On potential energy models for ea-based ab initio protein structure prediction

Authors:
Milan Mijajlovic;Mark J. Biggs;Dusan P. Djurdjevic
Affiliations:
Exobiology Branch, NASA Ames Research Center, Mail-Stop 239-4, Moffett Field, California 94035, USA.;School of Chemical Engineering, The University of Adelaide, South Australia, 5005, Australia. mark.biggs@adelaide.edu.au;P&G Technical Centres Ltd., Newcastle upon Tyne, UK.
Venue:
Evolutionary Computation
Year:
2010

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Abstract

Ab initio protein structure prediction involves determination of the three-dimensional (3D) conformation of proteins on the basis of their amino acid sequence, a potential energy (PE) model that captures the physics of the interatomic interactions, and a method to search for and identify the global minimum in the PE (or free energy) surface such as an evolutionary algorithm (EA). Many PE models have been proposed over the past three decades and more. There is currently no understanding of how the behavior of an EA is affected by the PE model used. The study reported here shows that the EA behavior can be profoundly affected: the EA performance obtained when using the ECEPP PE model is significantly worse than that obtained when using the Amber, OPLS, and CVFF PE models, and the optimal EA control parameter values for the ECEPP model also differ significantly from those associated with the other models.