Open AccessMining the Protein Data Bank to improve prediction of changes in protein-protein binding
Author(s) -
Samuel Coulbourn Flores,
Αθανάσιος Αλεξίου,
Anastasios Glaros
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0257614
Subject(s) - protein data bank , flexibility (engineering) , protein structure , in silico , protein structure prediction , data bank , computer science , computational biology , function (biology) , root mean square , sequence (biology) , set (abstract data type) , biological system , protein structure database , algorithm , bioinformatics , mathematics , biology , physics , statistics , genetics , biochemistry , sequence database , telecommunications , quantum mechanics , gene , programming language
Predicting the effect of mutations on protein-protein interactions is important for relating structure to function, as well as for in silico affinity maturation. The effect of mutations on protein-protein binding energy (ΔΔG) can be predicted by a variety of atomic simulation methods involving full or limited flexibility, and explicit or implicit solvent. Methods which consider only limited flexibility are naturally more economical, and many of them are quite accurate, however results are dependent on the atomic coordinate set used. In this work we perform a sequence and structure based search of the Protein Data Bank to find additional coordinate sets and repeat the calculation on each. The method increases precision and Positive Predictive Value, and decreases Root Mean Square Error, compared to using single structures. Given the ongoing growth of near-redundant structures in the Protein Data Bank, our method will only increase in applicability and accuracy.