A3D database: structure-based predictions of protein aggregation for the human proteome

Aleksandra E Badaczewska-Dawid, Javier Garcia-Pardo, Aleksander Kuriata, Jordi Pujols, Salvador Ventura, Sebastian Kmiecik, Lenore Cowen (Editor)

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)


Protein aggregation is associated with many human disorders and constitutes a major bottleneck for producing therapeutic proteins. Our knowledge of the human protein structures repertoire has dramatically increased with the recent development of the AlphaFold (AF) deep-learning method. This structural information can be used to understand better protein aggregation properties and the rational design of protein solubility. This article uses the Aggrescan3D (A3D) tool to compute the structure-based aggregation predictions for the human proteome and make the predictions available in a database form. In the A3D database, we analyze the AF-predicted human protein structures (for over 20.5 thousand unique Uniprot IDs) in terms of their aggregation properties using the A3D tool. Each entry of the A3D database provides a detailed analysis of the structure-based aggregation propensity computed with A3D. The A3D database implements simple but useful graphical tools for visualizing and interpreting protein structure datasets. It also enables testing the influence of user-selected mutations on protein solubility and stability, all integrated into a user-friendly interface.

Original languageEnglish
Pages (from-to)3121–3123
Number of pages3
Issue number11
Early online date21 Apr 2022
Publication statusPublished - 1 Jun 2022


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