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

Protein Engineering, Design & Selection : PEDS
Shing-Chung NganRam Samudrala

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.

References

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Citations

Mar 4, 2009·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Junfeng GuXicheng Wang
Mar 8, 2013·Proteins·Kristoffer Enøe Johansson, Thomas Hamelryck
Jan 6, 2009·Biochemical and Biophysical Research Communications·Junfeng GuXicheng Wang

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