Accelerating protein–protein interaction screens with reduced AlphaFold-Multimer sampling
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Abstract
Motivation: Discovering new protein–protein interactions (PPIs) across entire proteomes offers vast potential for understanding novel protein functions and elucidate system properties within or between an organism. While recent advances in computational structural biology, particularly AlphaFold-Multimer, have facilitated this task, scaling for large-scale screenings remains a challenge, requiring significant computational resources. Results: We evaluated the impact of reducing the number of models generated by AlphaFold-Multimer from five to one on the method’s ability to distinguish true PPIs from false ones. Our evaluation was conducted on a dataset containing both intra- and inter-species PPIs, which included proteins from bacterial and eukaryotic sources. We demonstrate that reducing the sampling does not compromise the accuracy of the method, offering a faster, efficient, and environmentally friendly solution for PPI predictions. Availability and implementation: The code used in this article is available at https://github.com/MIDIfactory/AlphaFastPPi. Note that the same can be achieved using the latest version of AlphaPulldown available at https://github.com/KosinskiLab/AlphaPulldown
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Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
Improved prediction of protein-protein interactions using AlphaFold2
Bryant, P., Pozzati, G., & Elofsson, A. (2021) · doi:10.1101/2021.09.15.460468 · Cited by 1,025 · see citing works
Quantifying the local adaptive landscape of a nascent bacterial community
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Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants
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Towards a structurally resolved human protein interaction network
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An in-depth review of AI-powered advancements in cancer drug discovery
Le, M. H. N., Nguyen, P. K., Nguyen, T. P. T., Nguyen, H. Q., Tam, D. N. H., Huynh, H. H., Huynh, P. K., & Le, N. Q. K. (2025) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2025.167680 · Cited by 15 · see citing works
- Authors
- Bellinzona, G., Sassera, D., & Bonvin, A. M. J. J.
- Affiliations
- University of Pavia (IT)
- Utrecht University (NL)
- Corresponding author
- Bonvin, Alexandre M J J
- Venue
- Bioinformatics Advances
- Publisher
- Oxford University Press (OUP)
- Published
- 2024
- Volume
- 4
- Issue
- 1
- Pages
- vbae153-vbae153
- ISSN
- 2635-0041 (ISSN-L)
- PMC
- PMC11513016
- OpenAlex
- W4403331552
- Cited by
- 13
- References
- 5
- Citations by year
- 2026: 4 · 2025: 9
- FWCI
- 1.77
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Bioinformatics Advances (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Funders
- HPC Cineca
- European Union Horizon 2020
- Rijksdienst voor Ondernemend Nederland LEAP project (DEI122053)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein-protein interaction, AlphaFold-Multimer, protein-protein interaction prediction, computational efficiency, protein structure prediction, false positive reduction
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