Journal
PROTEIN ENGINEERING DESIGN & SELECTION
Volume 19, Issue 5, Pages 187-193Publisher
OXFORD UNIV PRESS
DOI: 10.1093/protein/gzj018
Keywords
ab initio prediction; Bayesian; protein structure
Funding
- NIGMS NIH HHS [R33 GM068152, GM068152-01] Funding Source: Medline
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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.
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