Tools for computational design and high-throughput screening of therapeutic enzymes

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F22%3A00077602" target="_blank" >RIV/00159816:_____/22:00077602 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14310/22:00126167

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/abs/pii/S0169409X22000333?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S0169409X22000333?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.addr.2022.114143" target="_blank" >10.1016/j.addr.2022.114143</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Tools for computational design and high-throughput screening of therapeutic enzymes

Popis výsledku v původním jazyce

Therapeutic enzymes are valuable biopharmaceuticals in various biomedical applications. They have been successfully applied for fibrinolysis, cancer treatment, enzyme replacement therapies, and the treatment of rare diseases. Still, there is a permanent demand to find new or better therapeutic enzymes, which would be sufficiently soluble, stable, and active to meet specific medical needs. Here, we highlight the benefits of coupling computational approaches with high-throughput experimental technologies, which significantly accelerate the identification and engineering of catalytic therapeutic agents. New enzymes can be identified in genomic and metagenomic databases, which grow thanks to next generation sequencing technologies exponentially. Computational design and machine learning methods are being developed to improve catalytically potent enzymes and predict their properties to guide the selection of target enzymes. High-throughput experimental pipelines, increasingly relying on microfluidics, ensure functional screening and biochemical characterization of target enzymes to reach efficient therapeutic enzymes.(c) 2022 Elsevier B.V. All rights reserved.

Název v anglickém jazyce

Tools for computational design and high-throughput screening of therapeutic enzymes

Popis výsledku anglicky

Therapeutic enzymes are valuable biopharmaceuticals in various biomedical applications. They have been successfully applied for fibrinolysis, cancer treatment, enzyme replacement therapies, and the treatment of rare diseases. Still, there is a permanent demand to find new or better therapeutic enzymes, which would be sufficiently soluble, stable, and active to meet specific medical needs. Here, we highlight the benefits of coupling computational approaches with high-throughput experimental technologies, which significantly accelerate the identification and engineering of catalytic therapeutic agents. New enzymes can be identified in genomic and metagenomic databases, which grow thanks to next generation sequencing technologies exponentially. Computational design and machine learning methods are being developed to improve catalytically potent enzymes and predict their properties to guide the selection of target enzymes. High-throughput experimental pipelines, increasingly relying on microfluidics, ensure functional screening and biochemical characterization of target enzymes to reach efficient therapeutic enzymes.(c) 2022 Elsevier B.V. All rights reserved.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

30104 - Pharmacology and pharmacy

Projekt

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

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

ADVANCED DRUG DELIVERY REVIEWS

ISSN

0169-409X

e-ISSN

1872-8294

Svazek periodika

183

Číslo periodika v rámci svazku

April

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

16

Strana od-do

nestrankovano

Kód UT WoS článku

000793483600005

EID výsledku v databázi Scopus

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