Open AccessIntEnzyDB: an Integrated Structure–Kinetics Enzymology Database
Author(s) -
Bailu Yan,
Xinchun Ran,
Anvita Gollu,
Zihao Cheng,
Xiang Zhou,
Yiwen Chen,
Zhongyue Yang
Publication year - 2022
Publication title -
journal of chemical information and modeling
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.24
H-Index - 160
eISSN - 1549-960X
pISSN - 1549-9596
DOI - 10.1021/acs.jcim.2c01139
Subject(s) - database , biocatalysis , computer science , enzyme kinetics , function (biology) , kinetics , computational biology , enzyme , data mining , chemistry , biology , active site , physics , genetics , reaction mechanism , biochemistry , catalysis , quantum mechanics
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.