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Updated: May 11, 2026
09:42
Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes
Published on: January 16, 2016
A structural perspective on enzymes and their catalytic mechanisms
Neera Borkakoti1, António J M Ribeiro2, Janet M Thornton1
1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SD, UK.
Current Opinion in Structural Biology
|March 30, 2025
Summary
Recent advances in enzyme knowledge, driven by metagenomic sequencing and AI, enhance understanding of enzyme structures, functions, and
Area of Science:
- Biochemistry and Structural Biology
- Bioinformatics and Computational Biology
Background:
- Significant progress in understanding enzyme sequences, structures, and functions has been achieved.
- New enzyme data is continuously generated and annotated through structural and computational analyses of proteins.
Purpose of the Study:
- To analyze recent advancements in enzyme knowledge over the past two years.
- To review the impact of new data and computational approaches on enzyme research.
- To discuss future directions and driving forces in enzyme studies.
Main Methods:
- Analysis of newly garnered and annotated enzyme data.
- Review of structural and computational approaches for protein studies.
- Examination of AI-driven protein structure prediction techniques.
- Assessment of bioinformatic tools for enzyme mechanism prediction.
Main Results:
- A substantial increase in available enzyme data, fueled by metagenomic sequencing.
- Improved enzyme data resources and high coverage in the Protein Data Bank (PDB).
- Advancements in 'Catalysis in silico', including accurate enzyme structure and mechanism prediction.
- Focus on intrinsically disordered regions for enzyme regulation and specificity.
Conclusions:
- AI and computational methods are revolutionizing enzyme structure and function prediction.
- Metagenomics and improved data resources are accelerating enzyme discovery.
- Future research will likely focus on computational prediction of enzyme mechanisms and disordered regions.
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