title: 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.
authors.affiliation: Toyota Technological Institute at Chicago, Chicago, Illinois, USA.
authors.forename: Feng
authors.initials: F
authors.lastname: Zhao
authors.forename: Shuaicheng
authors.initials: S
authors.lastname: Li
authors.forename: Beckett W
authors.initials: BW
authors.lastname: Sterner
authors.forename: Jinbo
authors.initials: J
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008958:Models, Molecular; D015233:Models, Statistical; D011487:Protein Conformation; D017510:Protein Folding; D017433:Protein Structure, Secondary; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software