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AlphaFold Model Quality Self-Assessment Improvement Via Deep Graph Learning
Open accessPreprint — published as 10.1002/pro.70274 · Paper page
Abstract
In the past several years significant advances have been made in the field of deep-learning based computational modeling of proteins, with DeepMind’s AlphaFold2 being among the most prominent. Alongside the atom coordinates, these computationally modeled protein structures typically contain self-confidence metrics that can be used to gauge the relative modeling quality of individual residues, or the protein as a whole. Unfortunately, these scores are not always accurate, and may sometimes annotate poorly modeled regions of the protein as high confidence. Here, we introduce EQA-Fold to address this problem. EQA-Fold overhauls the LDDT prediction head of AlphaFold to provide more accurate self-confidence scores. We show that EQA-Fold is able to provide more accurate self-confidence scores than the standard AlphaFold architectures, as well as recent Model Quality Assessment protocols.
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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
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Metadata
- Authors
- Verburgt, J., Zhang, Z., & Kihara, D.
- Affiliations
- Purdue University West Lafayette (US)
- Corresponding author
- Kihara, Daisuke
- Venue
- Protein Science
- Publisher
- openRxiv
- Published
- 2024-07-22
- Volume
- 34
- Issue
- 9
- Pages
- e70274-e70274
- ISSN
- 0961-8368 (ISSN-L)
- PMC
- PMC12359199
- OpenAlex
- W4413285393
- Cited by
- 8
- References
- 1
- Citations by year
- 2026: 6 · 2025: 2
- FWCI
- 2.39
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- 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 (1925643, 2211598, 2003635, 1825941, 2151678, 2146026)
- National Institutes of Health (IIS2211598)
- National Institute of General Medical Sciences (GM132024, R01GM133840, T32GM132024)
- Division of Civil, Mechanical and Manufacturing Innovation (CMMI1825941)
- 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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