Evidence map›Paper›PMID 27818677›Full record

ArticleEURASIP journal on bioinformatics & systems biology2016

Enhancing interacting residue prediction with integrated contact matrix prediction in protein-protein interaction.

Tianchuan Du, Li Liao, Cathy H Wu

Abstract read
In one paragraph

Article in EURASIP journal on bioinformatics & systems biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Tianchuan DuDepartment of Computer and Information Sciences, University of Delaware, Newark, DE 19716 USA.
Li LiaoDepartment of Computer and Information Sciences, University of Delaware, Newark, DE 19716 USA.ORCID 0000-0002-1197-1879
Cathy H WuDepartment of Computer and Information Sciences, University of Delaware, Newark, DE 19716 USA.

Funding

Predictive Modeling & Optimal Control Framework for Model-Based Epidemic Response in DelawareP20GM103446 · NIGMS · UNIVERSITY OF DELAWARE · PI Anjana Narayan Bhat · 2012 to 2026
$67.2M
NIGMS NIH HHS P20 GM103446
6 · The paper itself

Abstract

Identifying the residues in a protein that are involved in protein-protein interaction and identifying the contact matrix for a pair of interacting proteins are two computational tasks at different levels of an in-depth analysis of protein-protein interaction. Various methods for solving these two problems have been reported in the literature. However, the interacting residue prediction and contact matrix prediction were handled by and large independently in those existing methods, though intuitively good prediction of interacting residues will help with predicting the contact matrix. In this work, we developed a novel protein interacting residue prediction system, contact matrix-interaction profile hidden Markov model (CM-ipHMM), with the integration of contact matrix prediction and the ipHMM interaction residue prediction. We propose to leverage what is learned from the contact matrix prediction and utilize the predicted contact matrix as "feedback" to enhance the interaction residue prediction. The CM-ipHMM model showed significant improvement over the previous method that uses the ipHMM for predicting interaction residues only. It indicates that the downstream contact matrix prediction could help the interaction site prediction.

Indexed as

Contact matrix predictionInteraction site predictionMachine learningProtein-protein interaction

Identifiers

PMID27818677
PMCPMC5075339

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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.