Machine Learning-Guided Protein Engineering

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Authors

KOUBA Petr KOHOUT Pavel HADDADI Faraneh BUSHUIEV Anton SAMUSEVICH Raman SEDLAR Jiri DAMBORSKÝ Jiří PLUSKAL Tomáš SIVIC Josef MAZURENKO Stanislav

Year of publication 2023
Type Article in Periodical
Magazine / Source ACS Catalysis
MU Faculty or unit

Faculty of Science

Citation
Web https://pubs.acs.org/doi/10.1021/acscatal.3c02743
Doi http://dx.doi.org/10.1021/acscatal.3c02743
Keywords activity; artificial intelligence; biocatalysis; deep learning; protein design
Attached files
Description Recent progress in engineering highly promising biocatalysts has increasingly involved machine learning methods. These methods leverage existing experimental and simulation data to aid in the discovery and annotation of promising enzymes, as well as in suggesting beneficial mutations for improving known targets. The field of machine learning for protein engineering is gathering steam, driven by recent success stories and notable progress in other areas. It already encompasses ambitious tasks such as understanding and predicting protein structure and function, catalytic efficiency, enantioselectivity, protein dynamics, stability, solubility, aggregation, and more. Nonetheless, the field is still evolving, with many challenges to overcome and questions to address. In this Perspective, we provide an overview of ongoing trends in this domain, highlight recent case studies, and examine the current limitations of machine learning-based methods. We emphasize the crucial importance of thorough experimental validation of emerging models before their use for rational protein design. We present our opinions on the fundamental problems and outline the potential directions for future research.
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