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

Shing-Chung Ngan; Michael T Inouye; Ram Samudrala

doi:10.1093/protein/gzj018

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

Shing-Chung Ngan·Michael T Inouye·Ram Samudrala

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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.

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Algorithms Amino Acids Bayes Theorem Computational Biology Models, Molecular Predictive Value of Tests Protein Conformation Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

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Shing-Chung Ngan, Michael T Inouye, Ram Samudrala. 2006-03-13. A knowledge-based scoring function based on residue triplets for protein structure prediction.. https://doi.org/10.1093/protein%2Fgzj018

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