ArticleProtein science : a publication of the Protein Society2022
Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants.
Article in Protein science : a publication of the Protein Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 193 papers.
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193 citing papers in PubMed.
- AlphaFold and Docking Approaches for Antibody-Antigen and Other Targets: Insights From CAPRI Rounds 47-55.Proteins · 2026Article
- Combining AI Structure Prediction and Integrative Modeling for Nanobody-Antigen Complexes.Journal of chemical information and modeling · 2026Article
- Comprehensive evaluation of AlphaFold/OpenFold prediction of experimentally unresolved proteins through novel metrics.Briefings in bioinformatics · 2026Article
- Structural characterization of representative odorant receptors in Rhynchophorus ferrugineus through high-throughput modelling and extended molecular dynamics simulations.Journal of computer-aided molecular design · 2026Article
- Challenging cases for AlphaFold: two multidomain proteins with zinc-binding-, phosphorylation- or dimerization-driven conformational changes.European biophysics journal : EBJ · 2026Article
- Bitter taste TAS2R14 and TAS2R46 receptors bound to G proteins: comparison of cryo-EM, AlphaFold, and molecular dynamics structures.European biophysics journal : EBJ · 2026Article
- Ensembles ofbioRxiv : the preprint server for biology · 2026Article
- Sheep erythrocyte potassium locus highlights MYADM family regulation of electroneutral potassium chloride cotransporters (KCC).BMC genomics · 2026Article
- Transfer learning for T-cell response prediction.BMC bioinformatics · 2026Article
- Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics.Bioactive materials · 2026Review
- Interpretable deep learning framework for mapping E3-substrate binding interfaces.Nature communications · 2026Article
- Comprehensive evaluation of artificial intelligence-empowered approaches for protein-aptamer complex prediction.Briefings in bioinformatics · 2026Article
- Benchmarking TCR-pMHC structure prediction: a unified evaluation and CDR3-based functional insights.Briefings in bioinformatics · 2026Article
- AlphaFold-driven discovery of oxysterol-binding protein-related protein-phosphoinositide 3-, 4-, and 5-phosphatase interactions using new generation confidence scores.Protein science : a publication of the Protein Society · 2026Article
- Peptide-protein docking: from physics-based models to generative intelligence.Chemical communications (Cambridge, England) · 2026Review
- G protein GαThe Journal of biological chemistry · 2026Article
- AI-Assisted Computed Structure Models for Pre-Ubiquitylation Complexes Assembled by Respiratory Syncytial Viral Suppressors of Cellular Interferon Response.International journal of molecular sciences · 2026Article
- Comparative evaluation of the prediction accuracy of AlphaFold and ESMFold for monomeric and dimeric proteins.NAR genomics and bioinformatics · 2026Article
- Ion Mobility Mass Spectrometry Guided Modeling with AlphaFold and Rosetta Improves Protein Complex Structure Prediction.bioRxiv : the preprint server for biology · 2026Article
- Mapping targetable sites on the human surfaceome for the design of novel binders.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
133 more citing papers are in PubMed but not listed here.
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4 authors.
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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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