Machine Learning-Guided Protein Engineering

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/68407700:21230/23:00370784 RIV/68407700:21730/23:00370784 RIV/00159816:_____/23:00079675 RIV/00216224:14310/23:00133331

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine Learning-Guided Protein Engineering

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Machine Learning-Guided Protein Engineering

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

ACS Catalysis

ISSN

2155-5435

e-ISSN

2155-5435

Svazek periodika

13

Číslo periodika v rámci svazku

21

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

33

Strana od-do

13863-13895

Kód UT WoS článku

001098449000001

EID výsledku v databázi Scopus

2-s2.0-85177214801