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

نویسندگان

  • Shing-Chung Ngan
  • Michael T. Inouye
  • Ram Samudrala
چکیده

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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عنوان ژورنال:

دوره 19  شماره 

صفحات  -

تاریخ انتشار 2006