Literature DB >> 26584123

Intermolecular Contact Potentials for Protein-Protein Interactions Extracted from Binding Free Energy Changes upon Mutation.

Iain H Moal1, Juan Fernandez-Recio1.   

Abstract

Understanding and predicting the energetics of protein-protein interactions is fundamental to the structural modeling of protein complexes. Binding free energy can be approximated as a sum of pairwise atomic or residue contact energies, which are commonly inferred from contact frequencies observed in experimental protein structures. However, such statistically inferred potentials require certain assumptions and approximation. Here, we explore the possibility of deriving atomic and residue contact potentials directly from experimental binding free energy changes following mutation and present a number of such potentials. The first set of potentials is obtained by unweighted least-squares fitting and bootsrap aggregating. The second set is calculated using a weighting scheme optimized against absolute binding affinity data, so as to account for the over-representation of certain complexes, residues, and families of interactions. The congruence of the potentials with known physical chemistry is investigated. The potentials are further validated by ranking and clustering protein-protein docking poses.

Year:  2013        PMID: 26584123     DOI: 10.1021/ct400295z

Source DB:  PubMed          Journal:  J Chem Theory Comput        ISSN: 1549-9618            Impact factor:   6.006


  10 in total

1.  Flex ddG: Rosetta Ensemble-Based Estimation of Changes in Protein-Protein Binding Affinity upon Mutation.

Authors:  Kyle A Barlow; Shane Ó Conchúir; Samuel Thompson; Pooja Suresh; James E Lucas; Markus Heinonen; Tanja Kortemme
Journal:  J Phys Chem B       Date:  2018-02-15       Impact factor: 2.991

2.  Updates to the Integrated Protein-Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2.

Authors:  Thom Vreven; Iain H Moal; Anna Vangone; Brian G Pierce; Panagiotis L Kastritis; Mieczyslaw Torchala; Raphael Chaleil; Brian Jiménez-García; Paul A Bates; Juan Fernandez-Recio; Alexandre M J J Bonvin; Zhiping Weng
Journal:  J Mol Biol       Date:  2015-07-29       Impact factor: 5.469

Review 3.  Implications of disease-related mutations at protein-protein interfaces.

Authors:  Dapeng Xiong; Dongjin Lee; Le Li; Qiuye Zhao; Haiyuan Yu
Journal:  Curr Opin Struct Biol       Date:  2021-12-24       Impact factor: 6.809

4.  An expanded benchmark for antibody-antigen docking and affinity prediction reveals insights into antibody recognition determinants.

Authors:  Johnathan D Guest; Thom Vreven; Jing Zhou; Iain Moal; Jeliazko R Jeliazkov; Jeffrey J Gray; Zhiping Weng; Brian G Pierce
Journal:  Structure       Date:  2021-02-03       Impact factor: 5.871

5.  The scoring of poses in protein-protein docking: current capabilities and future directions.

Authors:  Iain H Moal; Mieczyslaw Torchala; Paul A Bates; Juan Fernández-Recio
Journal:  BMC Bioinformatics       Date:  2013-10-01       Impact factor: 3.169

6.  IRaPPA: information retrieval based integration of biophysical models for protein assembly selection.

Authors:  Iain H Moal; Didier Barradas-Bautista; Brian Jiménez-García; Mieczyslaw Torchala; Arjan van der Velde; Thom Vreven; Zhiping Weng; Paul A Bates; Juan Fernández-Recio
Journal:  Bioinformatics       Date:  2017-06-15       Impact factor: 6.937

7.  SKEMPI 2.0: an updated benchmark of changes in protein-protein binding energy, kinetics and thermodynamics upon mutation.

Authors:  Justina Jankauskaite; Brian Jiménez-García; Justas Dapkunas; Juan Fernández-Recio; Iain H Moal
Journal:  Bioinformatics       Date:  2019-02-01       Impact factor: 6.937

8.  SAAMBE-3D: Predicting Effect of Mutations on Protein-Protein Interactions.

Authors:  Swagata Pahari; Gen Li; Adithya Krishna Murthy; Siqi Liang; Robert Fragoza; Haiyuan Yu; Emil Alexov
Journal:  Int J Mol Sci       Date:  2020-04-07       Impact factor: 5.923

9.  Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles.

Authors:  Jeffrey R Brender; Yang Zhang
Journal:  PLoS Comput Biol       Date:  2015-10-27       Impact factor: 4.475

10.  Generating quantitative binding landscapes through fractional binding selections combined with deep sequencing and data normalization.

Authors:  Michael Heyne; Niv Papo; Julia M Shifman
Journal:  Nat Commun       Date:  2020-01-15       Impact factor: 14.919

  10 in total

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