Virtual Bioprospecting of Interfacial Enzymes: Relating Sequence and Kinetics

Deposition of enzyme sequences greatly outruns any possibility of thorough experimental characterization. There seems to be a particular shortage of quantitative kinetic data, and this limits both structure–function analyses and the selection of biocatalysts for technical use. In this study, we present a virtual screening approach, which takes advantage of empirical scaling relations for interfacial enzymes in order to predict kinetic parameters from sequences. As an example, we analyzed an industrially important group of enzymes, namely, fungal cellulases from glycoside hydrolase family 7 (GH7). We screened this family and selected three previously uncharacterized enzymes, which were predicted to have high substrate-binding strength (a property that is desirable for biomass deconstruction). Generally, we found good agreement between the predicted and experimental kinetic parameters. In addition, one of the enzymes, Cel7C from Acremonium thermophilum, showed an unprecedented substrate-binding strength and outperformed the model enzyme, Cel7A from Trichoderma reesei by 50%, when tested on real biomass. We conclude that the method provides a means of computing kinetic parameters for hundreds of GH7 cellulases based only on the enzyme sequence, and surmise that similar approaches could be useful for other groups of enzymes within both engineering and discovery.