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AlphaFold model quality self‐assessment improvement via deep graph learning
Open accessPublished version of a preprint
Abstract
In recent years, significant advancements have been made in deep learning‐based computational modeling of proteins, with DeepMind's AlphaFold2 standing out as a landmark achievement. These computationally modeled protein structures not only provide atomic coordinates but also include self‐confidence metrics to assess the relative quality of the modeling, either for individual residues or the entire protein. However, these self‐confidence scores are not always reliable; for instance, poorly modeled regions of a protein may sometimes be assigned high confidence. To address this limitation, we introduce Equivariant Quality Assessment Folding (EQAFold), an enhanced framework that refines the Local Distance Difference Test prediction head of AlphaFold to generate more accurate self‐confidence scores. Our results demonstrate that EQAFold outperforms the standard AlphaFold architecture and recent model quality assessment protocols in providing more reliable confidence metrics. Source code for EQAFold is available at https://github.com/kiharalab/EQAFold_public .
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Accurate structure prediction of biomolecular interactions with AlphaFold 3
Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A. J., Bambrick, J., et al. (2024) · Nature · doi:10.1038/s41586-024-07487-w · Cited by 15,861
Evolutionary-scale prediction of atomic level protein structure with a language model
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De novo design of protein structure and function with RFdiffusion
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“The first example (Figure 3a) is a 208-residue long α -helical protein.”
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Metadata
- Authors
- Verburgt, J., Zhang, Z., & Kihara, D.
- Affiliations
- Purdue University West Lafayette (US)
- Department of Computer Science Purdue University Indiana USA
- Department of Biological Sciences Purdue University Indiana USA
- Corresponding author
- Kihara, Daisuke
- Venue
- Protein Science
- Publisher
- Wiley
- Published
- 2025-08-18
- Volume
- 34
- Issue
- 9
- Pages
- e70274-e70274
- ISSN
- 0961-8368 (ISSN-L)
- 1469-896X
- PMC
- PMC12359199
- OpenAlex
- W4413285393
- Cited by
- 8
- References
- 7
- Citations by year
- 2026: 6 · 2025: 2
- FWCI
- 2.39
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Protein Science (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (CMMI1825941, DBI2003635, DBI2146026, DMS2151678, IIS2211598, MCB1925643)
- National Institute of General Medical Sciences (T32GM132024)
- National Institutes of Health (R01GM133840)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Keywords
- AlphaFold, model quality assessment, deep graph learning, equivariant graph neural networks, protein structure prediction, confidence calibration
- MeSH terms
- Deep Learning, Models, Molecular, Protein Conformation, Proteins, Software, Protein Folding
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