ArticleProteins2023
The impact of AI-based modeling on the accuracy of protein assembly prediction: Insights from CASP15.
Article in Proteins, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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27 citing papers in PubMed.
- The Advantages of AI for Computational Protein Studies and Looking Ahead at the Next Challenges: Single Structures Are Not Enough.Journal of molecular biology · 2026Review
- ORIGAMI: Orientation-Aware Graph Neural Network for Assessing Multimeric Interfaces of Protein Complex Structures.Journal of chemical information and modeling · 2026Article
- ORIGAMI: Orientation-Aware Graph Neural Network for Assessing Multimeric Interfaces of Protein Complex Structures.bioRxiv : the preprint server for biology · 2026Article
- Reciprocal best matching: a new pipeline for scoring models with unknown stoichiometry in CASP experiments.BMC bioinformatics · 2026Article
- Deep-learning-based single-domain and multidomain protein structure prediction with D-I-TASSER.Nature biotechnology · 2026Article
- Efficient global accuracy estimation for protein complex structural models using multi-view representation learning.Cell reports methods · 2026Article
- A fully automated benchmarking suite to compare macromolecular complexes.Nature methods · 2026Review
- Progress and Bottlenecks for Deep Learning in Computational Structure Biology: CASP Round XVI.Proteins · 2026Article
- Model Quality Assessment for CASP16.Proteins · 2026Article
- Assessment of Nucleic Acid Structure Prediction in CASP16.Proteins · 2026Article
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- Blind prediction of complex water and ion ensembles around RNA in CASP16.bioRxiv : the preprint server for biology · 2025Article
- Dockground: The Resource Expands to Protein-RNA Interactome.Journal of molecular biology · 2025Article
- Assessment of Protein Complex Predictions in CASP16: Are we making progress?bioRxiv : the preprint server for biology · 2025Article
- Assessment of nucleic acid structure prediction in CASP16.bioRxiv : the preprint server for biology · 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
- M-DeepAssembly: enhanced DeepAssembly based on multi-objective multi-domain protein conformation sampling.BMC bioinformatics · 2025Article
- Recent advances and challenges in protein complex model accuracy estimation.Computational and structural biotechnology journal · 2024Review
- Integrative Modeling of Protein-Polypeptide Complexes by Bayesian Model Selection using AlphaFold and NMR Chemical Shift Perturbation Data.bioRxiv : the preprint server for biology · 2024Article
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Abstract
In CASP15, 87 predictors submitted around 11 000 models on 41 assembly targets. The community demonstrated exceptional performance in overall fold and interface contact predictions, achieving an impressive success rate of 90% (compared to 31% in CASP14). This remarkable accomplishment is largely due to the incorporation of DeepMind's AF2-Multimer approach into custom-built prediction pipelines. To evaluate the added value of participating methods, we compared the community models to the baseline AF2-Multimer predictor. In over 1/3 of cases, the community models were superior to the baseline predictor. The main reasons for this improved performance were the use of custom-built multiple sequence alignments, optimized AF2-Multimer sampling, and the manual assembly of AF2-Multimer-built subcomplexes. The best three groups, in order, are Zheng, Venclovas, and Wallner. Zheng and Venclovas reached a 73.2% success rate over all (41) cases, while Wallner attained 69.4% success rate over 36 cases. Nonetheless, challenges remain in predicting structures with weak evolutionary signals, such as nanobody-antigen, antibody-antigen, and viral complexes. Expectedly, modeling large complexes also remains challenging due to their high memory compute demands. In addition to the assembly category, we assessed the accuracy of modeling interdomain interfaces in the tertiary structure prediction targets. Models on seven targets featuring 17 unique interfaces were analyzed. Best predictors achieved a 76.5% success rate, with the UM-TBM group being the leader. In the interdomain category, we observed that the predictors faced challenges, as in the case of the assembly category, when the evolutionary signal for a given domain pair was weak or the structure was large. Overall, CASP15 witnessed unprecedented improvement in interface modeling, reflecting the AI revolution seen in CASP14.
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