If you use AlphaFold structures in your research, it is essential to cite the original papers to ensure proper attribution and acknowledge the significant work that has gone into creating this resource.

Primary citations:

• Jumper, J et al. Highly accurate protein structure prediction with AlphaFold. Nature (2021)
• Fleming J. et al. AlphaFold Protein Structure Database and 3D-Beacons: New Data and Capabilities. Journal of Molecular Biology, (2025)

If you use data from one of the collaboration datasets, please also cite the relevant dataset paper. 

Kinetoplastid dataset (2025) Wheeler Lab

• Wheeler RJ. A resource for improved predictions of Trypanosoma and Leishmania protein three-dimensional structure. PLoS One (2021)

AllTheBacteria dataset (2025) AllTheBacteria Consortium  

• Hunt M, et al. AllTheBacteria – all bacterial genomes assembled, available, and searchable. BioRXiV (2025)

BVFD dataset (2025) The BFVD Development Team

• Kim, RS et al. BFVD—a large repository of predicted viral protein structures. NAR (2024) 

Viro3D dataset (2025)

• Litvin, U et al. Viro3D: a comprehensive database of virus protein structure predictions. Mol Syst Biol (2025) 

If you use entries from the NVIDIA dataset, please cite the relevant methodological papers:

• Evans R. et al. Protein complex prediction with AlphaFold-Multimer. BioRXiV (2021)
• Steinegger M, Söding J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat Biotechnol (2017)
• Mirdita, M., Schütze, K., Moriwaki, Y. et al. ColabFold: making protein folding accessible to all. Nat Methods. (2022). 
• Kallenborn, F., Chacon, A., Hundt, C. et al. GPU-accelerated homology search with MMseqs2. Nat Methods. (2025)

If you use data from AlphaMissense in your work, please cite the following paper:

• Cheng, J et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science (2023)