Development of a Grid-based Statistical Potential for Protein Structure Prediction

A key component in protein structure prediction is the development of potentials that can discriminate native or near native structures from the wrong ones. Most previously developed statistical potentials are based on the preferred distances between any pair of residues. Here we explore the possible angle dependence between pairs of residues in addition to their distance dependence. For simplicity, we used a grid based partition of the space and analyzed relative geometric propensity between protein residue pairs. One thousand and nine non-redundant protein structures are studied in this paper. We have attached a local coordinate system to each amino acid, and spatial distributions of its nearby residues are investigated. Within the same distance range, there are clear differences in various grids. We have further developed a grid-based statistical potential, which incorporates both the distance dependence and angle dependence using a quasi-chemical approximation. The potential is tested against 32 decoy sets, and in 25 cases the native structure has the best score. This performance is comparable and in two cases better than best performance from previously developed distance-dependent statistical potentials on residue and atom level