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.
Ngan
Shing-Chung
SC
Computational Genomics Group, Department of Microbiology, University of Washington School of Medicine, Seattle, WA 98195, USA.
Inouye
Michael T
MT
Samudrala
Ram
R
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