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Multiobjective evolutionary algorithm with many tables for purely ab initio protein structure prediction
Author(s) -
Brasil Christiane Regina Soares,
Delbem Alexandre Claudio Botazzo,
da Silva Fernando Luís Barroso
Publication year - 2013
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.23315
Subject(s) - ab initio , protein structure prediction , solvation , van der waals force , computer science , space (punctuation) , algorithm , statistical physics , computational chemistry , protein structure , chemistry , physics , molecule , quantum mechanics , biochemistry , operating system
This article focuses on the development of an approach for ab initio protein structure prediction (PSP) without using any earlier knowledge from similar protein structures, as fragment‐based statistics or inference of secondary structures. Such an approach is called purely ab initio prediction. The article shows that well‐designed multiobjective evolutionary algorithms can predict relevant protein structures in a purely ab initio way. One challenge for purely ab initio PSP is the prediction of structures with β‐sheets. To work with such proteins, this research has also developed procedures to efficiently estimate hydrogen bond and solvation contribution energies. Considering van der Waals, electrostatic, hydrogen bond, and solvation contribution energies, the PSP is a problem with four energetic terms to be minimized. Each interaction energy term can be considered an objective of an optimization method. Combinatorial problems with four objectives have been considered too complex for the available multiobjective optimization (MOO) methods. The proposed approach, called “Multiobjective evolutionary algorithms with many tables” (MEAMT), can efficiently deal with four objectives through the combination thereof, performing a more adequate sampling of the objective space. Therefore, this method can better map the promising regions in this space, predicting structures in a purely ab initio way. In other words, MEAMT is an efficient optimization method for MOO, which explores simultaneously the search space as well as the objective space. MEAMT can predict structures with one or two domains with RMSDs comparable to values obtained by recently developed ab initio methods (GAPF CG , I‐PAES, and Quark) that use different levels of earlier knowledge. © 2013 Wiley Periodicals, Inc.