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AlphaCRV: A Pipeline for Identifying Accurate Binder Topologies in Mass-Modeling with AlphaFold
Open accessPreprint — published as 10.1093/bioadv/vbae131 · Paper page
Abstract
The speed and accuracy of deep learning-based structure prediction algorithms makes it now possible to perform in silico “pull-downs” to identify protein-protein interactions at a proteome-wide scale. However, existing scoring algorithms struggle to accurately identify correct interactions at such a large scale, resulting in an excessive number of false positives. 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.
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Citations
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
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Research works printed but not linked: 19
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Metadata
- Authors
- Guzmán-Vega, F. J., & Arold, S. T.
- Affiliations
- King Abdullah University of Science and Technology (SA)
- Corresponding author
- Arold, Stefan T.
- Venue
- Bioinformatics Advances
- Publisher
- openRxiv
- Published
- 2024-02-08
- Volume
- 4
- Issue
- 1
- Pages
- vbae131-vbae131
- ISSN
- 2635-0041 (ISSN-L)
- PMC
- PMC11405088
- OpenAlex
- W4402582206
- Cited by
- 4
- References
- 1
- Citations by year
- 2026: 2 · 2025: 2
- FWCI
- 0.54
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- 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, FCC/1/1976‐33, REI/1/4446–01)
- 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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