A3D 2.0 update for the prediction and optimization of protein solubility
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Protein aggregation propensity is a property imprinted in protein sequences and structures, being associated with the onset of human diseases and limiting the implementation of protein-based biotherapies. Computational approaches stand as cost-effective alternatives for reducing protein aggregation and increasing protein solubility. AGGRESCAN 3D (A3D) is a structure-based predictor of aggregation that takes into account the conformational context of a protein, aiming to identify aggregation-prone regions exposed in protein surfaces. Here we inspect the updated 2.0 version of the algorithm, which extends the application of A3D to previously inaccessible proteins and incorporates new modules to assist protein redesign. Among these features, the new server includes stability calculations and the possibility to optimize protein solubility using an experimentally validated computational pipeline. Finally, we employ defined examples to navigate the A3D RESTful service, a routine to handle extensive protein collections. Altogether, this chapter is conceived to train and assist A3D non-experts in the study of aggregation-prone regions and protein solubility redesign.
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There are some ML models that predict aggregation propensity using antibody sequences or structures.
“Furthermore, there are some ML models that predict aggregation propensity using antibody sequences or structures [27,28].”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [28]
A3D allows for the design of antibodies displaying significantly reduced aggregation propensity.
“A structure-based aggregation prediction tool, AGGRESCAN 3D (A3D) [28], allows for the design of antibodies displaying significantly reduced aggregation propensity.”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [28]
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Computational and artificial intelligence-based methods for antibody development
Kim, J., McFee, M., Fang, Q., Abdin, O., & Kim, P. M. (2023) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2022.12.005 · Cited by 178 · see citing works
- Authors
- Pujols, J., Iglesias, V., Santos, J., Kuriata, A., Kmiecik, S., & Ventura, S.
- Affiliations
- Universitat Autònoma de Barcelona (ES)
- University of Warsaw (PL)
- Corresponding author
- Ventura, Salvador
- Venue
- Methods in molecular biology
- Publisher
- Springer US
- Published
- 2022
- Volume
- 2406
- Pages
- 65-84
- ISSN
- 1064-3745 (ISSN-L)
- 1940-6029
- OpenAlex
- W4226227994
- Cited by
- 19
- References
- 46
- Citations by year
- 2026: 7 · 2025: 2 · 2024: 5 · 2023: 4 · 2022: 1
- FWCI
- 5.05
- Type
- book chapter
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Methods in molecular biology (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Ministerio de Economía y Competitividad (BIO2016–78,310-R)
- Institució Catalana de Recerca i Estudis Avançats (ICREA-Academia 2015, Academia 2015)
- Ministerio de Ciencia e Innovación (FPU17/01157, BIO2016-78310-R, FPU17)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein purification and stability
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein aggregation propensity, protein solubility, aggregation-prone regions, protein redesign, protein stability prediction, REST API, computational pipeline
- MeSH terms
- Algorithms, Humans, Proteins, Solubility, Protein Folding, Protein Aggregates
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