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Updated: Aug 27, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
Published on: November 3, 2011
GotEnzymes: an extensive database of enzyme parameter predictions
Feiran Li1,2, Yu Chen1,2, Mihail Anton1,3
1Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg SE-412 96, Sweden.
GotEnzymes is a new database providing AI-predicted enzyme parameters for millions of enzyme-compound pairs across thousands of organisms. This resource accelerates biological research by offering high-throughput enzyme property predictions.
Area of Science:
- Biochemistry
- Bioinformatics
- Systems Biology
Background:
- Experimental determination of enzyme parameters is limited, hindering comprehensive biological understanding and engineering.
- A significant gap exists in experimentally measured enzyme properties, especially across diverse organisms.
- High-throughput prediction methods are needed to explore the vast landscape of enzyme functions.
Purpose of the Study:
- To introduce GotEnzymes, a comprehensive database of AI-predicted enzyme parameters.
- To provide a publicly accessible resource for exploring enzyme properties across numerous organisms.
- To facilitate advancements in biological research through readily available enzyme data.
Main Methods:
- Utilized artificial intelligence (AI) approaches for high-throughput prediction of enzyme parameters.
- Developed an extensive database, GotEnzymes, to store and present these AI-driven predictions.
- Ensured public accessibility via an interactive web platform and programmatic access.
Main Results:
- The initial release of GotEnzymes contains predictions for over 25.7 million enzyme-compound pairs.
- Data spans across 8099 different organisms, offering broad coverage.
- Predicted enzyme parameters include turnover numbers, crucial for quantitative analysis.
Conclusions:
- GotEnzymes significantly expands the availability of predicted enzyme parameters.
- The database is expected to accelerate both experimental and computational biological research.
- Provides a valuable tool for researchers working with candidate enzymes and metabolic modeling.
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