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High‐resolution modeling of antibody structures by a combination of bioinformatics, expert knowledge, and molecular simulations
Author(s) -
Shirai Hiroki,
Ikeda Kazuyoshi,
Yamashita Kazuo,
Tsuchiya Yuko,
Sarmiento Jamica,
Liang Shide,
Morokata Tatsuaki,
Mizuguchi Kenji,
Higo Junichi,
Standley Daron M.,
Nakamura Haruki
Publication year - 2014
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.24591
Subject(s) - computer science , loop modeling , protein structure prediction , homology modeling , principal component analysis , force field (fiction) , molecular dynamics , data mining , artificial intelligence , pattern recognition (psychology) , protein structure , chemistry , biochemistry , computational chemistry , enzyme
ABSTRACT In the second antibody modeling assessment, we used a semiautomated template‐based structure modeling approach for 11 blinded antibody variable region (Fv) targets. The structural modeling method involved several steps, including template selection for framework and canonical structures of complementary determining regions (CDRs), homology modeling, energy minimization, and expert inspection. The submitted models for Fv modeling in Stage 1 had the lowest average backbone root mean square deviation (RMSD) (1.06 Å). Comparison to crystal structures showed the most accurate Fv models were generated for 4 out of 11 targets. We found that the successful modeling in Stage 1 mainly was due to expert‐guided template selection for CDRs, especially for CDR‐H3, based on our previously proposed empirical method (H3‐rules) and the use of position specific scoring matrix‐based scoring. Loop refinement using fragment assembly and multicanonical molecular dynamics (McMD) was applied to CDR‐H3 loop modeling in Stage 2. Fragment assembly and McMD produced putative structural ensembles with low free energy values that were scored based on the OSCAR all‐atom force field and conformation density in principal component analysis space, respectively, as well as the degree of consensus between the two sampling methods. The quality of 8 out of 10 targets improved as compared with Stage 1. For 4 out of 10 Stage‐2 targets, our method generated top‐scoring models with RMSD values of less than 1 Å. In this article, we discuss the strengths and weaknesses of our approach as well as possible directions for improvement to generate better predictions in the future. Proteins 2014; 82:1624–1635. © 2014 Wiley Periodicals, Inc.