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.