ArticleJournal of chemical information and modeling2022
IntEnzyDB: an Integrated Structure-Kinetics Enzymology Database.
Article in Journal of chemical information and modeling, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed.
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- QuantumPDB: A Workflow for High-Throughput Quantum Cluster Model Generation from Protein Structures.Journal of chemical information and modeling · 2026Article
- Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters.Journal of chemical information and modeling · 2026Review
- A structure-oriented kinetics dataset of enzyme-substrate interactions.Scientific data · 2025Article
- Physics-based modeling in the new era of enzyme engineering.Nature computational science · 2025Review
- Enzyme Databases in the Era of Omics and Artificial Intelligence.International journal of molecular sciences · 2023Review
- Mutexa: A Computational Ecosystem for Intelligent Protein Engineering.Journal of chemical theory and computation · 2023Review
- EnzyKR: a chirality-aware deep learning model for predicting the outcomes of the hydrolase-catalyzed kinetic resolution.Chemical science · 2023Article
- High-throughput computational investigation of protein electrostatics and cavity for SAM-dependent methyltransferases.Protein science : a publication of the Protein Society · 2023Article
- Data-driven enzyme engineering to identify function-enhancing enzymes.Protein engineering, design & selection : PEDS · 2023Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.