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

J 2024

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

PLANAS IGLESIAS, Joan; Simeon BORKO; Jan SWIATKOWSKI; Matej ELIAS; Martin HAVLÁSEK et al.

Základní údaje

Originální název

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

Autoři

Vydání

Nucleic Acids Research, Oxford, Oxford University Press, 2024, 0305-1048

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10608 Biochemistry and molecular biology

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 13.100

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14310/24:00136705

Organizační jednotka

Přírodovědecká fakulta

Klíčová slova anglicky

SECONDARY STRUCTURE; BETA; DATABASE; DESIGN

Štítky

14314010, 143180, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 4. 3. 2026 08:40, Mgr. Michaela Hylsová, Ph.D.

Anotace

V originále

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/.

Návaznosti

EF17_043/0009632, projekt VaV

Název: CETOCOEN Excellence

LM2018140, projekt VaV

Název: e-Infrastruktura CZ (Akronym: e-INFRA CZ)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, e-Infrastruktura CZ

LM2023055, projekt VaV

Název: Česká národní infrastruktura pro biologická data

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, ELIXIR-CZ: Česká národní infrastruktura pro biologická data

LM2023069, projekt VaV

Název: Výzkumná infrastruktura RECETOX

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Výzkumná infrastruktura RECETOX

LX22NPO5107, projekt VaV

Název: Národní ústav pro neurologický výzkum

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Národní ústav pro neurologický výzkum, 5.1 EXCELES

857560, interní kód MU
(Kód CEP: EF17_043/0009632)

Název: CETOCOEN Excellence (Akronym: CETOCOEN Excellence)

Investor: Evropská unie, CETOCOEN Excellence, Spreading excellence and widening participation

90254, velká výzkumná infrastruktura

Název: e-INFRA CZ II

Přiložené soubory

2421847.pdf

Citovat

PLANAS IGLESIAS, Joan; Simeon BORKO; Jan SWIATKOWSKI; Matej ELIAS; Martin HAVLÁSEK; Ondrej SALAMON; Ekaterina GRAKOVA; Antonín KUNKA; Tomas MARTINOVIC; Jiří DAMBORSKÝ; Jan MARTINOVIC a David BEDNÁŘ. AggreProt: a web server for predicting and engineering aggregation prone regions in proteins. Nucleic Acids Research. Oxford: Oxford University Press, 2024, roč. 52, W1, s. "W159"-"W169", 11 s. ISSN 0305-1048. Dostupné z: https://doi.org/10.1093/nar/gkae420.

@article{2421847,
   author = {Planas Iglesias, Joan and Borko, Simeon and Swiatkowski, Jan and Elias, Matej and Havlásek, Martin and Salamon, Ondrej and Grakova, Ekaterina and Kunka, Antonín and Martinovic, Tomas and Damborský, Jiří and Martinovic, Jan and Bednář, David},
   article_location = {Oxford},
   article_number = {W1},
   doi = {https://doi.org/10.1093/nar/gkae420},
   keywords = {SECONDARY STRUCTURE; BETA; DATABASE; DESIGN},
   language = {eng},
   issn = {0305-1048},
   journal = {Nucleic Acids Research},
   title = {AggreProt: a web server for predicting and engineering aggregation prone regions in proteins},
   url = {https://academic.oup.com/nar/article/52/W1/W159/7683054?login=true},
   volume = {52},
   year = {2024}
}

TY  - JOUR
ID  - 2421847
AU  - Planas Iglesias, Joan - Borko, Simeon - Swiatkowski, Jan - Elias, Matej - Havlásek, Martin - Salamon, Ondrej - Grakova, Ekaterina - Kunka, Antonín - Martinovic, Tomas - Damborský, Jiří - Martinovic, Jan - Bednář, David
PY  - 2024
TI  - AggreProt: a web server for predicting and engineering aggregation prone regions in proteins
JF  - Nucleic Acids Research
VL  - 52
IS  - W1
SP  - "W159"-"W169"
EP  - "W159"-"W169"
PB  - Oxford University Press
SN  - 03051048
KW  - SECONDARY STRUCTURE
KW  - BETA
KW  - DATABASE
KW  - DESIGN
UR  - https://academic.oup.com/nar/article/52/W1/W159/7683054?login=true
N2  - 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/.
ER  -

PLANAS IGLESIAS, Joan; Simeon BORKO; Jan SWIATKOWSKI; Matej ELIAS; Martin HAVLÁSEK; Ondrej SALAMON; Ekaterina GRAKOVA; Antonín KUNKA; Tomas MARTINOVIC; Jiří DAMBORSKÝ; Jan MARTINOVIC a David BEDNÁŘ. AggreProt: a web server for predicting and engineering aggregation prone regions in proteins. \textit{Nucleic Acids Research}. Oxford: Oxford University Press, 2024, roč.~52, W1, s.~''W159''-''W169'', 11 s. ISSN~0305-1048. Dostupné z: https://doi.org/10.1093/nar/gkae420.

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