Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants
Open accessPreprint — published as 10.1002/pro.4379 · Paper page
Abstract
High resolution experimental structural determination of protein-protein interactions has led to valuable mechanistic insights, yet due to the massive number of interactions and experimental limitations there is a need for computational methods that can accurately model their structures. Here we explore the use of the recently developed deep learning method, AlphaFold, to predict structures of protein complexes from sequence. With a benchmark of 152 diverse heterodimeric protein complexes, multiple implementations and parameters of AlphaFold were tested for accuracy. Remarkably, many cases had highly accurate models generated as top-ranked predictions, greatly surpassing the performance of unbound protein-protein docking, whereas antibody-antigen docking was largely unsuccessful. While AlphaFold-generated accuracy predictions were able to discriminate near-native models, previously developed scoring protocols improved performance. Our study demonstrates that end-to-end deep learning can accurately model transient protein complexes, and identifies areas for improvement to guide future developments to reliably model any protein-protein interaction of interest.
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Structural Features of Antibody-Peptide Recognition
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Beyond sequence: Structure-based machine learning
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- Authors
- Yin, R., Feng, B. Y., Varshney, A., & Pierce, B. G.
- Affiliations
- Institute for Bioscience and Biotechnology Research (US)
- University of Maryland, College Park (US)
- University of Maryland, Baltimore (US)
- Corresponding author
- Pierce, Brian G.
- Venue
- Protein Science
- Publisher
- openRxiv
- Published
- 2021-10-24
- Volume
- 31
- Issue
- 8
- Pages
- e4379-e4379
- ISSN
- 0961-8368 (ISSN-L)
- PMC
- PMC9278006
- OpenAlex
- W4285392187
- Cited by
- 335
- References
- 80
- Citations by year
- 2026: 47 · 2025: 67 · 2024: 111 · 2023: 90 · 2022: 19 · 2013: 1
- FWCI
- 23.42
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Protein Science (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- University Libraries (University of Maryland) (submittedVersion)
- Digital Repository at the University of Maryland (University of Maryland College Park)
- Funders
- National Institutes of Health (NIH-R01)
- National Institute of General Medical Sciences (R01GM126299, R35GM144083)
- 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
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- AlphaFold, AlphaFold-Multimer, protein complex structure prediction, protein-protein interaction, immune complexes, protein-protein docking, benchmarking, multiple sequence alignment, deep learning
- MeSH terms
- Algorithms, Protein Binding, Protein Conformation, Proteins, Sequence Alignment, Molecular Docking Simulation
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