Machine Learning-Guided Protein Engineering

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388963%3A_____%2F23%3A00576534" target="_blank" >RIV/61388963:_____/23:00576534 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21230/23:00370784 RIV/68407700:21730/23:00370784 RIV/00159816:_____/23:00079675 RIV/00216224:14310/23:00133331
Result on the web
<a href="https://doi.org/10.1021/acscatal.3c02743" target="_blank" >https://doi.org/10.1021/acscatal.3c02743</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1021/acscatal.3c02743" target="_blank" >10.1021/acscatal.3c02743</a>

Result language
angličtina
Original language name
Machine Learning-Guided Protein Engineering
Original language 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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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