Predicting Substrates by Docking High-Energy Intermediates to Enzyme Structures

With the emergence of sequences and even structures for proteins of unknown function, structure-based prediction of enzyme activity has become a pragmatic as well as an interesting question. Here we
investigate a method to predict substrates for enzymes of known structure by docking high-energy
intermediate forms of the potential substrates. A database of such high-energy transition-state analogues
was created from the KEGG metabolites. To reduce the number of possible reactions to consider, we
restricted ourselves to enzymes of the amidohydrolase superfamily. We docked each metabolite into seven
different amidohydrolases in both the ground-state and the high-energy intermediate forms. Docking the
high-energy intermediates improved the discrimination between decoys and substrates significantly over
the corresponding standard ground-state database, both by enrichment of the true substrates and by
geometric fidelity. To test this method prospectively, we attempted to predict the enantioselectivity of a set
of chiral substrates for phosphotriesterase, for both wild-type and mutant forms of this enzyme. The
stereoselectivity ratios of the six enzymes considered for those four substrate enantiomer pairs differed
over a range of 10- to 10 000-fold and underwent 20 switches in stereoselectivities for favored enantiomers,
compared to the wild type. The docking of the high-energy intermediates correctly predicted the
stereoselectivities for 18 of the 20 substrate/enzyme combinations when compared to subsequent
experimental synthesis and testing. The possible applications of this approach to other enzymes are
considered.