Enzyme/non-enzyme discrimination and prediction of enzyme active site location using charge-based methods.
Calculations of charge interactions complement analysis of a characterised active site, rationalising pH-dependence of activity and transition state stabilisation. Prediction of active site location through large DeltapK(a)s or electrostatic strain is relevant for structural genomics. We report a study of ionisable groups in a set of 20 enzymes, finding that false positives obscure predictive potential. In a larger set of 156 enzymes, peaks in solvent-space electrostatic properties are calculated. Both electric field and potential match well to active site location. The best correlation is found with electrostatic potential calculated from uniform charge density over enzyme volume, rather than from assignment of a standard atom-specific charge set. Studying a shell around each molecule, for 77% of enzymes the potential peak is within that 5% of the shell closest to the active site centre, and 86% within 10%. Active site identification by largest cleft, also with projection onto a shell, gives 58% of enzymes for which the centre of the largest cleft lies within 5% of the active site, and 70% within 10%. Dielectric boundary conditions emphasise clefts in the uniform charge density method, which is suited to recognition of binding pockets embedded within larger clefts. The variation of peak potential with distance from active site, and comparison between enzyme and non-enzyme sets, gives an optimal threshold distinguishing enzyme from non-enzyme. We find that 87% of the enzyme set exceeds the threshold as compared to 29% of the non-enzyme set. Enzyme/non-enzyme homologues, "structural genomics" annotated proteins and catalytic/non-catalytic RNAs are studied in this context.
Bate
Paul
P
Biomolecular Sciences Department, University of Manchester Institute of Science and Technology, Sackville Street, Manchester M60 1QD, UK.
Warwicker
Jim
J
D001665:Binding Sites; D004798:Enzymes / Q000737:chemistry / Q000378:metabolism*; D008958:Models, Molecular
D004798:Enzymes