# AlphaFold Protein Structure Database and 3D-Beacons: New Data and Capabilities

Article · Jennifer Fleming, Paulyna Magaña, Sreenath Sasidharan Nair, Maxim I. Tsenkov, Damian Bertoni, Ivanna Pidruchna, Marcelo Querino Lima Afonso, Adam Midlik, Urmila Paramval, Augustin Žídek, Agata Laydon, Oleg V. Kovalevskiy, Elisa Wong, Joshua Pan, Jun Cheng, Žiga Avsec, Clare Bycroft, Lai Hong Won, Meera Last, Milot Mirdita, Martin Steinegger, Mihály Váradi, Pushmeet Kohli, Sameer Velnankar

- Archive id: doi:10.1016/j.jmb.2025.168967
- Published: 2025-01-29
- Venue: Journal of Molecular Biology
- DOI: https://doi.org/10.1016/j.jmb.2025.168967
- PMID: 40133787
- Citations: 284
- Source URL: https://doi.org/10.1016/j.jmb.2025.168967
- Provenance: openalex+europepmc

## Abstract

The AlphaFold Protein Structure Database (https://alphafold.ebi.ac.uk/) has made significant strides in enhancing its utility and accessibility for the life science research community. The recent integration of AlphaMissense predictions enables access to the pathogenicity of human protein missense variants, with an innovative and interactive heatmap and 3D visualisation that display variant data at the residue level. Users can now toggle between structure model quality (pLDDT) and average pathogenicity scores, providing insights into the implications of specific residue changes. The Foldseek integration offers a rapid and accurate method for protein structure searches and comparisons. Bulk data download options further facilitate comprehensive data analysis and integration with other computational tools. The 3D-Beacons framework (https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/) has also been enhanced with detailed annotation endpoints (such as AlphaMissense data) and integrates LevyLab’s dataset of homomeric AlphaFold 2 models. These advancements, plus the release of the training module "AlphaFold: A Practical Guide", significantly improve the functionality and accessibility of these resources, enabling discoveries using structure data.

_Abstract reproduced under CC-BY (publisher license, via OpenAlex)._

Topics: [AI Protein Design & Structure Prediction](https://space-kitty.com/topics/protein-design)

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