AGGRESCAN and its evolution: A two-decade perspective on protein aggregation prediction

Protein aggregation is a widespread phenomenon with profound biological, biomedical, and biotechnological implications. In human disease, aberrant protein self-assembly is a hallmark of numerous neurodegenerative disorders, whereas in the biopharmaceutical industry, aggregation complicates the produ...

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Detalles Bibliográficos
Autores: Pesce, Giulia, Solé, Oriol, Bárcenas, Oriol, Ventura, Salvador
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2026
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/416508
Acceso en línea:http://hdl.handle.net/10261/416508
https://api.elsevier.com/content/abstract/scopus_id/105027836995
Access Level:acceso abierto
Palabra clave:Protein therapeutics
Aggregation-prone regions
Amyloid
Computational protein design
Molecular dynamics
Protein aggregation
http://metadata.un.org/sdg/3
http://metadata.un.org/sdg/9
http://metadata.un.org/sdg/11
Ensure healthy lives and promote well-being for all at all ages
Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation
Make cities and human settlements inclusive, safe, resilient and sustainable
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spelling AGGRESCAN and its evolution: A two-decade perspective on protein aggregation predictionPesce, GiuliaSolé, OriolBárcenas, OriolVentura, SalvadorProtein therapeuticsAggregation-prone regionsAmyloidComputational protein designMolecular dynamicsProtein aggregationhttp://metadata.un.org/sdg/3http://metadata.un.org/sdg/9http://metadata.un.org/sdg/11Ensure healthy lives and promote well-being for all at all agesBuild resilient infrastructure, promote inclusive and sustainable industrialization and foster innovationMake cities and human settlements inclusive, safe, resilient and sustainableProtein aggregation is a widespread phenomenon with profound biological, biomedical, and biotechnological implications. In human disease, aberrant protein self-assembly is a hallmark of numerous neurodegenerative disorders, whereas in the biopharmaceutical industry, aggregation complicates the production, stability, and formulation of therapeutic proteins. The Aggrescan platform was one of the first empirically based tools designed to predict aggregation-prone regions (APRs) within protein sequences. It has since expanded to incorporate three-dimensional structural contexts and environmental conditions. This review provides a comprehensive overview of the development, application, and impact of the Aggrescan family of tools, which includes AGGRESCAN, Aggrescan3D, and the recent Aggrescan4D. We examine the algorithmic foundations, empirical validation, and key use cases spanning fields from biotechnology to biomedical research. Additionally, we describe how the recent integration of AlphaFold models has enabled proteome-scale exploration of aggregation determinants. This review highlights how Aggrescan has evolved alongside with advances in the field, becoming a reliable and accessible tool for studying and redesigning protein aggregation.This work was supported by the Spanish Ministry of Science and Innovation (MICINN) PID2022-137963OB-I00 to S.V., by ICREA (ICREA-Academia 2020, Spain) to S.V., by CERCA Programme (Generalitat de Catalunya) to S.V., and 2021 SGR 00635 (AGAUR, Generalitat de Catalunya) to S.V.; O.B. was supported by the Spanish Ministry of Science and Innovation via a doctoral grant [FPU22/03656].Peer reviewedSpringer NatureConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202620262026info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/416508https://api.elsevier.com/content/abstract/scopus_id/105027836995reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésBiophysical Reviewshttps://doi.org/10.1007/s12551-025-01404-9Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/4165082026-05-22T06:33:51Z
dc.title.none.fl_str_mv AGGRESCAN and its evolution: A two-decade perspective on protein aggregation prediction
title AGGRESCAN and its evolution: A two-decade perspective on protein aggregation prediction
spellingShingle AGGRESCAN and its evolution: A two-decade perspective on protein aggregation prediction
Pesce, Giulia
Protein therapeutics
Aggregation-prone regions
Amyloid
Computational protein design
Molecular dynamics
Protein aggregation
http://metadata.un.org/sdg/3
http://metadata.un.org/sdg/9
http://metadata.un.org/sdg/11
Ensure healthy lives and promote well-being for all at all ages
Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation
Make cities and human settlements inclusive, safe, resilient and sustainable
title_short AGGRESCAN and its evolution: A two-decade perspective on protein aggregation prediction
title_full AGGRESCAN and its evolution: A two-decade perspective on protein aggregation prediction
title_fullStr AGGRESCAN and its evolution: A two-decade perspective on protein aggregation prediction
title_full_unstemmed AGGRESCAN and its evolution: A two-decade perspective on protein aggregation prediction
title_sort AGGRESCAN and its evolution: A two-decade perspective on protein aggregation prediction
dc.creator.none.fl_str_mv Pesce, Giulia
Solé, Oriol
Bárcenas, Oriol
Ventura, Salvador
author Pesce, Giulia
author_facet Pesce, Giulia
Solé, Oriol
Bárcenas, Oriol
Ventura, Salvador
author_role author
author2 Solé, Oriol
Bárcenas, Oriol
Ventura, Salvador
author2_role author
author
author
dc.contributor.none.fl_str_mv Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Protein therapeutics
Aggregation-prone regions
Amyloid
Computational protein design
Molecular dynamics
Protein aggregation
http://metadata.un.org/sdg/3
http://metadata.un.org/sdg/9
http://metadata.un.org/sdg/11
Ensure healthy lives and promote well-being for all at all ages
Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation
Make cities and human settlements inclusive, safe, resilient and sustainable
topic Protein therapeutics
Aggregation-prone regions
Amyloid
Computational protein design
Molecular dynamics
Protein aggregation
http://metadata.un.org/sdg/3
http://metadata.un.org/sdg/9
http://metadata.un.org/sdg/11
Ensure healthy lives and promote well-being for all at all ages
Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation
Make cities and human settlements inclusive, safe, resilient and sustainable
description Protein aggregation is a widespread phenomenon with profound biological, biomedical, and biotechnological implications. In human disease, aberrant protein self-assembly is a hallmark of numerous neurodegenerative disorders, whereas in the biopharmaceutical industry, aggregation complicates the production, stability, and formulation of therapeutic proteins. The Aggrescan platform was one of the first empirically based tools designed to predict aggregation-prone regions (APRs) within protein sequences. It has since expanded to incorporate three-dimensional structural contexts and environmental conditions. This review provides a comprehensive overview of the development, application, and impact of the Aggrescan family of tools, which includes AGGRESCAN, Aggrescan3D, and the recent Aggrescan4D. We examine the algorithmic foundations, empirical validation, and key use cases spanning fields from biotechnology to biomedical research. Additionally, we describe how the recent integration of AlphaFold models has enabled proteome-scale exploration of aggregation determinants. This review highlights how Aggrescan has evolved alongside with advances in the field, becoming a reliable and accessible tool for studying and redesigning protein aggregation.
publishDate 2026
dc.date.none.fl_str_mv 2026
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/416508
https://api.elsevier.com/content/abstract/scopus_id/105027836995
url http://hdl.handle.net/10261/416508
https://api.elsevier.com/content/abstract/scopus_id/105027836995
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Biophysical Reviews
https://doi.org/10.1007/s12551-025-01404-9
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Springer Nature
publisher.none.fl_str_mv Springer Nature
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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