On potential energy models for EA-based Ab initio protein structure prediction

Milan Mijajlovic, Mark J. Biggs, Dusan P. Djurdjevic

Research output: Contribution to journalArticle

4 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)255-275
Number of pages21
JournalEvolutionary Computation
Volume18
Issue number2
DOIs
Publication statusPublished - 2010

Keywords

  • Biochemistry
  • Biomaterials
  • Biomedical engineering
  • Bionanotechnology
  • EA adaptivity
  • Nanotechnology
  • Surface binding proteins

ASJC Scopus subject areas

  • Computational Mathematics

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