Open Enzyme Database: a community-wide repository for sharing enzyme data.

Area of Science:

• Biochemistry
• Bioinformatics
• Computational Biology

Background:

• Enzymes are crucial biological catalysts with diverse applications.
• Existing enzyme databases suffer from infrequent updates, lack of standardization, and limited machine learning support.
• There is a need for a comprehensive, up-to-date, and machine learning-ready enzyme repository.

Purpose of the Study:

• To develop the Open Enzyme Database (OED), a community-driven repository for enzyme data.
• To create a user-friendly web-based infrastructure for sharing and exploring enzyme information.
• To integrate advanced AI and cheminformatics tools for enzyme property prediction and discovery.

Main Methods:

• Development of a community-wide repository for enzyme data.
• Curation of enzyme kinetic parameters, structural data, reactions, assay conditions, and functional annotations.
• Implementation of artificial intelligence (AI) and cheminformatics algorithms for enzyme property prediction and identification.

Main Results:

• The first release of the OED provides curated data on enzyme kinetics, structures, reactions, and annotations.
• The database incorporates AI tools for predicting enzyme properties.
• Cheminformatics algorithms are included for identifying promising enzymes.
• The OED is accessible via a web platform.

Conclusions:

• The Open Enzyme Database (OED) provides a valuable resource for the scientific community.
• The OED is expected to accelerate research in biocatalysis, systems biology, synthetic biology, and enzyme engineering.
• The integration of AI tools within the OED will advance enzyme discovery and application development.