title: TERPRED: A Dynamic Structural Data Analysis Tool.
abstract: Computational protein structure prediction mainly involves the main-chain prediction and the side-chain confirmation determination. In this research, we developed a new structural bioinformatics tool, TERPRED for generating dynamic protein side-chain rotamer libraries. Compared with current various rotamer sampling methods, our work is unique in that it provides a method to generate a rotamer library dynamically based on small sequence fragments of a target protein. The Rotamer Generator provides a means for existing side-chain sampling methods using static pre-existing rotamer libraries, to sample from dynamic target-dependent libraries. Also, existing side-chain packing algorithms that require large rotamer libraries for optimal performance, could possibly utilize smaller, target-relevant libraries for improved speed.
authors.affiliation: University of Arkansas at Little Rock, Little Rock, AR 72204 USA.
authors.forename: Karl
authors.initials: K
authors.lastname: Walker
authors.affiliation: Arkansas State University, State University, AR 72467 USA.
authors.forename: Carole L
authors.initials: CL
authors.lastname: Cramer
authors.affiliation: University of Arkansas at Little Rock, Little Rock, AR 72204 USA.
authors.forename: Steven F
authors.initials: SF
authors.lastname: Jennings
authors.affiliation: Arkansas State University, State University, AR 72467 USA.
authors.forename: Xiuzhen
authors.initials: X
authors.lastname: Huang
keywords: protein side-chain; protein structure; rotamer library