Literature DB >> 20635422

i-Patch: interprotein contact prediction using local network information.

Rebecca Hamer1, Qiang Luo, Judith P Armitage, Gesine Reinert, Charlotte M Deane.   

Abstract

Biological processes are commonly controlled by precise protein-protein interactions. These connections rely on specific amino acids at the binding interfaces. Here we predict the binding residues of such interprotein complexes. We have developed a suite of methods, i-Patch, which predict the interprotein contact sites by considering the two proteins as a network, with residues as nodes and contacts as edges. i-Patch starts with two proteins, A and B, which are assumed to interact, but for which the structure of the complex is not available. However, we assume that for each protein, we have a reference structure and a multiple sequence alignment of homologues. i-Patch then uses the propensities of patches of residues to interact, to predict interprotein contact sites. i-Patch outperforms several other tested algorithms for prediction of interprotein contact sites. It gives 59% precision with 20% recall on a blind test set of 31 protein pairs. Combining the i-Patch scores with an existing correlated mutation algorithm, McBASC, using a logistic model gave little improvement. Results from a case study, on bacterial chemotaxis protein complexes, demonstrate that our predictions can identify contact residues, as well as suggesting unknown interfaces in multiprotein complexes. 2010 Wiley-Liss, Inc.

Mesh:

Substances:

Year:  2010        PMID: 20635422     DOI: 10.1002/prot.22792

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


  11 in total

Review 1.  Computational prediction of protein interfaces: A review of data driven methods.

Authors:  Li C Xue; Drena Dobbs; Alexandre M J J Bonvin; Vasant Honavar
Journal:  FEBS Lett       Date:  2015-10-13       Impact factor: 4.124

2.  Partner-aware prediction of interacting residues in protein-protein complexes from sequence data.

Authors:  Shandar Ahmad; Kenji Mizuguchi
Journal:  PLoS One       Date:  2011-12-14       Impact factor: 3.240

3.  Improving predictions of protein-protein interfaces by combining amino acid-specific classifiers based on structural and physicochemical descriptors with their weighted neighbor averages.

Authors:  Fábio R de Moraes; Izabella A P Neshich; Ivan Mazoni; Inácio H Yano; José G C Pereira; José A Salim; José G Jardine; Goran Neshich
Journal:  PLoS One       Date:  2014-01-28       Impact factor: 3.240

4.  Algorithmic approaches to protein-protein interaction site prediction.

Authors:  Tristan T Aumentado-Armstrong; Bogdan Istrate; Robert A Murgita
Journal:  Algorithms Mol Biol       Date:  2015-02-15       Impact factor: 1.405

Review 5.  Progress and challenges in predicting protein interfaces.

Authors:  Reyhaneh Esmaielbeiki; Konrad Krawczyk; Bernhard Knapp; Jean-Christophe Nebel; Charlotte M Deane
Journal:  Brief Bioinform       Date:  2015-05-13       Impact factor: 11.622

6.  Exploring the potential of 3D Zernike descriptors and SVM for protein-protein interface prediction.

Authors:  Sebastian Daberdaku; Carlo Ferrari
Journal:  BMC Bioinformatics       Date:  2018-02-06       Impact factor: 3.169

7.  Local network patterns in protein-protein interfaces.

Authors:  Qiang Luo; Rebecca Hamer; Gesine Reinert; Charlotte M Deane
Journal:  PLoS One       Date:  2013-03-08       Impact factor: 3.240

8.  Mutual information and variants for protein domain-domain contact prediction.

Authors:  Mireille Gomes; Rebecca Hamer; Gesine Reinert; Charlotte M Deane
Journal:  BMC Res Notes       Date:  2012-08-31

9.  Pattern to Knowledge: Deep Knowledge-Directed Machine Learning for Residue-Residue Interaction Prediction.

Authors:  Andrew K C Wong; Ho Yin Sze-To; Gary L Johanning
Journal:  Sci Rep       Date:  2018-10-04       Impact factor: 4.379

10.  Using B cell receptor lineage structures to predict affinity.

Authors:  Duncan K Ralph; Frederick A Matsen
Journal:  PLoS Comput Biol       Date:  2020-11-11       Impact factor: 4.475

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