Machine Learning-Guided Protein Engineering

0576534 - ÚOCHB 2024 RIV US eng J - Článek v odborném periodiku
Kouba, P. - Kohout, P. - Haddadi, F. - Bushuiev, A. - Samusevich, Raman - Sedlář, J. - Damborský, J. - Pluskal, Tomáš - Šivic, J. - Mazurenko, S.
Machine Learning-Guided Protein Engineering.
ACS Catalysis. Roč. 13, č. 21 (2023), s. 13863-13895. ISSN 2155-5435. E-ISSN 2155-5435
Grant CEP: GA ČR(CZ) GM21-11563M; GA MŠMT(CZ) LM2023055; GA MŠMT LX22NPO5102
GRANT EU: European Commission(XE) 891397 - KavaTarget
Výzkumná infrastruktura: RECETOX RI II - 90269
Institucionální podpora: RVO:61388963
Klíčová slova: activity * artificial intelligence * biocatalysis * deep learning * protein design
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impakt faktor: 12.9, rok: 2022
Způsob publikování: Open access
https://doi.org/10.1021/acscatal.3c02743

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.
Trvalý link: https://hdl.handle.net/11104/0346095