title: A Probabilistic Graphical Model for Ab Initio Folding.
abstract: Despite significant progress in recent years,ab initiofolding is still one of the most challenging problems in structural biology. This paper presents a probabilistic graphical model for ab initio folding, which employs Conditional Random Fields (CRFs) and directional statistics to model the relationship between the primary sequence of a protein and its three-dimensional structure. Different from the widely-used fragment assembly method and the lattice model for protein folding, our graphical model can explore protein conformations in a continuous space according to their probability. The probability of a protein conformation reflects its stability and is estimated from PSI-BLAST sequence profile and predicted secondary structure. Experimental results indicate that this new method compares favorably with the fragment assembly method and the lattice model.
authors: Toyota Technological Institute at Chicago, Chicago, IL 60637.
authors: Feng
authors: F
authors: Zhao
authors: Jian
authors: J
authors: Peng
authors: Joe
authors: J
authors: Debartolo
authors: Karl F
authors: KF
authors: Freed
authors: Tobin R
authors: TR
authors: Sosnick
authors: Jinbo
authors: J
authors: Xu
keywords: ab initio folding; conditional random fields (CRFs); directional statistics; fragment assembly; lattice model; protein structure prediction