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Discriminative learning for protein conformation sampling.

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.

SUBMITTER: Zhao F

PROVIDER: S-EPMC2826217 | biostudies-literature | 2008 Oct

REPOSITORIES: biostudies-literature

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Discriminative learning for protein conformation sampling.

Zhao Feng F Li Shuaicheng S Sterner Beckett W BW Xu Jinbo J

Proteins 20081001 1

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 quantifyi ...[more]

PMID: 18412258

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Discriminative learning for protein conformation sampling.

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Discriminative learning for protein conformation sampling.

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