Literature DB >> 14635122

Simplicial edge representation of protein structures and alpha contact potential with confidence measure.

Xiang Li1, Changyu Hu, Jie Liang.   

Abstract

Protein representation and potential function are two important ingredients for studying protein folding, equilibrium thermodynamics, and sequence design. We introduce a novel geometric representation of protein contact interactions using the edge simplices from the alpha shape of the protein structure. This representation can eliminate implausible neighbors that are not in physical contact, and can avoid spurious contact between two residues when a third residue is between them. We developed statistical alpha contact potential using an odds-ratio model. A studentized bootstrap method was then introduced to assess the 95% confidence intervals for each of the 210 propensity parameters. We found, with confidence, that there is significant long-range propensity (>30 residues apart) for hydrophobic interactions. We tested alpha contact potential for native structure discrimination using several sets of decoy structures, and found that it often performs comparably with atom-based potentials requiring many more parameters. We also show that accurate geometric representation is important, and that alpha contact potential has better performance than potential defined by cutoff distance between geometric centers of side chains. Hierarchical clustering of alpha contact potentials reveals natural grouping of residues. To explore the relationship between shape and physicochemical representations, we tested the minimum alphabet size necessary for native structure discrimination. We found that there is no significant difference in performance of discrimination when alphabet size varies from 7 to 20, if geometry is represented accurately by alpha simplicial edges. This result suggests that the geometry of packing plays an important role, but the specific residue types are often interchangeable. Copyright 2003 Wiley-Liss, Inc.

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Year:  2003        PMID: 14635122     DOI: 10.1002/prot.10442

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  14 in total

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Journal:  Protein Sci       Date:  2004-02       Impact factor: 6.725

2.  Self-complementarity within proteins: bridging the gap between binding and folding.

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3.  Generating properly weighted ensemble of conformations of proteins from sparse or indirect distance constraints.

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4.  Predicting protein folding rates from geometric contact and amino acid sequence.

Authors:  Zheng Ouyang; Jie Liang
Journal:  Protein Sci       Date:  2008-04-23       Impact factor: 6.725

5.  Conformational sampling and structure prediction of multiple interacting loops in soluble and β-barrel membrane proteins using multi-loop distance-guided chain-growth Monte Carlo method.

Authors:  Ke Tang; Samuel W K Wong; Jun S Liu; Jinfeng Zhang; Jie Liang
Journal:  Bioinformatics       Date:  2015-04-09       Impact factor: 6.937

6.  Multiscale Modeling of Cellular Epigenetic States: Stochasticity in Molecular Networks, Chromatin Folding in Cell Nuclei, and Tissue Pattern Formation of Cells.

Authors:  Jie Liang; Youfang Cao; Gamze Gursoy; Hammad Naveed; Anna Terebus; Jieling Zhao
Journal:  Crit Rev Biomed Eng       Date:  2015

7.  Predicting Oncogenic Missense Mutations.

Authors:  Xue Lei; Boshen Wang; Alan Perez-Rathke; Wei Tian; Chia-Yi Chou; Yan Yuan Tseng; Jie Liang
Journal:  IEEE EMBS Int Conf Biomed Health Inform       Date:  2019-09-12

8.  A pairwise residue contact area-based mean force potential for discrimination of native protein structure.

Authors:  Shahriar Arab; Mehdi Sadeghi; Changiz Eslahchi; Hamid Pezeshk; Armita Sheari
Journal:  BMC Bioinformatics       Date:  2010-01-09       Impact factor: 3.169

9.  Scoring function to predict solubility mutagenesis.

Authors:  Ye Tian; Christopher Deutsch; Bala Krishnamoorthy
Journal:  Algorithms Mol Biol       Date:  2010-10-07       Impact factor: 1.405

10.  Prediction of DNA-binding protein based on statistical and geometric features and support vector machines.

Authors:  Weiqiang Zhou; Hong Yan
Journal:  Proteome Sci       Date:  2011-10-14       Impact factor: 2.480

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