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

ABSTRACT: 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.

SUBMITTER: Ngan SC

PROVIDER: S-EPMC5441915 | biostudies-literature | 2006 May

REPOSITORIES: biostudies-literature

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

Ngan Shing-Chung SC Inouye Michael T MT Samudrala Ram R

Protein engineering, design & selection : PEDS 20060313 5

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 pr ...[more]

PMID: 16533801

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