Fine-Grained Statistical Torsion Angle Potentials are Effective in Discriminating Native Protein Structures

Alessandro      Albiero; Silvio   C.E.   Tosatto

Abstract

Modelling of drug targets requires the reliable selection of an accurate and representative structure from large ensembles of alternate models. Statistical potentials developed to discriminate native protein structures generally represent pairwise interactions between atoms, which are less sensitive to local conformational details. The discrimination of local distortions is therefore particularly difficult. Local interaction preferences, expressed through torsion angles, are rarely used, as some controversy exists in the literature regarding their discrimination power. The present study aims to benchmark the efficiency of different implementations of torsion angle propensities for selecting the native structure from ensembles of well-constructed decoys. Several statistical potentials derived from fine-grained discretisations of torsion angle space are constructed and evaluated. Results from a comparison with nine widely used statistical scoring functions show the torsion angle potentials to be more effective in recognising native structures and to improve with the number of torsion angles considered. These data suggest local structural propensities to be important for estimating the overall quality of native-like models.

Keywords: Ramachandran plot, amino acid propensities, statistical potential, knowledge-based potential, decoy sets, model evaluation