Literature DB >> 29779789

Local Interaction Signal Analysis Predicts Protein-Protein Binding Affinity.

Raffaele Raucci1, Elodie Laine2, Alessandra Carbone3.   

Abstract

Several models estimating the strength of the interaction between proteins in a complex have been proposed. By exploring the geometry of contact distribution at protein-protein interfaces, we provide an improved model of binding energy. Local interaction signal analysis (LISA) is a radial function based on terms describing favorable and non-favorable contacts obtained by density functional theory, the support-core-rim interface residue distribution, non-interacting charged residues and secondary structures contribution. The three-dimensional organization of the contacts and their contribution on localized hot-sites over the entire interaction surface were numerically evaluated. LISA achieves a correlation of 0.81 (and a root-mean-square error of 2.35 ± 0.38 kcal/mol) when tested on 125 complexes for which experimental measurements were realized. LISA's performance is stable for subsets defined by functional composition and extent of conformational changes upon complex formation. A large-scale comparison with 17 other functions demonstrated the power of the geometrical model in the understanding of complex binding.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Keywords:  atom-atom contact; binding affinity; density functional theory; electron density; favorable contact; non-covalent interaction; non-interacting surface; protein interface; protein-protein interaction; reduced density gradient

Mesh:

Substances:

Year:  2018        PMID: 29779789     DOI: 10.1016/j.str.2018.04.006

Source DB:  PubMed          Journal:  Structure        ISSN: 0969-2126            Impact factor:   5.006


  8 in total

1.  FRETting about the affinity of bimolecular protein-protein interactions.

Authors:  Tao Lin; Brandon L Scott; Adam D Hoppe; Suvobrata Chakravarty
Journal:  Protein Sci       Date:  2018-10       Impact factor: 6.725

Review 2.  Challenges in protein docking.

Authors:  Ilya A Vakser
Journal:  Curr Opin Struct Biol       Date:  2020-08-21       Impact factor: 6.809

3.  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

4.  Development of R7BP inhibitors through cross-linking coupled mass spectrometry and integrated modeling.

Authors:  Poorni R Adikaram; Jian-Hua Zhang; Claire M Kittock; Mritunjay Pandey; Sergio A Hassan; Nicole G Lue; Guanghui Wang; Marjan Gucek; William F Simonds
Journal:  Commun Biol       Date:  2019-09-13

5.  Structural insights into SARS-CoV-2 spike protein and its natural mutants found in Mexican population.

Authors:  Yudibeth Sixto-López; José Correa-Basurto; Martiniano Bello; Bruno Landeros-Rivera; Jose Antonio Garzón-Tiznado; Sarita Montaño
Journal:  Sci Rep       Date:  2021-02-25       Impact factor: 4.379

6.  Cold spots are universal in protein-protein interactions.

Authors:  Sagara N S Gurusinghe; Ben Oppenheimer; Julia M Shifman
Journal:  Protein Sci       Date:  2022-10       Impact factor: 6.993

7.  Deep Local Analysis evaluates protein docking conformations with locally oriented cubes.

Authors:  Yasser Mohseni Behbahani; Simon Crouzet; Elodie Laine; Alessandra Carbone
Journal:  Bioinformatics       Date:  2022-08-13       Impact factor: 6.931

8.  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

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.