AI Insight
Researchers used mRNA display, a high-throughput screening technique, to systematically map which peptide sequences can be modified by ProcM, a generalist enzyme that naturally processes multiple substrates rather than specializing in one. Machine learning analysis of the resulting data achieved 73% accuracy in predicting substrate compatibility, which is significantly lower than the near-perfect predictions possible for specialized enzymes, reflecting the inherent complexity and broader reactivity of generalist enzymes. This work expands understanding of ProcM's substrate tolerance beyond previously known targets and reveals fundamental differences between generalist and specialist enzyme behaviors.
Why it matters
Understanding how generalist enzymes like ProcM recognize substrates could enable rational engineering of modified peptides with therapeutic or biotechnological applications. The findings also establish benchmarks for computational prediction of enzyme-substrate compatibility in systems where enzymes naturally accept diverse substrates, which may require different analytical approaches than those developed for specialized enzymes.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
The biosynthetic machineries of ribosomally synthesized and post-translationally modified peptides (RiPPs) are often substrate tolerant. A remarkable example is the class II lanthipeptide synthetase ProcM, which naturally functions as a generalist enzyme that has not evolved to use a specific substrate during its evolutionary history. Although ProcM has been studied extensively, the sequence features associated with productive modification remain underexplored. In this study, we use the ultrahigh-throughput mRNA display technique to map the sequence compatibility of ProcM across a focused library. This approach expands the landscape of ProcM reactivity beyond native substrates and individually characterized variants. Machine learning (ML) is used as a tool to demonstrate that the selected dataset contains learnable signatures and classification architectures revealed a balanced accuracy of 0.73. This performance contrasts sharply with the near-perfect accuracy of specialized enzyme models as the sequence-fitness landscape of the generalist enzymes are characterized by class imbalance and limited by intrinsic dataset features. Our results provide a high-throughput view of ProcM reactivity and highlight differences with previous high-throughput studies on substrate selectivity of RiPP modification enzymes. Future studies will need to assess whether these differences are common when comparing generalist with specialist enzymes.