NEPRE: a Scoring Function for Protein Structures based on Neighbourhood Preference

Abstract

Protein structure prediction relies on two major components, a method to generate good models that are close to the native structure and a scoring function that can select the good models. Based on the statistics from known structures in the protein data bank, a statistical energy function is derived to reflect the amino acid neighbourhood preferences. The neighbourhood of one amino acid is defined by its contacting residues, and the energy function is determined by the neighbhoring residue types and relative positions. A scoring algorithm, Nepre, has been implemented and its performance was tested with several decoy sets. The results show that the Nepre program can be applied in model ranking to improve the success rate in structure predictions.