A knowledge-based scoring function based on residue triplets for protein structure prediction

• Published in: Protein engineering, design and selection Vol. 19; no. 5; pp. 187 - 193
• Main Authors: Ngan, Shing-Chung, Inouye, Michael T., Samudrala, Ram
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.05.2006; Oxford Publishing Limited (England)
• Subjects: ab initio prediction; Algorithms; Amino Acids - metabolism; Bayes Theorem; Bayesian; Computational Biology; Models, Molecular; Original; Predictive Value of Tests; Protein Conformation; protein structure; protein structure; prediction; Bayesian
• ISSN: 1741-0126; 1741-0134
• DOI: 10.1093/protein/gzj018

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.