MOTIVATION: Microbial biotransformation plays a central role in the environmental degradation of chemical contaminants, driven by the catalytic activities of diverse enzymes. However, linking specific enzymes to contaminant removal and predicting associated transformation products (TPs) under real-world conditions remain a major challenge. In this study, we present a self-supervised, contrastive fine-tuning strategy for reaction fingerprint learning, designed to improve the chemical relevance of BERT-based reaction embeddings for environmental biotransformation reactions. Specifically, we fine-tuned a BERT encoder such that the cosine similarity between its reaction fingerprints aligns with the Tanimoto similarity of traditional structure-based fingerprints. RESULTS: The resulting compact, 256-dimensional fingerprints, termed crxnfp, showed an improved ability to cluster reactions according to transformation type and focus attention on chemically meaningful reaction centers. Our crxnfp fingerprints were further validated in reaction classification tasks across multiple datasets, achieving superior or comparable performance relative to existing methods. Importantly, they enabled a similarity-based association of biotransformation rules and reactions from enviPath with enzyme annotations from the Rhea and UniProt databases, offering a scalable approach to enrich environmental biotransformation datasets with enzymatic information. Additionally, crxnfp was employed to identify specific enzyme classes involved in contaminant biotransformation, which were subsequently validated through experiments conducted in this study, achieving 91.3% accuracy at the third-level enzyme classification. The crxnfp fingerprints offer a promising solution to advance the understanding of contaminant biotransformation and guide the development of enzyme-informed strategies for contaminant management across diverse environmental contexts. AVAILABILITY AND IMPLEMENTATION: Code is available at https://github.com/zhangky12/crxnfp and https://github.com/zhangky12/crxnfp_knn.

Biocuration is essential to transform molecular sequence data into standardized, machine-readable resources. Such curated datasets enable comparative analysis, predictive modeling, and data integration across bioinformatics platforms. While professional biocuration is resource-intensive and usually limited to institutional settings, community-driven approaches can mobilize large-scale annotation of specialized datasets and are more resilient to disruptions in scientific funding. Here, we present a model for community-powered curation applied to the Minimum Information about a Biosynthetic Gene Cluster (MIBiG) repository. Through a framework of workflows for metadata capture, annotation validation, and contributor coordination, the MIBiG 4.0 initiative recruited 267 scientists across 178 institutions from 33 countries, volunteering an estimated 4000 h of work. These efforts expanded the MIBiG repository by 22% and enhanced its usability in downstream molecular data analyses in comparative genomic analyses, natural product discovery, and machine learning applications. We provide strategies and actionable lessons for adopting this model, supporting the sustainability of curated bioinformatics resources central to nucleic acid research and related fields.