Machine Learning-Guided Protein Engineering

0576534 - ÚOCHB 2024 RIV US eng J - Journal Article
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
R&D Projects: GA ČR(CZ) GM21-11563M; GA MŠMT(CZ) LM2023055; GA MŠMT LX22NPO5102
EU Projects: European Commission(XE) 891397 - KavaTarget
Research Infrastructure: RECETOX RI II - 90269
Institutional support: RVO:61388963
Keywords : activity * artificial intelligence * biocatalysis * deep learning * protein design
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 12.9, year: 2022
Method of publishing: 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.
Permanent Link: https://hdl.handle.net/11104/0346095