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High‐accuracy modeling of antibody structures by a search for minimum‐energy recombination of backbone fragments
Author(s) -
Norn Christoffer H.,
Lapidoth Gideon,
Fleishman Sarel J.
Publication year - 2017
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.25185
Subject(s) - loop modeling , template , homology modeling , benchmark (surveying) , sequence (biology) , computer science , monte carlo method , algorithm , low energy , identity (music) , energy (signal processing) , sequence alignment , computational biology , biological system , peptide sequence , protein structure prediction , protein structure , chemistry , physics , biology , mathematics , biochemistry , statistics , geodesy , atomic physics , acoustics , gene , programming language , geography , quantum mechanics , enzyme
Current methods for antibody structure prediction rely on sequence homology to known structures. Although this strategy often yields accurate predictions, models can be stereo‐chemically strained. Here, we present a fully automated algorithm, called AbPredict, that disregards sequence homology, and instead uses a Monte Carlo search for low‐energy conformations built from backbone segments and rigid‐body orientations that appear in antibody molecular structures. We find cases where AbPredict selects accurate loop templates with sequence identity as low as 10%, whereas the template of highest sequence identity diverges substantially from the query's conformation. Accordingly, in several cases reported in the recent Antibody Modeling Assessment benchmark, AbPredict models were more accurate than those from any participant, and the models' stereo‐chemical quality was consistently high. Furthermore, in two blind cases provided to us by crystallographers prior to structure determination, the method achieved <1.5 Ångstrom overall backbone accuracy. Accurate modeling of unstrained antibody structures will enable design and engineering of improved binders for biomedical research directly from sequence. Proteins 2016; 85:30–38. © 2016 Wiley Periodicals, Inc.