Catalytic residues in hydrolases: analysis of methods designed for ligand-binding site prediction

The comparison of eight tools applicable to ligand-binding site prediction is presented. The methods examined cover three types of approaches: the geometrical (CASTp, PASS, Pocket-Finder), the physicochemical (Q-SiteFinder, FOD) and the knowledge-based (ConSurf, SuMo, WebFEATURE). The accuracy of predictions was measured in reference to the catalytic residues documented in the Catalytic Site Atlas. The test was performed on a set comprising selected chains of hydrolases. The results were analysed with regard to size, polarity, secondary structure, accessible solvent area of predicted sites as well as parameters commonly used in machine learning (F-measure, MCC). The relative accuracies of predictions are presented in the ROC space, allowing determination of the optimal methods by means of the ROC convex hull. Additionally the minimum expected cost analysis was performed. Both advantages and disadvantages of the eight methods are presented. Characterization of protein chains in respect to the level of difficulty in the active site prediction is introduced. The main reasons for failures are discussed. Overall, the best performance offers SuMo followed by FOD, while Pocket-Finder is the best method among the geometrical approaches

Comparison of electrostatic potential around proteins calculated from Amber and AM1 charges: application to mutants of prion protein

On the basis of arguments of complementary fit of shape and charge polarity or hydrophobicity, molecular electrostatic potentials (MEPs) around proteins are commonly used to deduce likely sites for interaction with ligands or other proteins, including for variations such as mutations. But protein MEPs calculated classically from fixed force field descriptions, including those with implicit solvent models such as in Delphi, do not allow for repolarization of protein residues within the protein system; hence, their representations are likely to be variably inaccurate. Linear-scaling methods now allow calculation of MEPs quantum mechanically for systems as large as proteins, and can account for polarization explicitly. Here we compare MEPs derived from AM1 charge distributions calculated by Mopac2000 with those from the classical Amber force field. Our models are mutants of prion protein (PrP), a protein with an unusually high number of charged residues. The results demonstrate that static point charges, as used in most current force fields, cannot reproduce the MEP of macromolecules. Also, it is not sufficient to account for the influence of nearby atoms connected by chemical bonds; the influence of nearby atoms in space is at least as important. Thus, further progress in the accuracy and wider applicability of force fields requires proper accounting for polarization. Mopac2000 calculations can provide the necessary data for checking new force fields and/or parameter fitting