ArticleProteins2026
Massive Sampling Strategy for Antibody-Antigen Targets in CAPRI Round 55 With MassiveFold.
Article in Proteins, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Biomolecular Interaction Prediction in the Pre- and Post-AlphaFold Era: The 8th CAPRI Evaluation.Proteins · 2026Article
- Massive Sampling Strategy for Antibody-Antigen Targets in CAPRI Round 55 With MassiveFold.Proteins · 2026Article
- Multiobjective VScience advances · 2026Article
- Benchmarking AlphaFold and related deep learning approaches for modeling antibody and TCR antigen recognition.bioRxiv : the preprint server for biology · 2026Article
- Evaluating deep learning based structure prediction methods on antibody-antigen complexes.Bioinformatics (Oxford, England) · 2026Article
- Article
- Structural insights into clonal restriction and diversity in T cell recognition of two immunodominant SARS-CoV-2 nucleocapsid epitopes.Nature communications · 2025Article
- Evaluation of AlphaFold modeling for elucidation of nanobody-peptide epitope interactions.The Journal of biological chemistry · 2025Article
- Recent progress and future challenges in structure-based protein-protein interaction prediction.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Review
- Emerging frontiers in protein structure prediction following the AlphaFold revolution.Journal of the Royal Society, Interface · 2025Review
- Evaluation of Alphafold modeling for elucidation of nanobody-peptide epitope interactions.bioRxiv : the preprint server for biology · 2025Article
- MassiveFold: unveiling AlphaFold's hidden potential with optimized and parallelized massive sampling.Nature computational science · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Massive sampling with AlphaFold2 improves protein-protein complex predictions. This has been shown during the last CASP15-CAPRI blind prediction round by the AFsample tool. However, more difficult targets including antibody-antigen binding remain challenging. CAPRI Round 55 consisted of three antibody-antigen targets and one heterotrimer. We used our AlphaFold2-based MassiveFold, running 6 prediction pools, each with their own set of parameters, to produce in total more than 6000 predictions per target. We show here that massive sampling categorically produces acceptable to high quality predictions, however it is clear that the AlphaFold2 confidence score cannot be used to identify the best models in the set. We also show that, contrary to what was done before for CASP15-CAPRI with AFsample, increasing the sampling without activating the dropout provides the best models for most of the targets of Round 55.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.