Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants
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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 (43%) had near-native models (medium or high critical assessment of predicted interactions accuracy) generated as top-ranked predictions by AlphaFold, greatly surpassing the performance of unbound protein-protein docking (9% success rate for near-native top-ranked models), however AlphaFold modeling of antibody-antigen complexes within our set was unsuccessful. We identified sequence and structural features associated with lack of AlphaFold success, and we also investigated the impact of multiple sequence alignment input. Benchmarking of a multimer-optimized version of AlphaFold (AlphaFold-Multimer) with a set of recently released antibody-antigen structures confirmed a low rate of success for antibody-antigen complexes (11% success), and we found that T cell receptor-antigen complexes are likewise not accurately modeled by that algorithm, showing that adaptive immune recognition poses a challenge for the current AlphaFold algorithm and model. Overall, our study demonstrates that end-to-end deep learning can accurately model many transient protein complexes, and highlights areas of improvement for future developments to reliably model any protein-protein interaction of interest.
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Yin et al benchmarked 152 heterodimeric complexes and found AF2 and AF2-Multimer had a 51% success rate.
“Yin et al benchmarked 152 heterodimeric protein complexes revealing that AF2 and AF2-Multimer had a 51% success rate54,55”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [54]
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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
- Wiley
- Published
- 2022-07-13
- Volume
- 31
- Issue
- 8
- Pages
- e4379-e4379
- ISSN
- 0961-8368 (ISSN-L)
- 1469-896X
- PMC
- PMC9278006
- OpenAlex
- W4285392187
- Cited by
- 335
- References
- 72
- Citations by year
- 2026: 47 · 2025: 67 · 2024: 111 · 2023: 90 · 2022: 19 · 2013: 1
- FWCI
- 23.43
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- 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 (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, Benchmarking, Molecular Docking Simulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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