A knowledge-based scoring function based on residue triplets for protein structure prediction.
One of the general paradigms for ab initio protein structure prediction involves sampling the conformational space such that a large set of decoy (candidate) structures are generated and then selecting native-like conformations from those decoys using various scoring functions. In this study, based on a physical/geometric approach first suggested by Banavar and colleagues, we formulate a knowledge-based scoring function, which uses the radii of curvature formed among triplets of residues in a protein conformation. By analyzing its performance on various decoy sets, we determine a good set of parameters--the distance cutoff and the number of distance bins--to use for configuring such a function. Furthermore, we investigate the effect of using various approaches for compiling the prior distribution on the performance of the knowledge-based function. Possible extensions to the current form of the residue triplet scoring function are discussed.
Computational Genomics Group, Department of Microbiology, University of Washington School of Medicine, Seattle, WA 98195, USA.
Shing-Chung
SC
Ngan
Michael T
MT
Inouye
Ram
R
Samudrala
D000465:Algorithms; D000596:Amino Acids / Q000378:metabolism*; D001499:Bayes Theorem; D019295:Computational Biology; D008958:Models, Molecular; D011237:Predictive Value of Tests; D011487:Protein Conformation
D000596:Amino Acids