ArticleProteins2026
CASP16 Protein Monomer Structure Prediction Assessment.
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.
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Who cites it
12 citing papers in PubMed.
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- Reciprocal best matching: a new pipeline for scoring models with unknown stoichiometry in CASP experiments.BMC bioinformatics · 2026Article
- Immunoinformatics based designing of a broad-spectrum multi-epitope vaccine against co-infection of human metapneumovirus, respiratory syncytial virus, and influenza A virus.Scientific reports · 2026Article
- Unavailability of experimental 3D structural data on protein folding dynamics and necessity for a new generation of structure prediction methods in this context.Bioinformatics (Oxford, England) · 2026Article
- Predicting protein-carbohydrate binding sites: a deep learning approach integrating protein language model embeddings and structural features.Briefings in bioinformatics · 2026Article
- Progress and Bottlenecks for Deep Learning in Computational Structure Biology: CASP Round XVI.Proteins · 2026Article
- Practical Outcomes From CASP16 for Users in Need of Biomolecular Structure Prediction.Proteins · 2026Review
- CASP16 Protein Monomer Structure Prediction Assessment.Proteins · 2026Article
- Updates to the CASP Infrastructure in 2024.Proteins · 2026Article
- Article
- GhostFold: Accurate protein structure prediction using structure-constrained synthetic coevolutionary signals.bioRxiv : the preprint server for biology · 2025Article
- Heat Shock Protein and Disaggregase Influencing the Casein Structuralisation.International journal of molecular sciences · 2025Article
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7 authors.
Funding
Abstract
The assessment of monomer targets in the Critical Assessment of Structure Prediction Round 16 (CASP16) underscores that the problem of single-domain protein fold prediction is nearly solved-no target folds were incorrectly predicted across all Evaluation Units. However, challenges remain in accurately modeling truncated sequences, irregular secondary structures, and interaction-induced conformational changes. The release of AlphaFold3 (AF3) during CASP16, and its effective integration by many groups, demonstrated its superiority over AlphaFold2 (AF2), particularly in confidence estimation and model selection. Additional improvements in multiple sequence alignments (MSAs) and fragment-based prediction, that is, selecting the optimal fragment of the full sequence for modeling, also contributed to enhanced prediction accuracy. The top three groups-all from the Yang lab-consistently outperformed others across CASP16 monomer targets, reflecting their robust modeling pipelines and successful adoption of AF3. CASP16 also introduced three new challenges: Phase 0, in which stoichiometry was withheld; Phase 2, which supplied ~8000 MassiveFold models per target to test model selection strategies; and Model 6, which limited predictors to using MSAs provided by the organizers. While we evaluated group performance in these additional challenges, the insights gained were limited due to low participation and caveats in the design of experiments. We suggest improvements for the organization of these challenges and encourage broader engagement from the prediction community. The progress in monomer modeling from CASP15 to CASP16 was subtle, but more groups in CASP16 were able to outperform ColabFold, reflecting the community's improved ability in optimizing AF2 and the growing adoption of AF3. We anticipate that the recent release of the AF3 source code will stimulate future progress through user-driven optimization and innovations in model architecture. Finally, model ranking remains a persistent weakness across most groups, highlighting a critical area for future development.
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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.