A probabilistic and continuous model of protein conformational space for template-free modeling.

Content Provider	Europe PMC
Author	Zhao, Feng Peng, Jian Debartolo, Joe Freed, Karl F. Sosnick, Tobin R. Xu, Jinbo
Copyright Year	2010
Abstract	Abstract One of the major challenges with protein template-free modeling is an efficient sampling algorithm that can explore a huge conformation space quickly. The popular fragment assembly method constructs a conformation by stringing together short fragments extracted from the Protein Data Base (PDB). The discrete nature of this method may limit generated conformations to a subspace in which the native fold does not belong. Another worry is that a protein with really new fold may contain some fragments not in the PDB. This article presents a probabilistic model of protein conformational space to overcome the above two limitations. This probabilistic model employs directional statistics to model the distribution of backbone angles and 2nd-order Conditional Random Fields (CRFs) to describe sequence-angle relationship. Using this probabilistic model, we can sample protein conformations in a continuous space, as opposed to the widely used fragment assembly and lattice model methods that work in a discrete space. We show that when coupled with a simple energy function, this probabilistic method compares favorably with the fragment assembly method in the blind CASP8 evaluation, especially on alpha or small beta proteins. To our knowledge, this is the first probabilistic method that can search conformations in a continuous space and achieves favorable performance. Our method also generated three-dimensional (3D) models better than template-based methods for a couple of CASP8 hard targets. The method described in this article can also be applied to protein loop modeling, model refinement, and even RNA tertiary structure prediction.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC3203516&blobtype=pdf
Page Count	16
ISSN	10665277
Volume Number	17
DOI	10.1089/cmb.2009.0235
PubMed Central reference number	PMC3203516
Issue Number	6
PubMed reference number	20583926
Journal	Journal of Computational Biology [J Comput Biol]
e-ISSN	15578666
Language	English
Publisher	Mary Ann Liebert, Inc.
Publisher Date	2010-06-01
Publisher Place	USA
Access Restriction	Open
Rights License	Copyright 2010, Mary Ann Liebert, Inc.
Subject Keyword	conditional random fields (CRFs) directional statistics fragment assembly lattice model protein structure prediction template-free modeling
Content Type	Text
Resource Type	Article
Subject	Genetics Molecular Biology Computational Theory and Mathematics Modeling and Simulation Computational Mathematics