Enhancing luciferase activity and stability through generative modeling of natural enzyme sequences

Significance Generative models, when trained on natural protein sequences, have the capacity to generate novel sequences displaying enzyme activity. However, strategically inducing mutations to boost enzyme function remains challenging, given the intricacies of predicting sequence-function relationships, especially when aiming to enhance enzyme activity. Our research has found a way using generative models to interpret extant sequence diversity for various regions of an enzyme. This knowledge has enabled us to enhance enzyme activity or stability beyond those of the native enzyme in the experiment with a high success rate. These findings have crucial implications for enzyme engineering and shed light on the diverse factors that shape enzyme evolution.

• Publication year

  2023

• Venue

  Proceedings of the National Academy of Sciences of the United States of America

• Publication date

  2023-11-20

• Fields of study

  Biology, Medicine, Computer Science

• Identifiers
  DOI 10.1073/pnas.2312848120PMID 37983512PMCID 10691223
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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