OPUS-Beta: A Statistical Potential for Beta-Sheet Contact Pattern in Proteins

TL;DR

OPUS-Beta is a new statistical potential designed to evaluate beta-sheet contact patterns in proteins, improving native contact recognition over existing methods and aiding beta-sheet modeling.

Contribution

We introduce OPUS-Beta, a novel statistical potential for beta-sheet contact evaluation that operates independently of atomic coordinates and enhances recognition accuracy.

Findings

OPUS-Beta outperforms existing methods in recognizing native beta-contact patterns.

Combining OPUS-Beta with 2D-recursive neural networks improves top-1 and top-5 selection accuracy.

OPUS-Beta shows potential for use in beta-sheet protein modeling.

Abstract

Developing an accurate scoring function is essential for successfully predicting protein structures. In this study, we developed a statistical potential function, called OPUS-Beta, for energetically evaluating beta-sheet contact pattern (the entire residue-residue beta-contacts of a protein) independent of the atomic coordinate information. The OPUS-Beta potential contains five terms, i.e., a self-packing term, a pairwise inter-strand packing term, a pairwise intra-strand packing term, a lattice term and a hydrogen-bonding term. The results show that, in recognizing the native beta-contact pattern from decoys, OPUS-Beta potential outperforms the existing methods in literature, especially in combination with a method using 2D-recursive neural networks (about 5% and 23% improvements in top-1 and top-5 selections). We expect OPUS-Beta potential to be useful in beta-sheet modeling for proteins.