Identification of efficiency-enhancing enzyme mutations is significant for biocatalysis and biomedicine research but remains as a holy-grail challenge in computational chemistry and biology. Data-driven modeling presents a promising strategy to map sequence-structure-function relationships for enzyme catalysis and to inform catalyst-optimization strategies. However, collecting, cleaning, and validating structure and kinetics enzymology data are still very difficult. In this talk, I will introduce our ongoing efforts to construct an integrated structure-kinetics database, IntEnzyDB, which contains experimentally characterized enzyme structure and function data. I will discuss the architecture of the database that enables efficient data processing. I will describe the challenges and difficulties encounted in the database construction.

IntEnzyDB: An integrated structure-function database for enzymes

ACS Spring 2022

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