Experimentally validated deep learning control of protein aggregation

Year: 2026

Type: article

Abstract: The identification of aggregation-prone regions in proteins and their suppression through mutations is a powerful strategy to enhance protein solubility and yield, significantly expanding their potential applications. Here, we developed and experimentally validated a deep neural network-based predictor, AggreProt, that generates a residue-level aggregation profile for protein sequences. The model outperformed or matched current state-of-the-art algorithms, as validated on two independent datasets comprising hexapeptides and full-length proteins with annotated aggregation-prone regions. Importantly, we validated the model experimentally using a set of 34 hexapeptides identified in the model protein haloalkane dehalogenase LinB, along with seven proteins from the AmyPro database. Experimental results agreed with our predictions in 79% of cases and revealed inaccuracies in some database annotations. Finally, the algorithm's utility was demonstrated by identifying aggregation-prone regions in the LinB enzyme and designing mutations to suppress aggregation in its exposed regions. The resulting variants exhibited reduced aggregation propensity, improved solubility, and up to a 100% increase in yield compared to the wild type. AggreProt is freely available to the scientific community via a user-friendly web server: https://loschmidt.chemi.muni.cz/aggreprot .

Source: Communications Chemistry

DOI: 10.1038/s42004-026-02007-5

Authors: Vojtech Cima, Antonin Kunka, Joan Planas-Iglesias, Ekaterina Grakova, Martin Havlasek, Madhumalar Subramanian, Michal Beloch, Martin Marek, Katerina Slaninova, Jiri Damborsky, Zbynek Prokop, David Bednar, Jan Martinovic

Institutions: VSB - Technical University of Ostrava, Masaryk University, RECETOX, International Clinical Research Center, St. Anne's University Hospital Brno, St. Anne's University Hospital Brno, Technical University of Denmark

Language: English

Topic: Protein Structure and Dynamics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $3,690

Funders: European Commission, Ministerstvo Školství, Mládeže a Tělovýchovy, Grantová Agentura České Republiky, Horizon 2020 Framework Programme, HORIZON EUROPE Framework Programme, RECETOX Přírodovědecké Fakulty Masarykovy Univerzity

OpenAlex ID: W7155835592