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Gene ontology improves template selection in comparative protein docking
Author(s) -
Hadarovich Anna,
Anishchenko Ivan,
Tuzikov Alexander V.,
Kundrotas Petras J.,
Vakser Ilya A.
Publication year - 2019
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.25645
Subject(s) - gene ontology , structural similarity , computational biology , macromolecular docking , structural bioinformatics , computer science , docking (animal) , ranking (information retrieval) , protein structure prediction , protein structure , structural alignment , similarity (geometry) , protein function prediction , template , artificial intelligence , data mining , protein function , biology , gene , sequence alignment , genetics , peptide sequence , biochemistry , nursing , image (mathematics) , programming language , medicine , gene expression
Structural characterization of protein‐protein interactions is essential for our ability to study life processes at the molecular level. Computational modeling of protein complexes (protein docking) is important as the source of their structure and as a way to understand the principles of protein interaction. Rapidly evolving comparative docking approaches utilize target/template similarity metrics, which are often based on the protein structure. Although the structural similarity, generally, yields good performance, other characteristics of the interacting proteins (eg, function, biological process, and localization) may improve the prediction quality, especially in the case of weak target/template structural similarity. For the ranking of a pool of models for each target, we tested scoring functions that quantify similarity of Gene Ontology (GO) terms assigned to target and template proteins in three ontology domains—biological process, molecular function, and cellular component (GO‐score). The scoring functions were tested in docking of bound, unbound, and modeled proteins. The results indicate that the combined structural and GO‐terms functions improve the scoring, especially in the twilight zone of structural similarity, typical for protein models of limited accuracy.