Authors
Le Yuan, Jianan Canal Li, Miaoyuan Wen, David M Bianchi, Katherine Arneson, Bingji Guo, Sara Lambert, Chris Stephens, Tianhao Yu, Lijiang Fu, Christopher Pond, Matthew J Berry, Huimin Zhao
Published in
Nucleic acids research. Oct 15, 2026. Epub Oct 15, 2026.
Abstract
Enzyme function annotation is critical for decoding biological systems and enabling rational enzyme design. However, experimentally characterized enzyme functions remain available for only a small fraction of known protein sequences, resulting in a widening gap between rapidly expanding sequence space and reliable functional assignments. Recent advances in artificial intelligence (AI) have accelerated enzyme function annotation by facilitating accurate and high-throughput prediction from protein sequences. Here, we present CLEAN Database (CLEAN DB), a comprehensive resource encompassing >55 million enzyme function annotations across 1 098 305 strains, predicted by the CLEAN AI model. The database features an intuitive web interface that enables users to efficiently search, explore, and download enzyme function data through flexible query options. Additionally, powered by a protein language model, CLEAN DB supports mutation effect prediction through interactive heatmaps, structural overlays, and tabular data visualizations. We anticipate that CLEAN DB will serve as a valuable platform for advancing enzyme discovery and applications in synthetic biology, metabolic engineering, enzyme engineering, retrosynthesis, and drug discovery. The database is freely available at https://cleandb.platform.moleculemaker.org/.
PMID:
42876371
Bibliographic data and abstract were imported from PubMed on 15 Oct 2026.
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