of different purposes. The most effective strategies rely on the knowledge of the three-dimensional structure of the protein of interest. However it is often the case that an experimental structure is not available and that models of different quality are used instead. On the other hand, the relationship between the quality of a model and its appropriate use is not easy to derive in general, and so far it has been analyzed in detail only for specific application. RESULTS: This paper describes a database and related software tools that allow testing of a given structure based method on models of a protein representing different levels of accuracy. The comparison of the results of a computational experiment on the experimental structure and on a set of its decoy models will allow developers and users to assess which is the specific threshold of accuracy required to perform the task effectively. CONCLUSIONS: The ModelDB server automatically builds decoy models of different accuracy for a given protein of known structure and provides a set of useful tools for their analysis. Pre-computed data for a non-redundant set of deposited protein structures are available for analysis and download in the ModelDB database. IMPLEMENTATION, AVAILABILITY AND REQUIREMENTS: Project name: A resource for benchmarking the usefulness of protein structure models. Project home page: http://bl210.caspur.it/MODEL-DB/MODEL-DB_web/MODindex.php.Operating system(s): Platform independent. Programming language: Perl-BioPerl (program); mySQL, Perl DBI and DBD modules (database); php, JavaScript, Jmol scripting (web server). Other requirements: Java Runtime Environment v1.4 or later, Perl, BioPerl, CPAN modules, HHsearch, Modeller, LGA, NCBI Blast package, DSSP, Speedfill (Surfnet) and PSAIA. License: Free. Any restrictions to use by

Preclinical models used for immunogenicity prediction of therapeutic proteins.

Science.gov (United States)

Brinks, Vera; Weinbuch, Daniel; Baker, Matthew; Dean, Yann; Stas, Philippe; Kostense, Stefan; Rup, Bonita; Jiskoot, Wim

2013-07-01

All therapeutic proteins are potentially immunogenic. Antibodies formed against these drugs can decrease efficacy, leading to drastically increased therapeutic costs and in rare cases to serious and sometimes life threatening side-effects. Many efforts are therefore undertaken to develop therapeutic proteins with minimal immunogenicity. For this, immunogenicity prediction of candidate drugs during early drug development is essential. Several in silico, in vitro and in vivo models are used to predict immunogenicity of drug leads, to modify potentially immunogenic properties and to continue development of drug candidates with expected low immunogenicity. Despite the extensive use of these predictive models, their actual predictive value varies. Important reasons for this uncertainty are the limited/insufficient knowledge on the immune mechanisms underlying immunogenicity of therapeutic proteins, the fact that different predictive models explore different components of the immune system and the lack of an integrated clinical validation. In this review, we discuss the predictive models in use, summarize aspects of immunogenicity that these models predict and explore the merits and the limitations of each of the models.

Luciferase NanoLuc as a reporter for gene expression and protein levels in Saccharomyces cerevisiae.

Science.gov (United States)

Masser, Anna E; Kandasamy, Ganapathi; Kaimal, Jayasankar Mohanakrishnan; Andréasson, Claes

2016-05-01

Reporter proteins are essential tools in the study of biological processes and are employed to monitor changes in gene expression and protein levels. Luciferases are reporter proteins that enable rapid and highly sensitive detection with an outstanding dynamic range. Here we evaluated the usefulness of the 19 kDa luciferase NanoLuc (Nluc), derived from the deep sea shrimp Oplophorus gracilirostris, as a reporter protein in yeast. Cassettes with codon-optimized genes expressing yeast Nluc (yNluc) or its destabilized derivative yNlucPEST have been assembled in the context of the dominant drug resistance marker kanMX. The reporter proteins do not impair the growth of yeast cells and exhibit half-lives of 40 and 5 min, respectively. The commercial substrate Nano-Glo® is compatible with detection of yNluc bioluminescence in yeast using standard commercial substrate. © 2016 The Authors. Yeast published by John Wiley & Sons Ltd. © 2016 The Authors. Yeast published by John Wiley & Sons Ltd.

Protein structure analysis using the resonant recognition model and wavelet transforms

International Nuclear Information System (INIS)

Fang, Q.; Cosic, I.

1998-01-01

An approach based on the resonant recognition model and the discrete wavelet transform is introduced here for characterising proteins' biological function. The protein sequence is converted into a numerical series by assigning the electron-ion interaction potential to each amino acid from N-terminal to C-terminal. A set of peaks is found after performing a wavelet transform onto a numerical series representing a group of homologous proteins. These peaks are related to protein structural and functional properties and named characteristic vector of that protein group. Further more, the amino acids contributing mostly to a protein's biological functions, the so-called 'hot spots' amino acids, are predicted by the continuous wavelet transform. It is found that the hot spots are clustered around the protein's cleft structure. The wavelets approach provides a novel methods for amino acid sequence analysis as well as an expansion for the newly established macromolecular interaction model: the resonant recognition model. Copyright (1998) Australasian Physical and Engineering Sciences in Medicine

A generative, probabilistic model of local protein structure

DEFF Research Database (Denmark)

Boomsma, Wouter; Mardia, Kanti V.; Taylor, Charles C.

2008-01-01

Despite significant progress in recent years, protein structure prediction maintains its status as one of the prime unsolved problems in computational biology. One of the key remaining challenges is an efficient probabilistic exploration of the structural space that correctly reflects the relative...... conformational stabilities. Here, we present a fully probabilistic, continuous model of local protein structure in atomic detail. The generative model makes efficient conformational sampling possible and provides a framework for the rigorous analysis of local sequence-structure correlations in the native state...

Compare local pocket and global protein structure models by small structure patterns

KAUST Repository

Cui, Xuefeng

2015-09-09

Researchers proposed several criteria to assess the quality of predicted protein structures because it is one of the essential tasks in the Critical Assessment of Techniques for Protein Structure Prediction (CASP) competitions. Popular criteria include root mean squared deviation (RMSD), MaxSub score, TM-score, GDT-TS and GDT-HA scores. All these criteria require calculation of rigid transformations to superimpose the the predicted protein structure to the native protein structure. Yet, how to obtain the rigid transformations is unknown or with high time complexity, and, hence, heuristic algorithms were proposed. In this work, we carefully design various small structure patterns, including the ones specifically tuned for local pockets. Such structure patterns are biologically meaningful, and address the issue of relying on a sufficient number of backbone residue fragments for existing methods. We sample the rigid transformations from these small structure patterns; and the optimal superpositions yield by these small structures are refined and reported. As a result, among 11; 669 pairs of predicted and native local protein pocket models from the CASP10 dataset, the GDT-TS scores calculated by our method are significantly higher than those calculated by LGA. Moreover, our program is computationally much more efficient. Source codes and executables are publicly available at http://www.cbrc.kaust.edu.sa/prosta/

Modeling disordered regions in proteins using Rosetta.

Directory of Open Access Journals (Sweden)

Ray Yu-Ruei Wang

Full Text Available Protein structure prediction methods such as Rosetta search for the lowest energy conformation of the polypeptide chain. However, the experimentally observed native state is at a minimum of the free energy, rather than the energy. The neglect of the missing configurational entropy contribution to the free energy can be partially justified by the assumption that the entropies of alternative folded states, while very much less than unfolded states, are not too different from one another, and hence can be to a first approximation neglected when searching for the lowest free energy state. The shortcomings of current structure prediction methods may be due in part to the breakdown of this assumption. Particularly problematic are proteins with significant disordered regions which do not populate single low energy conformations even in the native state. We describe two approaches within the Rosetta structure modeling methodology for treating such regions. The first does not require advance knowledge of the regions likely to be disordered; instead these are identified by minimizing a simple free energy function used previously to model protein folding landscapes and transition states. In this model, residues can be either completely ordered or completely disordered; they are considered disordered if the gain in entropy outweighs the loss of favorable energetic interactions with the rest of the protein chain. The second approach requires identification in advance of the disordered regions either from sequence alone using for example the DISOPRED server or from experimental data such as NMR chemical shifts. During Rosetta structure prediction calculations the disordered regions make only unfavorable repulsive contributions to the total energy. We find that the second approach has greater practical utility and illustrate this with examples from de novo structure prediction, NMR structure calculation, and comparative modeling.

Conformational Sampling in Template-Free Protein Loop Structure Modeling: An Overview

OpenAIRE

Li, Yaohang

2013-01-01

Accurately modeling protein loops is an important step to predict three-dimensional structures as well as to understand functions of many proteins. Because of their high flexibility, modeling the three-dimensional structures of loops is difficult and is usually treated as a “mini protein folding problem” under geometric constraints. In the past decade, there has been remarkable progress in template-free loop structure modeling due to advances of computational methods as well as stably increas...

Protein Structure Classification and Loop Modeling Using Multiple Ramachandran Distributions

KAUST Repository

Najibi, Seyed Morteza; Maadooliat, Mehdi; Zhou, Lan; Huang, Jianhua Z.; Gao, Xin

2017-01-01

Recently, the study of protein structures using angular representations has attracted much attention among structural biologists. The main challenge is how to efficiently model the continuous conformational space of the protein structures based on the differences and similarities between different Ramachandran plots. Despite the presence of statistical methods for modeling angular data of proteins, there is still a substantial need for more sophisticated and faster statistical tools to model the large-scale circular datasets. To address this need, we have developed a nonparametric method for collective estimation of multiple bivariate density functions for a collection of populations of protein backbone angles. The proposed method takes into account the circular nature of the angular data using trigonometric spline which is more efficient compared to existing methods. This collective density estimation approach is widely applicable when there is a need to estimate multiple density functions from different populations with common features. Moreover, the coefficients of adaptive basis expansion for the fitted densities provide a low-dimensional representation that is useful for visualization, clustering, and classification of the densities. The proposed method provides a novel and unique perspective to two important and challenging problems in protein structure research: structure-based protein classification and angular-sampling-based protein loop structure prediction.

Protein Structure Classification and Loop Modeling Using Multiple Ramachandran Distributions

KAUST Repository

Najibi, Seyed Morteza

2017-02-08

Recently, the study of protein structures using angular representations has attracted much attention among structural biologists. The main challenge is how to efficiently model the continuous conformational space of the protein structures based on the differences and similarities between different Ramachandran plots. Despite the presence of statistical methods for modeling angular data of proteins, there is still a substantial need for more sophisticated and faster statistical tools to model the large-scale circular datasets. To address this need, we have developed a nonparametric method for collective estimation of multiple bivariate density functions for a collection of populations of protein backbone angles. The proposed method takes into account the circular nature of the angular data using trigonometric spline which is more efficient compared to existing methods. This collective density estimation approach is widely applicable when there is a need to estimate multiple density functions from different populations with common features. Moreover, the coefficients of adaptive basis expansion for the fitted densities provide a low-dimensional representation that is useful for visualization, clustering, and classification of the densities. The proposed method provides a novel and unique perspective to two important and challenging problems in protein structure research: structure-based protein classification and angular-sampling-based protein loop structure prediction.

Fusions between green fluorescent protein and beta-glucuronidase as sensitive and vital bifunctional reporters in plants.

Science.gov (United States)

Quaedvlieg, N E; Schlaman, H R; Admiraal, P C; Wijting, S E; Stougaard, J; Spaink, H P

1998-11-01

By fusing the genes encoding green fluorescent protein (GFP) and beta-glucuronidase (GUS) we have created a set of bifunctional reporter constructs which are optimized for use in transient and stable expression studies in plants. This approach makes it possible to combine the advantage of GUS, its high sensitivity in histochemical staining, with the advantages of GFP as a vital marker. The fusion proteins were functional in transient expression studies in tobacco using either DNA bombardment or potato virus X as a vector, and in stably transformed Arabidopsis thaliana and Lotus japonicus plants. The results show that high level of expression does not interfere with efficient stable transformation in A. thaliana and L. japonicus. Using confocal laser scanning microscopy we show that the fusion constructs are very suitable for promoter expression studies in all organs of living plants, including root nodules. The use of these reporter constructs in the model legume L. japonicus offers exciting new possibilities for the study of the root nodulation process.

Computational methods for constructing protein structure models from 3D electron microscopy maps.

Science.gov (United States)

Esquivel-Rodríguez, Juan; Kihara, Daisuke

2013-10-01

Protein structure determination by cryo-electron microscopy (EM) has made significant progress in the past decades. Resolutions of EM maps have been improving as evidenced by recently reported structures that are solved at high resolutions close to 3Å. Computational methods play a key role in interpreting EM data. Among many computational procedures applied to an EM map to obtain protein structure information, in this article we focus on reviewing computational methods that model protein three-dimensional (3D) structures from a 3D EM density map that is constructed from two-dimensional (2D) maps. The computational methods we discuss range from de novo methods, which identify structural elements in an EM map, to structure fitting methods, where known high resolution structures are fit into a low-resolution EM map. A list of available computational tools is also provided. Copyright © 2013 Elsevier Inc. All rights reserved.

Model of a DNA-protein complex of the architectural monomeric protein MC1 from Euryarchaea.

Directory of Open Access Journals (Sweden)

Françoise Paquet

Full Text Available In Archaea the two major modes of DNA packaging are wrapping by histone proteins or bending by architectural non-histone proteins. To supplement our knowledge about the binding mode of the different DNA-bending proteins observed across the three domains of life, we present here the first model of a complex in which the monomeric Methanogen Chromosomal protein 1 (MC1 from Euryarchaea binds to the concave side of a strongly bent DNA. In laboratory growth conditions MC1 is the most abundant architectural protein present in Methanosarcina thermophila CHTI55. Like most proteins that strongly bend DNA, MC1 is known to bind in the minor groove. Interaction areas for MC1 and DNA were mapped by Nuclear Magnetic Resonance (NMR data. The polarity of protein binding was determined using paramagnetic probes attached to the DNA. The first structural model of the DNA-MC1 complex we propose here was obtained by two complementary docking approaches and is in good agreement with the experimental data previously provided by electron microscopy and biochemistry. Residues essential to DNA-binding and -bending were highlighted and confirmed by site-directed mutagenesis. It was found that the Arg25 side-chain was essential to neutralize the negative charge of two phosphates that come very close in response to a dramatic curvature of the DNA.

Protein homology model refinement by large-scale energy optimization.

Science.gov (United States)

Park, Hahnbeom; Ovchinnikov, Sergey; Kim, David E; DiMaio, Frank; Baker, David

2018-03-20

Proteins fold to their lowest free-energy structures, and hence the most straightforward way to increase the accuracy of a partially incorrect protein structure model is to search for the lowest-energy nearby structure. This direct approach has met with little success for two reasons: first, energy function inaccuracies can lead to false energy minima, resulting in model degradation rather than improvement; and second, even with an accurate energy function, the search problem is formidable because the energy only drops considerably in the immediate vicinity of the global minimum, and there are a very large number of degrees of freedom. Here we describe a large-scale energy optimization-based refinement method that incorporates advances in both search and energy function accuracy that can substantially improve the accuracy of low-resolution homology models. The method refined low-resolution homology models into correct folds for 50 of 84 diverse protein families and generated improved models in recent blind structure prediction experiments. Analyses of the basis for these improvements reveal contributions from both the improvements in conformational sampling techniques and the energy function.

The research methods and model of protein turnover in animal

International Nuclear Information System (INIS)

Wu Xilin; Yang Feng

2002-01-01

The author discussed the concept and research methods of protein turnover in animal body. The existing problems and the research results of animal protein turnover in recent years were presented. Meanwhile, the measures to improve the models of animal protein turnover were analyzed

A simple quantitative model of macromolecular crowding effects on protein folding: Application to the murine prion protein(121-231)

Science.gov (United States)

Bergasa-Caceres, Fernando; Rabitz, Herschel A.

2013-06-01

A model of protein folding kinetics is applied to study the effects of macromolecular crowding on protein folding rate and stability. Macromolecular crowding is found to promote a decrease of the entropic cost of folding of proteins that produces an increase of both the stability and the folding rate. The acceleration of the folding rate due to macromolecular crowding is shown to be a topology-dependent effect. The model is applied to the folding dynamics of the murine prion protein (121-231). The differential effect of macromolecular crowding as a function of protein topology suffices to make non-native configurations relatively more accessible.

Possibilities of microscopic detection of isolated porcine proteins in model meat products

Directory of Open Access Journals (Sweden)

Michaela Petrášová

2016-05-01

Full Text Available In recent years, various protein additives intended for manufacture of meat products have increasing importance in the food industry. These ingredients include both, plant-origin as well as animal-origin proteins. Among animal proteins, blood plasma, milk protein or collagen are used most commonly. Collagen is obtained from pork, beef, and poultry or fish skin. Collagen does not contain all the essential amino acids, thus it is not a full protein in terms of essential amino acids supply for one's organism. However, it is rather rich in amino acids of glycine, hydroxyproline and proline which are almost absent in other proteins and their synthesis is very energy intensive. Collagen, which is added to the soft and small meat products in the form of isolated porcine protein, significantly affects the organoleptic properties of these products. This work focused on detection of isolated porcine protein in model meat products where detection of isolated porcine protein was verified by histological staining and light microscopy. Seven model meat products from poultry meat and 7 model meat products from beef and pork in the ratio of 1:1, which contained 2.5% concentration of various commercially produced isolated porcine proteins, were examined. These model meat products were histologically processed by means of cryosections and stained with hematoxylin-eosin staining, toluidine blue staining and Calleja. For the validation phase, Calleja was utilized. To determine the sensitivity and specificity, five model meat products containing the addition of isolated porcine protein and five model meat products free of it were used. The sensitivity was determined for isolated porcine protein at 1.00 and specificity was determined at 1.00. The detection limit of the method was at the level of 0.001% addition. Repeatability of the method was carried out using products with addition as well as without addition of isolated porcine protein and detection was repeated

A linear programming model for protein inference problem in shotgun proteomics.

Science.gov (United States)

Huang, Ting; He, Zengyou

2012-11-15

Assembling peptides identified from tandem mass spectra into a list of proteins, referred to as protein inference, is an important issue in shotgun proteomics. The objective of protein inference is to find a subset of proteins that are truly present in the sample. Although many methods have been proposed for protein inference, several issues such as peptide degeneracy still remain unsolved. In this article, we present a linear programming model for protein inference. In this model, we use a transformation of the joint probability that each peptide/protein pair is present in the sample as the variable. Then, both the peptide probability and protein probability can be expressed as a formula in terms of the linear combination of these variables. Based on this simple fact, the protein inference problem is formulated as an optimization problem: minimize the number of proteins with non-zero probabilities under the constraint that the difference between the calculated peptide probability and the peptide probability generated from peptide identification algorithms should be less than some threshold. This model addresses the peptide degeneracy issue by forcing some joint probability variables involving degenerate peptides to be zero in a rigorous manner. The corresponding inference algorithm is named as ProteinLP. We test the performance of ProteinLP on six datasets. Experimental results show that our method is competitive with the state-of-the-art protein inference algorithms. The source code of our algorithm is available at: https://sourceforge.net/projects/prolp/. zyhe@dlut.edu.cn. Supplementary data are available at Bioinformatics Online.

Development of a Model Protein Interaction Pair as a Benchmarking Tool for the Quantitative Analysis of 2-Site Protein-Protein Interactions.

Science.gov (United States)

Yamniuk, Aaron P; Newitt, John A; Doyle, Michael L; Arisaka, Fumio; Giannetti, Anthony M; Hensley, Preston; Myszka, David G; Schwarz, Fred P; Thomson, James A; Eisenstein, Edward

2015-12-01

A significant challenge in the molecular interaction field is to accurately determine the stoichiometry and stepwise binding affinity constants for macromolecules having >1 binding site. The mission of the Molecular Interactions Research Group (MIRG) of the Association of Biomolecular Resource Facilities (ABRF) is to show how biophysical technologies are used to quantitatively characterize molecular interactions, and to educate the ABRF members and scientific community on the utility and limitations of core technologies [such as biosensor, microcalorimetry, or analytic ultracentrifugation (AUC)]. In the present work, the MIRG has developed a robust model protein interaction pair consisting of a bivalent variant of the Bacillus amyloliquefaciens extracellular RNase barnase and a variant of its natural monovalent intracellular inhibitor protein barstar. It is demonstrated that this system can serve as a benchmarking tool for the quantitative analysis of 2-site protein-protein interactions. The protein interaction pair enables determination of precise binding constants for the barstar protein binding to 2 distinct sites on the bivalent barnase binding partner (termed binase), where the 2 binding sites were engineered to possess affinities that differed by 2 orders of magnitude. Multiple MIRG laboratories characterized the interaction using isothermal titration calorimetry (ITC), AUC, and surface plasmon resonance (SPR) methods to evaluate the feasibility of the system as a benchmarking model. Although general agreement was seen for the binding constants measured using solution-based ITC and AUC approaches, weaker affinity was seen for surface-based method SPR, with protein immobilization likely affecting affinity. An analysis of the results from multiple MIRG laboratories suggests that the bivalent barnase-barstar system is a suitable model for benchmarking new approaches for the quantitative characterization of complex biomolecular interactions.

Identify High-Quality Protein Structural Models by Enhanced K-Means.

Science.gov (United States)

Wu, Hongjie; Li, Haiou; Jiang, Min; Chen, Cheng; Lv, Qiang; Wu, Chuang

2017-01-01

Background. One critical issue in protein three-dimensional structure prediction using either ab initio or comparative modeling involves identification of high-quality protein structural models from generated decoys. Currently, clustering algorithms are widely used to identify near-native models; however, their performance is dependent upon different conformational decoys, and, for some algorithms, the accuracy declines when the decoy population increases. Results. Here, we proposed two enhanced K -means clustering algorithms capable of robustly identifying high-quality protein structural models. The first one employs the clustering algorithm SPICKER to determine the initial centroids for basic K -means clustering ( SK -means), whereas the other employs squared distance to optimize the initial centroids ( K -means++). Our results showed that SK -means and K -means++ were more robust as compared with SPICKER alone, detecting 33 (59%) and 42 (75%) of 56 targets, respectively, with template modeling scores better than or equal to those of SPICKER. Conclusions. We observed that the classic K -means algorithm showed a similar performance to that of SPICKER, which is a widely used algorithm for protein-structure identification. Both SK -means and K -means++ demonstrated substantial improvements relative to results from SPICKER and classical K -means.

A Novel Reporter Rat Strain That Conditionally Expresses the Bright Red Fluorescent Protein tdTomato.

Directory of Open Access Journals (Sweden)

Hiroyuki Igarashi

Full Text Available Despite the strength of the Cre/loxP recombination system in animal models, its application in rats trails that in mice because of the lack of relevant reporter strains. Here, we generated a floxed STOP tdTomato rat that conditionally expresses a red fluorescent protein variant (tdTomato in the presence of exogenous Cre recombinase. The tdTomato signal vividly visualizes neurons including their projection fibers and spines without any histological enhancement. In addition, a transgenic rat line (FLAME that ubiquitously expresses tdTomato was successfully established by injecting intracytoplasmic Cre mRNA into fertilized ova. Our rat reporter system will facilitate connectome studies as well as the visualization of the fine structures of genetically identified cells for long periods both in vivo and ex vivo. Furthermore, FLAME is an ideal model for organ transplantation research owing to improved traceability of cells/tissues.

NASCENT: an automatic protein interaction network generation tool for non-model organisms.

Science.gov (United States)

Banky, Daniel; Ordog, Rafael; Grolmusz, Vince

2009-04-24

Large quantity of reliable protein interaction data are available for model organisms in public depositories (e.g., MINT, DIP, HPRD, INTERACT). Most data correspond to experiments with the proteins of Saccharomyces cerevisiae, Drosophila melanogaster, Homo sapiens, Caenorhabditis elegans, Escherichia coli and Mus musculus. For other important organisms the data availability is poor or non-existent. Here we present NASCENT, a completely automatic web-based tool and also a downloadable Java program, capable of modeling and generating protein interaction networks even for non-model organisms. The tool performs protein interaction network modeling through gene-name mapping, and outputs the resulting network in graphical form and also in computer-readable graph-forms, directly applicable by popular network modeling software. http://nascent.pitgroup.org.

DeepQA: Improving the estimation of single protein model quality with deep belief networks

OpenAIRE

Cao, Renzhi; Bhattacharya, Debswapna; Hou, Jie; Cheng, Jianlin

2016-01-01

Background Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem. Results We introduce a novel single-model quality assessment method DeepQA based on deep belie...

A semi-nonparametric mixture model for selecting functionally consistent proteins.

Science.gov (United States)

Yu, Lianbo; Doerge, Rw

2010-09-28

High-throughput technologies have led to a new era of proteomics. Although protein microarray experiments are becoming more common place there are a variety of experimental and statistical issues that have yet to be addressed, and that will carry over to new high-throughput technologies unless they are investigated. One of the largest of these challenges is the selection of functionally consistent proteins. We present a novel semi-nonparametric mixture model for classifying proteins as consistent or inconsistent while controlling the false discovery rate and the false non-discovery rate. The performance of the proposed approach is compared to current methods via simulation under a variety of experimental conditions. We provide a statistical method for selecting functionally consistent proteins in the context of protein microarray experiments, but the proposed semi-nonparametric mixture model method can certainly be generalized to solve other mixture data problems. The main advantage of this approach is that it provides the posterior probability of consistency for each protein.

Fast Proton Titration Scheme for Multiscale Modeling of Protein Solutions.

Science.gov (United States)

Teixeira, Andre Azevedo Reis; Lund, Mikael; da Silva, Fernando Luís Barroso

2010-10-12

Proton exchange between titratable amino acid residues and the surrounding solution gives rise to exciting electric processes in proteins. We present a proton titration scheme for studying acid-base equilibria in Metropolis Monte Carlo simulations where salt is treated at the Debye-Hückel level. The method, rooted in the Kirkwood model of impenetrable spheres, is applied on the three milk proteins α-lactalbumin, β-lactoglobulin, and lactoferrin, for which we investigate the net-charge, molecular dipole moment, and charge capacitance. Over a wide range of pH and salt conditions, excellent agreement is found with more elaborate simulations where salt is explicitly included. The implicit salt scheme is orders of magnitude faster than the explicit analog and allows for transparent interpretation of physical mechanisms. It is shown how the method can be expanded to multiscale modeling of aqueous salt solutions of many biomolecules with nonstatic charge distributions. Important examples are protein-protein aggregation, protein-polyelectrolyte complexation, and protein-membrane association.

Genome-scale modeling of the protein secretory machinery in yeast

DEFF Research Database (Denmark)

Feizi, Amir; Österlund, Tobias; Petranovic, Dina

2013-01-01

The protein secretory machinery in Eukarya is involved in post-translational modification (PTMs) and sorting of the secretory and many transmembrane proteins. While the secretory machinery has been well-studied using classic reductionist approaches, a holistic view of its complex nature is lacking....... Here, we present the first genome-scale model for the yeast secretory machinery which captures the knowledge generated through more than 50 years of research. The model is based on the concept of a Protein Specific Information Matrix (PSIM: characterized by seven PTMs features). An algorithm...

The Phyre2 web portal for protein modeling, prediction and analysis.

Science.gov (United States)

Kelley, Lawrence A; Mezulis, Stefans; Yates, Christopher M; Wass, Mark N; Sternberg, Michael J E

2015-06-01

Phyre2 is a suite of tools available on the web to predict and analyze protein structure, function and mutations. The focus of Phyre2 is to provide biologists with a simple and intuitive interface to state-of-the-art protein bioinformatics tools. Phyre2 replaces Phyre, the original version of the server for which we previously published a paper in Nature Protocols. In this updated protocol, we describe Phyre2, which uses advanced remote homology detection methods to build 3D models, predict ligand binding sites and analyze the effect of amino acid variants (e.g., nonsynonymous SNPs (nsSNPs)) for a user's protein sequence. Users are guided through results by a simple interface at a level of detail they determine. This protocol will guide users from submitting a protein sequence to interpreting the secondary and tertiary structure of their models, their domain composition and model quality. A range of additional available tools is described to find a protein structure in a genome, to submit large number of sequences at once and to automatically run weekly searches for proteins that are difficult to model. The server is available at http://www.sbg.bio.ic.ac.uk/phyre2. A typical structure prediction will be returned between 30 min and 2 h after submission.

A transgenic Xenopus laevis reporter model to study lymphangiogenesis

Directory of Open Access Journals (Sweden)

Annelii Ny

2013-07-01

The importance of the blood- and lymph vessels in the transport of essential fluids, gases, macromolecules and cells in vertebrates warrants optimal insight into the regulatory mechanisms underlying their development. Mouse and zebrafish models of lymphatic development are instrumental for gene discovery and gene characterization but are challenging for certain aspects, e.g. no direct accessibility of embryonic stages, or non-straightforward visualization of early lymphatic sprouting, respectively. We previously demonstrated that the Xenopus tadpole is a valuable model to study the processes of lymphatic development. However, a fluorescent Xenopus reporter directly visualizing the lymph vessels was lacking. Here, we created transgenic Tg(Flk1:eGFP Xenopus laevis reporter lines expressing green fluorescent protein (GFP in blood- and lymph vessels driven by the Flk1 (VEGFR-2 promoter. We also established a high-resolution fluorescent dye labeling technique selectively and persistently visualizing lymphatic endothelial cells, even in conditions of impaired lymph vessel formation or drainage function upon silencing of lymphangiogenic factors. Next, we applied the model to dynamically document blood and lymphatic sprouting and patterning of the initially avascular tadpole fin. Furthermore, quantifiable models of spontaneous or induced lymphatic sprouting into the tadpole fin were developed for dynamic analysis of loss-of-function and gain-of-function phenotypes using pharmacologic or genetic manipulation. Together with angiography and lymphangiography to assess functionality, Tg(Flk1:eGFP reporter tadpoles readily allowed detailed lymphatic phenotyping of live tadpoles by fluorescence microscopy. The Tg(Flk1:eGFP tadpoles represent a versatile model for functional lymph/angiogenomics and drug screening.

Automated de novo phasing and model building of coiled-coil proteins.

Science.gov (United States)

Rämisch, Sebastian; Lizatović, Robert; André, Ingemar

2015-03-01

Models generated by de novo structure prediction can be very useful starting points for molecular replacement for systems where suitable structural homologues cannot be readily identified. Protein-protein complexes and de novo-designed proteins are examples of systems that can be challenging to phase. In this study, the potential of de novo models of protein complexes for use as starting points for molecular replacement is investigated. The approach is demonstrated using homomeric coiled-coil proteins, which are excellent model systems for oligomeric systems. Despite the stereotypical fold of coiled coils, initial phase estimation can be difficult and many structures have to be solved with experimental phasing. A method was developed for automatic structure determination of homomeric coiled coils from X-ray diffraction data. In a benchmark set of 24 coiled coils, ranging from dimers to pentamers with resolutions down to 2.5 Å, 22 systems were automatically solved, 11 of which had previously been solved by experimental phasing. The generated models contained 71-103% of the residues present in the deposited structures, had the correct sequence and had free R values that deviated on average by 0.01 from those of the respective reference structures. The electron-density maps were of sufficient quality that only minor manual editing was necessary to produce final structures. The method, named CCsolve, combines methods for de novo structure prediction, initial phase estimation and automated model building into one pipeline. CCsolve is robust against errors in the initial models and can readily be modified to make use of alternative crystallographic software. The results demonstrate the feasibility of de novo phasing of protein-protein complexes, an approach that could also be employed for other small systems beyond coiled coils.

Validation of protein models by a neural network approach

Directory of Open Access Journals (Sweden)

Fantucci Piercarlo

2008-01-01

Full Text Available Abstract Background The development and improvement of reliable computational methods designed to evaluate the quality of protein models is relevant in the context of protein structure refinement, which has been recently identified as one of the bottlenecks limiting the quality and usefulness of protein structure prediction. Results In this contribution, we present a computational method (Artificial Intelligence Decoys Evaluator: AIDE which is able to consistently discriminate between correct and incorrect protein models. In particular, the method is based on neural networks that use as input 15 structural parameters, which include energy, solvent accessible surface, hydrophobic contacts and secondary structure content. The results obtained with AIDE on a set of decoy structures were evaluated using statistical indicators such as Pearson correlation coefficients, Znat, fraction enrichment, as well as ROC plots. It turned out that AIDE performances are comparable and often complementary to available state-of-the-art learning-based methods. Conclusion In light of the results obtained with AIDE, as well as its comparison with available learning-based methods, it can be concluded that AIDE can be successfully used to evaluate the quality of protein structures. The use of AIDE in combination with other evaluation tools is expected to further enhance protein refinement efforts.

Generic framework for mining cellular automata models on protein-folding simulations.

Science.gov (United States)

Diaz, N; Tischer, I

2016-05-13

Cellular automata model identification is an important way of building simplified simulation models. In this study, we describe a generic architectural framework to ease the development process of new metaheuristic-based algorithms for cellular automata model identification in protein-folding trajectories. Our framework was developed by a methodology based on design patterns that allow an improved experience for new algorithms development. The usefulness of the proposed framework is demonstrated by the implementation of four algorithms, able to obtain extremely precise cellular automata models of the protein-folding process with a protein contact map representation. Dynamic rules obtained by the proposed approach are discussed, and future use for the new tool is outlined.

DeepQA: improving the estimation of single protein model quality with deep belief networks.

Science.gov (United States)

Cao, Renzhi; Bhattacharya, Debswapna; Hou, Jie; Cheng, Jianlin

2016-12-05

Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem. We introduce a novel single-model quality assessment method DeepQA based on deep belief network that utilizes a number of selected features describing the quality of a model from different perspectives, such as energy, physio-chemical characteristics, and structural information. The deep belief network is trained on several large datasets consisting of models from the Critical Assessment of Protein Structure Prediction (CASP) experiments, several publicly available datasets, and models generated by our in-house ab initio method. Our experiments demonstrate that deep belief network has better performance compared to Support Vector Machines and Neural Networks on the protein model quality assessment problem, and our method DeepQA achieves the state-of-the-art performance on CASP11 dataset. It also outperformed two well-established methods in selecting good outlier models from a large set of models of mostly low quality generated by ab initio modeling methods. DeepQA is a useful deep learning tool for protein single model quality assessment and protein structure prediction. The source code, executable, document and training/test datasets of DeepQA for Linux is freely available to non-commercial users at http://cactus.rnet.missouri.edu/DeepQA/ .

@TOME-2: a new pipeline for comparative modeling of protein-ligand complexes.

Science.gov (United States)

Pons, Jean-Luc; Labesse, Gilles

2009-07-01

@TOME 2.0 is new web pipeline dedicated to protein structure modeling and small ligand docking based on comparative analyses. @TOME 2.0 allows fold recognition, template selection, structural alignment editing, structure comparisons, 3D-model building and evaluation. These tasks are routinely used in sequence analyses for structure prediction. In our pipeline the necessary software is efficiently interconnected in an original manner to accelerate all the processes. Furthermore, we have also connected comparative docking of small ligands that is performed using protein-protein superposition. The input is a simple protein sequence in one-letter code with no comment. The resulting 3D model, protein-ligand complexes and structural alignments can be visualized through dedicated Web interfaces or can be downloaded for further studies. These original features will aid in the functional annotation of proteins and the selection of templates for molecular modeling and virtual screening. Several examples are described to highlight some of the new functionalities provided by this pipeline. The server and its documentation are freely available at http://abcis.cbs.cnrs.fr/AT2/

A lock-and-key model for protein–protein interactions

OpenAIRE

Morrison, Julie L.; Breitling, Rainer; Higham, Desmond J.; Gilbert, David R.

2006-01-01

Motivation: Protein–protein interaction networks are one of the major post-genomic data sources available to molecular biologists. They provide a comprehensive view of the global interaction structure of an organism’s proteome, as well as detailed information on specific interactions. Here we suggest a physical model of protein interactions that can be used to extract additional information at an intermediate level: It enables us to identify proteins which share biological interaction motifs,...

Protein buffering in model systems and in whole human saliva.

Directory of Open Access Journals (Sweden)

Andreas Lamanda

Full Text Available The aim of this study was to quantify the buffer attributes (value, power, range and optimum of two model systems for whole human resting saliva, the purified proteins from whole human resting saliva and single proteins. Two model systems, the first containing amyloglucosidase and lysozyme, and the second containing amyloglucosidase and alpha-amylase, were shown to provide, in combination with hydrogencarbonate and di-hydrogenphosphate, almost identical buffer attributes as whole human resting saliva. It was further demonstrated that changes in the protein concentration as small as 0.1% may change the buffer value of a buffer solution up to 15 times. Additionally, it was shown that there was a protein concentration change in the same range (0.16% between saliva samples collected at the time periods of 13:00 and others collected at 9:00 am and 17:00. The mode of the protein expression changed between these samples corresponded to the change in basic buffer power and the change of the buffer value at pH 6.7. Finally, SDS Page and Ruthenium II tris (bathophenantroline disulfonate staining unveiled a constant protein expression in all samples except for one 50 kDa protein band. As the change in the expression pattern of that 50 kDa protein band corresponded to the change in basic buffer power and the buffer value at pH 6.7, it was reasonable to conclude that this 50 kDa protein band may contain the protein(s belonging to the protein buffer system of human saliva.

Analysis and Ranking of Protein-Protein Docking Models Using Inter-Residue Contacts and Inter-Molecular Contact Maps

KAUST Repository

Oliva, Romina; Chermak, Edrisse; Cavallo, Luigi

2015-01-01

In view of the increasing interest both in inhibitors of protein-protein interactions and in protein drugs themselves, analysis of the three-dimensional structure of protein-protein complexes is assuming greater relevance in drug design. In the many cases where an experimental structure is not available, protein-protein docking becomes the method of choice for predicting the arrangement of the complex. However, reliably scoring protein-protein docking poses is still an unsolved problem. As a consequence, the screening of many docking models is usually required in the analysis step, to possibly single out the correct ones. Here, making use of exemplary cases, we review our recently introduced methods for the analysis of protein complex structures and for the scoring of protein docking poses, based on the use of inter-residue contacts and their visualization in inter-molecular contact maps. We also show that the ensemble of tools we developed can be used in the context of rational drug design targeting protein-protein interactions.

Analysis and Ranking of Protein-Protein Docking Models Using Inter-Residue Contacts and Inter-Molecular Contact Maps

KAUST Repository

Oliva, Romina

2015-07-01

In view of the increasing interest both in inhibitors of protein-protein interactions and in protein drugs themselves, analysis of the three-dimensional structure of protein-protein complexes is assuming greater relevance in drug design. In the many cases where an experimental structure is not available, protein-protein docking becomes the method of choice for predicting the arrangement of the complex. However, reliably scoring protein-protein docking poses is still an unsolved problem. As a consequence, the screening of many docking models is usually required in the analysis step, to possibly single out the correct ones. Here, making use of exemplary cases, we review our recently introduced methods for the analysis of protein complex structures and for the scoring of protein docking poses, based on the use of inter-residue contacts and their visualization in inter-molecular contact maps. We also show that the ensemble of tools we developed can be used in the context of rational drug design targeting protein-protein interactions.

Mechanical strength of 17,134 model proteins and cysteine slipknots.

Directory of Open Access Journals (Sweden)

Mateusz Sikora

2009-10-01

Full Text Available A new theoretical survey of proteins' resistance to constant speed stretching is performed for a set of 17,134 proteins as described by a structure-based model. The proteins selected have no gaps in their structure determination and consist of no more than 250 amino acids. Our previous studies have dealt with 7510 proteins of no more than 150 amino acids. The proteins are ranked according to the strength of the resistance. Most of the predicted top-strength proteins have not yet been studied experimentally. Architectures and folds which are likely to yield large forces are identified. New types of potent force clamps are discovered. They involve disulphide bridges and, in particular, cysteine slipknots. An effective energy parameter of the model is estimated by comparing the theoretical data on characteristic forces to the corresponding experimental values combined with an extrapolation of the theoretical data to the experimental pulling speeds. These studies provide guidance for future experiments on single molecule manipulation and should lead to selection of proteins for applications. A new class of proteins, involving cysteine slipknots, is identified as one that is expected to lead to the strongest force clamps known. This class is characterized through molecular dynamics simulations.

Anomalous diffusion in neutral evolution of model proteins

Science.gov (United States)

Nelson, Erik D.; Grishin, Nick V.

2015-06-01

Protein evolution is frequently explored using minimalist polymer models, however, little attention has been given to the problem of structural drift, or diffusion. Here, we study neutral evolution of small protein motifs using an off-lattice heteropolymer model in which individual monomers interact as low-resolution amino acids. In contrast to most earlier models, both the length and folded structure of the polymers are permitted to change. To describe structural change, we compute the mean-square distance (MSD) between monomers in homologous folds separated by n neutral mutations. We find that structural change is episodic, and, averaged over lineages (for example, those extending from a single sequence), exhibits a power-law dependence on n . We show that this exponent depends on the alignment method used, and we analyze the distribution of waiting times between neutral mutations. The latter are more disperse than for models required to maintain a specific fold, but exhibit a similar power-law tail.

Rock mechanics models evaluation report: Draft report

International Nuclear Information System (INIS)

1985-10-01

This report documents the evaluation of the thermal and thermomechanical models and codes for repository subsurface design and for design constraint analysis. The evaluation was based on a survey of the thermal and thermomechanical codes and models that are applicable to subsurface design, followed by a Kepner-Tregoe (KT) structured decision analysis of the codes and models. The end result of the KT analysis is a balanced, documented recommendation of the codes and models which are best suited to conceptual subsurface design for the salt repository. The various laws for modeling the creep of rock salt are also reviewed in this report. 37 refs., 1 fig., 7 tabs

A Comparison Study for DNA Motif Modeling on Protein Binding Microarray

KAUST Repository

Wong, Ka-Chun; Li, Yue; Peng, Chengbin; Wong, Hau-San

2015-01-01

Transcription Factor Binding Sites (TFBSs) are relatively short (5-15 bp) and degenerate. Identifying them is a computationally challenging task. In particular, Protein Binding Microarray (PBM) is a high-throughput platform that can measure the DNA binding preference of a protein in a comprehensive and unbiased manner; for instance, a typical PBM experiment can measure binding signal intensities of a protein to all possible DNA k-mers (k=810). Since proteins can often bind to DNA with different binding intensities, one of the major challenges is to build motif models which can fully capture the quantitative binding affinity data. To learn DNA motif models from the non-convex objective function landscape, several optimization methods are compared and applied to the PBM motif model building problem. In particular, representative methods from different optimization paradigms have been chosen for modeling performance comparison on hundreds of PBM datasets. The results suggest that the multimodal optimization methods are very effective for capturing the binding preference information from PBM data. In particular, we observe a general performance improvement using di-nucleotide modeling over mono-nucleotide modeling. In addition, the models learned by the best-performing method are applied to two independent applications: PBM probe rotation testing and ChIP-Seq peak sequence prediction, demonstrating its biological applicability.

A Comparison Study for DNA Motif Modeling on Protein Binding Microarray

KAUST Repository

Wong, Ka-Chun

2015-06-11

Transcription Factor Binding Sites (TFBSs) are relatively short (5-15 bp) and degenerate. Identifying them is a computationally challenging task. In particular, Protein Binding Microarray (PBM) is a high-throughput platform that can measure the DNA binding preference of a protein in a comprehensive and unbiased manner; for instance, a typical PBM experiment can measure binding signal intensities of a protein to all possible DNA k-mers (k=810). Since proteins can often bind to DNA with different binding intensities, one of the major challenges is to build motif models which can fully capture the quantitative binding affinity data. To learn DNA motif models from the non-convex objective function landscape, several optimization methods are compared and applied to the PBM motif model building problem. In particular, representative methods from different optimization paradigms have been chosen for modeling performance comparison on hundreds of PBM datasets. The results suggest that the multimodal optimization methods are very effective for capturing the binding preference information from PBM data. In particular, we observe a general performance improvement using di-nucleotide modeling over mono-nucleotide modeling. In addition, the models learned by the best-performing method are applied to two independent applications: PBM probe rotation testing and ChIP-Seq peak sequence prediction, demonstrating its biological applicability.

Conformational sampling in template-free protein loop structure modeling: an overview.

Science.gov (United States)

Li, Yaohang

2013-01-01

Accurately modeling protein loops is an important step to predict three-dimensional structures as well as to understand functions of many proteins. Because of their high flexibility, modeling the three-dimensional structures of loops is difficult and is usually treated as a "mini protein folding problem" under geometric constraints. In the past decade, there has been remarkable progress in template-free loop structure modeling due to advances of computational methods as well as stably increasing number of known structures available in PDB. This mini review provides an overview on the recent computational approaches for loop structure modeling. In particular, we focus on the approaches of sampling loop conformation space, which is a critical step to obtain high resolution models in template-free methods. We review the potential energy functions for loop modeling, loop buildup mechanisms to satisfy geometric constraints, and loop conformation sampling algorithms. The recent loop modeling results are also summarized.

CONFORMATIONAL SAMPLING IN TEMPLATE-FREE PROTEIN LOOP STRUCTURE MODELING: AN OVERVIEW

Directory of Open Access Journals (Sweden)

Yaohang Li

2013-02-01

Full Text Available Accurately modeling protein loops is an important step to predict three-dimensional structures as well as to understand functions of many proteins. Because of their high flexibility, modeling the three-dimensional structures of loops is difficult and is usually treated as a “mini protein folding problem” under geometric constraints. In the past decade, there has been remarkable progress in template-free loop structure modeling due to advances of computational methods as well as stably increasing number of known structures available in PDB. This mini review provides an overview on the recent computational approaches for loop structure modeling. In particular, we focus on the approaches of sampling loop conformation space, which is a critical step to obtain high resolution models in template-free methods. We review the potential energy functions for loop modeling, loop buildup mechanisms to satisfy geometric constraints, and loop conformation sampling algorithms. The recent loop modeling results are also summarized.

Exploration of freely available web-interfaces for comparative homology modelling of microbial proteins.

Science.gov (United States)

Nema, Vijay; Pal, Sudhir Kumar

2013-01-01

This study was conducted to find the best suited freely available software for modelling of proteins by taking a few sample proteins. The proteins used were small to big in size with available crystal structures for the purpose of benchmarking. Key players like Phyre2, Swiss-Model, CPHmodels-3.0, Homer, (PS)2, (PS)(2)-V(2), Modweb were used for the comparison and model generation. Benchmarking process was done for four proteins, Icl, InhA, and KatG of Mycobacterium tuberculosis and RpoB of Thermus Thermophilus to get the most suited software. Parameters compared during analysis gave relatively better values for Phyre2 and Swiss-Model. This comparative study gave the information that Phyre2 and Swiss-Model make good models of small and large proteins as compared to other screened software. Other software was also good but is often not very efficient in providing full-length and properly folded structure.

Toxicological relationships between proteins obtained from protein target predictions of large toxicity databases

International Nuclear Information System (INIS)

Nigsch, Florian; Mitchell, John B.O.

2008-01-01

The combination of models for protein target prediction with large databases containing toxicological information for individual molecules allows the derivation of 'toxiclogical' profiles, i.e., to what extent are molecules of known toxicity predicted to interact with a set of protein targets. To predict protein targets of drug-like and toxic molecules, we built a computational multiclass model using the Winnow algorithm based on a dataset of protein targets derived from the MDL Drug Data Report. A 15-fold Monte Carlo cross-validation using 50% of each class for training, and the remaining 50% for testing, provided an assessment of the accuracy of that model. We retained the 3 top-ranking predictions and found that in 82% of all cases the correct target was predicted within these three predictions. The first prediction was the correct one in almost 70% of cases. A model built on the whole protein target dataset was then used to predict the protein targets for 150 000 molecules from the MDL Toxicity Database. We analysed the frequency of the predictions across the panel of protein targets for experimentally determined toxicity classes of all molecules. This allowed us to identify clusters of proteins related by their toxicological profiles, as well as toxicities that are related. Literature-based evidence is provided for some specific clusters to show the relevance of the relationships identified

Towards an animal model of ovarian cancer: cataloging chicken blood proteins using combinatorial peptide ligand libraries coupled with shotgun proteomic analysis for translational research.

Science.gov (United States)

Ma, Yingying; Sun, Zeyu; de Matos, Ricardo; Zhang, Jing; Odunsi, Kunle; Lin, Biaoyang

2014-05-01

Epithelial ovarian cancer is the most deadly gynecological cancer around the world, with high morbidity in industrialized countries. Early diagnosis is key in reducing its morbidity rate. Yet, robust biomarkers, diagnostics, and animal models are still limited for ovarian cancer. This calls for broader omics and systems science oriented diagnostics strategies. In this vein, the domestic chicken has been used as an ovarian cancer animal model, owing to its high rate of developing spontaneous epithelial ovarian tumors. Chicken blood has thus been considered a surrogate reservoir from which cancer biomarkers can be identified. However, the presence of highly abundant proteins in chicken blood has compromised the applicability of proteomics tools to study chicken blood owing to a lack of immunodepletion methods. Here, we demonstrate that a combinatorial peptide ligand library (CPLL) can efficiently remove highly abundant proteins from chicken blood samples, consequently doubling the number of identified proteins. Using an integrated CPLL-1DGE-LC-MSMS workflow, we identified a catalog of 264 unique proteins. Functional analyses further suggested that most proteins were coagulation and complement factors, blood transport and binding proteins, immune- and defense-related proteins, proteases, protease inhibitors, cellular enzymes, or cell structure and adhesion proteins. Semiquantitative spectral counting analysis identified 10 potential biomarkers from the present chicken ovarian cancer model. Additionally, many human homologs of chicken blood proteins we have identified have been independently suggested as diagnostic biomarkers for ovarian cancer, further triangulating our novel observations reported here. In conclusion, the CPLL-assisted proteomic workflow using the chicken ovarian cancer model provides a feasible platform for translational research to identify ovarian cancer biomarkers and understand ovarian cancer biology. To the best of our knowledge, we report here

The evolution of the protein synthesis system. I - A model of a primitive protein synthesis system

Science.gov (United States)

Mizutani, H.; Ponnamperuma, C.

1977-01-01

A model is developed to describe the evolution of the protein synthesis system. The model is comprised of two independent autocatalytic systems, one including one gene (A-gene) and two activated amino acid polymerases (O and A-polymerases), and the other including the addition of another gene (N-gene) and a nucleotide polymerase. Simulation results have suggested that even a small enzymic activity and polymerase specificity could lead the system to the most accurate protein synthesis, as far as permitted by transitions to systems with higher accuracy.

Membrane Compartmentalization Reducing the Mobility of Lipids and Proteins within a Model Plasma Membrane.

Science.gov (United States)

Koldsø, Heidi; Reddy, Tyler; Fowler, Philip W; Duncan, Anna L; Sansom, Mark S P

2016-09-01

The cytoskeleton underlying cell membranes may influence the dynamic organization of proteins and lipids within the bilayer by immobilizing certain transmembrane (TM) proteins and forming corrals within the membrane. Here, we present coarse-grained resolution simulations of a biologically realistic membrane model of asymmetrically organized lipids and TM proteins. We determine the effects of a model of cytoskeletal immobilization of selected membrane proteins using long time scale coarse-grained molecular dynamics simulations. By introducing compartments with varying degrees of restraints within the membrane models, we are able to reveal how compartmentalization caused by cytoskeletal immobilization leads to reduced and anomalous diffusional mobility of both proteins and lipids. This in turn results in a reduced rate of protein dimerization within the membrane and of hopping of membrane proteins between compartments. These simulations provide a molecular realization of hierarchical models often invoked to explain single-molecule imaging studies of membrane proteins.

Interpretation of protein quantitation using the Bradford assay: comparison with two calculation models.

Science.gov (United States)

Ku, Hyung-Keun; Lim, Hyuk-Min; Oh, Kyong-Hwa; Yang, Hyo-Jin; Jeong, Ji-Seon; Kim, Sook-Kyung

2013-03-01

The Bradford assay is a simple method for protein quantitation, but variation in the results between proteins is a matter of concern. In this study, we compared and normalized quantitative values from two models for protein quantitation, where the residues in the protein that bind to anionic Coomassie Brilliant Blue G-250 comprise either Arg and Lys (Method 1, M1) or Arg, Lys, and His (Method 2, M2). Use of the M2 model yielded much more consistent quantitation values compared with use of the M1 model, which exhibited marked overestimations against protein standards. Copyright © 2012 Elsevier Inc. All rights reserved.

Compartmental modelling of the pharmacokinetics of a breast cancer resistance protein.

Science.gov (United States)

Grandjean, Thomas R B; Chappell, Mike J; Yates, James T W; Jones, Kevin; Wood, Gemma; Coleman, Tanya

2011-11-01

A mathematical model for the pharmacokinetics of Hoechst 33342 following administration into a culture medium containing a population of transfected cells (HEK293 hBCRP) with a potent breast cancer resistance protein inhibitor, Fumitremorgin C (FTC), present is described. FTC is reported to almost completely annul resistance mediated by BCRP in vitro. This non-linear compartmental model has seven macroscopic sub-units, with 14 rate parameters. It describes the relationship between the concentration of Hoechst 33342 and FTC, initially spiked in the medium, and the observed change in fluorescence due to Hoechst 33342 binding to DNA. Structural identifiability analysis has been performed using two methods, one based on the similarity transformation/exhaustive modelling approach and the other based on the differential algebra approach. The analyses demonstrated that all models derived are uniquely identifiable for the experiments/observations available. A kinetic modelling software package, namely FACSIMILE (MPCA Software, UK), was used for parameter fitting and to obtain numerical solutions for the system equations. Model fits gave very good agreement with in vitro data provided by AstraZeneca across a variety of experimental scenarios. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Model for Stress-induced Protein Degradation in Lemna minor1

Science.gov (United States)

Cooke, Robert J.; Roberts, Keith; Davies, David D.

1980-01-01

Transfer of Lemna minor fronds to adverse or stress conditions produces a large increase in the rate of protein degradation. Cycloheximide partially inhibits stress-induced protein degradation and also partially inhibits the protein degradation which occurs in the absence of stress. The increased protein degradation does not appear to be due to an increase in activity of soluble proteolytic enzymes. Biochemical evidence indicates that stress, perhaps acting via hormones, affects the permeability of certain membranes, particularly the tonoplast. A general model for stress-induced protein degradation is presented in which changes in membrane properties allow vacuolar proteolytic enzymes increased access to cytoplasmic proteins. PMID:16661588

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....

Illuminating the origins of spectral properties of green fluorescent proteins via proteochemometric and molecular modeling.

Science.gov (United States)

Nantasenamat, Chanin; Simeon, Saw; Owasirikul, Wiwat; Songtawee, Napat; Lapins, Maris; Prachayasittikul, Virapong; Wikberg, Jarl E S

2014-10-15

Green fluorescent protein (GFP) has immense utility in biomedical imaging owing to its autofluorescent nature. In efforts to broaden the spectral diversity of GFP, there have been several reports of engineered mutants via rational design and random mutagenesis. Understanding the origins of spectral properties of GFP could be achieved by means of investigating its structure-activity relationship. The first quantitative structure-property relationship study for modeling the spectral properties, particularly the excitation and emission maximas, of GFP was previously proposed by us some years ago in which quantum chemical descriptors were used for model development. However, such simplified model does not consider possible effects that neighboring amino acids have on the conjugated π-system of GFP chromophore. This study describes the development of a unified proteochemometric model in which the GFP chromophore and amino acids in its vicinity are both considered in the same model. The predictive performance of the model was verified by internal and external validation as well as Y-scrambling. Our strategy provides a general solution for elucidating the contribution that specific ligand and protein descriptors have on the investigated spectral property, which may be useful in engineering novel GFP variants with desired characteristics. Copyright © 2014 Wiley Periodicals, Inc.

An Efficient Null Model for Conformational Fluctuations in Proteins

DEFF Research Database (Denmark)

Harder, Tim Philipp; Borg, Mikael; Bottaro, Sandro

2012-01-01

Protein dynamics play a crucial role in function, catalytic activity, and pathogenesis. Consequently, there is great interest in computational methods that probe the conformational fluctuations of a protein. However, molecular dynamics simulations are computationally costly and therefore are often...... limited to comparatively short timescales. TYPHON is a probabilistic method to explore the conformational space of proteins under the guidance of a sophisticated probabilistic model of local structure and a given set of restraints that represent nonlocal interactions, such as hydrogen bonds or disulfide...... on conformational fluctuations that is in correspondence with experimental measurements. TYPHON provides a flexible, yet computationally efficient, method to explore possible conformational fluctuations in proteins....

Development of a green fluorescent protein metastatic-cancer chick-embryo drug-screen model.

Science.gov (United States)

Bobek, Vladimir; Plachy, Jiri; Pinterova, Daniela; Kolostova, Katarina; Boubelik, Michael; Jiang, Ping; Yang, Meng; Hoffman, Robert M

2004-01-01

The chick-embryo model has been an important tool to study tumor growth, metastasis, and angiogenesis. However, an imageable model with a genetic fluorescent tag in the growing and spreading cancer cells that is stable over time has not been developed. We report here the development of such an imageable fluorescent chick-embryo metastatic cancer model with the use of green fluorescent protein (GFP). Lewis lung carcinoma cells, stably expressing GFP, were injected on the 12th day of incubation in the chick embryo. GFP-Lewis lung carcinoma metastases were visualized by fluorescence, after seven days additional incubation, in the brain, heart, and sternum of the developing chick embryo, with the most frequent site being the brain. The combination of streptokinase and gemcitabine was evaluated in this GFP metastatic model. Twelve-day-old chick embryos were injected intravenously with GFP-Lewis lung cancer cells, along with these two agents either alone or in combination. The streptokinase-gemcitabine combination inhibited metastases at all sites. The effective dose of gemcitabine was found to be 10 mg/kg and streptokinase 2000 IU per embryo. The data in this report suggest that this new stably fluorescent imageable metastatic-cancer chick-embryo model will enable rapid screening of new antimetastatic agents.

Genome-scale metabolic model of Pichia pastoris with native and humanized glycosylation of recombinant proteins.

Science.gov (United States)

Irani, Zahra Azimzadeh; Kerkhoven, Eduard J; Shojaosadati, Seyed Abbas; Nielsen, Jens

2016-05-01

Pichia pastoris is used for commercial production of human therapeutic proteins, and genome-scale models of P. pastoris metabolism have been generated in the past to study the metabolism and associated protein production by this yeast. A major challenge with clinical usage of recombinant proteins produced by P. pastoris is the difference in N-glycosylation of proteins produced by humans and this yeast. However, through metabolic engineering, a P. pastoris strain capable of producing humanized N-glycosylated proteins was constructed. The current genome-scale models of P. pastoris do not address native nor humanized N-glycosylation, and we therefore developed ihGlycopastoris, an extension to the iLC915 model with both native and humanized N-glycosylation for recombinant protein production, but also an estimation of N-glycosylation of P. pastoris native proteins. This new model gives a better prediction of protein yield, demonstrates the effect of the different types of N-glycosylation of protein yield, and can be used to predict potential targets for strain improvement. The model represents a step towards a more complete description of protein production in P. pastoris, which is required for using these models to understand and optimize protein production processes. © 2015 Wiley Periodicals, Inc.

Predicted cycloartenol synthase protein from Kandelia obovata and Rhizophora stylosa using online software of Phyre2 and Swiss-model

Science.gov (United States)

Basyuni, M.; Sulistiyono, N.; Wati, R.; Sumardi; Oku, H.; Baba, S.; Sagami, H.

2018-03-01

Cloning of Kandelia obovata KcCAS gene (previously known as Kandelia candel) and Rhizophora stylosa RsCAS have already have been reported and encoded cycloartenol synthases. In this study, the predicted KcCAS and RsCAS protein were analyzed using online software of Phyre2 and Swiss-model. The protein modelling for KcCAS and RsCAS cycloartenol synthases was determined using Pyre2 had similar results with slightly different in sequence identity. By contrast, the Swiss-model for KcCAS slightly had higher sequence identity (47.31%) and Qmean (0.70) compared to RsCAS. No difference of ligands binding site which is considered as modulators for both cycloartenol synthases. The range of predicted protein derived from 91-757 amino acid residues with coverage sequence similarities 0.86, respectively from template model of lanosterol synthase from the human. Homology modelling revealed that 706 residues (93% of the amino acid sequence) had been modelled with 100.0% confidence by the single highest scoring template for both KcCAS and RsCAS using Phyre2. This coverage was more elevated than swiss-model predicted (86%). The present study suggested that both genes are responsible for the genesis of cycloartenol in these mangrove plants.

HIV-specific probabilistic models of protein evolution.

Directory of Open Access Journals (Sweden)

David C Nickle

2007-06-01

Full Text Available Comparative sequence analyses, including such fundamental bioinformatics techniques as similarity searching, sequence alignment and phylogenetic inference, have become a mainstay for researchers studying type 1 Human Immunodeficiency Virus (HIV-1 genome structure and evolution. Implicit in comparative analyses is an underlying model of evolution, and the chosen model can significantly affect the results. In general, evolutionary models describe the probabilities of replacing one amino acid character with another over a period of time. Most widely used evolutionary models for protein sequences have been derived from curated alignments of hundreds of proteins, usually based on mammalian genomes. It is unclear to what extent these empirical models are generalizable to a very different organism, such as HIV-1-the most extensively sequenced organism in existence. We developed a maximum likelihood model fitting procedure to a collection of HIV-1 alignments sampled from different viral genes, and inferred two empirical substitution models, suitable for describing between-and within-host evolution. Our procedure pools the information from multiple sequence alignments, and provided software implementation can be run efficiently in parallel on a computer cluster. We describe how the inferred substitution models can be used to generate scoring matrices suitable for alignment and similarity searches. Our models had a consistently superior fit relative to the best existing models and to parameter-rich data-driven models when benchmarked on independent HIV-1 alignments, demonstrating evolutionary biases in amino-acid substitution that are unique to HIV, and that are not captured by the existing models. The scoring matrices derived from the models showed a marked difference from common amino-acid scoring matrices. The use of an appropriate evolutionary model recovered a known viral transmission history, whereas a poorly chosen model introduced phylogenetic

Business Model Disclosures in Corporate Reports

Directory of Open Access Journals (Sweden)

Jan Michalak

2017-01-01

Full Text Available Purpose: In this paper, we investigate the development, the current state, and the potential of business model disclosures to illustrate where, why and how organizations might want to disclose their business models to their stakeholders. The description of the business model may be relevant to stakeholders if it helps them to comprehend the company ‘story’ and increase understanding of other provided data (i.e. financial statements, risk exposure, sustainability of operations. It can also aid stakeholders in the assessment of sustainability of business models and the whole company. To realize these goals, business model descriptions should fulfil requirements of users suggested by various guidelines. Design/Methodology/Approach: First, we review and analyse literature on business model disclosure and some of its antecedents, including voluntary disclosure of intellectual capital. We also discuss business model reporting incentives from the viewpoint of shareholders, stakeholders and legitimacy theory. Second, we compare and discuss reporting guidelines on strategic reports, intellectual capital reports, and integrated reports through the lens of their requirements for business model disclosure and the consequences of their use for corporate report users. Third, we present, analyse and compare examples of good corporate practices in business model reporting. Findings: In the examined reporting guidelines, we find similarities, e.g. mostly structural but also qualitative attributes, in their presented information: materiality, completeness, connectivity, future orientation and conciseness. We also identify important differences between their frameworks concerning the target audience of the reports, business model definitions and business model disclosure requirements. Discontinuation of intellectual capital reporting conforming to DATI guidelines provides important warnings for the proponents of voluntary disclosure – especially for

Using mathematical models to understand the effect of nanoscale roughness on protein adsorption for improving medical devices

Directory of Open Access Journals (Sweden)

Ercan B

2013-09-01

Full Text Available Batur Ercan,1 Dongwoo Khang,2 Joseph Carpenter,3 Thomas J Webster1 1Department of Chemical Engineering, Northeastern University, Boston, MA, USA; 2School of Materials Science and Engineering and Center for PRC and RIGET, Gyeongsang National University, Jinju, South Korea; 3School of Medicine, Stanford University, Stanford, CA, USA Abstract: Surface roughness and energy significantly influence protein adsorption on to biomaterials, which, in turn, controls select cellular adhesion to determine the success and longevity of an implant. To understand these relationships at a fundamental level, a model was originally proposed by Khang et al to correlate nanoscale surface properties (specifically, nanoscale roughness and energy to protein adsorption, which explained the greater cellular responses on nanostructured surfaces commonly reported in the literature today. To test this model for different surfaces from what was previously used to develop that model, in this study we synthesized highly ordered poly(lactic-co-glycolic acid surfaces of identical chemistry but altered nanoscale surface roughness and energy using poly(dimethylsiloxane molds of polystyrene beads. Fibronectin and collagen type IV adsorption studies showed a linear adsorption behavior as the surface nanoroughness increased. This supported the general trends observed by Khang et al. However, when fitting such data to the mathematical model established by Khang et al, a strong correlation did not result. Thus, this study demonstrated that the equation proposed by Khang et al to predict protein adsorption should be modified to accommodate for additional nanoscale surface property contributions (ie, surface charge to make the model more accurate. In summary, results from this study provided an important step in developing future mathematical models that can correlate surface properties (such as nanoscale roughness and surface energy to initial protein adsorption events important to

Comparative Study of Elastic Network Model and Protein Contact Network for Protein Complexes: The Hemoglobin Case

Directory of Open Access Journals (Sweden)

Guang Hu

2017-01-01

Full Text Available The overall topology and interfacial interactions play key roles in understanding structural and functional principles of protein complexes. Elastic Network Model (ENM and Protein Contact Network (PCN are two widely used methods for high throughput investigation of structures and interactions within protein complexes. In this work, the comparative analysis of ENM and PCN relative to hemoglobin (Hb was taken as case study. We examine four types of structural and dynamical paradigms, namely, conformational change between different states of Hbs, modular analysis, allosteric mechanisms studies, and interface characterization of an Hb. The comparative study shows that ENM has an advantage in studying dynamical properties and protein-protein interfaces, while PCN is better for describing protein structures quantitatively both from local and from global levels. We suggest that the integration of ENM and PCN would give a potential but powerful tool in structural systems biology.

Refinement of protein termini in template-based modeling using conformational space annealing.

Science.gov (United States)

Park, Hahnbeom; Ko, Junsu; Joo, Keehyoung; Lee, Julian; Seok, Chaok; Lee, Jooyoung

2011-09-01

The rapid increase in the number of experimentally determined protein structures in recent years enables us to obtain more reliable protein tertiary structure models than ever by template-based modeling. However, refinement of template-based models beyond the limit available from the best templates is still needed for understanding protein function in atomic detail. In this work, we develop a new method for protein terminus modeling that can be applied to refinement of models with unreliable terminus structures. The energy function for terminus modeling consists of both physics-based and knowledge-based potential terms with carefully optimized relative weights. Effective sampling of both the framework and terminus is performed using the conformational space annealing technique. This method has been tested on a set of termini derived from a nonredundant structure database and two sets of termini from the CASP8 targets. The performance of the terminus modeling method is significantly improved over our previous method that does not employ terminus refinement. It is also comparable or superior to the best server methods tested in CASP8. The success of the current approach suggests that similar strategy may be applied to other types of refinement problems such as loop modeling or secondary structure rearrangement. Copyright © 2011 Wiley-Liss, Inc.

Accurate protein structure modeling using sparse NMR data and homologous structure information.

Science.gov (United States)

Thompson, James M; Sgourakis, Nikolaos G; Liu, Gaohua; Rossi, Paolo; Tang, Yuefeng; Mills, Jeffrey L; Szyperski, Thomas; Montelione, Gaetano T; Baker, David

2012-06-19

While information from homologous structures plays a central role in X-ray structure determination by molecular replacement, such information is rarely used in NMR structure determination because it can be incorrect, both locally and globally, when evolutionary relationships are inferred incorrectly or there has been considerable evolutionary structural divergence. Here we describe a method that allows robust modeling of protein structures of up to 225 residues by combining (1)H(N), (13)C, and (15)N backbone and (13)Cβ chemical shift data, distance restraints derived from homologous structures, and a physically realistic all-atom energy function. Accurate models are distinguished from inaccurate models generated using incorrect sequence alignments by requiring that (i) the all-atom energies of models generated using the restraints are lower than models generated in unrestrained calculations and (ii) the low-energy structures converge to within 2.0 Å backbone rmsd over 75% of the protein. Benchmark calculations on known structures and blind targets show that the method can accurately model protein structures, even with very remote homology information, to a backbone rmsd of 1.2-1.9 Å relative to the conventional determined NMR ensembles and of 0.9-1.6 Å relative to X-ray structures for well-defined regions of the protein structures. This approach facilitates the accurate modeling of protein structures using backbone chemical shift data without need for side-chain resonance assignments and extensive analysis of NOESY cross-peak assignments.

Construction of a biodynamic model for Cry protein production studies.

Science.gov (United States)

Navarro-Mtz, Ana Karin; Pérez-Guevara, Fermín

2014-12-01

Mathematical models have been used from growth kinetic simulation to gen regulatory networks prediction for B. thuringiensis culture. However, this culture is a time dependent dynamic process where cells physiology suffers several changes depending on the changes in the cell environment. Therefore, through its culture, B. thuringiensis presents three phases related with the predominance of three major metabolic pathways: vegetative growth (Embded-Meyerhof-Parnas pathway), transition (γ-aminobutiric cycle) and sporulation (tricarboxylic acid cycle). There is not available a mathematical model that relates the different stages of cultivation with the metabolic pathway active on each one of them. Therefore, in the present study, and based on published data, a biodynamic model was generated to describe the dynamic of the three different phases based on their major metabolic pathways. The biodynamic model is used to study the interrelation between the different culture phases and their relationship with the Cry protein production. The model consists of three interconnected modules where each module represents one culture phase and its principal metabolic pathway. For model validation four new fermentations were done showing that the model constructed describes reasonably well the dynamic of the three phases. The main results of this model imply that poly-β-hydroxybutyrate is crucial for endospore and Cry protein production. According to the yields of dipicolinic acid and Cry from poly-β-hydroxybutyrate, calculated with the model, the endospore and Cry protein production are not just simultaneous and parallel processes they are also competitive processes.

Modeling structure of G protein-coupled receptors in huan genome

KAUST Repository

Zhang, Yang

2016-01-26

G protein-coupled receptors (or GPCRs) are integral transmembrane proteins responsible to various cellular signal transductions. Human GPCR proteins are encoded by 5% of human genes but account for the targets of 40% of the FDA approved drugs. Due to difficulties in crystallization, experimental structure determination remains extremely difficult for human GPCRs, which have been a major barrier in modern structure-based drug discovery. We proposed a new hybrid protocol, GPCR-I-TASSER, to construct GPCR structure models by integrating experimental mutagenesis data with ab initio transmembrane-helix assembly simulations, assisted by the predicted transmembrane-helix interaction networks. The method was tested in recent community-wide GPCRDock experiments and constructed models with a root mean square deviation 1.26 Å for Dopamine-3 and 2.08 Å for Chemokine-4 receptors in the transmembrane domain regions, which were significantly closer to the native than the best templates available in the PDB. GPCR-I-TASSER has been applied to model all 1,026 putative GPCRs in the human genome, where 923 are found to have correct folds based on the confidence score analysis and mutagenesis data comparison. The successfully modeled GPCRs contain many pharmaceutically important families that do not have previously solved structures, including Trace amine, Prostanoids, Releasing hormones, Melanocortins, Vasopressin and Neuropeptide Y receptors. All the human GPCR models have been made publicly available through the GPCR-HGmod database at http://zhanglab.ccmb.med.umich.edu/GPCR-HGmod/ The results demonstrate new progress on genome-wide structure modeling of transmembrane proteins which should bring useful impact on the effort of GPCR-targeted drug discovery.

Biosphere Model Report

Energy Technology Data Exchange (ETDEWEB)

D. W. Wu

2003-07-16

The purpose of this report is to document the biosphere model, the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), which describes radionuclide transport processes in the biosphere and associated human exposure that may arise as the result of radionuclide release from the geologic repository at Yucca Mountain. The biosphere model is one of the process models that support the Yucca Mountain Project (YMP) Total System Performance Assessment (TSPA) for the license application (LA), the TSPA-LA. The ERMYN model provides the capability of performing human radiation dose assessments. This report documents the biosphere model, which includes: (1) Describing the reference biosphere, human receptor, exposure scenarios, and primary radionuclides for each exposure scenario (Section 6.1); (2) Developing a biosphere conceptual model using site-specific features, events, and processes (FEPs), the reference biosphere, the human receptor, and assumptions (Section 6.2 and Section 6.3); (3) Building a mathematical model using the biosphere conceptual model and published biosphere models (Sections 6.4 and 6.5); (4) Summarizing input parameters for the mathematical model, including the uncertainty associated with input values (Section 6.6); (5) Identifying improvements in the ERMYN model compared with the model used in previous biosphere modeling (Section 6.7); (6) Constructing an ERMYN implementation tool (model) based on the biosphere mathematical model using GoldSim stochastic simulation software (Sections 6.8 and 6.9); (7) Verifying the ERMYN model by comparing output from the software with hand calculations to ensure that the GoldSim implementation is correct (Section 6.10); and (8) Validating the ERMYN model by corroborating it with published biosphere models; comparing conceptual models, mathematical models, and numerical results (Section 7).

Biosphere Model Report

Energy Technology Data Exchange (ETDEWEB)

M. A. Wasiolek

2003-10-27

The purpose of this report is to document the biosphere model, the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), which describes radionuclide transport processes in the biosphere and associated human exposure that may arise as the result of radionuclide release from the geologic repository at Yucca Mountain. The biosphere model is one of the process models that support the Yucca Mountain Project (YMP) Total System Performance Assessment (TSPA) for the license application (LA), the TSPA-LA. The ERMYN model provides the capability of performing human radiation dose assessments. This report documents the biosphere model, which includes: (1) Describing the reference biosphere, human receptor, exposure scenarios, and primary radionuclides for each exposure scenario (Section 6.1); (2) Developing a biosphere conceptual model using site-specific features, events, and processes (FEPs), the reference biosphere, the human receptor, and assumptions (Section 6.2 and Section 6.3); (3) Building a mathematical model using the biosphere conceptual model and published biosphere models (Sections 6.4 and 6.5); (4) Summarizing input parameters for the mathematical model, including the uncertainty associated with input values (Section 6.6); (5) Identifying improvements in the ERMYN model compared with the model used in previous biosphere modeling (Section 6.7); (6) Constructing an ERMYN implementation tool (model) based on the biosphere mathematical model using GoldSim stochastic simulation software (Sections 6.8 and 6.9); (7) Verifying the ERMYN model by comparing output from the software with hand calculations to ensure that the GoldSim implementation is correct (Section 6.10); and (8) Validating the ERMYN model by corroborating it with published biosphere models; comparing conceptual models, mathematical models, and numerical results (Section 7).

Biosphere Model Report

International Nuclear Information System (INIS)

D. W. Wu

2003-01-01

The purpose of this report is to document the biosphere model, the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), which describes radionuclide transport processes in the biosphere and associated human exposure that may arise as the result of radionuclide release from the geologic repository at Yucca Mountain. The biosphere model is one of the process models that support the Yucca Mountain Project (YMP) Total System Performance Assessment (TSPA) for the license application (LA), the TSPA-LA. The ERMYN model provides the capability of performing human radiation dose assessments. This report documents the biosphere model, which includes: (1) Describing the reference biosphere, human receptor, exposure scenarios, and primary radionuclides for each exposure scenario (Section 6.1); (2) Developing a biosphere conceptual model using site-specific features, events, and processes (FEPs), the reference biosphere, the human receptor, and assumptions (Section 6.2 and Section 6.3); (3) Building a mathematical model using the biosphere conceptual model and published biosphere models (Sections 6.4 and 6.5); (4) Summarizing input parameters for the mathematical model, including the uncertainty associated with input values (Section 6.6); (5) Identifying improvements in the ERMYN model compared with the model used in previous biosphere modeling (Section 6.7); (6) Constructing an ERMYN implementation tool (model) based on the biosphere mathematical model using GoldSim stochastic simulation software (Sections 6.8 and 6.9); (7) Verifying the ERMYN model by comparing output from the software with hand calculations to ensure that the GoldSim implementation is correct (Section 6.10); and (8) Validating the ERMYN model by corroborating it with published biosphere models; comparing conceptual models, mathematical models, and numerical results (Section 7)

Model documentation report: Transportation sector model of the National Energy Modeling System

Energy Technology Data Exchange (ETDEWEB)

1994-03-01

This report documents the objectives, analytical approach and development of the National Energy Modeling System (NEMS) Transportation Model (TRAN). The report catalogues and describes the model assumptions, computational methodology, parameter estimation techniques, model source code, and forecast results generated by the model. This document serves three purposes. First, it is a reference document providing a detailed description of TRAN for model analysts, users, and the public. Second, this report meets the legal requirements of the Energy Information Administration (EIA) to provide adequate documentation in support of its statistical and forecast reports (Public Law 93-275, 57(b)(1)). Third, it permits continuity in model development by providing documentation from which energy analysts can undertake model enhancements, data updates, and parameter refinements.

Electrostatics of cysteine residues in proteins: Parameterization and validation of a simple model

Science.gov (United States)

Salsbury, Freddie R.; Poole, Leslie B.; Fetrow, Jacquelyn S.

2013-01-01

One of the most popular and simple models for the calculation of pKas from a protein structure is the semi-macroscopic electrostatic model MEAD. This model requires empirical parameters for each residue to calculate pKas. Analysis of current, widely used empirical parameters for cysteine residues showed that they did not reproduce expected cysteine pKas; thus, we set out to identify parameters consistent with the CHARMM27 force field that capture both the behavior of typical cysteines in proteins and the behavior of cysteines which have perturbed pKas. The new parameters were validated in three ways: (1) calculation across a large set of typical cysteines in proteins (where the calculations are expected to reproduce expected ensemble behavior); (2) calculation across a set of perturbed cysteines in proteins (where the calculations are expected to reproduce the shifted ensemble behavior); and (3) comparison to experimentally determined pKa values (where the calculation should reproduce the pKa within experimental error). Both the general behavior of cysteines in proteins and the perturbed pKa in some proteins can be predicted reasonably well using the newly determined empirical parameters within the MEAD model for protein electrostatics. This study provides the first general analysis of the electrostatics of cysteines in proteins, with specific attention paid to capturing both the behavior of typical cysteines in a protein and the behavior of cysteines whose pKa should be shifted, and validation of force field parameters for cysteine residues. PMID:22777874

A transgenic model of transactivation by the Tax protein of HTLV-I.

Science.gov (United States)

Bieberich, C J; King, C M; Tinkle, B T; Jay, G

1993-09-01

The human T-lymphotropic virus type I (HTLV-I) Tax protein is a transcriptional regulatory protein that has been suggested to play a causal role in the development of several HTLV-I-associated diseases. Tax regulates expression of its own LTR and of certain cellular promoters perhaps by usurping the function of the host transcriptional machinery. We have established a transgenic mouse model system to define the spectrum of tissues in vivo that are capable of supporting Tax-mediated transcriptional transactivation. Transgenic mice carrying the HTLV-I LTR driving expression of the Escherichia coli beta-galactosidase (beta gal) gene were generated, and this LTR-beta gal gene was transcriptionally inactive in all tissues. When LTR-beta gal mice were mated to transgenic mice carrying the same LTR driving expression of the HTLV-I tax gene, mice that carried both transgenes showed restricted expression of the beta gal reporter gene in several tissues including muscle, bone, salivary glands, skin, and nerve. In addition, a dramatic increase in the number of beta gal-expressing cells was seen in response to wounding. These observations provide direct evidence for viral transactivation in vivo, delimit the tissues capable of supporting that transactivation, and provide a model system to study the mechanism of gene regulation by Tax.

Label-free detection of protein biomolecules secreted from a heart-on-a-chip model for drug cardiotoxicity evaluation

Science.gov (United States)

DeLuna, Frank; Zhang, Yu Shrike; Bustamante, Gilbert; Li, Le; Lauderdale, Matthew; Dokmeci, Mehmet R.; Khademhosseini, Ali; Ye, Jing Yong

2018-02-01

Efficient methods for the accurate analysis of drug toxicities are in urgent demand as failures of newly discovered drug candidates due to toxic side effects have resulted in about 30% of clinical attrition. The high failure rate is partly due to current inadequate models to study drug side effects, i.e., common animal models may fail due to its misrepresentation of human physiology. Therefore, much effort has been allocated in the development of organ-on-a-chip models which offer a variety of human organ models mimicking a multitude of human physiological conditions. However, it is extremely challenging to analyze the transient and long-term response of the organ models to drug treatments during drug toxicity tests, as the proteins secreted from the organ-on-a-chip model are minute due to its volumetric size, and current methods for detecting said biomolecules are not suitable for real-time monitoring. As protein biomolecules are being continuously secreted from the human organ model, fluorescence techniques are practically impossible to achieve real-time fluorescence labeling in the dynamically changing environment, thus making a label-free approach highly desirable for the organ-on-achip applications. In this paper, we report the use of a photonic-crystal biosensor integrated with a microfluidic system for sensitive label-free bioassays of secreted protein biomolecules from a heart-on-the-chip model created with cardiomyocytes derived from human induced pluripotent stem cells.

Random close packing in protein cores.

Science.gov (United States)

Gaines, Jennifer C; Smith, W Wendell; Regan, Lynne; O'Hern, Corey S

2016-03-01

Shortly after the determination of the first protein x-ray crystal structures, researchers analyzed their cores and reported packing fractions ϕ ≈ 0.75, a value that is similar to close packing of equal-sized spheres. A limitation of these analyses was the use of extended atom models, rather than the more physically accurate explicit hydrogen model. The validity of the explicit hydrogen model was proved in our previous studies by its ability to predict the side chain dihedral angle distributions observed in proteins. In contrast, the extended atom model is not able to recapitulate the side chain dihedral angle distributions, and gives rise to large atomic clashes at side chain dihedral angle combinations that are highly probable in protein crystal structures. Here, we employ the explicit hydrogen model to calculate the packing fraction of the cores of over 200 high-resolution protein structures. We find that these protein cores have ϕ ≈ 0.56, which is similar to results obtained from simulations of random packings of individual amino acids. This result provides a deeper understanding of the physical basis of protein structure that will enable predictions of the effects of amino acid mutations to protein cores and interfaces of known structure.

Protein restriction and cancer.

Science.gov (United States)

Yin, Jie; Ren, Wenkai; Huang, Xingguo; Li, Tiejun; Yin, Yulong

2018-03-26

Protein restriction without malnutrition is currently an effective nutritional intervention known to prevent diseases and promote health span from yeast to human. Recently, low protein diets are reported to be associated with lowered cancer incidence and mortality risk of cancers in human. In murine models, protein restriction inhibits tumor growth via mTOR signaling pathway. IGF-1, amino acid metabolic programing, FGF21, and autophagy may also serve as potential mechanisms of protein restriction mediated cancer prevention. Together, dietary intervention aimed at reducing protein intake can be beneficial and has the potential to be widely adopted and effective in preventing and treating cancers. Copyright © 2018 Elsevier B.V. All rights reserved.

Hidden Markov model approach for identifying the modular framework of the protein backbone.

Science.gov (United States)

Camproux, A C; Tuffery, P; Chevrolat, J P; Boisvieux, J F; Hazout, S

1999-12-01

The hidden Markov model (HMM) was used to identify recurrent short 3D structural building blocks (SBBs) describing protein backbones, independently of any a priori knowledge. Polypeptide chains are decomposed into a series of short segments defined by their inter-alpha-carbon distances. Basically, the model takes into account the sequentiality of the observed segments and assumes that each one corresponds to one of several possible SBBs. Fitting the model to a database of non-redundant proteins allowed us to decode proteins in terms of 12 distinct SBBs with different roles in protein structure. Some SBBs correspond to classical regular secondary structures. Others correspond to a significant subdivision of their bounding regions previously considered to be a single pattern. The major contribution of the HMM is that this model implicitly takes into account the sequential connections between SBBs and thus describes the most probable pathways by which the blocks are connected to form the framework of the protein structures. Validation of the SBBs code was performed by extracting SBB series repeated in recoding proteins and examining their structural similarities. Preliminary results on the sequence specificity of SBBs suggest promising perspectives for the prediction of SBBs or series of SBBs from the protein sequences.

A selection that reports on protein-protein interactions within a thermophilic bacterium.

Science.gov (United States)

Nguyen, Peter Q; Silberg, Jonathan J

2010-07-01

Many proteins can be split into fragments that exhibit enhanced function upon fusion to interacting proteins. While this strategy has been widely used to create protein-fragment complementation assays (PCAs) for discovering protein-protein interactions within mesophilic organisms, similar assays have not yet been developed for studying natural and engineered protein complexes at the temperatures where thermophilic microbes grow. We describe the development of a selection for protein-protein interactions within Thermus thermophilus that is based upon growth complementation by fragments of Thermotoga neapolitana adenylate kinase (AK(Tn)). Complementation studies with an engineered thermophile (PQN1) that is not viable above 75 degrees C because its adk gene has been replaced by a Geobacillus stearothermophilus ortholog revealed that growth could be restored at 78 degrees C by a vector that coexpresses polypeptides corresponding to residues 1-79 and 80-220 of AK(Tn). In contrast, PQN1 growth was not complemented by AK(Tn) fragments harboring a C156A mutation within the zinc-binding tetracysteine motif unless these fragments were fused to Thermotoga maritima chemotaxis proteins that heterodimerize (CheA and CheY) or homodimerize (CheX). This enhanced complementation is interpreted as arising from chemotaxis protein-protein interactions, since AK(Tn)-C156A fragments having only one polypeptide fused to a chemotaxis protein did not complement PQN1 to the same extent. This selection increases the maximum temperature where a PCA can be used to engineer thermostable protein complexes and to map protein-protein interactions.

A Novel Approach for Protein-Named Entity Recognition and Protein-Protein Interaction Extraction

Directory of Open Access Journals (Sweden)

Meijing Li

2015-01-01

Full Text Available Many researchers focus on developing protein-named entity recognition (Protein-NER or PPI extraction systems. However, the studies about these two topics cannot be merged well; then existing PPI extraction systems’ Protein-NER still needs to improve. In this paper, we developed the protein-protein interaction extraction system named PPIMiner based on Support Vector Machine (SVM and parsing tree. PPIMiner consists of three main models: natural language processing (NLP model, Protein-NER model, and PPI discovery model. The Protein-NER model, which is named ProNER, identifies the protein names based on two methods: dictionary-based method and machine learning-based method. ProNER is capable of identifying more proteins than dictionary-based Protein-NER model in other existing systems. The final discovered PPIs extracted via PPI discovery model are represented in detail because we showed the protein interaction types and the occurrence frequency through two different methods. In the experiments, the result shows that the performances achieved by our ProNER and PPI discovery model are better than other existing tools. PPIMiner applied this protein-named entity recognition approach and parsing tree based PPI extraction method to improve the performance of PPI extraction. We also provide an easy-to-use interface to access PPIs database and an online system for PPIs extraction and Protein-NER.

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.

Modelling responses of broiler chickens to dietary balanced protein

NARCIS (Netherlands)

Eits, R.M.

2004-01-01

Protein is an important nutrient for growing broiler chickens, as it affects broiler performance, feed cost as well as nitrogen excretion. The objective of this dissertation was to develop a growth model for broiler chickens that could be easily used by practical nutritionists. The model should

Mathematical Modeling of Protein Misfolding Mechanisms in Neurological Diseases: A Historical Overview.

Science.gov (United States)

Carbonell, Felix; Iturria-Medina, Yasser; Evans, Alan C

2018-01-01

Protein misfolding refers to a process where proteins become structurally abnormal and lose their specific 3-dimensional spatial configuration. The histopathological presence of misfolded protein (MP) aggregates has been associated as the primary evidence of multiple neurological diseases, including Prion diseases, Alzheimer's disease, Parkinson's disease, and Creutzfeldt-Jacob disease. However, the exact mechanisms of MP aggregation and propagation, as well as their impact in the long-term patient's clinical condition are still not well understood. With this aim, a variety of mathematical models has been proposed for a better insight into the kinetic rate laws that govern the microscopic processes of protein aggregation. Complementary, another class of large-scale models rely on modern molecular imaging techniques for describing the phenomenological effects of MP propagation over the whole brain. Unfortunately, those neuroimaging-based studies do not take full advantage of the tremendous capabilities offered by the chemical kinetics modeling approach. Actually, it has been barely acknowledged that the vast majority of large-scale models have foundations on previous mathematical approaches that describe the chemical kinetics of protein replication and propagation. The purpose of the current manuscript is to present a historical review about the development of mathematical models for describing both microscopic processes that occur during the MP aggregation and large-scale events that characterize the progression of neurodegenerative MP-mediated diseases.

Model system-guided protein interaction mapping for virus isolated from phloem tissue

Science.gov (United States)

Potato leafroll virus (PLRV) is an agriculturally important phloem-limited pathogen that causes significant yield loss in potato (Solanum tuberosum) and a model virus in the Luteoviridae. Encoding only a small repertoire of viral proteins, PLRV relies on carefully orchestrated protein-protein intera...

A modeling strategy for G-protein coupled receptors

Directory of Open Access Journals (Sweden)

Anna Kahler

2016-03-01

Full Text Available Cell responses can be triggered via G-protein coupled receptors (GPCRs that interact with small molecules, peptides or proteins and transmit the signal over the membrane via structural changes to activate intracellular pathways. GPCRs are characterized by a rather low sequence similarity and exhibit structural differences even for functionally closely related GPCRs. An accurate structure prediction for GPCRs is therefore not straightforward. We propose a computational approach that relies on the generation of several independent models based on different template structures, which are subsequently refined by molecular dynamics simulations. A comparison of their conformational stability and the agreement with GPCR-typical structural features is then used to select a favorable model. This strategy was applied to predict the structure of the herpesviral chemokine receptor US28 by generating three independent models based on the known structures of the chemokine receptors CXCR1, CXCR4, and CCR5. Model refinement and evaluation suggested that the model based on CCR5 exhibits the most favorable structural properties. In particular, the GPCR-typical structural features, such as a conserved water cluster or conserved non-covalent contacts, are present to a larger extent in the model based on CCR5 compared to the other models. A final model validation based on the recently published US28 crystal structure confirms that the CCR5-based model is the most accurate and exhibits 80.8% correctly modeled residues within the transmembrane helices. The structural agreement between the selected model and the crystal structure suggests that our modeling strategy may also be more generally applicable to other GPCRs of unknown structure.

Markov state models of protein misfolding

Science.gov (United States)

Sirur, Anshul; De Sancho, David; Best, Robert B.

2016-02-01

Markov state models (MSMs) are an extremely useful tool for understanding the conformational dynamics of macromolecules and for analyzing MD simulations in a quantitative fashion. They have been extensively used for peptide and protein folding, for small molecule binding, and for the study of native ensemble dynamics. Here, we adapt the MSM methodology to gain insight into the dynamics of misfolded states. To overcome possible flaws in root-mean-square deviation (RMSD)-based metrics, we introduce a novel discretization approach, based on coarse-grained contact maps. In addition, we extend the MSM methodology to include "sink" states in order to account for the irreversibility (on simulation time scales) of processes like protein misfolding. We apply this method to analyze the mechanism of misfolding of tandem repeats of titin domains, and how it is influenced by confinement in a chaperonin-like cavity.

On the application of the MARTINI coarse-grained model to immersion of a protein in a phospholipid bilayer

Energy Technology Data Exchange (ETDEWEB)

Mustafa, Ghulam, E-mail: Ghulam.Mustafa@h-its.org, E-mail: rebecca.wade@h-its.org; Nandekar, Prajwal P.; Yu, Xiaofeng [Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Schloß-Wolfsbrunnenweg 35, 69118 Heidelberg (Germany); Wade, Rebecca C., E-mail: Ghulam.Mustafa@h-its.org, E-mail: rebecca.wade@h-its.org [Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Schloß-Wolfsbrunnenweg 35, 69118 Heidelberg (Germany); Zentrum für Molekulare Biologie der Universität Heidelberg, DKFZ-ZMBH Alliance, INF 282, 69120 Heidelberg (Germany); Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, INF 368, 69120 Heidelberg (Germany)

2015-12-28

An important step in the simulation of a membrane protein in a phospholipid bilayer is the correct immersion of the protein in the bilayer. Crystal structures are determined without the bilayer. Particularly for proteins with monotopic domains, it can be unclear how deeply and in which orientation the protein is being inserted in the membrane. We have previously developed a procedure combining coarse-grain (CG) with all-atom (AA) molecular dynamics (MD) simulations to insert and simulate a cytochrome P450 (CYP) possessing an N-terminal transmembrane helix connected by a flexible linker region to a globular domain that dips into the membrane. The CG simulations provide a computationally efficient means to explore different orientations and conformations of the CYP in the membrane. Converged configurations obtained in the CG simulations are then refined in AA simulations. Here, we tested different variants of the MARTINI CG model, differing in the water model, the treatment of long-range non-bonded interactions, and the implementation (GROMACS 4.5.5 vs 5.0.4), for this purpose. We examined the behavior of the models for simulating a 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) bilayer in water and for the immersion of CYP3A4 in a POPC bilayer, and compared the CG-MD results with the previously reported experimental and simulation results. We also tested the methodology on a set of four other CYPs. Finally, we propose an optimized protocol for modeling such protein-membrane systems that provides the most plausible configurations and is computationally efficient; this incorporates the standard non-polar water model and the GROMACS 5.0.4 implementation with a reaction field treatment of long-range interactions.

On the application of the MARTINI coarse-grained model to immersion of a protein in a phospholipid bilayer

International Nuclear Information System (INIS)

Mustafa, Ghulam; Nandekar, Prajwal P.; Yu, Xiaofeng; Wade, Rebecca C.

2015-01-01

An important step in the simulation of a membrane protein in a phospholipid bilayer is the correct immersion of the protein in the bilayer. Crystal structures are determined without the bilayer. Particularly for proteins with monotopic domains, it can be unclear how deeply and in which orientation the protein is being inserted in the membrane. We have previously developed a procedure combining coarse-grain (CG) with all-atom (AA) molecular dynamics (MD) simulations to insert and simulate a cytochrome P450 (CYP) possessing an N-terminal transmembrane helix connected by a flexible linker region to a globular domain that dips into the membrane. The CG simulations provide a computationally efficient means to explore different orientations and conformations of the CYP in the membrane. Converged configurations obtained in the CG simulations are then refined in AA simulations. Here, we tested different variants of the MARTINI CG model, differing in the water model, the treatment of long-range non-bonded interactions, and the implementation (GROMACS 4.5.5 vs 5.0.4), for this purpose. We examined the behavior of the models for simulating a 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) bilayer in water and for the immersion of CYP3A4 in a POPC bilayer, and compared the CG-MD results with the previously reported experimental and simulation results. We also tested the methodology on a set of four other CYPs. Finally, we propose an optimized protocol for modeling such protein-membrane systems that provides the most plausible configurations and is computationally efficient; this incorporates the standard non-polar water model and the GROMACS 5.0.4 implementation with a reaction field treatment of long-range interactions

Hot-spot analysis for drug discovery targeting protein-protein interactions.

Science.gov (United States)

Rosell, Mireia; Fernández-Recio, Juan

2018-04-01

Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.

Electrostatics of cysteine residues in proteins: parameterization and validation of a simple model.

Science.gov (United States)

Salsbury, Freddie R; Poole, Leslie B; Fetrow, Jacquelyn S

2012-11-01

One of the most popular and simple models for the calculation of pK(a) s from a protein structure is the semi-macroscopic electrostatic model MEAD. This model requires empirical parameters for each residue to calculate pK(a) s. Analysis of current, widely used empirical parameters for cysteine residues showed that they did not reproduce expected cysteine pK(a) s; thus, we set out to identify parameters consistent with the CHARMM27 force field that capture both the behavior of typical cysteines in proteins and the behavior of cysteines which have perturbed pK(a) s. The new parameters were validated in three ways: (1) calculation across a large set of typical cysteines in proteins (where the calculations are expected to reproduce expected ensemble behavior); (2) calculation across a set of perturbed cysteines in proteins (where the calculations are expected to reproduce the shifted ensemble behavior); and (3) comparison to experimentally determined pK(a) values (where the calculation should reproduce the pK(a) within experimental error). Both the general behavior of cysteines in proteins and the perturbed pK(a) in some proteins can be predicted reasonably well using the newly determined empirical parameters within the MEAD model for protein electrostatics. This study provides the first general analysis of the electrostatics of cysteines in proteins, with specific attention paid to capturing both the behavior of typical cysteines in a protein and the behavior of cysteines whose pK(a) should be shifted, and validation of force field parameters for cysteine residues. Copyright © 2012 Wiley Periodicals, Inc.

Sequence-based prediction of protein-binding sites in DNA: comparative study of two SVM models.

Science.gov (United States)

Park, Byungkyu; Im, Jinyong; Tuvshinjargal, Narankhuu; Lee, Wook; Han, Kyungsook

2014-11-01

As many structures of protein-DNA complexes have been known in the past years, several computational methods have been developed to predict DNA-binding sites in proteins. However, its inverse problem (i.e., predicting protein-binding sites in DNA) has received much less attention. One of the reasons is that the differences between the interaction propensities of nucleotides are much smaller than those between amino acids. Another reason is that DNA exhibits less diverse sequence patterns than protein. Therefore, predicting protein-binding DNA nucleotides is much harder than predicting DNA-binding amino acids. We computed the interaction propensity (IP) of nucleotide triplets with amino acids using an extensive dataset of protein-DNA complexes, and developed two support vector machine (SVM) models that predict protein-binding nucleotides from sequence data alone. One SVM model predicts protein-binding nucleotides using DNA sequence data alone, and the other SVM model predicts protein-binding nucleotides using both DNA and protein sequences. In a 10-fold cross-validation with 1519 DNA sequences, the SVM model that uses DNA sequence data only predicted protein-binding nucleotides with an accuracy of 67.0%, an F-measure of 67.1%, and a Matthews correlation coefficient (MCC) of 0.340. With an independent dataset of 181 DNAs that were not used in training, it achieved an accuracy of 66.2%, an F-measure 66.3% and a MCC of 0.324. Another SVM model that uses both DNA and protein sequences achieved an accuracy of 69.6%, an F-measure of 69.6%, and a MCC of 0.383 in a 10-fold cross-validation with 1519 DNA sequences and 859 protein sequences. With an independent dataset of 181 DNAs and 143 proteins, it showed an accuracy of 67.3%, an F-measure of 66.5% and a MCC of 0.329. Both in cross-validation and independent testing, the second SVM model that used both DNA and protein sequence data showed better performance than the first model that used DNA sequence data. To the best of

Bayesian Proteoform Modeling Improves Protein Quantification of Global Proteomic Measurements

Energy Technology Data Exchange (ETDEWEB)

Webb-Robertson, Bobbie-Jo M.; Matzke, Melissa M.; Datta, Susmita; Payne, Samuel H.; Kang, Jiyun; Bramer, Lisa M.; Nicora, Carrie D.; Shukla, Anil K.; Metz, Thomas O.; Rodland, Karin D.; Smith, Richard D.; Tardiff, Mark F.; McDermott, Jason E.; Pounds, Joel G.; Waters, Katrina M.

2014-12-01

As the capability of mass spectrometry-based proteomics has matured, tens of thousands of peptides can be measured simultaneously, which has the benefit of offering a systems view of protein expression. However, a major challenge is that with an increase in throughput, protein quantification estimation from the native measured peptides has become a computational task. A limitation to existing computationally-driven protein quantification methods is that most ignore protein variation, such as alternate splicing of the RNA transcript and post-translational modifications or other possible proteoforms, which will affect a significant fraction of the proteome. The consequence of this assumption is that statistical inference at the protein level, and consequently downstream analyses, such as network and pathway modeling, have only limited power for biomarker discovery. Here, we describe a Bayesian model (BP-Quant) that uses statistically derived peptides signatures to identify peptides that are outside the dominant pattern, or the existence of multiple over-expressed patterns to improve relative protein abundance estimates. It is a research-driven approach that utilizes the objectives of the experiment, defined in the context of a standard statistical hypothesis, to identify a set of peptides exhibiting similar statistical behavior relating to a protein. This approach infers that changes in relative protein abundance can be used as a surrogate for changes in function, without necessarily taking into account the effect of differential post-translational modifications, processing, or splicing in altering protein function. We verify the approach using a dilution study from mouse plasma samples and demonstrate that BP-Quant achieves similar accuracy as the current state-of-the-art methods at proteoform identification with significantly better specificity. BP-Quant is available as a MatLab ® and R packages at https://github.com/PNNL-Comp-Mass-Spec/BP-Quant.

Modelling Transcapillary Transport of Fluid and Proteins in Hemodialysis Patients.

Directory of Open Access Journals (Sweden)

Mauro Pietribiasi

Full Text Available The kinetics of protein transport to and from the vascular compartment play a major role in the determination of fluid balance and plasma refilling during hemodialysis (HD sessions. In this study we propose a whole-body mathematical model describing water and protein shifts across the capillary membrane during HD and compare its output to clinical data while evaluating the impact of choosing specific values for selected parameters.The model follows a two-compartment structure (vascular and interstitial space and is based on balance equations of protein mass and water volume in each compartment. The capillary membrane was described according to the three-pore theory. Two transport parameters, the fractional contribution of large pores (αLP and the total hydraulic conductivity (LpS of the capillary membrane, were estimated from patient data. Changes in the intensity and direction of individual fluid and solute flows through each part of the transport system were analyzed in relation to the choice of different values of small pores radius and fractional conductivity, lymphatic sensitivity to hydraulic pressure, and steady-state interstitial-to-plasma protein concentration ratio.The estimated values of LpS and αLP were respectively 10.0 ± 8.4 mL/min/mmHg (mean ± standard deviation and 0.062 ± 0.041. The model was able to predict with good accuracy the profiles of plasma volume and serum total protein concentration in most of the patients (average root-mean-square deviation < 2% of the measured value.The applied model provides a mechanistic interpretation of fluid transport processes induced by ultrafiltration during HD, using a minimum of tuned parameters and assumptions. The simulated values of individual flows through each kind of pore and lymphatic absorption rate yielded by the model may suggest answers to unsolved questions on the relative impact of these not-measurable quantities on total vascular refilling and fluid balance.

Protein modelling of triterpene synthase genes from mangrove plants using Phyre2 and Swiss-model

Science.gov (United States)

Basyuni, M.; Wati, R.; Sulistiyono, N.; Hayati, R.; Sumardi; Oku, H.; Baba, S.; Sagami, H.

2018-03-01

Molecular cloning of five oxidosqualene cyclases (OSC) genes from Bruguiera gymnorrhiza, Kandelia candel, and Rhizophora stylosa had previously been cloned, characterized, and encoded mono and -multi triterpene synthases. The present study analyzed protein modelling of triterpene synthase genes from mangrove using Phyre2 and Swiss-model. The diversity was noted within protein modelling of triterpene synthases using Phyre2 from sequence identity (38-43%) and residue (696-703). RsM2 was distinguishable from others for template structure; it used lanosterol synthase as a template (PDB ID: w6j.1.A). By contrast, other genes used human lanosterol synthase (1w6k.1.A). The predicted bind sites were correlated with the product of triterpene synthase, the product of BgbAS was β-amyrin, while RsM1 contained a significant amount of β-amyrin. Similarly BgLUS and KcMS, both main products was lupeol, on the other hand, RsM2 with the outcome of taraxerol. Homology modelling revealed that 696 residues of BgbAS, BgLUS, RsM1, and RsM2 (91-92% of the amino acid sequence) had been modelled with 100% confidence by the single highest scoring template using Phyre2. This coverage was higher than Swiss-model (85-90%). The present study suggested that molecular cloning of triterpene genes provides useful tools for studying the protein modelling related regulation of isoprenoids biosynthesis in mangrove forests.

A feature-based approach to modeling protein-protein interaction hot spots.

Science.gov (United States)

Cho, Kyu-il; Kim, Dongsup; Lee, Doheon

2009-05-01

Identifying features that effectively represent the energetic contribution of an individual interface residue to the interactions between proteins remains problematic. Here, we present several new features and show that they are more effective than conventional features. By combining the proposed features with conventional features, we develop a predictive model for interaction hot spots. Initially, 54 multifaceted features, composed of different levels of information including structure, sequence and molecular interaction information, are quantified. Then, to identify the best subset of features for predicting hot spots, feature selection is performed using a decision tree. Based on the selected features, a predictive model for hot spots is created using support vector machine (SVM) and tested on an independent test set. Our model shows better overall predictive accuracy than previous methods such as the alanine scanning methods Robetta and FOLDEF, and the knowledge-based method KFC. Subsequent analysis yields several findings about hot spots. As expected, hot spots have a larger relative surface area burial and are more hydrophobic than other residues. Unexpectedly, however, residue conservation displays a rather complicated tendency depending on the types of protein complexes, indicating that this feature is not good for identifying hot spots. Of the selected features, the weighted atomic packing density, relative surface area burial and weighted hydrophobicity are the top 3, with the weighted atomic packing density proving to be the most effective feature for predicting hot spots. Notably, we find that hot spots are closely related to pi-related interactions, especially pi . . . pi interactions.

Biosphere Process Model Report

Energy Technology Data Exchange (ETDEWEB)

J. Schmitt

2000-05-25

To evaluate the postclosure performance of a potential monitored geologic repository at Yucca Mountain, a Total System Performance Assessment (TSPA) will be conducted. Nine Process Model Reports (PMRs), including this document, are being developed to summarize the technical basis for each of the process models supporting the TSPA model. These reports cover the following areas: (1) Integrated Site Model; (2) Unsaturated Zone Flow and Transport; (3) Near Field Environment; (4) Engineered Barrier System Degradation, Flow, and Transport; (5) Waste Package Degradation; (6) Waste Form Degradation; (7) Saturated Zone Flow and Transport; (8) Biosphere; and (9) Disruptive Events. Analysis/Model Reports (AMRs) contain the more detailed technical information used to support TSPA and the PMRs. The AMRs consists of data, analyses, models, software, and supporting documentation that will be used to defend the applicability of each process model for evaluating the postclosure performance of the potential Yucca Mountain repository system. This documentation will ensure the traceability of information from its source through its ultimate use in the TSPA-Site Recommendation (SR) and in the National Environmental Policy Act (NEPA) analysis processes. The objective of the Biosphere PMR is to summarize (1) the development of the biosphere model, and (2) the Biosphere Dose Conversion Factors (BDCFs) developed for use in TSPA. The Biosphere PMR does not present or summarize estimates of potential radiation doses to human receptors. Dose calculations are performed as part of TSPA and will be presented in the TSPA documentation. The biosphere model is a component of the process to evaluate postclosure repository performance and regulatory compliance for a potential monitored geologic repository at Yucca Mountain, Nevada. The biosphere model describes those exposure pathways in the biosphere by which radionuclides released from a potential repository could reach a human receptor

Biosphere Process Model Report

International Nuclear Information System (INIS)

Schmitt, J.

2000-01-01

To evaluate the postclosure performance of a potential monitored geologic repository at Yucca Mountain, a Total System Performance Assessment (TSPA) will be conducted. Nine Process Model Reports (PMRs), including this document, are being developed to summarize the technical basis for each of the process models supporting the TSPA model. These reports cover the following areas: (1) Integrated Site Model; (2) Unsaturated Zone Flow and Transport; (3) Near Field Environment; (4) Engineered Barrier System Degradation, Flow, and Transport; (5) Waste Package Degradation; (6) Waste Form Degradation; (7) Saturated Zone Flow and Transport; (8) Biosphere; and (9) Disruptive Events. Analysis/Model Reports (AMRs) contain the more detailed technical information used to support TSPA and the PMRs. The AMRs consists of data, analyses, models, software, and supporting documentation that will be used to defend the applicability of each process model for evaluating the postclosure performance of the potential Yucca Mountain repository system. This documentation will ensure the traceability of information from its source through its ultimate use in the TSPA-Site Recommendation (SR) and in the National Environmental Policy Act (NEPA) analysis processes. The objective of the Biosphere PMR is to summarize (1) the development of the biosphere model, and (2) the Biosphere Dose Conversion Factors (BDCFs) developed for use in TSPA. The Biosphere PMR does not present or summarize estimates of potential radiation doses to human receptors. Dose calculations are performed as part of TSPA and will be presented in the TSPA documentation. The biosphere model is a component of the process to evaluate postclosure repository performance and regulatory compliance for a potential monitored geologic repository at Yucca Mountain, Nevada. The biosphere model describes those exposure pathways in the biosphere by which radionuclides released from a potential repository could reach a human receptor

Biosphere Model Report

Energy Technology Data Exchange (ETDEWEB)

D.W. Wu; A.J. Smith

2004-11-08

The purpose of this report is to document the biosphere model, the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), which describes radionuclide transport processes in the biosphere and associated human exposure that may arise as the result of radionuclide release from the geologic repository at Yucca Mountain. The biosphere model is one of the process models that support the Yucca Mountain Project (YMP) Total System Performance Assessment (TSPA) for the license application (LA), TSPA-LA. The ERMYN provides the capability of performing human radiation dose assessments. This report documents the biosphere model, which includes: (1) Describing the reference biosphere, human receptor, exposure scenarios, and primary radionuclides for each exposure scenario (Section 6.1); (2) Developing a biosphere conceptual model using site-specific features, events, and processes (FEPs) (Section 6.2), the reference biosphere (Section 6.1.1), the human receptor (Section 6.1.2), and approximations (Sections 6.3.1.4 and 6.3.2.4); (3) Building a mathematical model using the biosphere conceptual model (Section 6.3) and published biosphere models (Sections 6.4 and 6.5); (4) Summarizing input parameters for the mathematical model, including the uncertainty associated with input values (Section 6.6); (5) Identifying improvements in the ERMYN compared with the model used in previous biosphere modeling (Section 6.7); (6) Constructing an ERMYN implementation tool (model) based on the biosphere mathematical model using GoldSim stochastic simulation software (Sections 6.8 and 6.9); (7) Verifying the ERMYN by comparing output from the software with hand calculations to ensure that the GoldSim implementation is correct (Section 6.10); (8) Validating the ERMYN by corroborating it with published biosphere models; comparing conceptual models, mathematical models, and numerical results (Section 7).

Biosphere Model Report

International Nuclear Information System (INIS)

D.W. Wu; A.J. Smith

2004-01-01

The purpose of this report is to document the biosphere model, the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), which describes radionuclide transport processes in the biosphere and associated human exposure that may arise as the result of radionuclide release from the geologic repository at Yucca Mountain. The biosphere model is one of the process models that support the Yucca Mountain Project (YMP) Total System Performance Assessment (TSPA) for the license application (LA), TSPA-LA. The ERMYN provides the capability of performing human radiation dose assessments. This report documents the biosphere model, which includes: (1) Describing the reference biosphere, human receptor, exposure scenarios, and primary radionuclides for each exposure scenario (Section 6.1); (2) Developing a biosphere conceptual model using site-specific features, events, and processes (FEPs) (Section 6.2), the reference biosphere (Section 6.1.1), the human receptor (Section 6.1.2), and approximations (Sections 6.3.1.4 and 6.3.2.4); (3) Building a mathematical model using the biosphere conceptual model (Section 6.3) and published biosphere models (Sections 6.4 and 6.5); (4) Summarizing input parameters for the mathematical model, including the uncertainty associated with input values (Section 6.6); (5) Identifying improvements in the ERMYN compared with the model used in previous biosphere modeling (Section 6.7); (6) Constructing an ERMYN implementation tool (model) based on the biosphere mathematical model using GoldSim stochastic simulation software (Sections 6.8 and 6.9); (7) Verifying the ERMYN by comparing output from the software with hand calculations to ensure that the GoldSim implementation is correct (Section 6.10); (8) Validating the ERMYN by corroborating it with published biosphere models; comparing conceptual models, mathematical models, and numerical results (Section 7)

A Mathematical Model of the Effect of Immunogenicity on Therapeutic Protein Pharmacokinetics

OpenAIRE

Chen, Xiaoying; Hickling, Timothy; Kraynov, Eugenia; Kuang, Bing; Parng, Chuenlei; Vicini, Paolo

2013-01-01

A mathematical pharmacokinetic/anti-drug-antibody (PK/ADA) model was constructed for quantitatively assessing immunogenicity for therapeutic proteins. The model is inspired by traditional pharmacokinetic/pharmacodynamic (PK/PD) models, and is based on the observed impact of ADA on protein drug clearance. The hypothesis for this work is that altered drug PK contains information about the extent and timing of ADA generation. By fitting drug PK profiles while accounting for ADA-mediated drug cle...

From the Protein's Perspective: The Benefits and Challenges of Protein Structure-Based Pharmacophore Modeling

NARCIS (Netherlands)

Sanders, M.P.A.; McGuire, R; Roumen, L.; de Esch, I.J.P.; de Vlieg, J; Klomp, J.P.G; de Graaf, C.

2011-01-01

A pharmacophore describes the arrangement of molecular features a ligand must contain to efficaciously bind a receptor. Pharmacophore models are developed to improve molecular understanding of ligand-protein interactions, and can be used as a tool to identify novel compounds that fulfil the

Analysing the origin of long-range interactions in proteins using lattice models

Directory of Open Access Journals (Sweden)

Unger Ron

2009-01-01

Full Text Available Abstract Background Long-range communication is very common in proteins but the physical basis of this phenomenon remains unclear. In order to gain insight into this problem, we decided to explore whether long-range interactions exist in lattice models of proteins. Lattice models of proteins have proven to capture some of the basic properties of real proteins and, thus, can be used for elucidating general principles of protein stability and folding. Results Using a computational version of double-mutant cycle analysis, we show that long-range interactions emerge in lattice models even though they are not an input feature of them. The coupling energy of both short- and long-range pairwise interactions is found to become more positive (destabilizing in a linear fashion with increasing 'contact-frequency', an entropic term that corresponds to the fraction of states in the conformational ensemble of the sequence in which the pair of residues is in contact. A mathematical derivation of the linear dependence of the coupling energy on 'contact-frequency' is provided. Conclusion Our work shows how 'contact-frequency' should be taken into account in attempts to stabilize proteins by introducing (or stabilizing contacts in the native state and/or through 'negative design' of non-native contacts.

Protein model discrimination using mutational sensitivity derived from deep sequencing.

Science.gov (United States)

Adkar, Bharat V; Tripathi, Arti; Sahoo, Anusmita; Bajaj, Kanika; Goswami, Devrishi; Chakrabarti, Purbani; Swarnkar, Mohit K; Gokhale, Rajesh S; Varadarajan, Raghavan

2012-02-08

A major bottleneck in protein structure prediction is the selection of correct models from a pool of decoys. Relative activities of ∼1,200 individual single-site mutants in a saturation library of the bacterial toxin CcdB were estimated by determining their relative populations using deep sequencing. This phenotypic information was used to define an empirical score for each residue (RankScore), which correlated with the residue depth, and identify active-site residues. Using these correlations, ∼98% of correct models of CcdB (RMSD ≤ 4Å) were identified from a large set of decoys. The model-discrimination methodology was further validated on eleven different monomeric proteins using simulated RankScore values. The methodology is also a rapid, accurate way to obtain relative activities of each mutant in a large pool and derive sequence-structure-function relationships without protein isolation or characterization. It can be applied to any system in which mutational effects can be monitored by a phenotypic readout. Copyright © 2012 Elsevier Ltd. All rights reserved.

Toward a rigorous network of protein-protein interactions of the model sulfate reducer Desulfovibrio vulgaris Hildenborough

Energy Technology Data Exchange (ETDEWEB)

Chhabra, S.R.; Joachimiak, M.P.; Petzold, C.J.; Zane, G.M.; Price, M.N.; Gaucher, S.; Reveco, S.A.; Fok, V.; Johanson, A.R.; Batth, T.S.; Singer, M.; Chandonia, J.M.; Joyner, D.; Hazen, T.C.; Arkin, A.P.; Wall, J.D.; Singh, A.K.; Keasling, J.D.

2011-05-01

Protein–protein interactions offer an insight into cellular processes beyond what may be obtained by the quantitative functional genomics tools of proteomics and transcriptomics. The aforementioned tools have been extensively applied to study E. coli and other aerobes and more recently to study the stress response behavior of Desulfovibrio 5 vulgaris Hildenborough, a model anaerobe and sulfate reducer. In this paper we present the first attempt to identify protein-protein interactions in an obligate anaerobic bacterium. We used suicide vector-assisted chromosomal modification of 12 open reading frames encoded by this sulfate reducer to append an eight amino acid affinity tag to the carboxy-terminus of the chosen proteins. Three biological replicates of the 10 ‘pulled-down’ proteins were separated and analyzed using liquid chromatography-mass spectrometry. Replicate agreement ranged between 35% and 69%. An interaction network among 12 bait and 90 prey proteins was reconstructed based on 134 bait-prey interactions computationally identified to be of high confidence. We discuss the biological significance of several unique metabolic features of D. vulgaris revealed by this protein-protein interaction data 15 and protein modifications that were observed. These include the distinct role of the putative carbon monoxide-induced hydrogenase, unique electron transfer routes associated with different oxidoreductases, and the possible role of methylation in regulating sulfate reduction.

Towards a rigorous network of protein-protein interactions of the model sulfate reducer Desulfovibrio vulgaris Hildenborough.

Directory of Open Access Journals (Sweden)

Swapnil R Chhabra

Full Text Available Protein-protein interactions offer an insight into cellular processes beyond what may be obtained by the quantitative functional genomics tools of proteomics and transcriptomics. The aforementioned tools have been extensively applied to study Escherichia coli and other aerobes and more recently to study the stress response behavior of Desulfovibrio vulgaris Hildenborough, a model obligate anaerobe and sulfate reducer and the subject of this study. Here we carried out affinity purification followed by mass spectrometry to reconstruct an interaction network among 12 chromosomally encoded bait and 90 prey proteins based on 134 bait-prey interactions identified to be of high confidence. Protein-protein interaction data are often plagued by the lack of adequate controls and replication analyses necessary to assess confidence in the results, including identification of potential false positives. We addressed these issues through the use of biological replication, exponentially modified protein abundance indices, results from an experimental negative control, and a statistical test to assign confidence to each putative interacting pair applicable to small interaction data studies. We discuss the biological significance of metabolic features of D. vulgaris revealed by these protein-protein interaction data and the observed protein modifications. These include the distinct role of the putative carbon monoxide-induced hydrogenase, unique electron transfer routes associated with different oxidoreductases, and the possible role of methylation in regulating sulfate reduction.

Modelling small-angle scattering data from complex protein-lipid systems

DEFF Research Database (Denmark)

Kynde, Søren Andreas Røssell

This thesis consists of two parts. The rst part is divided into five chapters. Chapter 1 gives a general introduction to the bio-molecular systems that have been studied. These are membrane proteins and their lipid environments in the form of phospholipid nanodiscs. Membrane proteins...... the techniques very well suited for the study of the nanodisc system. Chapter 3 explains two different modelling approaches that can be used in the analysis of small-angle scattering data from lipid-protein complexes. These are the continuous approach where the system of interest is modelled as a few regular...... combine the bene ts of each of the methods and give unique structural information about relevant bio-molecular complexes in solution. Chapter 4 describes the work behind a proposal of a small-angle neutron scattering instrument for the European Spallation Source under construction in Lund. The instrument...

Lessons from Animal Models of Cytoplasmic Intermediate Filament Proteins.

Science.gov (United States)

Bouameur, Jamal-Eddine; Magin, Thomas M

Cytoplasmic intermediate filaments (IFs) represent a major cytoskeletal network contributing to cell shape, adhesion and migration as well as to tissue resilience and renewal in numerous bilaterians, including mammals. The observation that IFs are dispensable in cultured mammalian cells, but cause tissue-specific, life-threatening disorders, has pushed the need to investigate their function in vivo. In keeping with human disease, the deletion or mutation of murine IF genes resulted in highly specific pathologies. Epidermal keratins, together with desmin, are essential to protect corresponding tissues against mechanical force but also participate in stabilizing cell adhesion and in inflammatory signalling. Surprisingly, other IF proteins contribute to tissue integrity to a much lesser extent than anticipated, pointing towards their role in stress situations. In support, the overexpression of small chaperones or the interference with inflammatory signalling in several settings has been shown to rescue severe tissue pathologies that resulted from the expression of mutant IF proteins. It stills remains an open issue whether the wide range of IF disorders share similar pathomechanisms. Moreover, we lack an understanding how IF proteins participate in signalling processes. Now, with a large number of mouse models in hand, the next challenge will be to develop organotypic cell culture models to dissect pathomechanisms at the molecular level, to employ Crispr/Cas-mediated genome engineering to optimize models and, finally, to combine available animal models with medicinal chemistry for the development of molecular therapies.

Structure of human Rad51 protein filament from molecular modeling and site-specific linear dichroism spectroscopy

KAUST Repository

Reymer, A.

2009-07-08

To get mechanistic insight into the DNA strand-exchange reaction of homologous recombination, we solved a filament structure of a human Rad51 protein, combining molecular modeling with experimental data. We build our structure on reported structures for central and N-terminal parts of pure (uncomplexed) Rad51 protein by aid of linear dichroism spectroscopy, providing angular orientations of substituted tyrosine residues of Rad51-dsDNA filaments in solution. The structure, validated by comparison with an electron microscopy density map and results from mutation analysis, is proposed to represent an active solution structure of the nucleo-protein complex. An inhomogeneously stretched double-stranded DNA fitted into the filament emphasizes the strategic positioning of 2 putative DNA-binding loops in a way that allows us speculate about their possibly distinct roles in nucleo-protein filament assembly and DNA strand-exchange reaction. The model suggests that the extension of a single-stranded DNA molecule upon binding of Rad51 is ensured by intercalation of Tyr-232 of the L1 loop, which might act as a docking tool, aligning protein monomers along the DNA strand upon filament assembly. Arg-235, also sitting on L1, is in the right position to make electrostatic contact with the phosphate backbone of the other DNA strand. The L2 loop position and its more ordered compact conformation makes us propose that this loop has another role, as a binding site for an incoming double-stranded DNA. Our filament structure and spectroscopic approach open the possibility of analyzing details along the multistep path of the strand-exchange reaction.

Global optimization of proteins using a dynamical lattice model: Ground states and energy landscapes

OpenAIRE

Dressel, F.; Kobe, S.

2004-01-01

A simple approach is proposed to investigate the protein structure. Using a low complexity model, a simple pairwise interaction and the concept of global optimization, we are able to calculate ground states of proteins, which are in agreement with experimental data. All possible model structures of small proteins are available below a certain energy threshold. The exact lowenergy landscapes for the trp cage protein (1L2Y) is presented showing the connectivity of all states and energy barriers.

Protein structure modelling and evaluation based on a 4-distance description of side-chain interactions

Directory of Open Access Journals (Sweden)

Inbar Yuval

2010-07-01

Full Text Available Abstract Background Accurate evaluation and modelling of residue-residue interactions within and between proteins is a key aspect of computational structure prediction including homology modelling, protein-protein docking, refinement of low-resolution structures, and computational protein design. Results Here we introduce a method for accurate protein structure modelling and evaluation based on a novel 4-distance description of residue-residue interaction geometry. Statistical 4-distance preferences were extracted from high-resolution protein structures and were used as a basis for a knowledge-based potential, called Hunter. We demonstrate that 4-distance description of side chain interactions can be used reliably to discriminate the native structure from a set of decoys. Hunter ranked the native structure as the top one in 217 out of 220 high-resolution decoy sets, in 25 out of 28 "Decoys 'R' Us" decoy sets and in 24 out of 27 high-resolution CASP7/8 decoy sets. The same concept was applied to side chain modelling in protein structures. On a set of very high-resolution protein structures the average RMSD was 1.47 Å for all residues and 0.73 Å for buried residues, which is in the range of attainable accuracy for a model. Finally, we show that Hunter performs as good or better than other top methods in homology modelling based on results from the CASP7 experiment. The supporting web site http://bioinfo.weizmann.ac.il/hunter/ was developed to enable the use of Hunter and for visualization and interactive exploration of 4-distance distributions. Conclusions Our results suggest that Hunter can be used as a tool for evaluation and for accurate modelling of residue-residue interactions in protein structures. The same methodology is applicable to other areas involving high-resolution modelling of biomolecules.

Modeling of allergen proteins found in sea food products

Directory of Open Access Journals (Sweden)

Nataly Galán-Freyle

2012-06-01

Full Text Available Shellfish are a source of food allergens, and their consumption is the cause of severe allergic reactions in humans. Tropomyosins, a family of muscle proteins, have been identified as the major allergens in shellfish and mollusks species. Nevertheless, few experimentally determined three-dimensional structures are available in the Protein Data Base (PDB. In this study, 3D models of several homologous of tropomyosins present in marine shellfish and mollusk species (Chaf 1, Met e1, Hom a1, Per v1, and Pen a1 were constructed, validated, and their immunoglobulin E binding epitopes were identified using bioinformatics tools. All protein models for these allergens consisted of long alpha-helices. Chaf 1, Met e1, and Hom a1 had six conserved regions with sequence similarities to known epitopes, whereas Per v1 and Pen a1 contained only one. Lipophilic potentials of identified epitopes revealed a high propensity of hydrophobic amino acids in the immunoglobulin E binding site. This information could be useful to design tropomyosin-specific immunotherapy for sea food allergies.

Model to predict inhomogeneous protein-sugar distribution in powders prepared by spray drying

NARCIS (Netherlands)

Grasmeijer, Niels; Frijlink, Henderik W.; Hinrichs, Wouter L. J.

2016-01-01

A protein can be stabilized by spray drying an aqueous solution of the protein and a sugar, thereby incorporating the protein into a glassy sugar matrix. For optimal stability, the protein should be homogeneously distributed inside the sugar matrix. The aim of this study was to develop a model that

Animal Models of Congenital Cardiomyopathies Associated With Mutations in Z-Line Proteins.

Science.gov (United States)

Bang, Marie-Louise

2017-01-01

The cardiac Z-line at the boundary between sarcomeres is a multiprotein complex connecting the contractile apparatus with the cytoskeleton and the extracellular matrix. The Z-line is important for efficient force generation and transmission as well as the maintenance of structural stability and integrity. Furthermore, it is a nodal point for intracellular signaling, in particular mechanosensing and mechanotransduction. Mutations in various genes encoding Z-line proteins have been associated with different cardiomyopathies, including dilated cardiomyopathy, hypertrophic cardiomyopathy, arrhythmogenic right ventricular cardiomyopathy, restrictive cardiomyopathy, and left ventricular noncompaction, and mutations even within the same gene can cause widely different pathologies. Animal models have contributed to a great advancement in the understanding of the physiological function of Z-line proteins and the pathways leading from mutations in Z-line proteins to cardiomyopathy, although genotype-phenotype prediction remains a great challenge. This review presents an overview of the currently available animal models for Z-line and Z-line associated proteins involved in human cardiomyopathies with special emphasis on knock-in and transgenic mouse models recapitulating the clinical phenotypes of human cardiomyopathy patients carrying mutations in Z-line proteins. Pros and cons of mouse models will be discussed and a future outlook will be given. J. Cell. Physiol. 232: 38-52, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Rubber particle proteins, HbREF and HbSRPP, show different interactions with model membranes.

Science.gov (United States)

Berthelot, Karine; Lecomte, Sophie; Estevez, Yannick; Zhendre, Vanessa; Henry, Sarah; Thévenot, Julie; Dufourc, Erick J; Alves, Isabel D; Peruch, Frédéric

2014-01-01

The biomembrane surrounding rubber particles from the hevea latex is well known for its content of numerous allergen proteins. HbREF (Hevb1) and HbSRPP (Hevb3) are major components, linked on rubber particles, and they have been shown to be involved in rubber synthesis or quality (mass regulation), but their exact function is still to be determined. In this study we highlighted the different modes of interactions of both recombinant proteins with various membrane models (lipid monolayers, liposomes or supported bilayers, and multilamellar vesicles) to mimic the latex particle membrane. We combined various biophysical methods (polarization-modulation-infrared reflection-adsorption spectroscopy (PM-IRRAS)/ellipsometry, attenuated-total reflectance Fourier-transform infrared (ATR-FTIR), solid-state nuclear magnetic resonance (NMR), plasmon waveguide resonance (PWR), fluorescence spectroscopy) to elucidate their interactions. Small rubber particle protein (SRPP) shows less affinity than rubber elongation factor (REF) for the membranes but displays a kind of "covering" effect on the lipid headgroups without disturbing the membrane integrity. Its structure is conserved in the presence of lipids. Contrarily, REF demonstrates higher membrane affinity with changes in its aggregation properties, the amyloid nature of REF, which we previously reported, is not favored in the presence of lipids. REF binds and inserts into membranes. The membrane integrity is highly perturbed, and we suspect that REF is even able to remove lipids from the membrane leading to the formation of mixed micelles. These two homologous proteins show affinity to all membrane models tested but neatly differ in their interacting features. This could imply differential roles on the surface of rubber particles. © 2013.

ROCK PROPERTIES MODEL ANALYSIS MODEL REPORT

International Nuclear Information System (INIS)

Clinton Lum

2002-01-01

The purpose of this Analysis and Model Report (AMR) is to document Rock Properties Model (RPM) 3.1 with regard to input data, model methods, assumptions, uncertainties and limitations of model results, and qualification status of the model. The report also documents the differences between the current and previous versions and validation of the model. The rock properties models are intended principally for use as input to numerical physical-process modeling, such as of ground-water flow and/or radionuclide transport. The constraints, caveats, and limitations associated with this model are discussed in the appropriate text sections that follow. This work was conducted in accordance with the following planning documents: WA-0344, ''3-D Rock Properties Modeling for FY 1998'' (SNL 1997, WA-0358), ''3-D Rock Properties Modeling for FY 1999'' (SNL 1999), and the technical development plan, Rock Properties Model Version 3.1, (CRWMS MandO 1999c). The Interim Change Notice (ICNs), ICN 02 and ICN 03, of this AMR were prepared as part of activities being conducted under the Technical Work Plan, TWP-NBS-GS-000003, ''Technical Work Plan for the Integrated Site Model, Process Model Report, Revision 01'' (CRWMS MandO 2000b). The purpose of ICN 03 is to record changes in data input status due to data qualification and verification activities. These work plans describe the scope, objectives, tasks, methodology, and implementing procedures for model construction. The constraints, caveats, and limitations associated with this model are discussed in the appropriate text sections that follow. The work scope for this activity consists of the following: (1) Conversion of the input data (laboratory measured porosity data, x-ray diffraction mineralogy, petrophysical calculations of bound water, and petrophysical calculations of porosity) for each borehole into stratigraphic coordinates; (2) Re-sampling and merging of data sets; (3) Development of geostatistical simulations of porosity; (4

Mixing Energy Models in Genetic Algorithms for On-Lattice Protein Structure Prediction

Directory of Open Access Journals (Sweden)

Mahmood A. Rashid

2013-01-01

Full Text Available Protein structure prediction (PSP is computationally a very challenging problem. The challenge largely comes from the fact that the energy function that needs to be minimised in order to obtain the native structure of a given protein is not clearly known. A high resolution 20×20 energy model could better capture the behaviour of the actual energy function than a low resolution energy model such as hydrophobic polar. However, the fine grained details of the high resolution interaction energy matrix are often not very informative for guiding the search. In contrast, a low resolution energy model could effectively bias the search towards certain promising directions. In this paper, we develop a genetic algorithm that mainly uses a high resolution energy model for protein structure evaluation but uses a low resolution HP energy model in focussing the search towards exploring structures that have hydrophobic cores. We experimentally show that this mixing of energy models leads to significant lower energy structures compared to the state-of-the-art results.

Oral sensitization to food proteins: A Brown Norway rat model

NARCIS (Netherlands)

Knippels, L.M.J.; Penninks, A.H.; Spanhaak, S.; Houben, G.F.

1998-01-01

Background: Although several in vivo antigenicity assays using parenteral immunization are operational, no adequate enteral sensitization models are available to study food allergy and allergenicity of food proteins. Objective: This paper describes the development of an enteral model for food

Model documentation report: Short-Term Hydroelectric Generation Model

International Nuclear Information System (INIS)

1993-08-01

The purpose of this report is to define the objectives of the Short- Term Hydroelectric Generation Model (STHGM), describe its basic approach, and to provide details on the model structure. This report is intended as a reference document for model analysts, users, and the general public. Documentation of the model is in accordance with the Energy Information Administration's (AYE) legal obligation to provide adequate documentation in support of its models (Public Law 94-385, Section 57.b.2). The STHGM performs a short-term (18 to 27- month) forecast of hydroelectric generation in the United States using an autoregressive integrated moving average (UREMIA) time series model with precipitation as an explanatory variable. The model results are used as input for the short-term Energy Outlook

Building alternate protein structures using the elastic network model.

Science.gov (United States)

Yang, Qingyi; Sharp, Kim A

2009-02-15

We describe a method for efficiently generating ensembles of alternate, all-atom protein structures that (a) differ significantly from the starting structure, (b) have good stereochemistry (bonded geometry), and (c) have good steric properties (absence of atomic overlap). The method uses reconstruction from a series of backbone framework structures that are obtained from a modified elastic network model (ENM) by perturbation along low-frequency normal modes. To ensure good quality backbone frameworks, the single force parameter ENM is modified by introducing two more force parameters to characterize the interaction between the consecutive carbon alphas and those within the same secondary structure domain. The relative stiffness of the three parameters is parameterized to reproduce B-factors, while maintaining good bonded geometry. After parameterization, violations of experimental Calpha-Calpha distances and Calpha-Calpha-Calpha pseudo angles along the backbone are reduced to less than 1%. Simultaneously, the average B-factor correlation coefficient improves to R = 0.77. Two applications illustrate the potential of the approach. (1) 102,051 protein backbones spanning a conformational space of 15 A root mean square deviation were generated from 148 nonredundant proteins in the PDB database, and all-atom models with minimal bonded and nonbonded violations were produced from this ensemble of backbone structures using the SCWRL side chain building program. (2) Improved backbone templates for homology modeling. Fifteen query sequences were each modeled on two targets. For each of the 30 target frameworks, dozens of improved templates could be produced In all cases, improved full atom homology models resulted, of which 50% could be identified blind using the D-Fire statistical potential. (c) 2008 Wiley-Liss, Inc.

Protein-Anchoring Therapy of Biglycan for Mdx Mouse Model of Duchenne Muscular Dystrophy.

Science.gov (United States)

Ito, Mikako; Ehara, Yuka; Li, Jin; Inada, Kosuke; Ohno, Kinji

2017-05-01

Duchenne muscular dystrophy (DMD) is a devastating muscle disease caused by loss-of-function mutations in DMD encoding dystrophin. No rational therapy is currently available. Utrophin is a paralog of dystrophin and is highly expressed at the neuromuscular junction. In mdx mice, utrophin is naturally upregulated throughout the muscle fibers, which mitigates muscular dystrophy. Protein-anchoring therapy was previously reported, in which a recombinant extracellular matrix (ECM) protein is delivered to and anchored to a specific target using its proprietary binding domains. Being prompted by a report that intramuscular and intraperitoneal injection of an ECM protein, biglycan, upregulates expression of utrophin and ameliorates muscle pathology in mdx mice, protein-anchoring therapy was applied to mdx mice. Recombinant adeno-associated virus serotype 8 (rAAV8) carrying hBGN encoding human biglycan was intravenously injected into 5-week-old mdx mice. The rAAV8-hBGN treatment improved motor deficits and decreased plasma creatine kinase activities. In muscle sections of treated mice, the number of central myonuclei and the distribution of myofiber sizes were improved. The treated mice increased gene expressions of utrophin and β1-syntrophin, as well as protein expressions of biglycan, utrophin, γ-sarcoglycan, dystrobrevin, and α1-syntrophin. The expression of hBGN in the skeletal muscle of the treated mice was 1.34-fold higher than that of the native mouse Bgn (mBgn). The low transduction efficiency and improved motor functions suggest that biglycan expressed in a small number of muscle fibers was likely to have been secreted and anchored to the cell surface throughout the whole muscular fibers. It is proposed that the protein-anchoring strategy can be applied not only to deficiency of an ECM protein as previously reported, but also to augmentation of a naturally induced ECM protein.

Application of model bread baking in the examination of arabinoxylan-protein complexes in rye bread.

Science.gov (United States)

Buksa, Krzysztof

2016-09-05

The changes in molecular mass of arabinoxylan (AX) and protein caused by bread baking process were examined using a model rye bread. Instead of the normal flour, the dough contained starch, water-extractable AX and protein which were isolated from rye wholemeal. From the crumb of selected model breads, starch was removed releasing AX-protein complexes, which were further examined by size exclusion chromatography. On the basis of the research, it was concluded that optimum model mix can be composed of 3-6% AX and 3-6% rye protein isolate at 94-88% of rye starch meaning with the most similar properties to low extraction rye flour. Application of model rye bread allowed to examine the interactions between AX and proteins. Bread baked with a share of AX, rye protein and starch, from which the complexes of the highest molar mass were isolated, was characterized by the strongest structure of the bread crumb. Copyright © 2016 Elsevier Ltd. All rights reserved.

Proteomic Identification of Altered Cerebral Proteins in the Complex Regional Pain Syndrome Animal Model

Directory of Open Access Journals (Sweden)

Francis Sahngun Nahm

2014-01-01

Full Text Available Background. Complex regional pain syndrome (CRPS is a rare but debilitating pain disorder. Although the exact pathophysiology of CRPS is not fully understood, central and peripheral mechanisms might be involved in the development of this disorder. To reveal the central mechanism of CRPS, we conducted a proteomic analysis of rat cerebrum using the chronic postischemia pain (CPIP model, a novel experimental model of CRPS. Materials and Methods. After generating the CPIP animal model, we performed a proteomic analysis of the rat cerebrum using a multidimensional protein identification technology, and screened the proteins differentially expressed between the CPIP and control groups. Results. A total of 155 proteins were differentially expressed between the CPIP and control groups: 125 increased and 30 decreased; expressions of proteins related to cell signaling, synaptic plasticity, regulation of cell proliferation, and cytoskeletal formation were increased in the CPIP group. However, proenkephalin A, cereblon, and neuroserpin were decreased in CPIP group. Conclusion. Altered expression of cerebral proteins in the CPIP model indicates cerebral involvement in the pathogenesis of CRPS. Further study is required to elucidate the roles of these proteins in the development and maintenance of CRPS.

Proteomic identification of altered cerebral proteins in the complex regional pain syndrome animal model.

Science.gov (United States)

Nahm, Francis Sahngun; Park, Zee-Yong; Nahm, Sang-Soep; Kim, Yong Chul; Lee, Pyung Bok

2014-01-01

Complex regional pain syndrome (CRPS) is a rare but debilitating pain disorder. Although the exact pathophysiology of CRPS is not fully understood, central and peripheral mechanisms might be involved in the development of this disorder. To reveal the central mechanism of CRPS, we conducted a proteomic analysis of rat cerebrum using the chronic postischemia pain (CPIP) model, a novel experimental model of CRPS. After generating the CPIP animal model, we performed a proteomic analysis of the rat cerebrum using a multidimensional protein identification technology, and screened the proteins differentially expressed between the CPIP and control groups. Results. A total of 155 proteins were differentially expressed between the CPIP and control groups: 125 increased and 30 decreased; expressions of proteins related to cell signaling, synaptic plasticity, regulation of cell proliferation, and cytoskeletal formation were increased in the CPIP group. However, proenkephalin A, cereblon, and neuroserpin were decreased in CPIP group. Altered expression of cerebral proteins in the CPIP model indicates cerebral involvement in the pathogenesis of CRPS. Further study is required to elucidate the roles of these proteins in the development and maintenance of CRPS.

Mannan-binding protein forms complexes with alpha-2-macroglobulin. A protein model for the interaction

DEFF Research Database (Denmark)

Storgaard, P; Holm Nielsen, E; Skriver, E

1995-01-01

We report that alpha-2-macroglobulin (alpha 2M) can form complexes with a high molecular weight porcine mannan-binding protein (pMBP-28). The alpha 2M/pMBP-28 complexes was isolated by PEG-precipitation and affinity chromatography on mannan-Sepharose, protein A-Sepharose and anti-IgM Sepharose......-PAGE, which reacted with antibodies against alpha 2M and pMBP-28, respectively, in Western blotting. Furthermore, alpha 2M/pMBP-28 complexes were demonstrated by electron microscopy. Fractionation of pMBP-containing D-mannose eluate from mannan-Sepharose on Superose 6 showed two protein peaks which reacted...... with anti-C1 s antibodies in ELISA, one of about 650-800 kDa, which in addition contained pMBP-28 and anti-alpha 2M reactive material, the other with an M(r) of 100-150 kDa. The latter peak revealed rhomboid molecules (7 x 15 nm) in the electron microscope and a 67 kDa band in SDS-PAGE under reducing...

Protein loop modeling using a new hybrid energy function and its application to modeling in inaccurate structural environments.

Directory of Open Access Journals (Sweden)

Hahnbeom Park

Full Text Available Protein loop modeling is a tool for predicting protein local structures of particular interest, providing opportunities for applications involving protein structure prediction and de novo protein design. Until recently, the majority of loop modeling methods have been developed and tested by reconstructing loops in frameworks of experimentally resolved structures. In many practical applications, however, the protein loops to be modeled are located in inaccurate structural environments. These include loops in model structures, low-resolution experimental structures, or experimental structures of different functional forms. Accordingly, discrepancies in the accuracy of the structural environment assumed in development of the method and that in practical applications present additional challenges to modern loop modeling methods. This study demonstrates a new strategy for employing a hybrid energy function combining physics-based and knowledge-based components to help tackle this challenge. The hybrid energy function is designed to combine the strengths of each energy component, simultaneously maintaining accurate loop structure prediction in a high-resolution framework structure and tolerating minor environmental errors in low-resolution structures. A loop modeling method based on global optimization of this new energy function is tested on loop targets situated in different levels of environmental errors, ranging from experimental structures to structures perturbed in backbone as well as side chains and template-based model structures. The new method performs comparably to force field-based approaches in loop reconstruction in crystal structures and better in loop prediction in inaccurate framework structures. This result suggests that higher-accuracy predictions would be possible for a broader range of applications. The web server for this method is available at http://galaxy.seoklab.org/loop with the PS2 option for the scoring function.

Protein Simulation Data in the Relational Model.

Science.gov (United States)

Simms, Andrew M; Daggett, Valerie

2012-10-01

High performance computing is leading to unprecedented volumes of data. Relational databases offer a robust and scalable model for storing and analyzing scientific data. However, these features do not come without a cost-significant design effort is required to build a functional and efficient repository. Modeling protein simulation data in a relational database presents several challenges: the data captured from individual simulations are large, multi-dimensional, and must integrate with both simulation software and external data sites. Here we present the dimensional design and relational implementation of a comprehensive data warehouse for storing and analyzing molecular dynamics simulations using SQL Server.

Denatured state is critical in determining the properties of model proteins designed on different folds

DEFF Research Database (Denmark)

Amatori, Andrea; Ferkinghoff-Borg, Jesper; Tiana, Guido

2008-01-01

The thermodynamics of proteins designed on three common folds (SH3, chymotrypsin inhibitor 2 [CI2], and protein G) is studied with a simplified C alpha, model and compared with the thermodynamics of proteins designed on random-generated folds. The model allows to design sequences to fold within a...

A study of quality measures for protein threading models

Directory of Open Access Journals (Sweden)

Rychlewski Leszek

2001-08-01

Full Text Available Abstract Background Prediction of protein structures is one of the fundamental challenges in biology today. To fully understand how well different prediction methods perform, it is necessary to use measures that evaluate their performance. Every two years, starting in 1994, the CASP (Critical Assessment of protein Structure Prediction process has been organized to evaluate the ability of different predictors to blindly predict the structure of proteins. To capture different features of the models, several measures have been developed during the CASP processes. However, these measures have not been examined in detail before. In an attempt to develop fully automatic measures that can be used in CASP, as well as in other type of benchmarking experiments, we have compared twenty-one measures. These measures include the measures used in CASP3 and CASP2 as well as have measures introduced later. We have studied their ability to distinguish between the better and worse models submitted to CASP3 and the correlation between them. Results Using a small set of 1340 models for 23 different targets we show that most methods correlate with each other. Most pairs of measures show a correlation coefficient of about 0.5. The correlation is slightly higher for measures of similar types. We found that a significant problem when developing automatic measures is how to deal with proteins of different length. Also the comparisons between different measures is complicated as many measures are dependent on the size of the target. We show that the manual assessment can be reproduced to about 70% using automatic measures. Alignment independent measures, detects slightly more of the models with the correct fold, while alignment dependent measures agree better when selecting the best models for each target. Finally we show that using automatic measures would, to a large extent, reproduce the assessors ranking of the predictors at CASP3. Conclusions We show that given a

Structure Based Thermostability Prediction Models for Protein Single Point Mutations with Machine Learning Tools.

Directory of Open Access Journals (Sweden)

Lei Jia

Full Text Available Thermostability issue of protein point mutations is a common occurrence in protein engineering. An application which predicts the thermostability of mutants can be helpful for guiding decision making process in protein design via mutagenesis. An in silico point mutation scanning method is frequently used to find "hot spots" in proteins for focused mutagenesis. ProTherm (http://gibk26.bio.kyutech.ac.jp/jouhou/Protherm/protherm.html is a public database that consists of thousands of protein mutants' experimentally measured thermostability. Two data sets based on two differently measured thermostability properties of protein single point mutations, namely the unfolding free energy change (ddG and melting temperature change (dTm were obtained from this database. Folding free energy change calculation from Rosetta, structural information of the point mutations as well as amino acid physical properties were obtained for building thermostability prediction models with informatics modeling tools. Five supervised machine learning methods (support vector machine, random forests, artificial neural network, naïve Bayes classifier, K nearest neighbor and partial least squares regression are used for building the prediction models. Binary and ternary classifications as well as regression models were built and evaluated. Data set redundancy and balancing, the reverse mutations technique, feature selection, and comparison to other published methods were discussed. Rosetta calculated folding free energy change ranked as the most influential features in all prediction models. Other descriptors also made significant contributions to increasing the accuracy of the prediction models.

PIN-G – A novel reporter for imaging and defining the effects of trafficking signals in membrane proteins

Directory of Open Access Journals (Sweden)

Hu Weiwen

2006-03-01

Full Text Available Abstract Background The identification of protein trafficking signals, and their interacting mechanisms, is a fundamental objective of modern biology. Unfortunately, the analysis of trafficking signals is complicated by their topography, hierarchical nature and regulation. Powerful strategies to test candidate motifs include their ability to direct simpler reporter proteins, to which they are fused, to the appropriate cellular compartment. However, present reporters are limited by their endogenous expression, paucity of cloning sites, and difficult detection in live cells. Results Consequently, we have engineered a mammalian expression vector encoding a novel trafficking reporter – pIN-G – consisting of a simple, type I integral protein bearing permissive intra/extracellular cloning sites, green fluorescent protein (GFP, cMyc and HA epitope tags. Fluorescence imaging, flow cytometry and biochemical assays of transfected HEK293 cells, confirm the size, topology and surface expression of PIN-G. Moreover, a pIN-G fusion construct, containing a Trans-Golgi Network (TGN targeting determinant, internalises rapidly from the cell surface and localises to the TGN. Additionally, another PIN-G fusion protein and its mutants reveal trafficking determinants in the cytoplasmic carboxy terminus of Kv1.4 voltage-gated potassium channels. Conclusion Together, these data indicate that pIN-G is a versatile, powerful, new reporter for analysing signals controlling membrane protein trafficking, surface expression and dynamics.

Improved contact prediction in proteins: Using pseudolikelihoods to infer Potts models

Science.gov (United States)

Ekeberg, Magnus; Lövkvist, Cecilia; Lan, Yueheng; Weigt, Martin; Aurell, Erik

2013-01-01

Spatially proximate amino acids in a protein tend to coevolve. A protein's three-dimensional (3D) structure hence leaves an echo of correlations in the evolutionary record. Reverse engineering 3D structures from such correlations is an open problem in structural biology, pursued with increasing vigor as more and more protein sequences continue to fill the data banks. Within this task lies a statistical inference problem, rooted in the following: correlation between two sites in a protein sequence can arise from firsthand interaction but can also be network-propagated via intermediate sites; observed correlation is not enough to guarantee proximity. To separate direct from indirect interactions is an instance of the general problem of inverse statistical mechanics, where the task is to learn model parameters (fields, couplings) from observables (magnetizations, correlations, samples) in large systems. In the context of protein sequences, the approach has been referred to as direct-coupling analysis. Here we show that the pseudolikelihood method, applied to 21-state Potts models describing the statistical properties of families of evolutionarily related proteins, significantly outperforms existing approaches to the direct-coupling analysis, the latter being based on standard mean-field techniques. This improved performance also relies on a modified score for the coupling strength. The results are verified using known crystal structures of specific sequence instances of various protein families. Code implementing the new method can be found at http://plmdca.csc.kth.se/.

The Ising model for prediction of disordered residues from protein sequence alone

International Nuclear Information System (INIS)

Lobanov, Michail Yu; Galzitskaya, Oxana V

2011-01-01

Intrinsically disordered regions serve as molecular recognition elements, which play an important role in the control of many cellular processes and signaling pathways. It is useful to be able to predict positions of disordered residues and disordered regions in protein chains using protein sequence alone. A new method (IsUnstruct) based on the Ising model for prediction of disordered residues from protein sequence alone has been developed. According to this model, each residue can be in one of two states: ordered or disordered. The model is an approximation of the Ising model in which the interaction term between neighbors has been replaced by a penalty for changing between states (the energy of border). The IsUnstruct has been compared with other available methods and found to perform well. The method correctly finds 77% of disordered residues as well as 87% of ordered residues in the CASP8 database, and 72% of disordered residues as well as 85% of ordered residues in the DisProt database

The ModFOLD4 server for the quality assessment of 3D protein models

OpenAIRE

McGuffin, Liam J.; Buenavista, Maria T.; Roche, Daniel B.

2013-01-01

Once you have generated a 3D model of a protein,\\ud how do you know whether it bears any resemblance\\ud to the actual structure? To determine the usefulness\\ud of 3D models of proteins, they must be assessed in\\ud terms of their quality by methods that predict their\\ud similarity to the native structure. The ModFOLD4\\ud server is the latest version of our leading independent\\ud server for the estimation of both the global and\\ud local (per-residue) quality of 3D protein models. The\\ud server ...

Exact protein distributions for stochastic models of gene expression using partitioning of Poisson processes.

Science.gov (United States)

Pendar, Hodjat; Platini, Thierry; Kulkarni, Rahul V

2013-04-01

Stochasticity in gene expression gives rise to fluctuations in protein levels across a population of genetically identical cells. Such fluctuations can lead to phenotypic variation in clonal populations; hence, there is considerable interest in quantifying noise in gene expression using stochastic models. However, obtaining exact analytical results for protein distributions has been an intractable task for all but the simplest models. Here, we invoke the partitioning property of Poisson processes to develop a mapping that significantly simplifies the analysis of stochastic models of gene expression. The mapping leads to exact protein distributions using results for mRNA distributions in models with promoter-based regulation. Using this approach, we derive exact analytical results for steady-state and time-dependent distributions for the basic two-stage model of gene expression. Furthermore, we show how the mapping leads to exact protein distributions for extensions of the basic model that include the effects of posttranscriptional and posttranslational regulation. The approach developed in this work is widely applicable and can contribute to a quantitative understanding of stochasticity in gene expression and its regulation.

Exact protein distributions for stochastic models of gene expression using partitioning of Poisson processes

Science.gov (United States)

Pendar, Hodjat; Platini, Thierry; Kulkarni, Rahul V.

2013-04-01

Stochasticity in gene expression gives rise to fluctuations in protein levels across a population of genetically identical cells. Such fluctuations can lead to phenotypic variation in clonal populations; hence, there is considerable interest in quantifying noise in gene expression using stochastic models. However, obtaining exact analytical results for protein distributions has been an intractable task for all but the simplest models. Here, we invoke the partitioning property of Poisson processes to develop a mapping that significantly simplifies the analysis of stochastic models of gene expression. The mapping leads to exact protein distributions using results for mRNA distributions in models with promoter-based regulation. Using this approach, we derive exact analytical results for steady-state and time-dependent distributions for the basic two-stage model of gene expression. Furthermore, we show how the mapping leads to exact protein distributions for extensions of the basic model that include the effects of posttranscriptional and posttranslational regulation. The approach developed in this work is widely applicable and can contribute to a quantitative understanding of stochasticity in gene expression and its regulation.

Reporter-Based Synthetic Genetic Array Analysis: A Functional Genomics Approach for Investigating Transcript or Protein Abundance Using Fluorescent Proteins in Saccharomyces cerevisiae.

Science.gov (United States)

Göttert, Hendrikje; Mattiazzi Usaj, Mojca; Rosebrock, Adam P; Andrews, Brenda J

2018-01-01

Fluorescent reporter genes have long been used to quantify various cell features such as transcript and protein abundance. Here, we describe a method, reporter synthetic genetic array (R-SGA) analysis, which allows for the simultaneous quantification of any fluorescent protein readout in thousands of yeast strains using an automated pipeline. R-SGA combines a fluorescent reporter system with standard SGA analysis and can be used to examine any array-based strain collection available to the yeast community. This protocol describes the R-SGA methodology for screening different arrays of yeast mutants including the deletion collection, a collection of temperature-sensitive strains for the assessment of essential yeast genes and a collection of inducible overexpression strains. We also present an alternative pipeline for the analysis of R-SGA output strains using flow cytometry of cells in liquid culture. Data normalization for both pipelines is discussed.

Protein carbonylation, protein aggregation and neuronal cell death in a murine model of multiple sclerosis

Science.gov (United States)

Dasgupta, Anushka

Many studies have suggested that oxidative stress plays an important role in the pathophysiology of both multiple sclerosis (MS) and its animal model experimental autoimmune encephalomyelitis (EAE). Yet, the mechanism by which oxidative stress leads to tissue damage in these disorders is unclear. Recent work from our laboratory has revealed that protein carbonylation, a major oxidative modification caused by severe and/or chronic oxidative stress conditions, is elevated in MS and EAE. Furthermore, protein carbonylation has been shown to alter protein structure leading to misfolding/aggregation. These findings prompted me to hypothesize that carbonylated proteins, formed as a consequence of oxidative stress and/or decreased proteasomal activity, promote protein aggregation to mediate neuronal apoptosis in vitro and in EAE. To test this novel hypothesis, I first characterized protein carbonylation, protein aggregation and apoptosis along the spinal cord during the course of myelin-oligodendrocyte glycoprotein (MOG)35-55 peptide-induced EAE in C57BL/6 mice [Chapter 2]. The results show that carbonylated proteins accumulate throughout the course of the disease, albeit by different mechanisms: increased oxidative stress in acute EAE and decreased proteasomal activity in chronic EAE. I discovered not only that there is a temporal correlation between protein carbonylation and apoptosis but also that carbonyl levels are significantly higher in apoptotic cells. A high number of juxta-nuclear and cytoplasmic protein aggregates containing the majority of the oxidized proteins are also present during the course of EAE, which seems to be due to reduced autophagy. In chapter 3, I show that when gluthathione levels are reduced to those in EAE spinal cord, both neuron-like PC12 (nPC12) cells and primary neuronal cultures accumulate carbonylated proteins and undergo cell death (both by necrosis and apoptosis). Immunocytochemical and biochemical studies also revealed a temporal

A mathematical model for generating bipartite graphs and its application to protein networks

International Nuclear Information System (INIS)

Nacher, J C; Ochiai, T; Hayashida, M; Akutsu, T

2009-01-01

Complex systems arise in many different contexts from large communication systems and transportation infrastructures to molecular biology. Most of these systems can be organized into networks composed of nodes and interacting edges. Here, we present a theoretical model that constructs bipartite networks with the particular feature that the degree distribution can be tuned depending on the probability rate of fundamental processes. We then use this model to investigate protein-domain networks. A protein can be composed of up to hundreds of domains. Each domain represents a conserved sequence segment with specific functional tasks. We analyze the distribution of domains in Homo sapiens and Arabidopsis thaliana organisms and the statistical analysis shows that while (a) the number of domain types shared by k proteins exhibits a power-law distribution, (b) the number of proteins composed of k types of domains decays as an exponential distribution. The proposed mathematical model generates bipartite graphs and predicts the emergence of this mixing of (a) power-law and (b) exponential distributions. Our theoretical and computational results show that this model requires (1) growth process and (2) copy mechanism.

A mathematical model for generating bipartite graphs and its application to protein networks

Science.gov (United States)

Nacher, J. C.; Ochiai, T.; Hayashida, M.; Akutsu, T.

2009-12-01

Complex systems arise in many different contexts from large communication systems and transportation infrastructures to molecular biology. Most of these systems can be organized into networks composed of nodes and interacting edges. Here, we present a theoretical model that constructs bipartite networks with the particular feature that the degree distribution can be tuned depending on the probability rate of fundamental processes. We then use this model to investigate protein-domain networks. A protein can be composed of up to hundreds of domains. Each domain represents a conserved sequence segment with specific functional tasks. We analyze the distribution of domains in Homo sapiens and Arabidopsis thaliana organisms and the statistical analysis shows that while (a) the number of domain types shared by k proteins exhibits a power-law distribution, (b) the number of proteins composed of k types of domains decays as an exponential distribution. The proposed mathematical model generates bipartite graphs and predicts the emergence of this mixing of (a) power-law and (b) exponential distributions. Our theoretical and computational results show that this model requires (1) growth process and (2) copy mechanism.

A mathematical model for generating bipartite graphs and its application to protein networks

Energy Technology Data Exchange (ETDEWEB)

Nacher, J C [Department of Complex Systems, Future University-Hakodate (Japan); Ochiai, T [Faculty of Engineering, Toyama Prefectural University (Japan); Hayashida, M; Akutsu, T [Bioinformatics Center, Institute for Chemical Research, Kyoto University (Japan)

2009-12-04

Complex systems arise in many different contexts from large communication systems and transportation infrastructures to molecular biology. Most of these systems can be organized into networks composed of nodes and interacting edges. Here, we present a theoretical model that constructs bipartite networks with the particular feature that the degree distribution can be tuned depending on the probability rate of fundamental processes. We then use this model to investigate protein-domain networks. A protein can be composed of up to hundreds of domains. Each domain represents a conserved sequence segment with specific functional tasks. We analyze the distribution of domains in Homo sapiens and Arabidopsis thaliana organisms and the statistical analysis shows that while (a) the number of domain types shared by k proteins exhibits a power-law distribution, (b) the number of proteins composed of k types of domains decays as an exponential distribution. The proposed mathematical model generates bipartite graphs and predicts the emergence of this mixing of (a) power-law and (b) exponential distributions. Our theoretical and computational results show that this model requires (1) growth process and (2) copy mechanism.

Formulation of probabilistic models of protein structure in atomic detail using the reference ratio method

DEFF Research Database (Denmark)

Valentin, Jan B.; Andreetta, Christian; Boomsma, Wouter

2014-01-01

We propose a method to formulate probabilistic models of protein structure in atomic detail, for a given amino acid sequence, based on Bayesian principles, while retaining a close link to physics. We start from two previously developed probabilistic models of protein structure on a local length s....... The results indicate that the proposed method and the probabilistic models show considerable promise for probabilistic protein structure prediction and related applications. © 2013 Wiley Periodicals, Inc....

The unfolded protein response has a protective role in yeast models of classic galactosemia

Directory of Open Access Journals (Sweden)

Evandro A. De-Souza

2014-01-01

Full Text Available Classic galactosemia is a human autosomal recessive disorder caused by mutations in the GALT gene (GAL7 in yeast, which encodes the enzyme galactose-1-phosphate uridyltransferase. Here we show that the unfolded protein response pathway is triggered by galactose in two yeast models of galactosemia: lithium-treated cells and the gal7Δ mutant. The synthesis of galactose-1-phosphate is essential to trigger the unfolded protein response under these conditions because the deletion of the galactokinase-encoding gene GAL1 completely abolishes unfolded protein response activation and galactose toxicity. Impairment of the unfolded protein response in both yeast models makes cells even more sensitive to galactose, unmasking its cytotoxic effect. These results indicate that endoplasmic reticulum stress is induced under galactosemic conditions and underscores the importance of the unfolded protein response pathway to cellular adaptation in these models of classic galactosemia.

A generic whole body physiologically based pharmacokinetic model for therapeutic proteins in PK-Sim.

Science.gov (United States)

Niederalt, Christoph; Kuepfer, Lars; Solodenko, Juri; Eissing, Thomas; Siegmund, Hans-Ulrich; Block, Michael; Willmann, Stefan; Lippert, Jörg

2018-04-01

Proteins are an increasingly important class of drugs used as therapeutic as well as diagnostic agents. A generic physiologically based pharmacokinetic (PBPK) model was developed in order to represent at whole body level the fundamental mechanisms driving the distribution and clearance of large molecules like therapeutic proteins. The model was built as an extension of the PK-Sim model for small molecules incorporating (i) the two-pore formalism for drug extravasation from blood plasma to interstitial space, (ii) lymph flow, (iii) endosomal clearance and (iv) protection from endosomal clearance by neonatal Fc receptor (FcRn) mediated recycling as especially relevant for antibodies. For model development and evaluation, PK data was used for compounds with a wide range of solute radii. The model supports the integration of knowledge gained during all development phases of therapeutic proteins, enables translation from pre-clinical species to human and allows predictions of tissue concentration profiles which are of relevance for the analysis of on-target pharmacodynamic effects as well as off-target toxicity. The current implementation of the model replaces the generic protein PBPK model available in PK-Sim since version 4.2 and becomes part of the Open Systems Pharmacology Suite.

eMatchSite: sequence order-independent structure alignments of ligand binding pockets in protein models.

Directory of Open Access Journals (Sweden)

Michal Brylinski

2014-09-01

Full Text Available Detecting similarities between ligand binding sites in the absence of global homology between target proteins has been recognized as one of the critical components of modern drug discovery. Local binding site alignments can be constructed using sequence order-independent techniques, however, to achieve a high accuracy, many current algorithms for binding site comparison require high-quality experimental protein structures, preferably in the bound conformational state. This, in turn, complicates proteome scale applications, where only various quality structure models are available for the majority of gene products. To improve the state-of-the-art, we developed eMatchSite, a new method for constructing sequence order-independent alignments of ligand binding sites in protein models. Large-scale benchmarking calculations using adenine-binding pockets in crystal structures demonstrate that eMatchSite generates accurate alignments for almost three times more protein pairs than SOIPPA. More importantly, eMatchSite offers a high tolerance to structural distortions in ligand binding regions in protein models. For example, the percentage of correctly aligned pairs of adenine-binding sites in weakly homologous protein models is only 4-9% lower than those aligned using crystal structures. This represents a significant improvement over other algorithms, e.g. the performance of eMatchSite in recognizing similar binding sites is 6% and 13% higher than that of SiteEngine using high- and moderate-quality protein models, respectively. Constructing biologically correct alignments using predicted ligand binding sites in protein models opens up the possibility to investigate drug-protein interaction networks for complete proteomes with prospective systems-level applications in polypharmacology and rational drug repositioning. eMatchSite is freely available to the academic community as a web-server and a stand-alone software distribution at http://www.brylinski.org/ematchsite.

Green Fluorescent Protein as a Model for Protein Crystal Growth Studies

Science.gov (United States)

Agena, Sabine; Smith, Lori; Karr, Laurel; Pusey, Marc

1998-01-01

Green fluorescent protein (GFP) from jellyfish Aequorea Victoria has become a popular marker for e.g. mutagenesis work. Its fluorescent property, which originates from a chromophore located in the center of the molecule, makes it widely applicable as a research too]. GFP clones have been produced with a variety of spectral properties, such as blue and yellow emitting species. The protein is a single chain of molecular weight 27 kDa and its structure has been determined at 1.9 Angstrom resolution. The combination of GFP's fluorescent property, the knowledge of its several crystallization conditions, and its increasing use in biophysical and biochemical studies, all led us to consider it as a model material for macromolecular crystal growth studies. Initial preparations of GFP were from E.coli with yields of approximately 5 mg/L of culture media. Current yields are now in the 50 - 120 mg/L range, and we hope to further increase this by expression of the GFP gene in the Pichia system. The results of these efforts and of preliminary crystal growth studies will be presented.

Formulation of probabilistic models of protein structure in atomic detail using the reference ratio method.

Science.gov (United States)

Valentin, Jan B; Andreetta, Christian; Boomsma, Wouter; Bottaro, Sandro; Ferkinghoff-Borg, Jesper; Frellsen, Jes; Mardia, Kanti V; Tian, Pengfei; Hamelryck, Thomas

2014-02-01

We propose a method to formulate probabilistic models of protein structure in atomic detail, for a given amino acid sequence, based on Bayesian principles, while retaining a close link to physics. We start from two previously developed probabilistic models of protein structure on a local length scale, which concern the dihedral angles in main chain and side chains, respectively. Conceptually, this constitutes a probabilistic and continuous alternative to the use of discrete fragment and rotamer libraries. The local model is combined with a nonlocal model that involves a small number of energy terms according to a physical force field, and some information on the overall secondary structure content. In this initial study we focus on the formulation of the joint model and the evaluation of the use of an energy vector as a descriptor of a protein's nonlocal structure; hence, we derive the parameters of the nonlocal model from the native structure without loss of generality. The local and nonlocal models are combined using the reference ratio method, which is a well-justified probabilistic construction. For evaluation, we use the resulting joint models to predict the structure of four proteins. The results indicate that the proposed method and the probabilistic models show considerable promise for probabilistic protein structure prediction and related applications. Copyright © 2013 Wiley Periodicals, Inc.

Induction of complement proteins in a mouse model for cerebral microvascular Aβ deposition

Directory of Open Access Journals (Sweden)

DeFilippis Kelly

2007-09-01

Full Text Available Abstract The deposition of amyloid β-protein (Aβ in cerebral vasculature, known as cerebral amyloid angiopathy (CAA, is a common pathological feature of Alzheimer's disease and related disorders. In familial forms of CAA single mutations in the Aβ peptide have been linked to the increase of vascular Aβ deposits accompanied by a strong localized activation of glial cells and elevated expression of neuroinflammatory mediators including complement proteins. We have developed human amyloid-β precursor protein transgenic mice harboring two CAA Aβ mutations (Dutch E693Q and Iowa D694N that mimic the prevalent cerebral microvascular Aβ deposition observed in those patients, and the Swedish mutations (K670N/M671L to increase Aβ production. In these Tg-SwDI mice, we have reported predominant fibrillar Aβ along microvessels in the thalamic region and diffuse plaques in cortical region. Concurrently, activated microglia and reactive astrocytes have been detected primarily in association with fibrillar cerebral microvascular Aβ in this model. Here we show that three native complement components in classical and alternative complement pathways, C1q, C3, and C4, are elevated in Tg-SwDI mice in regions rich in fibrillar microvascular Aβ. Immunohistochemical staining of all three proteins was increased in thalamus, hippocampus, and subiculum, but not frontal cortex. Western blot analysis showed significant increases of all three proteins in the thalamic region (with hippocampus as well as the cortical region, except C3 that was below detection level in cortex. Also, in the thalamic region (with hippocampus, C1q and C3 mRNAs were significantly up-regulated. These complement proteins appeared to be expressed largely by activated microglial cells associated with the fibrillar microvascular Aβ deposits. Our findings demonstrate that Tg-SwDI mice exhibit elevated complement protein expression in response to fibrillar vascular Aβ deposition that is

Fragment-based modelling of single stranded RNA bound to RNA recognition motif containing proteins

Science.gov (United States)

de Beauchene, Isaure Chauvot; de Vries, Sjoerd J.; Zacharias, Martin

2016-01-01

Abstract Protein-RNA complexes are important for many biological processes. However, structural modeling of such complexes is hampered by the high flexibility of RNA. Particularly challenging is the docking of single-stranded RNA (ssRNA). We have developed a fragment-based approach to model the structure of ssRNA bound to a protein, based on only the protein structure, the RNA sequence and conserved contacts. The conformational diversity of each RNA fragment is sampled by an exhaustive library of trinucleotides extracted from all known experimental protein–RNA complexes. The method was applied to ssRNA with up to 12 nucleotides which bind to dimers of the RNA recognition motifs (RRMs), a highly abundant eukaryotic RNA-binding domain. The fragment based docking allows a precise de novo atomic modeling of protein-bound ssRNA chains. On a benchmark of seven experimental ssRNA–RRM complexes, near-native models (with a mean heavy-atom deviation of <3 Å from experiment) were generated for six out of seven bound RNA chains, and even more precise models (deviation < 2 Å) were obtained for five out of seven cases, a significant improvement compared to the state of the art. The method is not restricted to RRMs but was also successfully applied to Pumilio RNA binding proteins. PMID:27131381

Modeling the time evolution of the nanoparticle-protein corona in a body fluid.

Directory of Open Access Journals (Sweden)

Daniele Dell'Orco

Full Text Available BACKGROUND: Nanoparticles in contact with biological fluids interact with proteins and other biomolecules, thus forming a dynamic corona whose composition varies over time due to continuous protein association and dissociation events. Eventually equilibrium is reached, at which point the continued exchange will not affect the composition of the corona. RESULTS: We developed a simple and effective dynamic model of the nanoparticle protein corona in a body fluid, namely human plasma. The model predicts the time evolution and equilibrium composition of the corona based on affinities, stoichiometries and rate constants. An application to the interaction of human serum albumin, high density lipoprotein (HDL and fibrinogen with 70 nm N-iso-propylacrylamide/N-tert-butylacrylamide copolymer nanoparticles is presented, including novel experimental data for HDL. CONCLUSIONS: The simple model presented here can easily be modified to mimic the interaction of the nanoparticle protein corona with a novel biological fluid or compartment once new data will be available, thus opening novel applications in nanotoxicity and nanomedicine.

Interaction of sucralose with whey protein: Experimental and molecular modeling studies

Science.gov (United States)

Zhang, Hongmei; Sun, Shixin; Wang, Yanqing; Cao, Jian

2017-12-01

The objective of this research was to study the interactions of sucralose with whey protein isolate (WPI) by using the three-dimensional fluorescence spectroscopy, circular dichroism spectroscopy and molecular modeling. The results showed that the peptide strands structure of WPI had been changed by sucralose. Sucralose binding induced the secondary structural changes and increased content of aperiodic structure of WPI. Sucralose decreased the thermal stability of WPI and acted as a structure destabilizer during the thermal unfolding process of protein. In addition, the existence of sucralose decreased the reversibility of the unfolding of WPI. Nonetheless, sucralose-WPI complex was less stable than protein alone. The molecular modeling result showed that van der Waals and hydrogen bonding interactions contribute to the complexation free binding energy. There are more than one possible binding sites of WPI with sucralose by surface binding mode.

PCVMZM: Using the Probabilistic Classification Vector Machines Model Combined with a Zernike Moments Descriptor to Predict Protein-Protein Interactions from Protein Sequences.

Science.gov (United States)

Wang, Yanbin; You, Zhuhong; Li, Xiao; Chen, Xing; Jiang, Tonghai; Zhang, Jingting

2017-05-11

Protein-protein interactions (PPIs) are essential for most living organisms' process. Thus, detecting PPIs is extremely important to understand the molecular mechanisms of biological systems. Although many PPIs data have been generated by high-throughput technologies for a variety of organisms, the whole interatom is still far from complete. In addition, the high-throughput technologies for detecting PPIs has some unavoidable defects, including time consumption, high cost, and high error rate. In recent years, with the development of machine learning, computational methods have been broadly used to predict PPIs, and can achieve good prediction rate. In this paper, we present here PCVMZM, a computational method based on a Probabilistic Classification Vector Machines (PCVM) model and Zernike moments (ZM) descriptor for predicting the PPIs from protein amino acids sequences. Specifically, a Zernike moments (ZM) descriptor is used to extract protein evolutionary information from Position-Specific Scoring Matrix (PSSM) generated by Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then, PCVM classifier is used to infer the interactions among protein. When performed on PPIs datasets of Yeast and H. Pylori , the proposed method can achieve the average prediction accuracy of 94.48% and 91.25%, respectively. In order to further evaluate the performance of the proposed method, the state-of-the-art support vector machines (SVM) classifier is used and compares with the PCVM model. Experimental results on the Yeast dataset show that the performance of PCVM classifier is better than that of SVM classifier. The experimental results indicate that our proposed method is robust, powerful and feasible, which can be used as a helpful tool for proteomics research.

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.

Subcellular localization for Gram positive and Gram negative bacterial proteins using linear interpolation smoothing model.

Science.gov (United States)

Saini, Harsh; Raicar, Gaurav; Dehzangi, Abdollah; Lal, Sunil; Sharma, Alok

2015-12-07

Protein subcellular localization is an important topic in proteomics since it is related to a protein׳s overall function, helps in the understanding of metabolic pathways, and in drug design and discovery. In this paper, a basic approximation technique from natural language processing called the linear interpolation smoothing model is applied for predicting protein subcellular localizations. The proposed approach extracts features from syntactical information in protein sequences to build probabilistic profiles using dependency models, which are used in linear interpolation to determine how likely is a sequence to belong to a particular subcellular location. This technique builds a statistical model based on maximum likelihood. It is able to deal effectively with high dimensionality that hinders other traditional classifiers such as Support Vector Machines or k-Nearest Neighbours without sacrificing performance. This approach has been evaluated by predicting subcellular localizations of Gram positive and Gram negative bacterial proteins. Copyright © 2015 Elsevier Ltd. All rights reserved.

Synthesis, physicochemical and biological properties of poly-α-amino acids - the simplest of protein models

International Nuclear Information System (INIS)

Katchalski-Katzir, Ephraim

1996-01-01

During the 1950s, linear and multichain poly-α-amino acids were synthesized by polymerization of the corresponding N-carboxy-amino acid anhydrides in solution in the presence of suitable catalysts. The resulting homo- and heteropolymers have since been widely employed as simple protein models. Under appropriate conditions, poly-α-amino acids, in the solid state and in solution, were found to acquire conformations of an α-helix and β-parallel and antiparallel pleased sheets, or to exist as random coils. Their use in experimental and theoretical investigations of helix-coil transitions helped to shed new light on the mechanisms involved in protein denaturation. Poly-α-amino acids played an important role in the deciphering of the genetic code. In addition, analysis of the antigenicity of poly-α-amino acids led to the clucidation of the factors determining the antigenicity of proteins and peptides. Interest in the biological and physicochemical characteristics of poly-α-amino acids was recently renewed because of the reported novel finding that some copolymers of amino acids are effective as drugs in multiple sclerosis, and that glutamine repeats and reiteration of other amino acids occur in inherited neurodegenerative diseases. The presence of repeating sequences of amino acids in proteins, and of nucleotides in DNA, raises many interesting questions about their respective roles in determining protein structure and function, and gene performance and regulation. (author). 35 refs, 3 figs, 2 tabs

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

Directory of Open Access Journals (Sweden)

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

Systematic identification of yeast proteins extracted into model wine during aging on the yeast lees.

Science.gov (United States)

Rowe, Jeffrey D; Harbertson, James F; Osborne, James P; Freitag, Michael; Lim, Juyun; Bakalinsky, Alan T

2010-02-24

Total protein and protein-associated mannan concentrations were measured, and individual proteins were identified during extraction into model wines over 9 months of aging on the yeast lees following completion of fermentations by seven wine strains of Saccharomyces cerevisiae. In aged wines, protein-associated mannan increased about 6-fold (+/-66%), while total protein only increased 2-fold (+/-20%), which resulted in a significantly greater protein-associated mannan/total protein ratio for three strains. A total of 219 proteins were identified among all wine samples taken over the entire time course. Of the 17 "long-lived" proteins detected in all 9 month samples, 13 were cell wall mannoproteins, and four were glycolytic enzymes. Most cytosolic proteins were not detected after 6 months. Native mannosylated yeast invertase was assayed for binding to wine tannin and was found to have a 10-fold lower affinity than nonglycosylated bovine serum albumin. Enrichment of mannoproteins in the aged model wines implies greater solution stability than other yeast proteins and the possibility that their contributions to wine quality may persist long after bottling.

Selective Advantage of Recombination in Evolving Protein Populations:. a Lattice Model Study

Science.gov (United States)

Williams, Paul D.; Pollock, David D.; Goldstein, Richard A.

Recent research has attempted to clarify the contributions of several mutational processes, such as substitutions or homologous recombination. Simplistic, tractable protein models, which determine the compact native structure phenotype from the sequence genotype, are well-suited to such studies. In this paper, we use a lattice-protein model to examine the effects of point mutation and homologous recombination on evolving populations of proteins. We find that while the majority of mutation and recombination events are neutral or deleterious, recombination is far more likely to be beneficial. This results in a faster increase in fitness during evolution, although the final fitness level is not significantly changed. This transient advantage provides an evolutionary advantage to subpopulations that undergo recombination, allowing fixation of recombination to occur in the population.

Mathematical modeling and comparison of protein size distribution in different plant, animal, fungal and microbial species reveals a negative correlation between protein size and protein number, thus providing insight into the evolution of proteomes

Directory of Open Access Journals (Sweden)

Tiessen Axel

2012-02-01

Full Text Available Abstract Background The sizes of proteins are relevant to their biochemical structure and for their biological function. The statistical distribution of protein lengths across a diverse set of taxa can provide hints about the evolution of proteomes. Results Using the full genomic sequences of over 1,302 prokaryotic and 140 eukaryotic species two datasets containing 1.2 and 6.1 million proteins were generated and analyzed statistically. The lengthwise distribution of proteins can be roughly described with a gamma type or log-normal model, depending on the species. However the shape parameter of the gamma model has not a fixed value of 2, as previously suggested, but varies between 1.5 and 3 in different species. A gamma model with unrestricted shape parameter described best the distributions in ~48% of the species, whereas the log-normal distribution described better the observed protein sizes in 42% of the species. The gamma restricted function and the sum of exponentials distribution had a better fitting in only ~5% of the species. Eukaryotic proteins have an average size of 472 aa, whereas bacterial (320 aa and archaeal (283 aa proteins are significantly smaller (33-40% on average. Average protein sizes in different phylogenetic groups were: Alveolata (628 aa, Amoebozoa (533 aa, Fornicata (543 aa, Placozoa (453 aa, Eumetazoa (486 aa, Fungi (487 aa, Stramenopila (486 aa, Viridiplantae (392 aa. Amino acid composition is biased according to protein size. Protein length correlated negatively with %C, %M, %K, %F, %R, %W, %Y and positively with %D, %E, %Q, %S and %T. Prokaryotic proteins had a different protein size bias for %E, %G, %K and %M as compared to eukaryotes. Conclusions Mathematical modeling of protein length empirical distributions can be used to asses the quality of small ORFs annotation in genomic releases (detection of too many false positive small ORFs. There is a negative correlation between average protein size and total number of

Effect of secretory pathway gene overexpression on secretion of a fluorescent reporter protein in Aspergillus nidulans

DEFF Research Database (Denmark)

Schalén, Martin; Anyaogu, Diana Chinyere; Hoof, Jakob Blæsbjerg

2016-01-01

roles in the process have been identified through transcriptomics. The assignment of function to these genes has been enabled in combination with gene deletion studies. In this work, 14 genes known to play a role in protein secretion in filamentous fungi were overexpressed in Aspergillus nidulans....... The background strain was a fluorescent reporter secreting mRFP. The overall effect of the overexpressions could thus be easily monitored through fluorescence measurements, while the effects on physiology were determined in batch cultivations and surface growth studies. Results: Fourteen protein secretion...... pathway related genes were overexpressed with a tet-ON promoter in the RFP-secreting reporter strain and macromorphology, physiology and protein secretion were monitored when the secretory genes were induced. Overexpression of several of the chosen genes was shown to cause anomalies on growth, micro...

Truly Absorbed Microbial Protein Synthesis, Rumen Bypass Protein, Endogenous Protein, and Total Metabolizable Protein from Starchy and Protein-Rich Raw Materials

NARCIS (Netherlands)

Parand, Ehsan; Vakili, Alireza; Mesgaran, Mohsen Danesh; Duinkerken, Van Gert; Yu, Peiqiang

2015-01-01

This study was carried out to measure truly absorbed microbial protein synthesis, rumen bypass protein, and endogenous protein loss, as well as total metabolizable protein, from starchy and protein-rich raw feed materials with model comparisons. Predictions by the DVE2010 system as a more

Geologic Framework Model Analysis Model Report

Energy Technology Data Exchange (ETDEWEB)

R. Clayton

2000-12-19

The purpose of this report is to document the Geologic Framework Model (GFM), Version 3.1 (GFM3.1) with regard to data input, modeling methods, assumptions, uncertainties, limitations, and validation of the model results, qualification status of the model, and the differences between Version 3.1 and previous versions. The GFM represents a three-dimensional interpretation of the stratigraphy and structural features of the location of the potential Yucca Mountain radioactive waste repository. The GFM encompasses an area of 65 square miles (170 square kilometers) and a volume of 185 cubic miles (771 cubic kilometers). The boundaries of the GFM were chosen to encompass the most widely distributed set of exploratory boreholes (the Water Table or WT series) and to provide a geologic framework over the area of interest for hydrologic flow and radionuclide transport modeling through the unsaturated zone (UZ). The depth of the model is constrained by the inferred depth of the Tertiary-Paleozoic unconformity. The GFM was constructed from geologic map and borehole data. Additional information from measured stratigraphy sections, gravity profiles, and seismic profiles was also considered. This interim change notice (ICN) was prepared in accordance with the Technical Work Plan for the Integrated Site Model Process Model Report Revision 01 (CRWMS M&O 2000). The constraints, caveats, and limitations associated with this model are discussed in the appropriate text sections that follow. The GFM is one component of the Integrated Site Model (ISM) (Figure l), which has been developed to provide a consistent volumetric portrayal of the rock layers, rock properties, and mineralogy of the Yucca Mountain site. The ISM consists of three components: (1) Geologic Framework Model (GFM); (2) Rock Properties Model (RPM); and (3) Mineralogic Model (MM). The ISM merges the detailed project stratigraphy into model stratigraphic units that are most useful for the primary downstream models and the

Geologic Framework Model Analysis Model Report

International Nuclear Information System (INIS)

Clayton, R.

2000-01-01

The purpose of this report is to document the Geologic Framework Model (GFM), Version 3.1 (GFM3.1) with regard to data input, modeling methods, assumptions, uncertainties, limitations, and validation of the model results, qualification status of the model, and the differences between Version 3.1 and previous versions. The GFM represents a three-dimensional interpretation of the stratigraphy and structural features of the location of the potential Yucca Mountain radioactive waste repository. The GFM encompasses an area of 65 square miles (170 square kilometers) and a volume of 185 cubic miles (771 cubic kilometers). The boundaries of the GFM were chosen to encompass the most widely distributed set of exploratory boreholes (the Water Table or WT series) and to provide a geologic framework over the area of interest for hydrologic flow and radionuclide transport modeling through the unsaturated zone (UZ). The depth of the model is constrained by the inferred depth of the Tertiary-Paleozoic unconformity. The GFM was constructed from geologic map and borehole data. Additional information from measured stratigraphy sections, gravity profiles, and seismic profiles was also considered. This interim change notice (ICN) was prepared in accordance with the Technical Work Plan for the Integrated Site Model Process Model Report Revision 01 (CRWMS M and O 2000). The constraints, caveats, and limitations associated with this model are discussed in the appropriate text sections that follow. The GFM is one component of the Integrated Site Model (ISM) (Figure l), which has been developed to provide a consistent volumetric portrayal of the rock layers, rock properties, and mineralogy of the Yucca Mountain site. The ISM consists of three components: (1) Geologic Framework Model (GFM); (2) Rock Properties Model (RPM); and (3) Mineralogic Model (MM). The ISM merges the detailed project stratigraphy into model stratigraphic units that are most useful for the primary downstream models and

ProDis-ContSHC: learning protein dissimilarity measures and hierarchical context coherently for protein-protein comparison in protein database retrieval.

Science.gov (United States)

Wang, Jingyan; Gao, Xin; Wang, Quanquan; Li, Yongping

2012-05-08

The need to retrieve or classify protein molecules using structure or sequence-based similarity measures underlies a wide range of biomedical applications. Traditional protein search methods rely on a pairwise dissimilarity/similarity measure for comparing a pair of proteins. This kind of pairwise measures suffer from the limitation of neglecting the distribution of other proteins and thus cannot satisfy the need for high accuracy of the retrieval systems. Recent work in the machine learning community has shown that exploiting the global structure of the database and learning the contextual dissimilarity/similarity measures can improve the retrieval performance significantly. However, most existing contextual dissimilarity/similarity learning algorithms work in an unsupervised manner, which does not utilize the information of the known class labels of proteins in the database. In this paper, we propose a novel protein-protein dissimilarity learning algorithm, ProDis-ContSHC. ProDis-ContSHC regularizes an existing dissimilarity measure dij by considering the contextual information of the proteins. The context of a protein is defined by its neighboring proteins. The basic idea is, for a pair of proteins (i, j), if their context N(i) and N(j) is similar to each other, the two proteins should also have a high similarity. We implement this idea by regularizing dij by a factor learned from the context N(i) and N(j).Moreover, we divide the context to hierarchial sub-context and get the contextual dissimilarity vector for each protein pair. Using the class label information of the proteins, we select the relevant (a pair of proteins that has the same class labels) and irrelevant (with different labels) protein pairs, and train an SVM model to distinguish between their contextual dissimilarity vectors. The SVM model is further used to learn a supervised regularizing factor. Finally, with the new Supervised learned Dissimilarity measure, we update the Protein Hierarchial

Modeling of the structure of ribosomal protein L1 from the archaeon Haloarcula marismortui

Science.gov (United States)

Nevskaya, N. A.; Kljashtorny, V. G.; Vakhrusheva, A. V.; Garber, M. B.; Nikonov, S. V.

2017-07-01

The halophilic archaeon Haloarcula marismortui proliferates in the Dead Sea at extremely high salt concentrations (higher than 3 M). This is the only archaeon, for which the crystal structure of the ribosomal 50S subunit was determined. However, the structure of the functionally important side protuberance containing the abnormally negatively charged protein L1 (HmaL1) was not visualized. Attempts to crystallize HmaL1 in the isolated state or as its complex with RNA using normal salt concentrations (≤500 mM) failed. A theoretical model of HmaL1 was built based on the structural data for homologs of the protein L1 from other organisms, and this model was refined by molecular dynamics methods. Analysis of this model showed that the protein HmaL1 can undergo aggregation due to the presence of a cluster of positive charges unique for proteins L1. This cluster is located at the RNA-protein interface, which interferes with the crystallization of HmaL1 and the binding of the latter to RNA.

Sculpting proteins interactively: continual energy minimization embedded in a graphical modeling system.

Science.gov (United States)

Surles, M C; Richardson, J S; Richardson, D C; Brooks, F P

1994-02-01

We describe a new paradigm for modeling proteins in interactive computer graphics systems--continual maintenance of a physically valid representation, combined with direct user control and visualization. This is achieved by a fast algorithm for energy minimization, capable of real-time performance on all atoms of a small protein, plus graphically specified user tugs. The modeling system, called Sculpt, rigidly constrains bond lengths, bond angles, and planar groups (similar to existing interactive modeling programs), while it applies elastic restraints to minimize the potential energy due to torsions, hydrogen bonds, and van der Waals and electrostatic interactions (similar to existing batch minimization programs), and user-specified springs. The graphical interface can show bad and/or favorable contacts, and individual energy terms can be turned on or off to determine their effects and interactions. Sculpt finds a local minimum of the total energy that satisfies all the constraints using an augmented Lagrange-multiplier method; calculation time increases only linearly with the number of atoms because the matrix of constraint gradients is sparse and banded. On a 100-MHz MIPS R4000 processor (Silicon Graphics Indigo), Sculpt achieves 11 updates per second on a 20-residue fragment and 2 updates per second on an 80-residue protein, using all atoms except non-H-bonding hydrogens, and without electrostatic interactions. Applications of Sculpt are described: to reverse the direction of bundle packing in a designed 4-helix bundle protein, to fold up a 2-stranded beta-ribbon into an approximate beta-barrel, and to design the sequence and conformation of a 30-residue peptide that mimics one partner of a protein subunit interaction. Computer models that are both interactive and physically realistic (within the limitations of a given force field) have 2 significant advantages: (1) they make feasible the modeling of very large changes (such as needed for de novo design), and

Modelling strategy report

Energy Technology Data Exchange (ETDEWEB)

Smith, P.A. [SAM Switzerland GmbH (Switzerland); Brommundt, J.; Mayer, G. [AF-Consult Switzerland Ltd, Baden (Switzerland)] [and others

2012-01-15

This report presents a preliminary plan for the development and application of models for the probabilistic sensitivity analyses and the deterministic calculation cases that will be used to analyse scenarios in the 2012 safety case. The plan addresses both groundwater flow modelling and radionuclide transport modelling, primarily of the repository near field and of the geosphere. It also addresses the measures that will be applied during the implementation and documentation of the calculations that will contribute to the quality assurance of the safety case. It is expected that this plan will be refined and amended as experience is gained during the carrying out of the work. (orig.)

Modelling strategy report

International Nuclear Information System (INIS)

Smith, P.A.; Brommundt, J.; Mayer, G.

2012-01-01

This report presents a preliminary plan for the development and application of models for the probabilistic sensitivity analyses and the deterministic calculation cases that will be used to analyse scenarios in the 2012 safety case. The plan addresses both groundwater flow modelling and radionuclide transport modelling, primarily of the repository near field and of the geosphere. It also addresses the measures that will be applied during the implementation and documentation of the calculations that will contribute to the quality assurance of the safety case. It is expected that this plan will be refined and amended as experience is gained during the carrying out of the work. (orig.)

Genome-scale metabolic model of Pichia pastoris with native and humanized glycosylation of recombinant proteins

DEFF Research Database (Denmark)

Irani, Zahra Azimzadeh; Kerkhoven, Eduard J.; Shojaosadati, Seyed Abbas

2016-01-01

prediction of protein yield, demonstrates the effect of the different types of N-glycosylation of protein yield, and can be used to predict potential targets for strain improvement. The model represents a step towards a more complete description of protein production in P. pastoris, which is required...... for using these models to understand and optimize protein production processes....

Protein Nano-Object Integrator (ProNOI for generating atomic style objects for molecular modeling

Directory of Open Access Journals (Sweden)

Smith Nicholas

2012-12-01

Full Text Available Abstract Background With the progress of nanotechnology, one frequently has to model biological macromolecules simultaneously with nano-objects. However, the atomic structures of the nano objects are typically not available or they are solid state entities. Because of that, the researchers have to investigate such nano systems by generating models of the nano objects in a manner that the existing software be able to carry the simulations. In addition, it should allow generating composite objects with complex shape by combining basic geometrical figures and embedding biological macromolecules within the system. Results Here we report the Protein Nano-Object Integrator (ProNOI which allows for generating atomic-style geometrical objects with user desired shape and dimensions. Unlimited number of objects can be created and combined with biological macromolecules in Protein Data Bank (PDB format file. Once the objects are generated, the users can use sliders to manipulate their shape, dimension and absolute position. In addition, the software offers the option to charge the objects with either specified surface or volumetric charge density and to model them with user-desired dielectric constants. According to the user preference, the biological macromolecule atoms can be assigned charges and radii according to four different force fields: Amber, Charmm, OPLS and PARSE. The biological macromolecules and the atomic-style objects are exported as a position, charge and radius (PQR file, or if a default dielectric constant distribution is not selected, it is exported as a position, charge, radius and epsilon (PQRE file. As illustration of the capabilities of the ProNOI, we created a composite object in a shape of a robot, aptly named the Clemson Robot, whose parts are charged with various volumetric charge densities and holds the barnase-barstar protein complex in its hand. Conclusions The Protein Nano-Object Integrator (ProNOI is a convenient tool for

Structural characterisation of medically relevant protein assemblies by integrating mass spectrometry with computational modelling.

Science.gov (United States)

Politis, Argyris; Schmidt, Carla

2018-03-20

Structural mass spectrometry with its various techniques is a powerful tool for the structural elucidation of medically relevant protein assemblies. It delivers information on the composition, stoichiometries, interactions and topologies of these assemblies. Most importantly it can deal with heterogeneous mixtures and assemblies which makes it universal among the conventional structural techniques. In this review we summarise recent advances and challenges in structural mass spectrometric techniques. We describe how the combination of the different mass spectrometry-based methods with computational strategies enable structural models at molecular levels of resolution. These models hold significant potential for helping us in characterizing the function of protein assemblies related to human health and disease. In this review we summarise the techniques of structural mass spectrometry often applied when studying protein-ligand complexes. We exemplify these techniques through recent examples from literature that helped in the understanding of medically relevant protein assemblies. We further provide a detailed introduction into various computational approaches that can be integrated with these mass spectrometric techniques. Last but not least we discuss case studies that integrated mass spectrometry and computational modelling approaches and yielded models of medically important protein assembly states such as fibrils and amyloids. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

Comparing side chain packing in soluble proteins, protein-protein interfaces, and transmembrane proteins.

Science.gov (United States)

Gaines, J C; Acebes, S; Virrueta, A; Butler, M; Regan, L; O'Hern, C S

2018-05-01

We compare side chain prediction and packing of core and non-core regions of soluble proteins, protein-protein interfaces, and transmembrane proteins. We first identified or created comparable databases of high-resolution crystal structures of these 3 protein classes. We show that the solvent-inaccessible cores of the 3 classes of proteins are equally densely packed. As a result, the side chains of core residues at protein-protein interfaces and in the membrane-exposed regions of transmembrane proteins can be predicted by the hard-sphere plus stereochemical constraint model with the same high prediction accuracies (>90%) as core residues in soluble proteins. We also find that for all 3 classes of proteins, as one moves away from the solvent-inaccessible core, the packing fraction decreases as the solvent accessibility increases. However, the side chain predictability remains high (80% within 30°) up to a relative solvent accessibility, rSASA≲0.3, for all 3 protein classes. Our results show that ≈40% of the interface regions in protein complexes are "core", that is, densely packed with side chain conformations that can be accurately predicted using the hard-sphere model. We propose packing fraction as a metric that can be used to distinguish real protein-protein interactions from designed, non-binding, decoys. Our results also show that cores of membrane proteins are the same as cores of soluble proteins. Thus, the computational methods we are developing for the analysis of the effect of hydrophobic core mutations in soluble proteins will be equally applicable to analyses of mutations in membrane proteins. © 2018 Wiley Periodicals, Inc.

Improved catalyzed reporter deposition, iCARD.

Science.gov (United States)

Lohse, Jesper; Petersen, Kenneth Heesche; Woller, Nina Claire; Pedersen, Hans Christian; Skladtchikova, Galina; Jørgensen, Rikke Malene

2014-06-18

Novel reporters have been synthesized with extended hydrophilic linkers that in combination with polymerizing cross-linkers result in very efficient reporter deposition. By utilizing antibodies to stain HER2 proteins in a cell line model it is demonstrated that the method is highly specific and sensitive with virtually no background. The detection of HER2 proteins in tissue was used to visualize individual antigens as small dots visible in a microscope. Image analysis-assisted counting of fluorescent or colored dots allowed assessment of relative protein levels in tissue. Taken together, we have developed novel reporters that improve the CARD method allowing highly sensitive in situ detection of proteins in tissue. Our findings suggest that in situ protein quantification in biological samples can be performed by object recognition and enumeration of dots, rather than intensity-based fluorescent or colorimetric assays.

Maillard-reaction-induced modification and aggregation of proteins and hardening of texture in protein bar model systems.

Science.gov (United States)

Zhou, Peng; Guo, Mufan; Liu, Dasong; Liu, Xiaoming; Labuza, Teodore P

2013-03-01

The hardening of high-protein bars causes problems in their acceptability to consumers. The objective of this study was to determine the progress of the Maillard reaction in model systems of high-protein nutritional bars containing reducing sugars, and to illustrate the influences of the Maillard reaction on the modification and aggregation of proteins and the hardening of bar matrices during storage. The progress of the Maillard reaction, glycation, and aggregation of proteins, and textural changes in bar matrices were investigated during storage at 25, 35, and 45 °C. The initial development of the Maillard reaction caused little changes in hardness; however, further storage resulted in dramatic modification of protein with formation of high-molecular-weight polymers, resulting in the hardening in texture. The replacement of reducing sugars with nonreducing ingredients such as sugar alcohols in the formula minimized the changes in texture. The hardening of high-protein bars causes problems in their acceptability to consumers. Maillard reaction is one of the mechanisms contributing to the hardening of bar matrix, particularly for the late stage of storage. The replacement of reducing sugars with nonreducing ingredients such as sugar alcohols in the formula will minimize the changes in texture. © 2013 Institute of Food Technologists®

In Silico Characterization and Structural Modeling of Dermacentor andersoni p36 Immunosuppressive Protein

Directory of Open Access Journals (Sweden)

Martin Omulindi Oyugi

2018-01-01

Full Text Available Ticks cause approximately $17–19 billion economic losses to the livestock industry globally. Development of recombinant antitick vaccine is greatly hindered by insufficient knowledge and understanding of proteins expressed by ticks. Ticks secrete immunosuppressant proteins that modulate the host’s immune system during blood feeding; these molecules could be a target for antivector vaccine development. Recombinant p36, a 36 kDa immunosuppressor from the saliva of female Dermacentor andersoni, suppresses T-lymphocytes proliferation in vitro. To identify potential unique structural and dynamic properties responsible for the immunosuppressive function of p36 proteins, this study utilized bioinformatic tool to characterize and model structure of D. andersoni p36 protein. Evaluation of p36 protein family as suitable vaccine antigens predicted a p36 homolog in Rhipicephalus appendiculatus, the tick vector of East Coast fever, with an antigenicity score of 0.7701 that compares well with that of Bm86 (0.7681, the protein antigen that constitute commercial tick vaccine Tickgard™. Ab initio modeling of the D. andersoni p36 protein yielded a 3D structure that predicted conserved antigenic region, which has potential of binding immunomodulating ligands including glycerol and lactose, found located within exposed loop, suggesting a likely role in immunosuppressive function of tick p36 proteins. Laboratory confirmation of these preliminary results is necessary in future studies.

Looping and clustering model for the organization of protein-DNA complexes on the bacterial genome

Science.gov (United States)

Walter, Jean-Charles; Walliser, Nils-Ole; David, Gabriel; Dorignac, Jérôme; Geniet, Frédéric; Palmeri, John; Parmeggiani, Andrea; Wingreen, Ned S.; Broedersz, Chase P.

2018-03-01

The bacterial genome is organized by a variety of associated proteins inside a structure called the nucleoid. These proteins can form complexes on DNA that play a central role in various biological processes, including chromosome segregation. A prominent example is the large ParB-DNA complex, which forms an essential component of the segregation machinery in many bacteria. ChIP-Seq experiments show that ParB proteins localize around centromere-like parS sites on the DNA to which ParB binds specifically, and spreads from there over large sections of the chromosome. Recent theoretical and experimental studies suggest that DNA-bound ParB proteins can interact with each other to condense into a coherent 3D complex on the DNA. However, the structural organization of this protein-DNA complex remains unclear, and a predictive quantitative theory for the distribution of ParB proteins on DNA is lacking. Here, we propose the looping and clustering model, which employs a statistical physics approach to describe protein-DNA complexes. The looping and clustering model accounts for the extrusion of DNA loops from a cluster of interacting DNA-bound proteins that is organized around a single high-affinity binding site. Conceptually, the structure of the protein-DNA complex is determined by a competition between attractive protein interactions and loop closure entropy of this protein-DNA cluster on the one hand, and the positional entropy for placing loops within the cluster on the other. Indeed, we show that the protein interaction strength determines the ‘tightness’ of the loopy protein-DNA complex. Thus, our model provides a theoretical framework for quantitatively computing the binding profiles of ParB-like proteins around a cognate (parS) binding site.

Classification of Beta-lactamases and penicillin binding proteins using ligand-centric network models.

Directory of Open Access Journals (Sweden)

Hakime Öztürk

Full Text Available β-lactamase mediated antibiotic resistance is an important health issue and the discovery of new β-lactam type antibiotics or β-lactamase inhibitors is an area of intense research. Today, there are about a thousand β-lactamases due to the evolutionary pressure exerted by these ligands. While β-lactamases hydrolyse the β-lactam ring of antibiotics, rendering them ineffective, Penicillin-Binding Proteins (PBPs, which share high structural similarity with β-lactamases, also confer antibiotic resistance to their host organism by acquiring mutations that allow them to continue their participation in cell wall biosynthesis. In this paper, we propose a novel approach to include ligand sharing information for classifying and clustering β-lactamases and PBPs in an effort to elucidate the ligand induced evolution of these β-lactam binding proteins. We first present a detailed summary of the β-lactamase and PBP families in the Protein Data Bank, as well as the compounds they bind to. Then, we build two different types of networks in which the proteins are represented as nodes, and two proteins are connected by an edge with a weight that depends on the number of shared identical or similar ligands. These models are analyzed under three different edge weight settings, namely unweighted, weighted, and normalized weighted. A detailed comparison of these six networks showed that the use of ligand sharing information to cluster proteins resulted in modules comprising proteins with not only sequence similarity but also functional similarity. Consideration of ligand similarity highlighted some interactions that were not detected in the identical ligand network. Analysing the β-lactamases and PBPs using ligand-centric network models enabled the identification of novel relationships, suggesting that these models can be used to examine other protein families to obtain information on their ligand induced evolutionary paths.

MATHEMATICAL AND COMPUTATIONAL MODELLING OF RIBOSOMAL MOVEMENT AND PROTEIN SYNTHESIS: AN OVERVIEW

Directory of Open Access Journals (Sweden)

Tobias von der Haar

2012-04-01

Full Text Available Translation or protein synthesis consists of a complex system of chemical reactions, which ultimately result in decoding of the mRNA and the production of a protein. The complexity of this reaction system makes it difficult to quantitatively connect its input parameters (such as translation factor or ribosome concentrations, codon composition of the mRNA, or energy availability to output parameters (such as protein synthesis rates or ribosome densities on mRNAs. Mathematical and computational models of translation have now been used for nearly five decades to investigate translation, and to shed light on the relationship between the different reactions in the system. This review gives an overview over the principal approaches used in the modelling efforts, and summarises some of the major findings that were made.

Model of OSBP-Mediated Cholesterol Supply to Aichi Virus RNA Replication Sites Involving Protein-Protein Interactions among Viral Proteins, ACBD3, OSBP, VAP-A/B, and SAC1.

Science.gov (United States)

Ishikawa-Sasaki, Kumiko; Nagashima, Shigeo; Taniguchi, Koki; Sasaki, Jun

2018-04-15

Positive-strand RNA viruses, including picornaviruses, utilize cellular machinery for genome replication. Previously, we reported that each of the 2B, 2BC, 2C, 3A, and 3AB proteins of Aichi virus (AiV), a picornavirus, forms a complex with the Golgi apparatus protein ACBD3 and phosphatidylinositol 4-kinase IIIβ (PI4KB) at viral RNA replication sites (replication organelles [ROs]), enhancing PI4KB-dependent phosphatidylinositol 4-phosphate (PI4P) production. Here, we demonstrate AiV hijacking of the cellular cholesterol transport system involving oxysterol-binding protein (OSBP), a PI4P-binding cholesterol transfer protein. AiV RNA replication was inhibited by silencing cellular proteins known to be components of this pathway, OSBP, the ER membrane proteins VAPA and VAPB (VAP-A/B), the PI4P-phosphatase SAC1, and PI-transfer protein β. OSBP, VAP-A/B, and SAC1 were present at RNA replication sites. We also found various previously unknown interactions among the AiV proteins (2B, 2BC, 2C, 3A, and 3AB), ACBD3, OSBP, VAP-A/B, and SAC1, and the interactions were suggested to be involved in recruiting the component proteins to AiV ROs. Importantly, the OSBP-2B interaction enabled PI4P-independent recruitment of OSBP to AiV ROs, indicating preferential recruitment of OSBP among PI4P-binding proteins. Protein-protein interaction-based OSBP recruitment has not been reported for other picornaviruses. Cholesterol was accumulated at AiV ROs, and inhibition of OSBP-mediated cholesterol transfer impaired cholesterol accumulation and AiV RNA replication. Electron microscopy showed that AiV-induced vesicle-like structures were close to ER membranes. Altogether, we conclude that AiV directly recruits the cholesterol transport machinery through protein-protein interactions, resulting in formation of membrane contact sites between the ER and AiV ROs and cholesterol supply to the ROs. IMPORTANCE Positive-strand RNA viruses utilize host pathways to modulate the lipid composition of

Connecting Protein Structure to Intermolecular Interactions: A Computer Modeling Laboratory

Science.gov (United States)

Abualia, Mohammed; Schroeder, Lianne; Garcia, Megan; Daubenmire, Patrick L.; Wink, Donald J.; Clark, Ginevra A.

2016-01-01

An understanding of protein folding relies on a solid foundation of a number of critical chemical concepts, such as molecular structure, intra-/intermolecular interactions, and relating structure to function. Recent reports show that students struggle on all levels to achieve these understandings and use them in meaningful ways. Further, several…

Bluetongue virus non-structural protein 1 is a positive regulator of viral protein synthesis

Directory of Open Access Journals (Sweden)

Boyce Mark

2012-08-01

Full Text Available Abstract Background Bluetongue virus (BTV is a double-stranded RNA (dsRNA virus of the Reoviridae family, which encodes its genes in ten linear dsRNA segments. BTV mRNAs are synthesised by the viral RNA-dependent RNA polymerase (RdRp as exact plus sense copies of the genome segments. Infection of mammalian cells with BTV rapidly replaces cellular protein synthesis with viral protein synthesis, but the regulation of viral gene expression in the Orbivirus genus has not been investigated. Results Using an mRNA reporter system based on genome segment 10 of BTV fused with GFP we identify the protein characteristic of this genus, non-structural protein 1 (NS1 as sufficient to upregulate translation. The wider applicability of this phenomenon among the viral genes is demonstrated using the untranslated regions (UTRs of BTV genome segments flanking the quantifiable Renilla luciferase ORF in chimeric mRNAs. The UTRs of viral mRNAs are shown to be determinants of the amount of protein synthesised, with the pre-expression of NS1 increasing the quantity in each case. The increased expression induced by pre-expression of NS1 is confirmed in virus infected cells by generating a replicating virus which expresses the reporter fused with genome segment 10, using reverse genetics. Moreover, NS1-mediated upregulation of expression is restricted to mRNAs which lack the cellular 3′ poly(A sequence identifying the 3′ end as a necessary determinant in specifically increasing the translation of viral mRNA in the presence of cellular mRNA. Conclusions NS1 is identified as a positive regulator of viral protein synthesis. We propose a model of translational regulation where NS1 upregulates the synthesis of viral proteins, including itself, and creates a positive feedback loop of NS1 expression, which rapidly increases the expression of all the viral proteins. The efficient translation of viral reporter mRNAs among cellular mRNAs can account for the observed

Bluetongue virus non-structural protein 1 is a positive regulator of viral protein synthesis.

Science.gov (United States)

Boyce, Mark; Celma, Cristina C P; Roy, Polly

2012-08-29

Bluetongue virus (BTV) is a double-stranded RNA (dsRNA) virus of the Reoviridae family, which encodes its genes in ten linear dsRNA segments. BTV mRNAs are synthesised by the viral RNA-dependent RNA polymerase (RdRp) as exact plus sense copies of the genome segments. Infection of mammalian cells with BTV rapidly replaces cellular protein synthesis with viral protein synthesis, but the regulation of viral gene expression in the Orbivirus genus has not been investigated. Using an mRNA reporter system based on genome segment 10 of BTV fused with GFP we identify the protein characteristic of this genus, non-structural protein 1 (NS1) as sufficient to upregulate translation. The wider applicability of this phenomenon among the viral genes is demonstrated using the untranslated regions (UTRs) of BTV genome segments flanking the quantifiable Renilla luciferase ORF in chimeric mRNAs. The UTRs of viral mRNAs are shown to be determinants of the amount of protein synthesised, with the pre-expression of NS1 increasing the quantity in each case. The increased expression induced by pre-expression of NS1 is confirmed in virus infected cells by generating a replicating virus which expresses the reporter fused with genome segment 10, using reverse genetics. Moreover, NS1-mediated upregulation of expression is restricted to mRNAs which lack the cellular 3' poly(A) sequence identifying the 3' end as a necessary determinant in specifically increasing the translation of viral mRNA in the presence of cellular mRNA. NS1 is identified as a positive regulator of viral protein synthesis. We propose a model of translational regulation where NS1 upregulates the synthesis of viral proteins, including itself, and creates a positive feedback loop of NS1 expression, which rapidly increases the expression of all the viral proteins. The efficient translation of viral reporter mRNAs among cellular mRNAs can account for the observed replacement of cellular protein synthesis with viral protein

Evolving Transcription Factor Binding Site Models From Protein Binding Microarray Data

KAUST Repository

Wong, Ka-Chun; Peng, Chengbin; Li, Yue

2016-01-01

Protein binding microarray (PBM) is a high-throughput platform that can measure the DNA binding preference of a protein in a comprehensive and unbiased manner. In this paper, we describe the PBM motif model building problem. We apply several evolutionary computation methods and compare their performance with the interior point method, demonstrating their performance advantages. In addition, given the PBM domain knowledge, we propose and describe a novel method called kmerGA which makes domain-specific assumptions to exploit PBM data properties to build more accurate models than the other models built. The effectiveness and robustness of kmerGA is supported by comprehensive performance benchmarking on more than 200 datasets, time complexity analysis, convergence analysis, parameter analysis, and case studies. To demonstrate its utility further, kmerGA is applied to two real world applications: 1) PBM rotation testing and 2) ChIP-Seq peak sequence prediction. The results support the biological relevance of the models learned by kmerGA, and thus its real world applicability.

Evolving Transcription Factor Binding Site Models From Protein Binding Microarray Data

KAUST Repository

Wong, Ka-Chun

2016-02-02

Protein binding microarray (PBM) is a high-throughput platform that can measure the DNA binding preference of a protein in a comprehensive and unbiased manner. In this paper, we describe the PBM motif model building problem. We apply several evolutionary computation methods and compare their performance with the interior point method, demonstrating their performance advantages. In addition, given the PBM domain knowledge, we propose and describe a novel method called kmerGA which makes domain-specific assumptions to exploit PBM data properties to build more accurate models than the other models built. The effectiveness and robustness of kmerGA is supported by comprehensive performance benchmarking on more than 200 datasets, time complexity analysis, convergence analysis, parameter analysis, and case studies. To demonstrate its utility further, kmerGA is applied to two real world applications: 1) PBM rotation testing and 2) ChIP-Seq peak sequence prediction. The results support the biological relevance of the models learned by kmerGA, and thus its real world applicability.

Alpha-1 antitrypsin protein and gene therapies decrease autoimmunity and delay arthritis development in mouse model

Directory of Open Access Journals (Sweden)

Atkinson Mark A

2011-02-01

Full Text Available Abstract Background Alpha-1 antitrypsin (AAT is a multi-functional protein that has anti-inflammatory and tissue protective properties. We previously reported that human AAT (hAAT gene therapy prevented autoimmune diabetes in non-obese diabetic (NOD mice and suppressed arthritis development in combination with doxycycline in mice. In the present study we investigated the feasibility of hAAT monotherapy for the treatment of chronic arthritis in collagen-induced arthritis (CIA, a mouse model of rheumatoid arthritis (RA. Methods DBA/1 mice were immunized with bovine type II collagen (bCII to induce arthritis. These mice were pretreated either with hAAT protein or with recombinant adeno-associated virus vector expressing hAAT (rAAV-hAAT. Control groups received saline injections. Arthritis development was evaluated by prevalence of arthritis and arthritic index. Serum levels of B-cell activating factor of the TNF-α family (BAFF, antibodies against both bovine (bCII and mouse collagen II (mCII were tested by ELISA. Results Human AAT protein therapy as well as recombinant adeno-associated virus (rAAV8-mediated hAAT gene therapy significantly delayed onset and ameliorated disease development of arthritis in CIA mouse model. Importantly, hAAT therapies significantly reduced serum levels of BAFF and autoantibodies against bCII and mCII, suggesting that the effects are mediated via B-cells, at least partially. Conclusion These results present a new drug for arthritis therapy. Human AAT protein and gene therapies are able to ameliorate and delay arthritis development and reduce autoimmunity, indicating promising potential of these therapies as a new treatment strategy for RA.

Multiscale modeling and simulation of microtubule–motor-protein assemblies

Science.gov (United States)

Gao, Tong; Blackwell, Robert; Glaser, Matthew A.; Betterton, M. D.; Shelley, Michael J.

2016-01-01

Microtubules and motor proteins self-organize into biologically important assemblies including the mitotic spindle and the centrosomal microtubule array. Outside of cells, microtubule-motor mixtures can form novel active liquid-crystalline materials driven out of equilibrium by adenosine triphosphate–consuming motor proteins. Microscopic motor activity causes polarity-dependent interactions between motor proteins and microtubules, but how these interactions yield larger-scale dynamical behavior such as complex flows and defect dynamics is not well understood. We develop a multiscale theory for microtubule-motor systems in which Brownian dynamics simulations of polar microtubules driven by motors are used to study microscopic organization and stresses created by motor-mediated microtubule interactions. We identify polarity-sorting and crosslink tether relaxation as two polar-specific sources of active destabilizing stress. We then develop a continuum Doi-Onsager model that captures polarity sorting and the hydrodynamic flows generated by these polar-specific active stresses. In simulations of active nematic flows on immersed surfaces, the active stresses drive turbulent flow dynamics and continuous generation and annihilation of disclination defects. The dynamics follow from two instabilities, and accounting for the immersed nature of the experiment yields unambiguous characteristic length and time scales. When turning off the hydrodynamics in the Doi-Onsager model, we capture formation of polar lanes as observed in the Brownian dynamics simulation. PMID:26764729

Multiscale modeling and simulation of microtubule-motor-protein assemblies.

Science.gov (United States)

Gao, Tong; Blackwell, Robert; Glaser, Matthew A; Betterton, M D; Shelley, Michael J

2015-01-01

Microtubules and motor proteins self-organize into biologically important assemblies including the mitotic spindle and the centrosomal microtubule array. Outside of cells, microtubule-motor mixtures can form novel active liquid-crystalline materials driven out of equilibrium by adenosine triphosphate-consuming motor proteins. Microscopic motor activity causes polarity-dependent interactions between motor proteins and microtubules, but how these interactions yield larger-scale dynamical behavior such as complex flows and defect dynamics is not well understood. We develop a multiscale theory for microtubule-motor systems in which Brownian dynamics simulations of polar microtubules driven by motors are used to study microscopic organization and stresses created by motor-mediated microtubule interactions. We identify polarity-sorting and crosslink tether relaxation as two polar-specific sources of active destabilizing stress. We then develop a continuum Doi-Onsager model that captures polarity sorting and the hydrodynamic flows generated by these polar-specific active stresses. In simulations of active nematic flows on immersed surfaces, the active stresses drive turbulent flow dynamics and continuous generation and annihilation of disclination defects. The dynamics follow from two instabilities, and accounting for the immersed nature of the experiment yields unambiguous characteristic length and time scales. When turning off the hydrodynamics in the Doi-Onsager model, we capture formation of polar lanes as observed in the Brownian dynamics simulation.

Multiscale modeling and simulation of microtubule-motor-protein assemblies

Science.gov (United States)

Gao, Tong; Blackwell, Robert; Glaser, Matthew A.; Betterton, M. D.; Shelley, Michael J.

2015-12-01

Microtubules and motor proteins self-organize into biologically important assemblies including the mitotic spindle and the centrosomal microtubule array. Outside of cells, microtubule-motor mixtures can form novel active liquid-crystalline materials driven out of equilibrium by adenosine triphosphate-consuming motor proteins. Microscopic motor activity causes polarity-dependent interactions between motor proteins and microtubules, but how these interactions yield larger-scale dynamical behavior such as complex flows and defect dynamics is not well understood. We develop a multiscale theory for microtubule-motor systems in which Brownian dynamics simulations of polar microtubules driven by motors are used to study microscopic organization and stresses created by motor-mediated microtubule interactions. We identify polarity-sorting and crosslink tether relaxation as two polar-specific sources of active destabilizing stress. We then develop a continuum Doi-Onsager model that captures polarity sorting and the hydrodynamic flows generated by these polar-specific active stresses. In simulations of active nematic flows on immersed surfaces, the active stresses drive turbulent flow dynamics and continuous generation and annihilation of disclination defects. The dynamics follow from two instabilities, and accounting for the immersed nature of the experiment yields unambiguous characteristic length and time scales. When turning off the hydrodynamics in the Doi-Onsager model, we capture formation of polar lanes as observed in the Brownian dynamics simulation.

Connecting protein and mRNA burst distributions for stochastic models of gene expression

International Nuclear Information System (INIS)

Elgart, Vlad; Jia, Tao; Fenley, Andrew T; Kulkarni, Rahul

2011-01-01

The intrinsic stochasticity of gene expression can lead to large variability in protein levels for genetically identical cells. Such variability in protein levels can arise from infrequent synthesis of mRNAs which in turn give rise to bursts of protein expression. Protein expression occurring in bursts has indeed been observed experimentally and recent studies have also found evidence for transcriptional bursting, i.e. production of mRNAs in bursts. Given that there are distinct experimental techniques for quantifying the noise at different stages of gene expression, it is of interest to derive analytical results connecting experimental observations at different levels. In this work, we consider stochastic models of gene expression for which mRNA and protein production occurs in independent bursts. For such models, we derive analytical expressions connecting protein and mRNA burst distributions which show how the functional form of the mRNA burst distribution can be inferred from the protein burst distribution. Additionally, if gene expression is repressed such that observed protein bursts arise only from single mRNAs, we show how observations of protein burst distributions (repressed and unrepressed) can be used to completely determine the mRNA burst distribution. Assuming independent contributions from individual bursts, we derive analytical expressions connecting means and variances for burst and steady-state protein distributions. Finally, we validate our general analytical results by considering a specific reaction scheme involving regulation of protein bursts by small RNAs. For a range of parameters, we derive analytical expressions for regulated protein distributions that are validated using stochastic simulations. The analytical results obtained in this work can thus serve as useful inputs for a broad range of studies focusing on stochasticity in gene expression

APOLLO: a quality assessment service for single and multiple protein models.

Science.gov (United States)

Wang, Zheng; Eickholt, Jesse; Cheng, Jianlin

2011-06-15

We built a web server named APOLLO, which can evaluate the absolute global and local qualities of a single protein model using machine learning methods or the global and local qualities of a pool of models using a pair-wise comparison approach. Based on our evaluations on 107 CASP9 (Critical Assessment of Techniques for Protein Structure Prediction) targets, the predicted quality scores generated from our machine learning and pair-wise methods have an average per-target correlation of 0.671 and 0.917, respectively, with the true model quality scores. Based on our test on 92 CASP9 targets, our predicted absolute local qualities have an average difference of 2.60 Å with the actual distances to native structure. http://sysbio.rnet.missouri.edu/apollo/. Single and pair-wise global quality assessment software is also available at the site.

Insulin as a model to teach three-dimensional structure of proteins

Directory of Open Access Journals (Sweden)

João Batista Teixeira da Rocha

2018-02-01

Proteins are the most ubiquitous macromolecules found in the living cells and have innumerous physiological functions. Therefore, it is fundamental to build a solid knowledge about the proteins three dimensional structure to better understand the living state. The hierarchical structure of proteins is usually studied in the undergraduate discipline of Biochemistry. Here we described pedagogical interventions designed to increase the preservice teacher chemistry students’ knowledge about protein structure. The activities were made using alternative and cheap materials to encourage the application of these simple methodologies by the future teachers in the secondary school. From the primary structure of insulin chains, students had to construct a three-dimensional structure of insulin. After the activities, the students highlighted an improvement of their previous knowledge about proteins structure. The construction of a tridimensional model together with other activities seems to be an efficient way to promote the learning about the structure of proteins to undergraduate students. The methodology used was inexpensiveness and simple and it can be used both in the university and in the high-school.

Entropic potential field formed for a linear-motor protein near a filament: Statistical-mechanical analyses using simple models.

Science.gov (United States)

Amano, Ken-Ichi; Yoshidome, Takashi; Iwaki, Mitsuhiro; Suzuki, Makoto; Kinoshita, Masahiro

2010-07-28

We report a new progress in elucidating the mechanism of the unidirectional movement of a linear-motor protein (e.g., myosin) along a filament (e.g., F-actin). The basic concept emphasized here is that a potential field is entropically formed for the protein on the filament immersed in solvent due to the effect of the translational displacement of solvent molecules. The entropic potential field is strongly dependent on geometric features of the protein and the filament, their overall shapes as well as details of the polyatomic structures. The features and the corresponding field are judiciously adjusted by the binding of adenosine triphosphate (ATP) to the protein, hydrolysis of ATP into adenosine diphosphate (ADP)+Pi, and release of Pi and ADP. As the first step, we propose the following physical picture: The potential field formed along the filament for the protein without the binding of ATP or ADP+Pi to it is largely different from that for the protein with the binding, and the directed movement is realized by repeated switches from one of the fields to the other. To illustrate the picture, we analyze the spatial distribution of the entropic potential between a large solute and a large body using the three-dimensional integral equation theory. The solute is modeled as a large hard sphere. Two model filaments are considered as the body: model 1 is a set of one-dimensionally connected large hard spheres and model 2 is a double helical structure formed by two sets of connected large hard spheres. The solute and the filament are immersed in small hard spheres forming the solvent. The major findings are as follows. The solute is strongly confined within a narrow space in contact with the filament. Within the space there are locations with sharply deep local potential minima along the filament, and the distance between two adjacent locations is equal to the diameter of the large spheres constituting the filament. The potential minima form a ringlike domain in model 1

Domain analyses of Usher syndrome causing Clarin-1 and GPR98 protein models.

Science.gov (United States)

Khan, Sehrish Haider; Javed, Muhammad Rizwan; Qasim, Muhammad; Shahzadi, Samar; Jalil, Asma; Rehman, Shahid Ur

2014-01-01

Usher syndrome is an autosomal recessive disorder that causes hearing loss, Retinitis Pigmentosa (RP) and vestibular dysfunction. It is clinically and genetically heterogeneous disorder which is clinically divided into three types i.e. type I, type II and type III. To date, there are about twelve loci and ten identified genes which are associated with Usher syndrome. A mutation in any of these genes e.g. CDH23, CLRN1, GPR98, MYO7A, PCDH15, USH1C, USH1G, USH2A and DFNB31 can result in Usher syndrome or non-syndromic deafness. These genes provide instructions for making proteins that play important roles in normal hearing, balance and vision. Studies have shown that protein structures of only seven genes have been determined experimentally and there are still three genes whose structures are unavailable. These genes are Clarin-1, GPR98 and Usherin. In the absence of an experimentally determined structure, homology modeling and threading often provide a useful 3D model of a protein. Therefore in the current study Clarin-1 and GPR98 proteins have been analyzed for signal peptide, domains and motifs. Clarin-1 protein was found to be without any signal peptide and consists of prokar lipoprotein domain. Clarin-1 is classified within claudin 2 super family and consists of twelve motifs. Whereas, GPR98 has a 29 amino acids long signal peptide and classified within GPCR family 2 having Concanavalin A-like lectin/glucanase superfamily. It was found to be consists of GPS and G protein receptor F2 domains and twenty nine motifs. Their 3D structures have been predicted using I-TASSER server. The model of Clarin-1 showed only α-helix but no beta sheets while model of GPR98 showed both α-helix and β sheets. The predicted structures were then evaluated and validated by MolProbity and Ramachandran plot. The evaluation of the predicted structures showed 78.9% residues of Clarin-1 and 78.9% residues of GPR98 within favored regions. The findings of present study has resulted in the

Camel milk protein hydrolysates with improved technofunctional properties and enhanced antioxidant potential in in vitro and in food model systems.

Science.gov (United States)

Al-Shamsi, Kholoud Awad; Mudgil, Priti; Hassan, Hassan Mohamed; Maqsood, Sajid

2018-01-01

Camel milk protein hydrolysates (CMPH) were generated using proteolytic enzymes, such as alcalase, bromelain, and papain, to explore the effect on the technofunctional properties and antioxidant potential under in vitro and in real food model systems. Characterization of the CMPH via degree of hydrolysis, sodium dodecyl sulfate-PAGE, and HPLC revealed that different proteins in camel milk underwent degradation at different degrees after enzymatic hydrolysis using 3 different enzymes for 2, 4, and 6 h, with papain displaying the highest degradation. Technofunctional properties, such as emulsifying activity index, surface hydrophobicity, and protein solubility, were higher in CMPH than unhydrolyzed camel milk proteins. However, the water and fat absorption capacity were lower in CMPH compared with unhydrolyzed camel milk proteins. Antioxidant properties as assessed by 2,2-azinobis(3-ethylbenzthiazoline-6-sulfonic acid) and 2,2-diphenyl-1-picrylhydrazyl radical scavenging activities and metal-chelating activity were enhanced after hydrolysis, in contrast to ferric-reducing antioxidant power which showed a decrease after hydrolysis. The CMPH were also tested in real food model systems for their potential to inhibit lipid peroxidation in fish mince and grape seed oil-in-water emulsion, and we found that papain-produced hydrolysate displayed higher inhibition than alcalase- and bromelain-produced hydrolysates. Therefore, the CMPH demonstrated effective antioxidant potential in vitro as well as in real food systems and showed enhanced functional properties, which guarantees their potential applications in functional foods. The present study is one of few reports available on CMPH being explored in vitro as well as in real food model systems. Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Orthogonal Electric Field Measurements near the Green Fluorescent Protein Fluorophore through Stark Effect Spectroscopy and pKa Shifts Provide a Unique Benchmark for Electrostatics Models.

Science.gov (United States)

Slocum, Joshua D; First, Jeremy T; Webb, Lauren J

2017-07-20

Measurement of the magnitude, direction, and functional importance of electric fields in biomolecules has been a long-standing experimental challenge. pK a shifts of titratable residues have been the most widely implemented measurements of the local electrostatic environment around the labile proton, and experimental data sets of pK a shifts in a variety of systems have been used to test and refine computational prediction capabilities of protein electrostatic fields. A more direct and increasingly popular technique to measure electric fields in proteins is Stark effect spectroscopy, where the change in absorption energy of a chromophore relative to a reference state is related to the change in electric field felt by the chromophore. While there are merits to both of these methods and they are both reporters of local electrostatic environment, they are fundamentally different measurements, and to our knowledge there has been no direct comparison of these two approaches in a single protein. We have recently demonstrated that green fluorescent protein (GFP) is an ideal model system for measuring changes in electric fields in a protein interior caused by amino acid mutations using both electronic and vibrational Stark effect chromophores. Here we report the changes in pK a of the GFP fluorophore in response to the same mutations and show that they are in excellent agreement with Stark effect measurements. This agreement in the results of orthogonal experiments reinforces our confidence in the experimental results of both Stark effect and pK a measurements and provides an excellent target data set to benchmark diverse protein electrostatics calculations. We used this experimental data set to test the pK a prediction ability of the adaptive Poisson-Boltzmann solver (APBS) and found that a simple continuum dielectric model of the GFP interior is insufficient to accurately capture the measured pK a and Stark effect shifts. We discuss some of the limitations of this

Uncoupling of Protein Aggregation and Neurodegeneration in a Mouse Amyotrophic Lateral Sclerosis Model.

Science.gov (United States)

Lee, Joo-Yong; Kawaguchi, Yoshiharu; Li, Ming; Kapur, Meghan; Choi, Su Jin; Kim, Hak-June; Park, Song-Yi; Zhu, Haining; Yao, Tso-Pang

2015-01-01

Aberrant accumulation of protein aggregates is a pathological hallmark of many neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS). Although a buildup of protein aggregates frequently leads to cell death, whether it is the key pathogenic factor in driving neurodegenerative disease remains controversial. HDAC6, a cytosolic ubiquitin-binding deacetylase, has emerged as an important regulator of ubiquitin-dependent quality control autophagy, a lysosome-dependent degradative system responsible for the disposal of misfolded protein aggregates and damaged organelles. Here, we show that in cell models HDAC6 plays a protective role against multiple disease-associated and aggregation-prone cytosolic proteins by facilitating their degradation. We further show that HDAC6 is required for efficient localization of lysosomes to protein aggregates, indicating that lysosome targeting to autophagic substrates is regulated. Supporting a critical role of HDAC6 in protein aggregate disposal in vivo, genetic ablation of HDAC6 in a transgenic SOD1G93A mouse, a model of ALS, leads to dramatic accumulation of ubiquitinated SOD1G93A protein aggregates. Surprisingly, despite a robust buildup of SOD1G93A aggregates, deletion of HDAC6 only moderately modified the motor phenotypes. These findings indicate that SOD1G93A aggregation is not the only determining factor to drive neurodegeneration in ALS, and that HDAC6 likely modulates neurodegeneration through additional mechanisms beyond protein aggregate clearance. © 2015 S. Karger AG, Basel.

ProDis-ContSHC: Learning protein dissimilarity measures and hierarchical context coherently for protein-protein comparison in protein database retrieval

KAUST Repository

Wang, Jim Jing-Yan

2012-05-08

Background: The need to retrieve or classify protein molecules using structure or sequence-based similarity measures underlies a wide range of biomedical applications. Traditional protein search methods rely on a pairwise dissimilarity/similarity measure for comparing a pair of proteins. This kind of pairwise measures suffer from the limitation of neglecting the distribution of other proteins and thus cannot satisfy the need for high accuracy of the retrieval systems. Recent work in the machine learning community has shown that exploiting the global structure of the database and learning the contextual dissimilarity/similarity measures can improve the retrieval performance significantly. However, most existing contextual dissimilarity/similarity learning algorithms work in an unsupervised manner, which does not utilize the information of the known class labels of proteins in the database.Results: In this paper, we propose a novel protein-protein dissimilarity learning algorithm, ProDis-ContSHC. ProDis-ContSHC regularizes an existing dissimilarity measure dij by considering the contextual information of the proteins. The context of a protein is defined by its neighboring proteins. The basic idea is, for a pair of proteins (i, j), if their context N (i) and N (j) is similar to each other, the two proteins should also have a high similarity. We implement this idea by regularizing dij by a factor learned from the context N (i) and N (j). Moreover, we divide the context to hierarchial sub-context and get the contextual dissimilarity vector for each protein pair. Using the class label information of the proteins, we select the relevant (a pair of proteins that has the same class labels) and irrelevant (with different labels) protein pairs, and train an SVM model to distinguish between their contextual dissimilarity vectors. The SVM model is further used to learn a supervised regularizing factor. Finally, with the new Supervised learned Dissimilarity measure, we update

Energy landscape, structure and rate effects on strength properties of alpha-helical proteins

International Nuclear Information System (INIS)

Bertaud, Jeremie; Hester, Joshua; Jimenez, Daniel D; Buehler, Markus J

2010-01-01

The strength of protein domains is crucial to identify the mechanical role of protein domains in biological processes such as mechanotransduction, tissue mechanics and tissue remodeling. Whereas the concept of strength has been widely investigated for engineered materials, the strength of fundamental protein material building blocks and how it depends on structural parameters such as the chemical bonding, the protein filament length and the timescale of observation or deformation velocity remains poorly understood. Here we report a systematic analysis of the influence of key parameters that define the energy landscape of the strength properties of alpha-helical protein domains, including energy barriers, unfolding and refolding distances, the locations of folded and unfolded states, as well as variations of the length and pulling velocity of alpha-helical protein filaments. The analysis is facilitated by the development of a double-well mesoscale potential formulation, utilized here to carry out a systematic numerical analysis of the behavior of alpha-helices. We compare the results against widely used protein strength models based on the Bell model, one of the simplest models used to characterize the strength of protein filaments. We find that, whereas Bell-type models are a reasonable approximation to describe the rupture of alpha-helical protein domains for a certain range of pulling speeds and values of energy barriers, the model ceases to hold for very large energy barriers and for very small pulling speeds, in agreement with earlier findings. We conclude with an application of our mesoscale model to investigate the effect of the length of alpha-helices on their mechanical strength. We find a weakening effect as the length of alpha-helical proteins increases, followed by an asymptotic regime in which the strength remains constant. We compare strand lengths found in biological proteins with the scaling law of strength versus alpha-helix filament length. The

Semantic role labeling for protein transport predicates

Directory of Open Access Journals (Sweden)

Martin James H

2008-06-01

Full Text Available Abstract Background Automatic semantic role labeling (SRL is a natural language processing (NLP technique that maps sentences to semantic representations. This technique has been widely studied in the recent years, but mostly with data in newswire domains. Here, we report on a SRL model for identifying the semantic roles of biomedical predicates describing protein transport in GeneRIFs – manually curated sentences focusing on gene functions. To avoid the computational cost of syntactic parsing, and because the boundaries of our protein transport roles often did not match up with syntactic phrase boundaries, we approached this problem with a word-chunking paradigm and trained support vector machine classifiers to classify words as being at the beginning, inside or outside of a protein transport role. Results We collected a set of 837 GeneRIFs describing movements of proteins between cellular components, whose predicates were annotated for the semantic roles AGENT, PATIENT, ORIGIN and DESTINATION. We trained these models with the features of previous word-chunking models, features adapted from phrase-chunking models, and features derived from an analysis of our data. Our models were able to label protein transport semantic roles with 87.6% precision and 79.0% recall when using manually annotated protein boundaries, and 87.0% precision and 74.5% recall when using automatically identified ones. Conclusion We successfully adapted the word-chunking classification paradigm to semantic role labeling, applying it to a new domain with predicates completely absent from any previous studies. By combining the traditional word and phrasal role labeling features with biomedical features like protein boundaries and MEDPOST part of speech tags, we were able to address the challenges posed by the new domain data and subsequently build robust models that achieved F-measures as high as 83.1. This system for extracting protein transport information from Gene

Polyphenol-enriched berry extracts naturally modulate reactive proteins in model foods.

Science.gov (United States)

Lila, Mary Ann; Schneider, Maggie; Devlin, Amy; Plundrich, Nathalie; Laster, Scott; Foegeding, E Allen

2017-12-13

Healthy foods like polyphenol-rich berries and high quality edible proteins are in demand in today's functional food marketplace, but it can be difficult to formulate convenient food products with physiologically-relevant amounts of these ingredients and still maintain product quality. In part, this is because proteins can interact with other food ingredients and precipitate destabilizing events, which can disrupt food structure and diminish shelf life. Proteins in foods can also interact with human receptors to provoke adverse consequences such as allergies. When proteins and polyphenols were pre-aggregated into stable colloidal particles prior to use as ingredients, highly palatable food formulations (with reduced astringency of polyphenols) could be prepared, and the overall structural properties of food formulations were significantly improved. All of the nutritive and phytoactive benefits of the proteins and concentrated polyphenols remained highly bioavailable, but the protein molecules in the particle matrix did not self-aggregate into networks or react with other food ingredients. Both the drainage half-life (a marker of structural stability) and the yield stress (resistance to flow) of model foams made with the protein-polyphenol particles were increased in a dose-dependent manner. Of high significance in this complexation process, the reactive allergenic epitopes of certain proteins were effectively blunted by binding with polyphenols, attenuating the allergenicity of the food proteins. Porcine macrophages produced TNF-α proinflammatory cytokine when provoked with whey protein, but, this response was blocked completely when the cells were stimulated with particles that complexed whey protein with cinnamon-derived polyphenols. Cytokine and chemokine production characteristic of allergic reactions were blocked by the polyphenols, allowing for the potential creation of hypoallergenic protein-berry polyphenol enriched foods.

Towards predictive resistance models for agrochemicals by combining chemical and protein similarity via proteochemometric modelling.

Science.gov (United States)

van Westen, Gerard J P; Bender, Andreas; Overington, John P

2014-10-01

Resistance to pesticides is an increasing problem in agriculture. Despite practices such as phased use and cycling of 'orthogonally resistant' agents, resistance remains a major risk to national and global food security. To combat this problem, there is a need for both new approaches for pesticide design, as well as for novel chemical entities themselves. As summarized in this opinion article, a technique termed 'proteochemometric modelling' (PCM), from the field of chemoinformatics, could aid in the quantification and prediction of resistance that acts via point mutations in the target proteins of an agent. The technique combines information from both the chemical and biological domain to generate bioactivity models across large numbers of ligands as well as protein targets. PCM has previously been validated in prospective, experimental work in the medicinal chemistry area, and it draws on the growing amount of bioactivity information available in the public domain. Here, two potential applications of proteochemometric modelling to agrochemical data are described, based on previously published examples from the medicinal chemistry literature.

Motor Gasoline Market Model documentation report

International Nuclear Information System (INIS)

1993-09-01

The purpose of this report is to define the objectives of the Motor Gasoline Market Model (MGMM), describe its basic approach and to provide detail on model functions. This report is intended as a reference document for model analysts, users, and the general public. The MGMM performs a short-term (6- to 9-month) forecast of demand and price for motor gasoline in the US market; it also calculates end of month stock levels. The model is used to analyze certain market behavior assumptions or shocks and to determine the effect on market price, demand and stock level

Final project report

Energy Technology Data Exchange (ETDEWEB)

Nitin S. Baliga and Leroy Hood

2008-11-12

The proposed overarching goal for this project was the following: Data integration, simulation and visualization will facilitate metabolic and regulatory network prediction, exploration, and formulation of hypotheses. We stated three specific aims to achieve the overarching goal of this project: (1) Integration of multiple levels of information such as mRNA and protein levels, predicted protein-protein interactions/associations and gene function will enable construction of models describing environmental response and dynamic behavior. (2) Flexible tools for network inference will accelerate our understanding of biological systems. (3) Flexible exploration and queries of model hypotheses will provide focus and reveal novel dependencies. The underlying philosophy of these proposed aims is that an iterative cycle of experiments, experimental design, and verification will lead to a comprehensive and predictive model that will shed light on systems level mechanisms involved in responses elicited by living systems upon sensing a change in their environment. In the previous years report we demonstrated considerable progress in development of data standards, regulatory network inference and data visualization and exploration. We are pleased to report that several manuscripts describing these procedures have been published in top international peer reviewed journals including Genome Biology, PNAS, and Cell. The abstracts of these manuscripts are given and they summarize our accomplishments in this project.

Emergence of modularity and disassortativity in protein-protein interaction networks.

Science.gov (United States)

Wan, Xi; Cai, Shuiming; Zhou, Jin; Liu, Zengrong

2010-12-01

In this paper, we present a simple evolution model of protein-protein interaction networks by introducing a rule of small-preference duplication of a node, meaning that the probability of a node chosen to duplicate is inversely proportional to its degree, and subsequent divergence plus nonuniform heterodimerization based on some plausible mechanisms in biology. We show that our model cannot only reproduce scale-free connectivity and small-world pattern, but also exhibit hierarchical modularity and disassortativity. After comparing the features of our model with those of real protein-protein interaction networks, we believe that our model can provide relevant insights into the mechanism underlying the evolution of protein-protein interaction networks. © 2010 American Institute of Physics.

The interface of protein structure, protein biophysics, and molecular evolution

Science.gov (United States)

Liberles, David A; Teichmann, Sarah A; Bahar, Ivet; Bastolla, Ugo; Bloom, Jesse; Bornberg-Bauer, Erich; Colwell, Lucy J; de Koning, A P Jason; Dokholyan, Nikolay V; Echave, Julian; Elofsson, Arne; Gerloff, Dietlind L; Goldstein, Richard A; Grahnen, Johan A; Holder, Mark T; Lakner, Clemens; Lartillot, Nicholas; Lovell, Simon C; Naylor, Gavin; Perica, Tina; Pollock, David D; Pupko, Tal; Regan, Lynne; Roger, Andrew; Rubinstein, Nimrod; Shakhnovich, Eugene; Sjölander, Kimmen; Sunyaev, Shamil; Teufel, Ashley I; Thorne, Jeffrey L; Thornton, Joseph W; Weinreich, Daniel M; Whelan, Simon

2012-01-01

Abstract The interface of protein structural biology, protein biophysics, molecular evolution, and molecular population genetics forms the foundations for a mechanistic understanding of many aspects of protein biochemistry. Current efforts in interdisciplinary protein modeling are in their infancy and the state-of-the art of such models is described. Beyond the relationship between amino acid substitution and static protein structure, protein function, and corresponding organismal fitness, other considerations are also discussed. More complex mutational processes such as insertion and deletion and domain rearrangements and even circular permutations should be evaluated. The role of intrinsically disordered proteins is still controversial, but may be increasingly important to consider. Protein geometry and protein dynamics as a deviation from static considerations of protein structure are also important. Protein expression level is known to be a major determinant of evolutionary rate and several considerations including selection at the mRNA level and the role of interaction specificity are discussed. Lastly, the relationship between modeling and needed high-throughput experimental data as well as experimental examination of protein evolution using ancestral sequence resurrection and in vitro biochemistry are presented, towards an aim of ultimately generating better models for biological inference and prediction. PMID:22528593

Computational Modeling of Proteins based on Cellular Automata: A Method of HP Folding Approximation.

Science.gov (United States)

Madain, Alia; Abu Dalhoum, Abdel Latif; Sleit, Azzam

2018-06-01

The design of a protein folding approximation algorithm is not straightforward even when a simplified model is used. The folding problem is a combinatorial problem, where approximation and heuristic algorithms are usually used to find near optimal folds of proteins primary structures. Approximation algorithms provide guarantees on the distance to the optimal solution. The folding approximation approach proposed here depends on two-dimensional cellular automata to fold proteins presented in a well-studied simplified model called the hydrophobic-hydrophilic model. Cellular automata are discrete computational models that rely on local rules to produce some overall global behavior. One-third and one-fourth approximation algorithms choose a subset of the hydrophobic amino acids to form H-H contacts. Those algorithms start with finding a point to fold the protein sequence into two sides where one side ignores H's at even positions and the other side ignores H's at odd positions. In addition, blocks or groups of amino acids fold the same way according to a predefined normal form. We intend to improve approximation algorithms by considering all hydrophobic amino acids and folding based on the local neighborhood instead of using normal forms. The CA does not assume a fixed folding point. The proposed approach guarantees one half approximation minus the H-H endpoints. This lower bound guaranteed applies to short sequences only. This is proved as the core and the folds of the protein will have two identical sides for all short sequences.

Biosphere Model Report, Errata 1

Energy Technology Data Exchange (ETDEWEB)

M. Wasolek

2003-09-18

The purpose of this report is to document the biosphere model, the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), which describes radionuclide transport processes in the biosphere and associated human exposure that may arise as the result of radionuclide release from the geologic repository at Yucca Mountain. The biosphere model is one of the process models that support the Yucca Mountain Project (YMP) Total System Performance Assessment (TSPA) for the license application (LA), the TSPA-LA. The ERMYN model provides the capability of performing human radiation dose assessments. This report documents the biosphere model, which includes: (1) Describing the reference biosphere, human receptor, exposure scenarios, and primary radionuclides for each exposure scenario (Section 6.1); (2) Developing a biosphere conceptual model using site-specific features, events, and processes (FEPs), the reference biosphere, the human receptor, and assumptions (Section 6.2 and Section 6.3); (3) Building a mathematical model using the biosphere conceptual model and published biosphere models (Sections 6.4 and 6.5); (4) Summarizing input parameters for the mathematical model, including the uncertainty associated with input values (Section 6.6); (5) Identifying improvements in the ERMYN model compared with the model used in previous biosphere modeling (Section 6.7); (6) Constructing an ERMYN implementation tool (model) based on the biosphere mathematical model using GoldSim stochastic simulation software (Sections 6.8 and 6.9); (7) Verifying the ERMYN model by comparing output from the software with hand calculations to ensure that the GoldSim implementation is correct (Section 6.10); (8) Validating the ERMYN model by corroborating it with published biosphere models; comparing conceptual models, mathematical models, and numerical results (Section 7).

Biosphere Model Report, Errata 1

International Nuclear Information System (INIS)

Wasolek, M.

2003-01-01

The purpose of this report is to document the biosphere model, the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), which describes radionuclide transport processes in the biosphere and associated human exposure that may arise as the result of radionuclide release from the geologic repository at Yucca Mountain. The biosphere model is one of the process models that support the Yucca Mountain Project (YMP) Total System Performance Assessment (TSPA) for the license application (LA), the TSPA-LA. The ERMYN model provides the capability of performing human radiation dose assessments. This report documents the biosphere model, which includes: (1) Describing the reference biosphere, human receptor, exposure scenarios, and primary radionuclides for each exposure scenario (Section 6.1); (2) Developing a biosphere conceptual model using site-specific features, events, and processes (FEPs), the reference biosphere, the human receptor, and assumptions (Section 6.2 and Section 6.3); (3) Building a mathematical model using the biosphere conceptual model and published biosphere models (Sections 6.4 and 6.5); (4) Summarizing input parameters for the mathematical model, including the uncertainty associated with input values (Section 6.6); (5) Identifying improvements in the ERMYN model compared with the model used in previous biosphere modeling (Section 6.7); (6) Constructing an ERMYN implementation tool (model) based on the biosphere mathematical model using GoldSim stochastic simulation software (Sections 6.8 and 6.9); (7) Verifying the ERMYN model by comparing output from the software with hand calculations to ensure that the GoldSim implementation is correct (Section 6.10); (8) Validating the ERMYN model by corroborating it with published biosphere models; comparing conceptual models, mathematical models, and numerical results (Section 7)

Glutamine reduces myocardial cell apoptosis in a rat model of sepsis by promoting expression of heat shock protein 90.

Science.gov (United States)

Li, Wanxia; Tao, Shaoyu; Wu, Qinghua; Wu, Tao; Tao, Ran; Fan, Jun

2017-12-01

Myocardial cell injury and cardiac myocyte apoptosis are associated with sepsis. Glutamine (Gln) has been reported to repair myocardial cell injury. The aim of this study was to explore the role of Gln on cardiac myocytes in a cecal ligation and puncture (CLP) model of sepsis in Wistar rats. Following induction of sepsis in a CLP rat model, viral encoding heat shock protein 90 (Hsp90) gene and Hsp90dsDNA were designed to express and knockdown Hsp90, respectively. Rat cardiac tissues were examined histologically, and apoptosis was detected by terminal deoxynucleotidyl transferase dUTP nick end labeling staining. The expression of B-cell lymphoma-2 (Bcl-2), Bcl-2-associated X protein, Hsp90, p53 upregulated modulator of apoptosis, and p53 was measured by western blotting and real-time polymerase chain reaction. Caspase-3, caspase-8, and caspase-9 were detected by enzyme-linked immunosorbent assay. Rat cardiac myocyte damage induced by CLP was reduced by Gln treatment and Hsp90 overexpression, and these changes were reversed by Hsp90 knockdown. Bcl-2 expression, Bcl-2-associated X protein, p53, p53 upregulated modulator of apoptosis, caspase-8, caspase-9, and caspase-3 activities were significantly upregulated in the CLP model, which were reduced by Gln treatment and Hsp90 overexpression. Gln reduced apoptosis of cardiac myocytes in a rat model of sepsis, by promoting Hsp90 expression. Further studies are needed to determine the possible therapeutic action of Gln in sepsis in human tissue. Copyright © 2017 Elsevier Inc. All rights reserved.

Summary of the Supplemental Model Reports Supporting the Disposal Criticality Analysis Methodology Topical Report

International Nuclear Information System (INIS)

Brownson, D. A.

2002-01-01

The Department of Energy (DOE) Office of Civilian Radioactive Waste Management (OCRWM) has committed to a series of model reports documenting the methodology to be utilized in the Disposal Criticality Analysis Methodology Topical Report (YMP 2000). These model reports detail and provide validation of the methodology to be utilized for criticality analyses related to: (1) Waste form/waste package degradation; (2) Waste package isotopic inventory; (3) Criticality potential of degraded waste form/waste package configurations (effective neutron multiplication factor); (4) Probability of criticality (for each potential critical configuration as well as total event); and (5) Criticality consequences. This purpose of this summary report is to provide a status of the model reports and a schedule for their completion. This report also provides information relative to the model report content and validation. The model reports and their revisions are being generated as a result of: (1) Commitments made in the Disposal Criticality Analysis Methodology Topical Report (YMP 2000); (2) Open Items from the Safety Evaluation Report (Reamer 2000); (3) Key Technical Issue agreements made during DOE/U.S. Nuclear Regulatory Commission (NRC) Technical Exchange Meeting (Reamer and Williams 2000); and (4) NRC requests for additional information (Schlueter 2002)

Citrate synthase proteins in extremophilic organisms: Studies within a structure-based model

International Nuclear Information System (INIS)

Różycki, Bartosz; Cieplak, Marek

2014-01-01

We study four citrate synthase homodimeric proteins within a structure-based coarse-grained model. Two of these proteins come from thermophilic bacteria, one from a cryophilic bacterium and one from a mesophilic organism; three are in the closed and two in the open conformations. Even though the proteins belong to the same fold, the model distinguishes the properties of these proteins in a way which is consistent with experiments. For instance, the thermophilic proteins are more stable thermodynamically than their mesophilic and cryophilic homologues, which we observe both in the magnitude of thermal fluctuations near the native state and in the kinetics of thermal unfolding. The level of stability correlates with the average coordination number for amino acid contacts and with the degree of structural compactness. The pattern of positional fluctuations along the sequence in the closed conformation is different than in the open conformation, including within the active site. The modes of correlated and anticorrelated movements of pairs of amino acids forming the active site are very different in the open and closed conformations. Taken together, our results show that the precise location of amino acid contacts in the native structure appears to be a critical element in explaining the similarities and differences in the thermodynamic properties, local flexibility, and collective motions of the different forms of the enzyme

Effective comparative analysis of protein-protein interaction networks by measuring the steady-state network flow using a Markov model.

Science.gov (United States)

Jeong, Hyundoo; Qian, Xiaoning; Yoon, Byung-Jun

2016-10-06

Comparative analysis of protein-protein interaction (PPI) networks provides an effective means of detecting conserved functional network modules across different species. Such modules typically consist of orthologous proteins with conserved interactions, which can be exploited to computationally predict the modules through network comparison. In this work, we propose a novel probabilistic framework for comparing PPI networks and effectively predicting the correspondence between proteins, represented as network nodes, that belong to conserved functional modules across the given PPI networks. The basic idea is to estimate the steady-state network flow between nodes that belong to different PPI networks based on a Markov random walk model. The random walker is designed to make random moves to adjacent nodes within a PPI network as well as cross-network moves between potential orthologous nodes with high sequence similarity. Based on this Markov random walk model, we estimate the steady-state network flow - or the long-term relative frequency of the transitions that the random walker makes - between nodes in different PPI networks, which can be used as a probabilistic score measuring their potential correspondence. Subsequently, the estimated scores can be used for detecting orthologous proteins in conserved functional modules through network alignment. Through evaluations based on multiple real PPI networks, we demonstrate that the proposed scheme leads to improved alignment results that are biologically more meaningful at reduced computational cost, outperforming the current state-of-the-art algorithms. The source code and datasets can be downloaded from http://www.ece.tamu.edu/~bjyoon/CUFID .

[Drosophila melanogaster as a model for studying the function of animal viral proteins].

Science.gov (United States)

Omelianchuk, L V; Iudina, O S

2011-07-01

Studies in which Drosophila melanogaster individuals carrying transgenes of animal viruses were used to analyze the action of animal viral proteins on the cell are reviewed. The data presented suggest that host specificity of viruses is determined by their proteins responsible for the penetration of the virus into the cell, while viral proteins responsible for interactions with the host cell are much less host-specific. Due to this, the model of Drosophila with its developed system of searching for genetic interactions can be used to find intracellular targets for the action of viral proteins of the second group.

In silico functional elucidation of uncharacterized proteins of Chlamydia abortus strain LLG.

Science.gov (United States)

Singh, Gagandeep; Sharma, Dixit; Singh, Vikram; Rani, Jyoti; Marotta, Francessco; Kumar, Manoj; Mal, Gorakh; Singh, Birbal

2017-03-01

This study reports structural modeling, molecular dynamics profiling of hypothetical proteins in Chlamydia abortus genome database. The hypothetical protein sequences were extracted from C. abortus LLG Genome Database for functional elucidation using in silico methods. Fifty-one proteins with their roles in defense, binding and transporting other biomolecules were unraveled. Forty-five proteins were found to be nonhomologous to proteins present in hosts infected by C. abortus . Of these, 31 proteins were related to virulence. The structural modeling of two proteins, first, WP_006344020.1 (phosphorylase) and second, WP_006344325.1 (chlamydial protease/proteasome-like activity factor) were accomplished. The conserved active sites necessary for the catalytic function were analyzed. The finally concluded proteins are envisioned as possible targets for developing drugs to curtail chlamydial infections, however, and should be validated by molecular biological methods.

Sytemic lupus erythematosus presenting with protein losing enteropathy in a resource limited centre: a case report

Directory of Open Access Journals (Sweden)

Ratnayake Eranda C

2012-01-01

Full Text Available Abstract Introduction Systemic lupus erythematosus is a disease which may initially present with varying symptoms, most commonly a photosensitive rash and arthritis. Protein losing enteropathy is a recognized but rare presenting manifestation. Diagnosing protein losing enteropathy in resource limited centres is challenging but possible through the exclusion of other possible causes of hypoalbunaemia. Case Presentation We report a case of protein losing gastroenteropathy secondary to intestinal lymphangiectasia as the initial manifestation of systemic lupus erythematosus in a 57 year old Sri Lankan (South Asian male patient. The diagnosis was made by the exclusion of other causes of hypoalbuminaemia as the gold standard investigations for protein losing enteropathy were not available at this centre. Conclusions Protein losing enteropathy is a diagnosis of exclusion in resource limited centres in the world. Systemic lupus erythematosus should be considered in the differential diagnosis of protein losing enteropathy. Intestinal lymphangiectasia should also be recognized as a possible pathophysiological mechanism.

A Finite Element Solution of Lateral Periodic Poisson–Boltzmann Model for Membrane Channel Proteins

Science.gov (United States)

Xu, Jingjie; Lu, Benzhuo

2018-01-01

Membrane channel proteins control the diffusion of ions across biological membranes. They are closely related to the processes of various organizational mechanisms, such as: cardiac impulse, muscle contraction and hormone secretion. Introducing a membrane region into implicit solvation models extends the ability of the Poisson–Boltzmann (PB) equation to handle membrane proteins. The use of lateral periodic boundary conditions can properly simulate the discrete distribution of membrane proteins on the membrane plane and avoid boundary effects, which are caused by the finite box size in the traditional PB calculations. In this work, we: (1) develop a first finite element solver (FEPB) to solve the PB equation with a two-dimensional periodicity for membrane channel proteins, with different numerical treatments of the singular charges distributions in the channel protein; (2) add the membrane as a dielectric slab in the PB model, and use an improved mesh construction method to automatically identify the membrane channel/pore region even with a tilt angle relative to the z-axis; and (3) add a non-polar solvation energy term to complete the estimation of the total solvation energy of a membrane protein. A mesh resolution of about 0.25 Å (cubic grid space)/0.36 Å (tetrahedron edge length) is found to be most accurate in linear finite element calculation of the PB solvation energy. Computational studies are performed on a few exemplary molecules. The results indicate that all factors, the membrane thickness, the length of periodic box, membrane dielectric constant, pore region dielectric constant, and ionic strength, have individually considerable influence on the solvation energy of a channel protein. This demonstrates the necessity to treat all of those effects in the PB model for membrane protein simulations. PMID:29495644

Application of TZERO calibrated modulated temperature differential scanning calorimetry to characterize model protein formulations.

Science.gov (United States)

Badkar, Aniket; Yohannes, Paulos; Banga, Ajay

2006-02-17

The objective of this study was to evaluate the feasibility of using T(ZERO) modulated temperature differential scanning calorimetry (MDSC) as a novel technique to characterize protein solutions using lysozyme as a model protein and IgG as a model monoclonal antibody. MDSC involves the application of modulated heating program, along with the standard heating program that enables the separation of overlapping thermal transitions. Although characterization of unfolding transitions for protein solutions requires the application of high sensitive DSC, separation of overlapping transitions like aggregation and other exothermic events may be possible only by use of MDSC. A newer T(ZERO) calibrated MDSC model from TA instruments that has improved sensitivity than previous models was used. MDSC analysis showed total, reversing and non-reversing heat flow signals. Total heat flow signals showed a combination of melting endotherms and overlapping exothermic events. Under the operating conditions used, the melting endotherms were seen in reversing heat flow signal while the exothermic events were seen in non-reversing heat flow signal. This enabled the separation of overlapping thermal transitions, improved data analysis and decreased baseline noise. MDSC was used here for characterization of lysozyme solutions, but its feasibility for characterizing therapeutic protein solutions needs further assessment.

A network model to correlate conformational change and the impedance spectrum of single proteins

Energy Technology Data Exchange (ETDEWEB)

Alfinito, Eleonora; Pennetta, Cecilia; Reggiani, Lino [Dipartimento di Ingegneria dell' Innovazione, Universita del Salento, Via Arnesano, Lecce (Italy); Consorzio Nazionale Interuniversitario per le Scienze Fisiche della Materia (CNISM) (Italy)

2008-02-13

Integrated nanodevices based on proteins or biomolecules are attracting increasing interest in today's research. In fact, it has been shown that proteins such as azurin and bacteriorhodopsin manifest some electrical properties that are promising for the development of active components of molecular electronic devices. Here we focus on two relevant kinds of protein: bovine rhodopsin, prototype of G-protein-coupled-receptor (GPCR) proteins, and the enzyme acetylcholinesterase (AChE), whose inhibition is one of the most qualified treatments of Alzheimer's disease. Both these proteins exert their function starting with a conformational change of their native structure. Our guess is that such a change should be accompanied with a detectable variation of their electrical properties. To investigate this conjecture, we present an impedance network model of proteins, able to estimate the different impedance spectra associated with the different configurations. The distinct types of conformational change of rhodopsin and AChE agree with their dissimilar electrical responses. In particular, for rhodopsin the model predicts variations of the impedance spectra up to about 30%, while for AChE the same variations are limited to about 10%, which supports the existence of a dynamical equilibrium between its native and complexed states.

Random close packing in protein cores

OpenAIRE

Gaines, Jennifer C.; Smith, W. Wendell; Regan, Lynne; O'Hern, Corey S.

2015-01-01

Shortly after the determination of the first protein x-ray crystal structures, researchers analyzed their cores and reported packing fractions $\\phi \\approx 0.75$, a value that is similar to close packing equal-sized spheres. A limitation of these analyses was the use of `extended atom' models, rather than the more physically accurate `explicit hydrogen' model. The validity of using the explicit hydrogen model is proved by its ability to predict the side chain dihedral angle distributions obs...

Molecular modeling of the conformational dynamics of the cellular prion protein

Science.gov (United States)

Nguyen, Charles; Colling, Ian; Bartz, Jason; Soto, Patricia

2014-03-01

Prions are infectious agents responsible for transmissible spongiform encephalopathies (TSEs), a type of fatal neurodegenerative disease in mammals. Prions propagate biological information by conversion of the non-pathological version of the prion protein to the infectious conformation, PrPSc. A wealth of knowledge has shed light on the nature and mechanism of prion protein conversion. In spite of the significance of this problem, we are far from fully understanding the conformational dynamics of the cellular isoform. To remedy this situation we employ multiple biomolecular modeling techniques such as docking and molecular dynamics simulations to map the free energy landscape and determine what specific regions of the prion protein are most conductive to binding. The overall goal is to characterize the conformational dynamics of the cell form of the prion protein, PrPc, to gain insight into inhibition pathways against misfolding. NE EPSCoR FIRST Award to Patricia Soto.

Self-organized critical model for protein folding

Science.gov (United States)

Moret, M. A.

2011-09-01

The major factor that drives a protein toward collapse and folding is the hydrophobic effect. At the folding process a hydrophobic core is shielded by the solvent-accessible surface area of the protein. We study the fractal behavior of 5526 protein structures present in the Brookhaven Protein Data Bank. Power laws of protein mass, volume and solvent-accessible surface area are measured independently. The present findings indicate that self-organized criticality is an alternative explanation for the protein folding. Also we note that the protein packing is an independent and constant value because the self-similar behavior of the volumes and protein masses have the same fractal dimension. This power law guarantees that a protein is a complex system. From the analyzed data, q-Gaussian distributions seem to fit well this class of systems.

Efficient expression of SRK intracellular domain by a modeling-based protein engineering.

Science.gov (United States)

Murase, Kohji; Hirano, Yoshinori; Takayama, Seiji; Hakoshima, Toshio

2017-03-01

S-locus protein kinase (SRK) is a receptor kinase that plays a critical role in self-recognition in the Brassicaceae self-incompatibility (SI) response. SRK is activated by binding of its ligand S-locus protein 11 (SP11) and subsequently induced phosphorylation of the intracellular kinase domain. However, a detailed activation mechanism of SRK is still largely unknown because of the difficulty in stably expressing SRK recombinant proteins. Here, we performed modeling-based protein engineering of the SRK kinase domain for stable expression in Escherichia coli. The engineered SRK intracellular domain was expressed about 54-fold higher production than wild type SRK, without loss of the kinase activity, suggesting it could be useful for further biochemical and structural studies. Copyright © 2016 Elsevier Inc. All rights reserved.

Bacterial Production, Characterization and Protein Modeling of a Novel Monofuctional Isoform of FAD Synthase in Humans: An Emergency Protein?

Directory of Open Access Journals (Sweden)

Piero Leone

2018-01-01

Full Text Available FAD synthase (FADS, EC 2.7.7.2 is the last essential enzyme involved in the pathway of biosynthesis of Flavin cofactors starting from Riboflavin (Rf. Alternative splicing of the human FLAD1 gene generates different isoforms of the enzyme FAD synthase. Besides the well characterized isoform 1 and 2, other FADS isoforms with different catalytic domains have been detected, which are splice variants. We report the characterization of one of these novel isoforms, a 320 amino acid protein, consisting of the sole C-terminal 3′-phosphoadenosine 5′-phosphosulfate (PAPS reductase domain (named FADS6. This isoform has been previously detected in Riboflavin-Responsive (RR-MADD and Non-responsive Multiple Acyl-CoA Dehydrogenase Deficiency (MADD patients with frameshift mutations of FLAD1 gene. To functionally characterize the hFADS6, it has been over-expressed in Escherichia coli and purified with a yield of 25 mg·L−1 of cell culture. The protein has a monomeric form, it binds FAD and is able to catalyze FAD synthesis (kcat about 2.8 min−1, as well as FAD pyrophosphorolysis in a strictly Mg2+-dependent manner. The synthesis of FAD is inhibited by HgCl2. The enzyme lacks the ability to hydrolyze FAD. It behaves similarly to PAPS. Combining threading and ab-initio strategy a 3D structural model for such isoform has been built. The relevance to human physio-pathology of this FADS isoform is discussed.

Animal proteins in feed : annual report 2009-2010 of the Dutch National Reference Laboratory

NARCIS (Netherlands)

Raamsdonk, van L.W.D.; Scholtens-Toma, I.M.J.; Vliege, J.J.M.; Pinckaers, V.G.Z.; Groot, M.J.; Ossenkoppele, J.S.; Ruth, van S.M.

2011-01-01

RIKILT serves as the only official control laboratory for animal proteins in feeds in the Netherlands in the framework of Directive 882/2004/EC. As National Reference Laboratory (NRL), RIKILT participated in 2 annual proficiency tests during the reporting period, in 2 additional interlaboratory

Chemical effects of ionizing radiation on the individual amino acids within intact and pure protein molecules. Final report

International Nuclear Information System (INIS)

Freidberg, F.

1977-01-01

Progress is reported on the following research projects: gamma radiation induced chemical and molecular weight changes in proteins; the free radical pattern for the irradiated protein; similarities in the mechanism of action of ionizing and of uv radiation; and spin trapping in the study of gamma radiolysis

Overproduction of a Model Sec- and Tat-Dependent Secretory Protein Elicits Different Cellular Responses in Streptomyces lividans.

Directory of Open Access Journals (Sweden)

Sonia Gullón

Full Text Available Streptomyces lividans is considered an efficient host for the secretory production of homologous and heterologous proteins. To identify possible bottlenecks in the protein production process, a comparative transcriptomic approach was adopted to study cellular responses during the overproduction of a Sec-dependent model protein (alpha-amylase and a Tat-dependent model protein (agarase in Streptomyces lividans. The overproduction of the model secretory proteins via the Sec or the Tat route in S. lividans does elicit a different major cell response in the bacterium. The stringent response is a bacterial response to nutrients' depletion, which naturally occurs at late times of the bacterial cell growth. While the induction of the stringent response at the exponential phase of growth may limit overall productivity in the case of the Tat route, the induction of that response does not take place in the case of the Sec route, which comparatively is an advantage in secretory protein production processes. Hence, this study identifies a potential major drawback in the secretory protein production process depending on the secretory route, and provides clues to improving S. lividans as a protein production host.

Mineralogic Model (MM3.0) Analysis Model Report

Energy Technology Data Exchange (ETDEWEB)

C. Lum

2002-02-12

The purpose of this report is to document the Mineralogic Model (MM), Version 3.0 (MM3.0) with regard to data input, modeling methods, assumptions, uncertainties, limitations and validation of the model results, qualification status of the model, and the differences between Version 3.0 and previous versions. A three-dimensional (3-D) Mineralogic Model was developed for Yucca Mountain to support the analyses of hydrologic properties, radionuclide transport, mineral health hazards, repository performance, and repository design. Version 3.0 of the MM was developed from mineralogic data obtained from borehole samples. It consists of matrix mineral abundances as a function of x (easting), y (northing), and z (elevation), referenced to the stratigraphic framework defined in Version 3.1 of the Geologic Framework Model (GFM). The MM was developed specifically for incorporation into the 3-D Integrated Site Model (ISM). The MM enables project personnel to obtain calculated mineral abundances at any position, within any region, or within any stratigraphic unit in the model area. The significance of the MM for key aspects of site characterization and performance assessment is explained in the following subsections. This work was conducted in accordance with the Development Plan for the MM (CRWMS M&O 2000). The planning document for this Rev. 00, ICN 02 of this AMR is Technical Work Plan, TWP-NBS-GS-000003, Technical Work Plan for the Integrated Site Model, Process Model Report, Revision 01 (CRWMS M&O 2000). The purpose of this ICN is to record changes in the classification of input status by the resolution of the use of TBV software and data in this report. Constraints and limitations of the MM are discussed in the appropriate sections that follow. The MM is one component of the ISM, which has been developed to provide a consistent volumetric portrayal of the rock layers, rock properties, and mineralogy of the Yucca Mountain site. The ISM consists of three components: (1

Mineralogic Model (MM3.0) Analysis Model Report

International Nuclear Information System (INIS)

Lum, C.

2002-01-01

The purpose of this report is to document the Mineralogic Model (MM), Version 3.0 (MM3.0) with regard to data input, modeling methods, assumptions, uncertainties, limitations and validation of the model results, qualification status of the model, and the differences between Version 3.0 and previous versions. A three-dimensional (3-D) Mineralogic Model was developed for Yucca Mountain to support the analyses of hydrologic properties, radionuclide transport, mineral health hazards, repository performance, and repository design. Version 3.0 of the MM was developed from mineralogic data obtained from borehole samples. It consists of matrix mineral abundances as a function of x (easting), y (northing), and z (elevation), referenced to the stratigraphic framework defined in Version 3.1 of the Geologic Framework Model (GFM). The MM was developed specifically for incorporation into the 3-D Integrated Site Model (ISM). The MM enables project personnel to obtain calculated mineral abundances at any position, within any region, or within any stratigraphic unit in the model area. The significance of the MM for key aspects of site characterization and performance assessment is explained in the following subsections. This work was conducted in accordance with the Development Plan for the MM (CRWMS M and O 2000). The planning document for this Rev. 00, ICN 02 of this AMR is Technical Work Plan, TWP-NBS-GS-000003, Technical Work Plan for the Integrated Site Model, Process Model Report, Revision 01 (CRWMS M and O 2000). The purpose of this ICN is to record changes in the classification of input status by the resolution of the use of TBV software and data in this report. Constraints and limitations of the MM are discussed in the appropriate sections that follow. The MM is one component of the ISM, which has been developed to provide a consistent volumetric portrayal of the rock layers, rock properties, and mineralogy of the Yucca Mountain site. The ISM consists of three components

Application of a New Statistical Model for Measurement Error to the Evaluation of Dietary Self-report Instruments.

Science.gov (United States)

Freedman, Laurence S; Midthune, Douglas; Carroll, Raymond J; Commins, John M; Arab, Lenore; Baer, David J; Moler, James E; Moshfegh, Alanna J; Neuhouser, Marian L; Prentice, Ross L; Rhodes, Donna; Spiegelman, Donna; Subar, Amy F; Tinker, Lesley F; Willett, Walter; Kipnis, Victor

2015-11-01

Most statistical methods that adjust analyses for dietary measurement error treat an individual's usual intake as a fixed quantity. However, usual intake, if defined as average intake over a few months, varies over time. We describe a model that accounts for such variation and for the proximity of biomarker measurements to self-reports within the framework of a meta-analysis, and apply it to the analysis of data on energy, protein, potassium, and sodium from a set of five large validation studies of dietary self-report instruments using recovery biomarkers as reference instruments. We show that this time-varying usual intake model fits the data better than the fixed usual intake assumption. Using this model, we estimated attenuation factors and correlations with true longer-term usual intake for single and multiple 24-hour dietary recalls (24HRs) and food frequency questionnaires (FFQs) and compared them with those obtained under the "fixed" method. Compared with the fixed method, the estimates using the time-varying model showed slightly larger values of the attenuation factor and correlation coefficient for FFQs and smaller values for 24HRs. In some cases, the difference between the fixed method estimate and the new estimate for multiple 24HRs was substantial. With the new method, while four 24HRs had higher estimated correlations with truth than a single FFQ for absolute intakes of protein, potassium, and sodium, for densities the correlations were approximately equal. Accounting for the time element in dietary validation is potentially important, and points toward the need for longer-term validation studies.

The potential of chitosan in enhancing peptide and protein absorption across the TR146 cell culture model-an in vitro model of the buccal epithelium

DEFF Research Database (Denmark)

Portero, Ana; Remuñán-López, Carmen; Nielsen, Hanne Mørck

2002-01-01

To investigate the potential of chitosan (CS) to enhance buccal peptide and protein absorption, the TR146 cell culture model, a model of the buccal epithelium, was used.......To investigate the potential of chitosan (CS) to enhance buccal peptide and protein absorption, the TR146 cell culture model, a model of the buccal epithelium, was used....

Change detection in the dynamics of an intracellular protein synthesis model using nonlinear Kalman filtering.

Science.gov (United States)

Rigatos, Gerasimos G; Rigatou, Efthymia G; Djida, Jean Daniel

2015-10-01

A method for early diagnosis of parametric changes in intracellular protein synthesis models (e.g. the p53 protein - mdm2 inhibitor model) is developed with the use of a nonlinear Kalman Filtering approach (Derivative-free nonlinear Kalman Filter) and of statistical change detection methods. The intracellular protein synthesis dynamic model is described by a set of coupled nonlinear differential equations. It is shown that such a dynamical system satisfies differential flatness properties and this allows to transform it, through a change of variables (diffeomorphism), to the so-called linear canonical form. For the linearized equivalent of the dynamical system, state estimation can be performed using the Kalman Filter recursion. Moreover, by applying an inverse transformation based on the previous diffeomorphism it becomes also possible to obtain estimates of the state variables of the initial nonlinear model. By comparing the output of the Kalman Filter (which is assumed to correspond to the undistorted dynamical model) with measurements obtained from the monitored protein synthesis system, a sequence of differences (residuals) is obtained. The statistical processing of the residuals with the use of x2 change detection tests, can provide indication within specific confidence intervals about parametric changes in the considered biological system and consequently indications about the appearance of specific diseases (e.g. malignancies).

Protein nanoparticles for therapeutic protein delivery.

Science.gov (United States)

Herrera Estrada, L P; Champion, J A

2015-06-01

Therapeutic proteins can face substantial challenges to their activity, requiring protein modification or use of a delivery vehicle. Nanoparticles can significantly enhance delivery of encapsulated cargo, but traditional small molecule carriers have some limitations in their use for protein delivery. Nanoparticles made from protein have been proposed as alternative carriers and have benefits specific to therapeutic protein delivery. This review describes protein nanoparticles made by self-assembly, including protein cages, protein polymers, and charged or amphipathic peptides, and by desolvation. It presents particle fabrication and delivery characterization for a variety of therapeutic and model proteins, as well as comparison of the features of different protein nanoparticles.

Identification of Pentatricopeptide Repeat Proteins in the Model Organism Dictyostelium discoideum

Directory of Open Access Journals (Sweden)

Sam Manna

2013-01-01

Full Text Available Pentatricopeptide repeat (PPR proteins are RNA binding proteins with functions in organelle RNA metabolism. They are found in all eukaryotes but have been most extensively studied in plants. We report on the identification of 12 PPR-encoding genes in the genome of the protist Dictyostelium discoideum, with potential homologs in other members of the same lineage and some predicted novel functions for the encoded gene products in protists. For one of the gene products, we show that it localizes to the mitochondria, and we also demonstrate that antisense inhibition of its expression leads to slower growth, a phenotype associated with mitochondrial dysfunction.

The EMEFS model evaluation. An interim report

Energy Technology Data Exchange (ETDEWEB)

Barchet, W.R. [Pacific Northwest Lab., Richland, WA (United States); Dennis, R.L. [Environmental Protection Agency, Research Triangle Park, NC (United States); Seilkop, S.K. [Analytical Sciences, Inc., Durham, NC (United States); Banic, C.M.; Davies, D.; Hoff, R.M.; Macdonald, A.M.; Mickle, R.E.; Padro, J.; Puckett, K. [Atmospheric Environment Service, Downsview, ON (Canada); Byun, D.; McHenry, J.N. [Computer Sciences Corp., Research Triangle Park, NC (United States); Karamchandani, P.; Venkatram, A. [ENSR Consulting and Engineering, Camarillo, CA (United States); Fung, C.; Misra, P.K. [Ontario Ministry of the Environment, Toronto, ON (Canada); Hansen, D.A. [Electric Power Research Inst., Palo Alto, CA (United States); Chang, J.S. [State Univ. of New York, Albany, NY (United States). Atmospheric Sciences Research Center

1991-12-01

The binational Eulerian Model Evaluation Field Study (EMEFS) consisted of several coordinated data gathering and model evaluation activities. In the EMEFS, data were collected by five air and precipitation monitoring networks between June 1988 and June 1990. Model evaluation is continuing. This interim report summarizes the progress made in the evaluation of the Regional Acid Deposition Model (RADM) and the Acid Deposition and Oxidant Model (ADOM) through the December 1990 completion of a State of Science and Technology report on model evaluation for the National Acid Precipitation Assessment Program (NAPAP). Because various assessment applications of RADM had to be evaluated for NAPAP, the report emphasizes the RADM component of the evaluation. A protocol for the evaluation was developed by the model evaluation team and defined the observed and predicted values to be used and the methods by which the observed and predicted values were to be compared. Scatter plots and time series of predicted and observed values were used to present the comparisons graphically. Difference statistics and correlations were used to quantify model performance. 64 refs., 34 figs., 6 tabs.

Influence of a protein on percolation phenomena in water-in-oil micro-emulsions

International Nuclear Information System (INIS)

Huruguen, Jean-Pierre

1991-01-01

This research thesis addresses the study of a small protein named cytochrome c which has a peculiar affinity with the inner wall of droplets. This affinity is such that it increases the available interface in the system. The author first presents the properties and the solubilizing power of the ternary system made of AOT (sodium diethyl-hexyl sulfosuccinate, a surfactant), water and iso-octane. Then, he reports the study of the influence and behaviour of the protein in a dense micellar AOT/water/isooctane system: study of percolation phenomena and of light diffusion. The next part reports the structural study of the AOT/water/isooctane system in presence of the protein: models of polymer solutions, methods of exploitation of the diffused intensity, experimental conditions, study by X ray diffusion. The study of the reaction behaviour of the protein in dense medium is then reported: presentation of pulsed radiolysis, experimental results in presence or absence of cytochrome c. In the last part, the author reports the structural study of de-mixed phases: structural models, phase diagram, X and neutron diffusion of de-mixed phases, result interpretation [fr

Modeling of protein electrophoresis in silica colloidal crystals having brush layers of polyacrylamide

Science.gov (United States)

Birdsall, Robert E.; Koshel, Brooke M.; Hua, Yimin; Ratnayaka, Saliya N.; Wirth, Mary J.

2013-01-01

Sieving of proteins in silica colloidal crystals of mm dimensions is characterized for particle diameters of nominally 350 and 500 nm, where the colloidal crystals are chemically modified with a brush layer of polyacrylamide. A model is developed that relates the reduced electrophoretic mobility to the experimentally measurable porosity. The model fits the data with no adjustable parameters for the case of silica colloidal crystals packed in capillaries, for which independent measurements of the pore radii were made from flow data. The model also fits the data for electrophoresis in a highly ordered colloidal crystal formed in a channel, where the unknown pore radius was used as a fitting parameter. Plate heights as small as 0.4 μm point to the potential for miniaturized separations. Band broadening increases as the pore radius approaches the protein radius, indicating that the main contribution to broadening is the spatial heterogeneity of the pore radius. The results quantitatively support the notion that sieving occurs for proteins in silica colloidal crystals, and facilitate design of new separations that would benefit from miniaturization. PMID:23229163

Identification of key residues for protein conformational transition using elastic network model.

Science.gov (United States)

Su, Ji Guo; Xu, Xian Jin; Li, Chun Hua; Chen, Wei Zu; Wang, Cun Xin

2011-11-07

Proteins usually undergo conformational transitions between structurally disparate states to fulfill their functions. The large-scale allosteric conformational transitions are believed to involve some key residues that mediate the conformational movements between different regions of the protein. In the present work, a thermodynamic method based on the elastic network model is proposed to predict the key residues involved in protein conformational transitions. In our method, the key functional sites are identified as the residues whose perturbations largely influence the free energy difference between the protein states before and after transition. Two proteins, nucleotide binding domain of the heat shock protein 70 and human/rat DNA polymerase β, are used as case studies to identify the critical residues responsible for their open-closed conformational transitions. The results show that the functionally important residues mainly locate at the following regions for these two proteins: (1) the bridging point at the interface between the subdomains that control the opening and closure of the binding cleft; (2) the hinge region between different subdomains, which mediates the cooperative motions between the corresponding subdomains; and (3) the substrate binding sites. The similarity in the positions of the key residues for these two proteins may indicate a common mechanism in their conformational transitions.

Mathematical model of the binding of allosteric effectors to the Escherichia coli PII signal transduction protein GlnB.

Science.gov (United States)

da Rocha, Ricardo Alves; Weschenfelder, Thiago André; de Castilhos, Fernanda; de Souza, Emanuel Maltempi; Huergo, Luciano Fernandes; Mitchell, David Alexander

2013-04-16

PII proteins are important regulators of nitrogen metabolism in a wide variety of organisms: the binding of the allosteric effectors ATP, ADP, and 2-oxoglutarate (2-OG) to PII proteins affects their ability to interact with target proteins. We modeled the simultaneous binding of ATP, ADP, and 2-OG to one PII protein, namely GlnB of Escherichia coli, using a modeling approach that allows the prediction of the proportions of individual binding states. Four models with different binding rules were compared. We selected one of these models (that assumes that the binding of the first nucleotide to GlnB makes it harder for subsequent nucleotides to bind) and used it to explore how physiological concentrations of ATP, ADP, and 2-OG would affect the proportions of those states of GlnB that interact with the target proteins ATase and NtrB. Our simulations indicate that GlnB can, as suggested by previous researchers, act as a sensor of both 2-OG and the ATP:ADP ratio. We conclude that our modeling approach will be an important tool in future studies concerning the PII binding states and their interactions with target proteins.

Introducing a Clustering Step in a Consensus Approach for the Scoring of Protein-Protein Docking Models

KAUST Repository

Chermak, Edrisse; De Donato, Renato; Lensink, Marc F.; Petta, Andrea; Serra, Luigi; Scarano, Vittorio; Cavallo, Luigi; Oliva, Romina

2016-01-01

Correctly scoring protein-protein docking models to single out native-like ones is an open challenge. It is also an object of assessment in CAPRI (Critical Assessment of PRedicted Interactions), the community-wide blind docking experiment. We introduced in the field the first pure consensus method, CONSRANK, which ranks models based on their ability to match the most conserved contacts in the ensemble they belong to. In CAPRI, scorers are asked to evaluate a set of available models and select the top ten ones, based on their own scoring approach. Scorers' performance is ranked based on the number of targets/interfaces for which they could provide at least one correct solution. In such terms, blind testing in CAPRI Round 30 (a joint prediction round with CASP11) has shown that critical cases for CONSRANK are represented by targets showing multiple interfaces or for which only a very small number of correct solutions are available. To address these challenging cases, CONSRANK has now been modified to include a contact-based clustering of the models as a preliminary step of the scoring process. We used an agglomerative hierarchical clustering based on the number of common inter-residue contacts within the models. Two criteria, with different thresholds, were explored in the cluster generation, setting either the number of common contacts or of total clusters. For each clustering approach, after selecting the top (most populated) ten clusters, CONSRANK was run on these clusters and the top-ranked model for each cluster was selected, in the limit of 10 models per target. We have applied our modified scoring approach, Clust-CONSRANK, to SCORE_SET, a set of CAPRI scoring models made recently available by CAPRI assessors, and to the subset of homodimeric targets in CAPRI Round 30 for which CONSRANK failed to include a correct solution within the ten selected models. Results show that, for the challenging cases, the clustering step typically enriches the ten top ranked

Introducing a Clustering Step in a Consensus Approach for the Scoring of Protein-Protein Docking Models

KAUST Repository

Chermak, Edrisse

2016-11-15

Correctly scoring protein-protein docking models to single out native-like ones is an open challenge. It is also an object of assessment in CAPRI (Critical Assessment of PRedicted Interactions), the community-wide blind docking experiment. We introduced in the field the first pure consensus method, CONSRANK, which ranks models based on their ability to match the most conserved contacts in the ensemble they belong to. In CAPRI, scorers are asked to evaluate a set of available models and select the top ten ones, based on their own scoring approach. Scorers\\' performance is ranked based on the number of targets/interfaces for which they could provide at least one correct solution. In such terms, blind testing in CAPRI Round 30 (a joint prediction round with CASP11) has shown that critical cases for CONSRANK are represented by targets showing multiple interfaces or for which only a very small number of correct solutions are available. To address these challenging cases, CONSRANK has now been modified to include a contact-based clustering of the models as a preliminary step of the scoring process. We used an agglomerative hierarchical clustering based on the number of common inter-residue contacts within the models. Two criteria, with different thresholds, were explored in the cluster generation, setting either the number of common contacts or of total clusters. For each clustering approach, after selecting the top (most populated) ten clusters, CONSRANK was run on these clusters and the top-ranked model for each cluster was selected, in the limit of 10 models per target. We have applied our modified scoring approach, Clust-CONSRANK, to SCORE_SET, a set of CAPRI scoring models made recently available by CAPRI assessors, and to the subset of homodimeric targets in CAPRI Round 30 for which CONSRANK failed to include a correct solution within the ten selected models. Results show that, for the challenging cases, the clustering step typically enriches the ten top ranked

In silico sequence analysis and homology modeling of predicted beta-amylase 7-like protein in Brachypodium distachyon L.

Directory of Open Access Journals (Sweden)

ERTUĞRUL FILIZ

2014-04-01

Full Text Available Beta-amylase (β-amylase, EC 3.2.1.2 is an enzyme that catalyses hydrolysis of glucosidic bonds in polysaccharides. In this study, we analyzed protein sequence of predicted beta-amylase 7-like protein in Brachypodium distachyon. pI (isoelectric point value was found as 5.23 in acidic character, while the instability index (II was found as 50.28 with accepted unstable protein. The prediction of subcellular localization was revealed that the protein may reside in chloroplast by using CELLO v.2.5. The 3D structure of protein was performed using comparative homology modeling with SWISS-MODEL. The accuracy of the predicted 3D structure was checked using Ramachandran plot analysis showed that 95.4% in favored region. The results of our study contribute to understanding of β-amylase protein structure in grass species and will be scientific base for 3D modeling of beta-amylase proteins in further studies.

Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment

KAUST Repository

Lensink, Marc F.; Velankar, Sameer; Kryshtafovych, Andriy; Huang, Shen-You; Schneidman-Duhovny, Dina; Sali, Andrej; Segura, Joan; Fernandez-Fuentes, Narcis; Viswanath, Shruthi; Elber, Ron; Grudinin, Sergei; Popov, Petr; Neveu, Emilie; Lee, Hasup; Baek, Minkyung; Park, Sangwoo; Heo, Lim; Rie Lee, Gyu; Seok, Chaok; Qin, Sanbo; Zhou, Huan-Xiang; Ritchie, David W.; Maigret, Bernard; Devignes, Marie-Dominique; Ghoorah, Anisah; Torchala, Mieczyslaw; Chaleil, Raphaë l A.G.; Bates, Paul A.; Ben-Zeev, Efrat; Eisenstein, Miriam; Negi, Surendra S.; Weng, Zhiping; Vreven, Thom; Pierce, Brian G.; Borrman, Tyler M.; Yu, Jinchao; Ochsenbein, Franç oise; Guerois, Raphaë l; Vangone, Anna; Rodrigues, Joã o P.G.L.M.; van Zundert, Gydo; Nellen, Mehdi; Xue, Li; Karaca, Ezgi; Melquiond, Adrien S.J.; Visscher, Koen; Kastritis, Panagiotis L.; Bonvin, Alexandre M.J.J.; Xu, Xianjin; Qiu, Liming; Yan, Chengfei; Li, Jilong; Ma, Zhiwei; Cheng, Jianlin; Zou, Xiaoqin; Shen, Yang; Peterson, Lenna X.; Kim, Hyung-Rae; Roy, Amit; Han, Xusi; Esquivel-Rodriguez, Juan; Kihara, Daisuke; Yu, Xiaofeng; Bruce, Neil J.; Fuller, Jonathan C.; Wade, Rebecca C.; Anishchenko, Ivan; Kundrotas, Petras J.; Vakser, Ilya A.; Imai, Kenichiro; Yamada, Kazunori; Oda, Toshiyuki; Nakamura, Tsukasa; Tomii, Kentaro; Pallara, Chiara; Romero-Durana, Miguel; Jimé nez-Garcí a, Brian; Moal, Iain H.; Fé rnandez-Recio, Juan; Joung, Jong Young; Kim, Jong Yun; Joo, Keehyoung; Lee, Jooyoung; Kozakov, Dima; Vajda, Sandor; Mottarella, Scott; Hall, David R.; Beglov, Dmitri; Mamonov, Artem; Xia, Bing; Bohnuud, Tanggis; Del Carpio, Carlos A.; Ichiishi, Eichiro; Marze, Nicholas; Kuroda, Daisuke; Roy Burman, Shourya S.; Gray, Jeffrey J.; Chermak, Edrisse; Cavallo, Luigi; Oliva, Romina; Tovchigrechko, Andrey; Wodak, Shoshana J.

2016-01-01

We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein-protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. © 2016 Wiley Periodicals, Inc.

Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment

KAUST Repository

Lensink, Marc F.

2016-04-28

We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein-protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. © 2016 Wiley Periodicals, Inc.

On the Effect of Sphere-Overlap on Super Coarse-Grained Models of Protein Assemblies

Science.gov (United States)

Degiacomi, Matteo T.

2018-05-01

Ion mobility mass spectrometry (IM/MS) can provide structural information on intact protein complexes. Such data, including connectivity and collision cross sections (CCS) of assemblies' subunits, can in turn be used as a guide to produce representative super coarse-grained models. These models are constituted by ensembles of overlapping spheres, each representing a protein subunit. A model is considered plausible if the CCS and sphere-overlap levels of its subunits fall within predetermined confidence intervals. While the first is determined by experimental error, the latter is based on a statistical analysis on a range of protein dimers. Here, we first propose a new expression to describe the overlap between two spheres. Then we analyze the effect of specific overlap cutoff choices on the precision and accuracy of super coarse-grained models. Finally, we propose a method to determine overlap cutoff levels on a per-case scenario, based on collected CCS data, and show that it can be applied to the characterization of the assembly topology of symmetrical homo-multimers. [Figure not available: see fulltext.

Preliminary structural characterization of human SOUL, a haem-binding protein

International Nuclear Information System (INIS)

Freire, Filipe; Romão, Maria João; Macedo, Anjos L.; Aveiro, Susana S.; Goodfellow, Brian J.; Carvalho, Ana Luísa

2009-01-01

This manuscript describes the overexpression, purification and crystallization of human SOUL protein (hSOUL). hSOUL is a 23 kDa haem-binding protein that was first identified as the PP23 protein isolated from human full-term placenta. Human SOUL (hSOUL) is a 23 kDa haem-binding protein that was first identified as the PP 23 protein isolated from human full-term placentas. Here, the overexpression, purification and crystallization of hSOUL are reported. The crystals belonged to space group P6 4 22, with unit-cell parameters a = b = 145, c = 60 Å and one protein molecule in the asymmetric unit. X-ray diffraction data were collected to 3.5 Å resolution at the ESRF. A preliminary model of the three-dimensional structure of hSOUL was obtained by molecular replacement using the structures of murine p22HBP, obtained by solution NMR, as search models

Molecular structure, dynamics and hydration studies of soybean storage proteins and model systems by nuclear magnetic resonance

International Nuclear Information System (INIS)

Kakalis, L.T.

1989-01-01

The potential of high-resolution 13 C NMR for the characterization of soybean storage proteins was explored. The spectra of a commercial soy protein isolate as well as those of alkali-denatured 7S and 11S soybean globulins were well resolved and tentatively assigned. Relaxation measurements indicated fast motion for several side chains and the protein backbone. Protein fractions (11S and 7S) were also investigated at various states of molecular association. The large size of the multisubunit soybean storage proteins affected adversely both the resolution and the sensitivity of their 13 C NMR spectra. A comparison of 17 O and 2 H NMR relaxation rates of water in solutions of lysozyme (a model system) as a function of concentration, pH and magnetic field suggested that only 17 O monitors directly the hydration of lysozyme. Analysis of 17 O NMR lysozyme hydration data in terms of a two-state, fast-exchange, anisotropic model resulted in hydration parameters which are consistent with the protein's physico-chemical properties. The same model was applied to the calculation of the amount and mobility of bound water in soy protein dispersions by means of 17 O NMR relaxation measurements as a function of protein concentration. The protein concentration dependences of 1 H transverse NMR relaxation measurements at various pH and ionic strength values were fitted by a viral expansion. The interpretation of the data was based on the effects of protein aggregation, salt binding and protein group ionization on the NMR measurements. In all cases, relaxation rates showed a linear dependence on protein activity