Discriminative learning for protein conformation sampling

Feng Zhao, Shuaicheng Li, Beckett W. Sterner, Jinbo Xu

Research output: Contribution to journalArticle

30 Scopus citations

Abstract

Protein structure prediction without using templates (i.e., ab initio folding) is one of the most challenging problems in structural biology. In particular, conformation sampling poses as a major bottleneck of ab initio folding. This article presents CRFSampler, an extensible protein conformation sampler, built on a probabilistic graphical model Conditional Random Fields (CRFs). Using a discriminative learning method, CRFSampler can automatically learn more than ten thousand parameters quantifying the relationship among primary sequence, secondary structure, and (pseudo) backbone angles. Using only compactness and self-avoiding constraints, CRFSampler can efficiently generate protein-like conformations from primary sequence and predicted secondary structure. CRFSampler is also very flexible in that a variety of model topologies and feature sets can be defined to model the sequence-structure relationship without worrying about parameter estimation. Our experimental results demonstrate that using a simple set of features, CRFSampler can generate decoys with much higher quality than the most recent HMM model.

Original languageEnglish (US)
Pages (from-to)228-240
Number of pages13
JournalProteins: Structure, Function and Genetics
Volume73
Issue number1
DOIs
StatePublished - Oct 2008

Keywords

  • Conditional random fields (CRFs)
  • Discriminative learning
  • Protein conformation sampling

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology

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