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Constructing side chains on near-native main chains for ab initio protein structure prediction

  • Cereon Genomics
  • Stanford University

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

Is there value in constructing side chains while searching protein conformational space during an ab initio simulation? If so, what is the most computationally efficient method for constructing these side chains? To answer these questions, four published approaches were used to construct side chain conformations on a range of near-native main chains generated by ab initio protein structure prediction methods. The accuracy of these approaches was compared with a naive approach that selects the most frequently observed rotamer for a given amino acid to construct side chains. An all-atom conditional probability discriminatory function is useful at selecting conformations with overall low all-atom root mean square deviation (r.m.s.d.) and the discrimination improves on sets that are closer to the native conformation. In addition, the naive approach performs as well as more sophisticated methods in terms of the percentage of χ1 angles built accurately and the all-atom r.m.s.d., between the native and near-native conformations. The results suggest that the naive method would be extremely useful for fast and efficient side chain construction on vast numbers of conformations for ab initio prediction of protein structure.

Original languageEnglish
Pages (from-to)453-457
Number of pages5
JournalProtein Engineering
Volume13
Issue number7
DOIs
StatePublished - 2000

Keywords

  • Conditional probability
  • Discriminatory function
  • Knowledge-based
  • Protein structure prediction
  • Side chain construction

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