ArticleMethods in molecular biology (Clifton, N.J.)2022
A3D 2.0 Update for the Prediction and Optimization of Protein Solubility.
Article in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 13 citations in OpenAlex.
- Beyond affinity: AI-supported developability assessment and multi-objective optimization in antibody development.Antibody therapeutics · 2026Review
- Structural insights into insect-selective sodium channel toxins drive AI-enhanced biopesticide design.Nature communications · 2026Article
- AGGRESCAN and its evolution: A two-decade perspective on protein aggregation prediction.Biophysical reviews · 2026Review
- Characterization of BRCA1-Associated Protein-1 (BAP1) Aggregation Properties Induced by Cancer-Associated Mutations.Chembiochem : a European journal of chemical biology · 2025Article
- AI Prediction of Structural Stability of Nanoproteins Based on Structures and Residue Properties by Mean Pooled Dual Graph Convolutional Network.Interdisciplinary sciences, computational life sciences · 2025Article
- Aggrescan4D: A comprehensive tool for pH-dependent analysis and engineering of protein aggregation propensity.Protein science : a publication of the Protein Society · 2024Review
- A3D Model Organism Database (A3D-MODB): a database for proteome aggregation predictions in model organisms.Nucleic acids research · 2024Article
- A3DyDB: exploring structural aggregation propensities in the yeast proteome.Microbial cell factories · 2023Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
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.