Discriminative learning for protein conformation sampling

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

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


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
Issue number1
StatePublished - Oct 2008
Externally publishedYes


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

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology


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