ArticleNature communications2025
Harnessing protein language model for structure-based discovery of highly efficient and robust PET hydrolases.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The diversity of PET degrading enzymes: A systematic review of sequence, structure, and function.Protein science : a publication of the Protein Society · 2025Pooled it
- Deep Learning-driven synergistic engineering of PET hydrolase for post-consumer PET depolymerization.Synthetic and systems biotechnology · 2027Article
- A versatile Bacillus esterase reveals a carboxylesterase blueprint for PET intermediate degradation.Applied microbiology and biotechnology · 2026Article
- Experimental, Genomic, and Structural Evidence Supporting Putative PET-Hydrolases in Thermophilic Bacteria Isolated From Hot Springs in Cajamarca, Peru.MicrobiologyOpen · 2026Article
- Structure-Guided Extremophile Genome Mining Expands the PETase Landscape and Reveals PET-Hydrolysing True Lipase Lineages.Microbial biotechnology · 2026Article
- Article
- A fluorescence-activated droplet sorting assay for ultra-high-throughput screening of PET hydrolases based on a pH indicator.RSC chemical biology · 2026Article
- Protein foundation models: a comprehensive survey.Science China. Life sciences · 2026Review
- Computational glycosyltransferases masked deoxynivalenol toxicity and halted FHB spread in wheat grains.Journal of advanced research · 2026Article
- Reflections on bio-based PET and plastic waste management: a responsible research and innovation approach.Nature communications · 2026Review
- EngineeringiScience · 2026Article
- Leveraging artificial intelligence for efficient microbial production.Bioresource technology · 2026Review
- Unlocking Enzyme Discovery: Leveraging Multi-Omics, Machine Learning, and De Novo Design.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Machine Learning-Guided Identification of PET Hydrolases from Natural Diversity.ACS catalysis · 2025Article
- EngineeringInternational journal of molecular sciences · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plastic waste, particularly polyethylene terephthalate (PET), presents significant environmental challenges, driving extensive research into enzymatic biodegradation. However, existing PET hydrolases (PETases) are limited by narrow sequence diversity and suboptimal performance. This study introduces VenusMine, a protein discovery pipeline that integrates protein language models (PLMs) with a representation tree to identify PETases based on structural similarity using sequence information. Using the crystal structure of IsPETase as a template, VenusMine identifies and clusters target proteins. Candidates are further screened using PLM-based assessments of solubility and thermostability, leading to the selection of 34 proteins for biochemical validation. Results reveal that 14 candidates exhibit PET degradation activity across 30-60 °C. Notably, a PET hydrolase from Kibdelosporangium banguiense (KbPETase) demonstrates a melting temperature (T
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