AlphaCRV: a pipeline for identifying accurate binder topologies in mass-modeling with AlphaFold
Open accessPublished version of a preprint
Abstract
Motivation: The speed and accuracy of deep learning-based structure prediction algorithms make it now possible to perform in silico “pull-downs” to identify protein–protein interactions on a proteome-wide scale. However, on such a large scale, existing scoring algorithms are often insufficient to discriminate biologically relevant interactions from false positives. Results: Here, we introduce AlphaCRV, a Python package that helps identify correct interactors in a one-against-many AlphaFold screen by clustering, ranking, and visualizing conserved binding topologies, based on protein sequence and fold. Availability and implementation: AlphaCRV is a Python package for Linux, freely available at https://github.com/strubelab/AlphaCRV
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Cites
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
Accurate structure prediction of biomolecular interactions with AlphaFold 3
Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A. J., Bambrick, J., et al. (2024) · Nature · doi:10.1038/s41586-024-07487-w · Cited by 15,861 · see citing works
Evolutionary-scale prediction of atomic level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2022) · doi:10.1101/2022.07.20.500902 · Cited by 5,523 · 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
Ahdritz, G., Bouatta, N., Floristean, C., Kadyan, S., Xia, Q., Gerecke, W., O’Donnell, T. J., Berenberg, D., Fisk, I., Zanichelli, N., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02272-z · Cited by 439 · cited 1× · see citing works
“…in algorithmic efficiency have further accelerated modeling speed, enabling proteome–scale interaction predictions as a computational alternative to high-throughput experimental investigations (Mirdita et al. 2022, Wallner 2023, Yu et al. 2023, Abramson et al. 2024, Ahdritz et al. 2024).”
BackgroundTowards a structurally resolved human protein interaction network
Burke, D. F., Bryant, P., Barrio-Hernandez, I., Memon, D., Pozzati, G., Shenoy, A., Zhu, W., Dunham, A. S., Albanese, P., Keller, A., et al. (2023) · Nature Structural & Molecular Biology · doi:10.1038/s41594-022-00910-8 · Cited by 269 · see citing works
Systematic discovery of protein interaction interfaces using AlphaFold and experimental validation
Lee, C. Y., Hubrich, D., Varga, J. K., Schäfer, C., Welzel, M., Schumbera, E., Djokic, M., Strom, J. M., Schönfeld, J., Geist, J. L., et al. (2024) · Molecular Systems Biology · doi:10.1038/s44320-023-00005-6 · Cited by 105 · cited 2× · see citing works
“…complex prediction by AlphaFold-Multimer may be enhanced, e.g. by testing different domains or fragments of the interacting proteins (Bret et al. 2024, Lee et al. 2024), or by enforcing a dropout at inference time to increase sampling of different conformations (Johansson-Åkhe and Wallner 2022).”
BackgroundMethodSystematic discovery of protein interaction interfaces using AlphaFold and experimental validation
Lee, C. Y., Hubrich, D., Varga, J. K., Schäfer, C., Welzel, M., Schumbera, E., Đokić, M., Strom, J. M., Schönfeld, J., Geist, J. L., et al. (2023) · doi:10.1101/2023.08.07.552219 · Cited by 105 · see citing works
From interaction networks to interfaces, scanning intrinsically disordered regions using AlphaFold2
Bret, H., Gao, J., Zea, D. J., Andreani, J., & Guerois, R. (2024) · Nature Communications · doi:10.1038/s41467-023-44288-7 · Cited by 103 · see citing works
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- Authors
- Guzmán-Vega, F. J., & Arold, S. T.
- Affiliations
- Biological and Environmental Science and Engineering Division, Computational Biology Research Center, King Abdullah University of Science and Technology , Thuwal 23955-6900,
- King Abdullah University of Science and Technology (SA)
- Corresponding author
- Arold, Stefan T
- Venue
- Bioinformatics Advances
- Publisher
- Oxford University Press (OUP)
- Published
- 2024
- Volume
- 4
- Issue
- 1
- Pages
- vbae131-vbae131
- ISSN
- 2635-0041 (ISSN-L)
- PMC
- PMC11405088
- OpenAlex
- W4402582206
- Cited by
- 4
- References
- 9
- Citations by year
- 2026: 2 · 2025: 2
- FWCI
- 0.54
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Bioinformatics Advances (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- King Abdullah University of Science and Technology (FCC/1/1976-33 and REI/1/4446-01)
- Office of Sponsored Research
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- protein-protein interaction, AlphaFold, mass modeling, protein-protein interaction network, clustering, protein structure prediction, false positive reduction
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