Literature DB >> 23476023

Specificity and affinity quantification of protein-protein interactions.

Zhiqiang Yan1, Liyong Guo, Liang Hu, Jin Wang.   

Abstract

MOTIVATION: Most biological processes are mediated by the protein-protein interactions. Determination of the protein-protein structures and insight into their interactions are vital to understand the mechanisms of protein functions. Currently, compared with the isolated protein structures, only a small fraction of protein-protein structures are experimentally solved. Therefore, the computational docking methods play an increasing role in predicting the structures and interactions of protein-protein complexes. The scoring function of protein-protein interactions is the key responsible for the accuracy of the computational docking. Previous scoring functions were mostly developed by optimizing the binding affinity which determines the stability of the protein-protein complex, but they are often lack of the consideration of specificity which determines the discrimination of native protein-protein complex against competitive ones.
RESULTS: We developed a scoring function (named as SPA-PP, specificity and affinity of the protein-protein interactions) by incorporating both the specificity and affinity into the optimization strategy. The testing results and comparisons with other scoring functions show that SPA-PP performs remarkably on both predictions of binding pose and binding affinity. Thus, SPA-PP is a promising quantification of protein-protein interactions, which can be implemented into the protein docking tools and applied for the predictions of protein-protein structure and affinity. AVAILABILITY: The algorithm is implemented in C language, and the code can be downloaded from http://dl.dropbox.com/u/1865642/Optimization.cpp.

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Year:  2013        PMID: 23476023     DOI: 10.1093/bioinformatics/btt121

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  10 in total

1.  Incorporating specificity into optimization: evaluation of SPA using CSAR 2014 and CASF 2013 benchmarks.

Authors:  Zhiqiang Yan; Jin Wang
Journal:  J Comput Aided Mol Des       Date:  2016-02-15       Impact factor: 3.686

2.  Minimalistic predictor of protein binding energy: contribution of solvation factor to protein binding.

Authors:  Jeong-Mo Choi; Adrian W R Serohijos; Sean Murphy; Dennis Lucarelli; Leo L Lofranco; Andrew Feldman; Eugene I Shakhnovich
Journal:  Biophys J       Date:  2015-02-17       Impact factor: 4.033

3.  SPA-LN: a scoring function of ligand-nucleic acid interactions via optimizing both specificity and affinity.

Authors:  Zhiqiang Yan; Jin Wang
Journal:  Nucleic Acids Res       Date:  2017-07-07       Impact factor: 16.971

4.  A minimal model of protein-protein binding affinities.

Authors:  Joël Janin
Journal:  Protein Sci       Date:  2014-10-25       Impact factor: 6.725

Review 5.  Perspectives on the landscape and flux theory for describing emergent behaviors of the biological systems.

Authors:  Jin Wang
Journal:  J Biol Phys       Date:  2021-11-25       Impact factor: 1.365

6.  The universal statistical distributions of the affinity, equilibrium constants, kinetics and specificity in biomolecular recognition.

Authors:  Xiliang Zheng; Jin Wang
Journal:  PLoS Comput Biol       Date:  2015-04-17       Impact factor: 4.475

7.  Optimizing scoring function of protein-nucleic acid interactions with both affinity and specificity.

Authors:  Zhiqiang Yan; Jin Wang
Journal:  PLoS One       Date:  2013-09-30       Impact factor: 3.240

8.  Improving the accuracy of high-throughput protein-protein affinity prediction may require better training data.

Authors:  Raquel Dias; Bryan Kolaczkowski
Journal:  BMC Bioinformatics       Date:  2017-03-23       Impact factor: 3.169

9.  mCSM: predicting the effects of mutations in proteins using graph-based signatures.

Authors:  Douglas E V Pires; David B Ascher; Tom L Blundell
Journal:  Bioinformatics       Date:  2013-11-26       Impact factor: 6.937

10.  Discovery of Small Molecule NSC290956 as a Therapeutic Agent for KRas Mutant Non-Small-Cell Lung Cancer.

Authors:  Jiaxin Zhang; Zuojia Liu; Wenjing Zhao; Xunzhe Yin; Xiliang Zheng; Chuanbo Liu; Jin Wang; Erkang Wang
Journal:  Front Pharmacol       Date:  2022-01-05       Impact factor: 5.810

  10 in total

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