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Discriminative learning for protein conformation sampling
Author(s) -
Zhao Feng,
Li Shuaicheng,
Sterner Beckett W.,
Xu Jinbo
Publication year - 2008
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.22057
Subject(s) - discriminative model , conditional random field , folding (dsp implementation) , computer science , graphical model , protein structure prediction , protein folding , sequence (biology) , artificial intelligence , bottleneck , pattern recognition (psychology) , probabilistic logic , sampling (signal processing) , feature (linguistics) , machine learning , protein structure , biology , engineering , biochemistry , electrical engineering , embedded system , filter (signal processing) , computer vision , linguistics , genetics , philosophy
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. Proteins 2008. © 2008 Wiley‐Liss, Inc.