ArticleProtein science : a publication of the Protein Society2025
Assessing scoring metrics for AlphaFold2 and AlphaFold3 protein complex predictions.
Article in Protein science : a publication of the Protein Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Deep learning based design of buried hydrogen bond networks with HBDesigner.bioRxiv : the preprint server for biology · 2026Article
- A simple probabilistic AlphaFold interaction score.Protein science : a publication of the Protein Society · 2026Article
- Ensembles ofbioRxiv : the preprint server for biology · 2026Article
- AlphaFold-Multimer modelling of linked nAChR subunits challenges concatemer design assumptions.Scientific reports · 2026Article
- Confidence scoring for deep learning-predicted antibody-antigen complexes: AntiConf as a precision-driven metric.Briefings in bioinformatics · 2026Article
- Diversity and distribution of bacterial DNA polymerases.Nucleic acids research · 2026Article
- NanoporeDB: a structural resource of multimeric protein nanopores for single-molecule sensing.GigaScience · 2026Article
- Direct binding of chromosome axis and cohesin complexes underlies meiotic chromosome architecture in fungi and plants.bioRxiv : the preprint server for biology · 2026Article
- Structural quality-tier assessment for TCR-pMHC functional enrichment.Frontiers in immunology · 2026Article
- ProNA3D: Distance-Based Analysis of Nucleic Acid-Containing Interfaces.Computational and structural biotechnology journal · 2026Article
- Integrated transcriptomic and metabolomic profiling reveals coordinated regulatory networks associated with mosaic disease resistance in sugarcane.Frontiers in plant science · 2026Article
- Assessing scoring metrics for AlphaFold2 and AlphaFold3 protein complex predictions.Protein science : a publication of the Protein Society · 2025Article
- NanoDeNovo: De Novo Design of Anti-Poliovirus I Sabin Strain Nanobodies by Semi-Automated Computational Pipeline.International journal of molecular sciences · 2025Article
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Authors and funding
4 authors.
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Abstract
Recent breakthroughs in AI-driven protein structure prediction have revolutionized structural biology, unlocking new possibilities to model complex biomolecular interactions. We evaluated widely used scoring metrics for assessing models predicted by ColabFold with templates, ColabFold without templates, and AlphaFold3. We benchmarked the optimal cutoffs for these assessment scores using a set of 223 heterodimeric, high-resolution protein structures and their predictions. Our results show that ColabFold with templates and AlphaFold3 perform similarly, and both outperform ColabFold without templates. However, the assessment scores perform best on ColabFold without templates. Furthermore, interface-specific scores are more reliable for evaluating protein complex predictions compared to the corresponding global scores. Notably, ipTM and model confidence achieve the best discrimination between correct and incorrect predictions. Based on our results, we developed a weighted combined score, C2Qscore, to improve model quality assessment. We used C2Qscore to analyze dimers from large assemblies solved by cryoEM, revealing potential limitations of the existing metrics when multiple configurations of heterodimers are possible. This study provides insights into the strengths and weaknesses of current scores and offers guidance for improving protein complex model assessment under realistic use case conditions. C2Qscore has been integrated as a tool into our ChimeraX plug-in PICKLUSTER v.2.0 and is also available as a command-line tool on https://gitlab.com/topf-lab/c2qscore.
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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.