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Machine learning-assisted discovery of AdMysD for enhanced porphyra-334 biosynthesis.

Longfei Yuan Shuting Feng Sheng Xie Qian Liu Kai Ni Jinjing Tan Liqun Li Jieru Xie Yuexuan Li Nan Wang
Jul 2026 · Bioorganic chemistry (Print) · Vol 181, pp. 110312 · 0 citations · 43 references
Medicine

Abstract

Mycosporine-like amino acids (MAAs) are functional secondary metabolites renowned for their exceptional UV protection, antioxidant properties, and environmental resilience. In the MAA biosynthetic pathway, MysD is the pivotal enzyme mediating the chemical transition of the cyclohexenone core into a cyclohexenimine-type scaffold. This MysD-catalyzed secondary amino acid modification not only dictates the chemical diversity of MAAs but also facilitates a crucial bathochromic shift, moving the UV absorption maximum from the UVB range into the high-penetration UVA region. Despite its significance, MysD remains the rate-limiting step in the biosynthesis of iminomycosporine-like amino acids. To date, only eight MysD enzymes have been heterologously validated, with even fewer subjected to biochemical characterization, which severely restricts the use of conventional supervised machine learning for enzyme discovery. In this study, we developed an integrated, data-driven screening framework combining Sequence Similarity Networks (SSN), deep representation learning (UniRep), and Positive-Unlabeled Bagging (PU Bagging) to explore the MysD functional landscape. This pipeline effectively compressed the search space from approximately 951 unannotated homologues to a prioritized 42 candidates. Experimental validation led to the discovery of AdMysD from Aphanothece hegewaldii, which exhibited a 3-fold increase in catalytic efficiency for porphyra-334 production relative to the previously established benchmark, NlMysD. Notably, while demonstrating a primary preference for l-Thr, AdMysD displayed significant substrate promiscuity by accepting l-Ser, l-Ala, and l-Cys to produce iminomycosporine derivatives. Our findings provide a biocatalytic tool for the efficient production of MAAs and demonstrate the potential of a PU-learning-based prioritization strategy for identifying rare enzyme families with sparse functional annotations.

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