Literature DB >> 23930922

Mining the characteristic interaction patterns on protein-protein binding interfaces.

Yan Li1, Zhihai Liu, Li Han, Chengke Li, Renxiao Wang.   

Abstract

Protein-protein interactions are observed in various biological processes. They are important for understanding the underlying molecular mechanisms and can be potential targets for developing small-molecule regulators of such processes. Previous studies suggest that certain residues on protein-protein binding interfaces are "hot spots". As an extension to this concept, we have developed a residue-based method to identify the characteristic interaction patterns (CIPs) on protein-protein binding interfaces, in which each pattern is a cluster of four contacting residues. Systematic analysis was conducted on a nonredundant set of 1,222 protein-protein binding interfaces selected out of the entire Protein Data Bank. Favored interaction patterns across different protein-protein binding interfaces were retrieved by considering both geometrical and chemical conservations. As demonstrated on two test tests, our method was able to predict hot spot residues on protein-protein binding interfaces with good recall scores and acceptable precision scores. By analyzing the function annotations and the evolutionary tree of the protein-protein complexes in our data set, we also observed that protein-protein interfaces sharing common characteristic interaction patterns are normally associated with identical or similar biological functions.

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Year:  2013        PMID: 23930922     DOI: 10.1021/ci400241s

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  3 in total

1.  Exploring the mechanistic insights of Cas scaffolding protein family member 4 with protein tyrosine kinase 2 in Alzheimer's disease by evaluating protein interactions through molecular docking and dynamic simulations.

Authors:  Mubashir Hassan; Saba Shahzadi; Hany Alashwal; Nazar Zaki; Sung-Yum Seo; Ahmed A Moustafa
Journal:  Neurol Sci       Date:  2018-05-22       Impact factor: 3.307

2.  Data Mining Approach for Extraction of Useful Information About Biologically Active Compounds from Publications.

Authors:  Olga A Tarasova; Nadezhda Yu Biziukova; Dmitry A Filimonov; Vladimir V Poroikov; Marc C Nicklaus
Journal:  J Chem Inf Model       Date:  2019-09-10       Impact factor: 4.956

3.  Mechanistic insights into TNFR1/MADD death domains in Alzheimer's disease through conformational molecular dynamic analysis.

Authors:  Mubashir Hassan; Sara Zahid; Hany Alashwal; Andrzej Kloczkowski; Ahmed A Moustafa
Journal:  Sci Rep       Date:  2021-06-10       Impact factor: 4.379

  3 in total

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