Structure Prediction and Computational Protein Design for Efficient Biocatalysts and Bioactive Proteins

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F25%3A00082483" target="_blank" >RIV/00159816:_____/25:00082483 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14310/25:00140783

Výsledek na webu

<a href="https://onlinelibrary.wiley.com/doi/10.1002/anie.202421686" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1002/anie.202421686</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/anie.202421686" target="_blank" >10.1002/anie.202421686</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Structure Prediction and Computational Protein Design for Efficient Biocatalysts and Bioactive Proteins

Popis výsledku v původním jazyce

The ability to predict and design protein structures has led to numerous applications in medicine, diagnostics and sustainable chemical manufacture. In addition, the wealth of predicted protein structures has advanced our understanding of how life&apos;s molecules function and interact. Honouring the work that has fundamentally changed the way scientists research and engineer proteins, the Nobel Prize in Chemistry in 2024 was awarded to David Baker for computational protein design and jointly to Demis Hassabis and John Jumper, who developed AlphaFold for machine-learning-based protein structure prediction. Here, we highlight notable contributions to the development of these computational tools and their importance for the design of functional proteins that are applied in organic synthesis. Notably, both technologies have the potential to impact drug discovery as any therapeutic protein target can now be modelled, allowing the de novo design of peptide binders and the identification of small molecule ligands through in silico docking of large compound libraries. Looking ahead, we highlight future research directions in protein engineering, medicinal chemistry and material design that are enabled by this transformative shift in protein science.

Název v anglickém jazyce

Structure Prediction and Computational Protein Design for Efficient Biocatalysts and Bioactive Proteins

Popis výsledku anglicky

The ability to predict and design protein structures has led to numerous applications in medicine, diagnostics and sustainable chemical manufacture. In addition, the wealth of predicted protein structures has advanced our understanding of how life&apos;s molecules function and interact. Honouring the work that has fundamentally changed the way scientists research and engineer proteins, the Nobel Prize in Chemistry in 2024 was awarded to David Baker for computational protein design and jointly to Demis Hassabis and John Jumper, who developed AlphaFold for machine-learning-based protein structure prediction. Here, we highlight notable contributions to the development of these computational tools and their importance for the design of functional proteins that are applied in organic synthesis. Notably, both technologies have the potential to impact drug discovery as any therapeutic protein target can now be modelled, allowing the de novo design of peptide binders and the identification of small molecule ligands through in silico docking of large compound libraries. Looking ahead, we highlight future research directions in protein engineering, medicinal chemistry and material design that are enabled by this transformative shift in protein science.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10608 - Biochemistry and molecular biology

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Angewandte Chemie-International Edition

ISSN

1433-7851

e-ISSN

—

Svazek periodika

64

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

9

Strana od-do

"e202421686"

Kód UT WoS článku

001368059400001

EID výsledku v databázi Scopus

2-s2.0-85211125246