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PEDS Advance Access published online on March 13, 2006
Protein Engineering Design and Selection, doi:10.1093/protein/gzj018
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received August 23, 2005
Revised December 30, 2005
Accepted January 9, 2006

Article

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

Shing-Chung Ngan 1, Michael T. Inouye 1, and Ram Samudrala 1 *

1 Computational Genomics Group, Department of Microbiology, University of Washington School of Medicine, Seattle, WA 98195, USA

* To whom correspondence should be addressed.
Ram Samudrala, E-mail: ram{at}compbio.washington.edu


  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.

Keywords: ab initio prediction; Bayesian; protein structure.
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