Evidence map›Paper›PMID 21682895›Full record

ArticleBMC bioinformatics2011

HomPPI: a class of sequence homology based protein-protein interface prediction methods.

Li C Xue, Drena Dobbs, Vasant Honavar

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Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.

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50citing papers in PubMed
–field-weighted citation impact
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3 · Its place in the literature

Who cites it

50 citing papers in PubMed.

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  11. Residue-Residue Interaction Prediction via Stacked Meta-Learning.International journal of molecular sciences · 2021
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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Li C XueDepartment of Computer Science, Iowa State University, Ames, IA 50011, USA. me.lixue@gmail.com
Drena Dobbs
Vasant Honavar

Funding

Discovery of Protein Seq.Struct.Func.RelationshipsR33GM066387 · NIGMS · IOWA STATE UNIVERSITY · PI HONAVAR, VASANT G. · 2005 to 2006
$545k
Protein Sequence Structure Function RelationshipsR21GM066387 · NIGMS · IOWA STATE UNIVERSITY · PI HONAVAR, VASANT G. · 2003 to 2004
$291k
NIGMS NIH HHS GM066387NIGMS NIH HHS R33 GM066387
6 · The paper itself

Abstract

backgroundAlthough homology-based methods are among the most widely used methods for predicting the structure and function of proteins, the question as to whether interface sequence conservation can be effectively exploited in predicting protein-protein interfaces has been a subject of debate.

resultsWe studied more than 300,000 pair-wise alignments of protein sequences from structurally characterized protein complexes, including both obligate and transient complexes. We identified sequence similarity criteria required for accurate homology-based inference of interface residues in a query protein sequence.Based on these analyses, we developed HomPPI, a class of sequence homology-based methods for predicting protein-protein interface residues. We present two variants of HomPPI: (i) NPS-HomPPI (Non partner-specific HomPPI), which can be used to predict interface residues of a query protein in the absence of knowledge of the interaction partner; and (ii) PS-HomPPI (Partner-specific HomPPI), which can be used to predict the interface residues of a query protein with a specific target protein.Our experiments on a benchmark dataset of obligate homodimeric complexes show that NPS-HomPPI can reliably predict protein-protein interface residues in a given protein, with an average correlation coefficient (CC) of 0.76, sensitivity of 0.83, and specificity of 0.78, when sequence homologs of the query protein can be reliably identified. NPS-HomPPI also reliably predicts the interface residues of intrinsically disordered proteins. Our experiments suggest that NPS-HomPPI is competitive with several state-of-the-art interface prediction servers including those that exploit the structure of the query proteins. The partner-specific classifier, PS-HomPPI can, on a large dataset of transient complexes, predict the interface residues of a query protein with a specific target, with a CC of 0.65, sensitivity of 0.69, and specificity of 0.70, when homologs of both the query and the target can be reliably identified. The HomPPI web server is available at http://homppi.cs.iastate.edu/.

conclusionsSequence homology-based methods offer a class of computationally efficient and reliable approaches for predicting the protein-protein interface residues that participate in either obligate or transient interactions. For query proteins involved in transient interactions, the reliability of interface residue prediction can be improved by exploiting knowledge of putative interaction partners.

Indexed as

SoftwareAlgorithmsAmino Acid SequenceHumansProteinsSequence Analysis, ProteinSequence HomologyProteins

Identifiers

PMID21682895
PMCPMC3213298

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.