Evidence map›Paper›PMID 41909611›Full record

ReviewBiophysical reviews2026

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

Giulia Pesce, Oriol Solé, Oriol Bárcenas, Salvador Ventura

Abstract readReview
In one paragraph

Review in Biophysical reviews, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Giulia PesceInstitut de Biotecnologia i Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193 Spain.
Oriol SoléInstitut de Biotecnologia i Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193 Spain.
Oriol BárcenasInstitut de Biotecnologia i Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193 Spain.
Salvador VenturaInstitut de Biotecnologia i Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193 Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Aggregation-prone regionsAmyloidComputational protein designMolecular dynamicsProtein aggregationProtein therapeutics

Identifiers

PMID41909611
PMCPMC13031676

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.