Integrating multiple scoring functions to improve protein loop structure conformation space sampling

Yaohang Li, Ionel Rata, Eric Jakobsson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this article, we present a new protein structure modeling approach based on multi-scoring functions sampling. The rationale is to integrate multiple carefully-selected physics- or knowledge-based scoring functions to tolerate insensitivity and inaccuracy existing in an individual scoring function so as to improve protein structure modeling accuracy. We apply the multi-scoring function sampling approach to protein loop backbone structure modeling. Our computational results show that sampling the scoring function space of a physics-based soft-sphere potential function and a knowledge-based scoring function based on pairwise atoms distance has led to resolution improvement in the predicted decoy populations in a set of 12-residue benchmark loop targets.

Original languageEnglish (US)
Title of host publication2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010
Pages37-44
Number of pages8
DOIs
StatePublished - Aug 20 2010
Event2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010 - Montreal, QC, Canada
Duration: May 2 2010May 5 2010

Publication series

Name2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010

Conference

Conference2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2010
Country/TerritoryCanada
CityMontreal, QC
Period5/2/105/5/10

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Biomedical Engineering

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