ArticleProteins2026
Structure Modeling Protocols for Protein Multimer and RNA in CASP16 With Enhanced MSAs, Model Ranking, and Deep Learning.
Article in Proteins, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Hierarchical breakdown of RNA structure prediction in CASP16: from reliable local helices to speculative multimer assembly.Bioinformatics (Oxford, England) · 2026Article
- RNAbpFlow: base pair-augmented SE(3) flow matching for conditional RNA 3D structure generation.Nature methods · 2026Article
- Article
- Structure Modeling Protocols for Protein Multimer and RNA in CASP16 With Enhanced MSAs, Model Ranking, and Deep Learning.Proteins · 2026Article
- The power and limits of predicting inter-protein exon-exon interactions using protein 3D structures.Bioinformatics advances · 2026Article
- Queryome: Orchestrating Retrieval, Reasoning, and Synthesis across Biomedical Literature.bioRxiv : the preprint server for biology · 2025Article
- High-accuracy protein complex structure modeling based on sequence-derived structure complementarity.Nature communications · 2025Article
- RNAbpFlow: Base pair-augmented SE(3)-flow matching for conditional RNA 3D structure generation.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
We present the methods and results of our protein complex and RNA structure predictions at CASP16. Our approach integrated multiple state-of-the-art deep learning models with a consensus-based scoring method. To enhance the depth of multiple sequence alignments (MSAs), we employed a large metagenomic sequence database. Model ranking was performed with a state-of-the-art consensus ranking method, to which we added more scoring terms. These predictions were further refined manually based on literature evidence. For RNA, we adopted an ensemble approach that incorporated multiple state-of-the-art methods, centered around our NuFold framework. As a result, our KiharaLab group ranked first in protein complex prediction and third in RNA structure prediction. A detailed analysis of targets that significantly differed from those of other groups highlighted both the strengths of our MSA and scoring strategies, as well as areas requiring further improvement.
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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 Socratic graph.