AggreProt: a web server for predicting and engineering aggregation prone regions in proteins

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F24%3A00081379" target="_blank" >RIV/00159816:_____/24:00081379 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14310/24:00136705 RIV/61989100:27740/24:10255789

Výsledek na webu

<a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC11223854/pdf/gkae420.pdf" target="_blank" >https://pmc.ncbi.nlm.nih.gov/articles/PMC11223854/pdf/gkae420.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1093/nar/gkae420" target="_blank" >10.1093/nar/gkae420</a>

Jazyk výsledku

angličtina

Název v původním jazyce

AggreProt: a web server for predicting and engineering aggregation prone regions in proteins

Popis výsledku v původním jazyce

Recombinant proteins play pivotal roles in numerous applications including industrial biocatalysts or therapeutics. Despite the recent progress in computational protein structure prediction, protein solubility and reduced aggregation propensity remain challenging attributes to design. Identification of aggregation-prone regions is essential for understanding misfolding diseases or designing efficient protein-based technologies, and as such has a great socio-economic impact. Here, we introduce AggreProt, a user-friendly webserver that automatically exploits an ensemble of deep neural networks to predict aggregation-prone regions (APRs) in protein sequences. Trained on experimentally evaluated hexapeptides, AggreProt compares to or outperforms state-of-the-art algorithms on two independent benchmark datasets. The server provides per-residue aggregation profiles along with information on solvent accessibility and transmembrane propensity within an intuitive interface with interactive sequence and structure viewers for comprehensive analysis. We demonstrate AggreProt efficacy in predicting differential aggregation behaviours in proteins on several use cases, which emphasize its potential for guiding protein engineering strategies towards decreased aggregation propensity and improved solubility. The webserver is freely available and accessible at https://loschmidt.chemi.muni.cz/aggreprot/. Graphical Abstract

Název v anglickém jazyce

AggreProt: a web server for predicting and engineering aggregation prone regions in proteins

Popis výsledku anglicky

Recombinant proteins play pivotal roles in numerous applications including industrial biocatalysts or therapeutics. Despite the recent progress in computational protein structure prediction, protein solubility and reduced aggregation propensity remain challenging attributes to design. Identification of aggregation-prone regions is essential for understanding misfolding diseases or designing efficient protein-based technologies, and as such has a great socio-economic impact. Here, we introduce AggreProt, a user-friendly webserver that automatically exploits an ensemble of deep neural networks to predict aggregation-prone regions (APRs) in protein sequences. Trained on experimentally evaluated hexapeptides, AggreProt compares to or outperforms state-of-the-art algorithms on two independent benchmark datasets. The server provides per-residue aggregation profiles along with information on solvent accessibility and transmembrane propensity within an intuitive interface with interactive sequence and structure viewers for comprehensive analysis. We demonstrate AggreProt efficacy in predicting differential aggregation behaviours in proteins on several use cases, which emphasize its potential for guiding protein engineering strategies towards decreased aggregation propensity and improved solubility. The webserver is freely available and accessible at https://loschmidt.chemi.muni.cz/aggreprot/. Graphical Abstract

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10608 - Biochemistry and molecular biology

Projekt

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

Návaznosti

—

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Nucleic Acids Research

ISSN

0305-1048

e-ISSN

1362-4962

Svazek periodika

52

Číslo periodika v rámci svazku

W1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

"W159"-"W169"

Kód UT WoS článku

001233323700001

EID výsledku v databázi Scopus

—