IntEnzyDB: an Integrated Structure–Kinetics Enzymology Database

Data-driven modeling has emerged as a new paradigm for
biocatalyst
design and discovery. Biocatalytic databases that integrate enzyme
structure and function data are in urgent need. Here we describe IntEnzyDB
as an integrated structure–kinetics database for facile statistical
modeling and machine learning. IntEnzyDB employs a relational database
architecture with a flattened data structure, which allows rapid data
operation. This architecture also makes it easy for IntEnzyDB to incorporate
more types of enzyme function data. IntEnzyDB contains enzyme kinetics
and structure data from six enzyme commission classes. Using 1050
enzyme structure–kinetics pairs, we investigated the efficiency-perturbing
propensities of mutations that are close or distal to the active site.
The statistical results show that efficiency-enhancing mutations are
globally encoded and that deleterious mutations are much more likely
to occur in close mutations than in distal mutations. Finally, we
describe a web interface that allows public users to access enzymology
data stored in IntEnzyDB. IntEnzyDB will provide a computational facility
for data-driven modeling in biocatalysis and molecular evolution.