AlphaFold illuminates half of the dark human proteins
Open accessAbstract
We investigate the use of confidence scores to evaluate the accuracy of a given AlphaFold (AF2) protein model for drug discovery. Prediction of accuracy is improved by not considering confidence scores below 80 due to the effects of disorder. On a set of recent crystal structures, 95% are likely to have accurate folds. Conformational discordance in the training set has a much more significant effect on accuracy than sequence divergence. We propose criteria for models and residues that are possibly useful for virtual screening. Based on these criteria, AF2 provides models for half of understudied (dark) human proteins and two-thirds of residues in those models.
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Previous analysis suggests AF2 predicts about 50% of residues across all proteins with high confidence.
“Previous analysis suggests that AF2 predicts on average ~50% of residues across all proteins with high confidence17,59”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [59]
For most globular proteins, AF2 gives accurate, reliable models with high pLDDT (>70) or low PAE (<5 Å) that match experimental native structures.
“For the majority of globular proteins, AF2 provides accurate, reliable models with high pLDDT (> 70) or low PAE values (< 5 Å) highlighting confidence in the prediction of the position of the atomic coordinates, which match experimentally determined native structures5,17,23,59”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [59]
To date, AlphaFold illuminates half of the dark human proteins.
“To date, AlphaFold illuminates half of the dark human proteins.”
From AlphaFold, Artificial Intelligence (AI), and Allostery (2022) · cites this paper as [10]
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- [2]National Center for Biotechnology Information. [ cited 16 Sep 2021]. Available: https://www.ncbi.nlm.nih.gov/
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“Previous analysis suggests that AF2 predicts on average ~50% of residues across all proteins with high confidence 17,59 .”
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- Authors
- Binder, J. L., Berendzen, J., Stevens, A. O., He, Y., Wang, J., Dokholyan, N. V., & Oprea, T. I.
- Affiliations
- University of New Mexico (US)
- Pennsylvania State University (US)
- University of Copenhagen (DK)
- Corresponding author
- Oprea, Tudor I.
- ORCID
- Venue
- Current Opinion in Structural Biology
- Publisher
- Elsevier BV
- Published
- 2022-06
- Volume
- 74
- Pages
- 102372
- ISSN
- 0959-440X (ISSN-L)
- PMC
- PMC10669925
- OpenAlex
- W4224126566
- Cited by
- 81
- References
- 45
- Citations by year
- 2026: 8 · 2025: 20 · 2024: 18 · 2023: 23 · 2022: 12
- FWCI
- 1.90
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 319 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Current Opinion in Structural Biology (acceptedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, other-oa)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion, other-oa)
- Funders
- National Institutes of Health
- University of New Mexico (U24CA224370)
- National Science Foundation (2137558, DGE-1939267)
- Passano Foundation (R35 GM134864)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- AlphaFold2, protein structure prediction, confidence scores, intrinsically disordered proteins, sequence divergence, virtual screening, drug discovery, model accuracy assessment
- MeSH terms
- Furylfuramide, Humans, Proteins, Protein Folding, Drug Discovery
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