Real-time structure search and structure classification for AlphaFold protein models
Open accessPublished version of a preprint
Abstract
Last year saw a breakthrough in protein structure prediction, where the AlphaFold2 method showed a substantial improvement in the modeling accuracy. Following the software release of AlphaFold2, predicted structures by AlphaFold2 for proteins in 21 species were made publicly available via the AlphaFold Database. Here, to facilitate structural analysis and application of AlphaFold2 models, we provide the infrastructure, 3D-AF-Surfer, which allows real-time structure-based search for the AlphaFold2 models. In 3D-AF-Surfer, structures are represented with 3D Zernike descriptors (3DZD), which is a rotationally invariant, mathematical representation of 3D shapes. We developed a neural network that takes 3DZDs of proteins as input and retrieves proteins of the same fold more accurately than direct comparison of 3DZDs. Using 3D-AF-Surfer, we report structure classifications of AlphaFold2 models and discuss the correlation between confidence levels of AlphaFold2 models and intrinsic disordered regions.
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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 · see citing works
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“Soon after the release of the AlphaFold2 code, predicted structure models by AlphaFold2 for proteins from 21 major model species have been released at the AlphaFold Protein Structure Database 14 .”
MethodEnd-to-End Differentiable Learning of Protein Structure
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AlphaFold, Artificial Intelligence (AI), and Allostery
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DEGRONOPEDIA: a web server for proteome-wide inspection of degrons
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- Authors
- Aderinwale, T., Bharadwaj, V., Christoffer, C., Terashi, G., Zhang, Z., Jahandideh, R., Kagaya, Y., & Kihara, D.
- Affiliations
- Purdue University West Lafayette (US)
- Corresponding author
- Kihara, Daisuke
- Venue
- Communications Biology
- Published
- 2022-04-05
- Volume
- 5
- Issue
- 1
- Pages
- 316-316
- ISSN
- 2399-3642 (ISSN-L)
- PMC
- PMC8983703
- OpenAlex
- W4226320411
- Cited by
- 61
- References
- 6
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 18 · 2023: 14 · 2022: 15
- FWCI
- 4.57
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Communications Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (R01GM133840, R01GM123055, 3R01GM133840-02S1)
- NSF | BIO | Division of Biological Infrastructure (DBI2003635)
- NSF | BIO | Division of Molecular and Cellular Biosciences (MCB1925643)
- NSF | ENG/OAD | Division of Civil, Mechanical and Manufacturing Innovation (CMMI1825941)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- AlphaFold2, protein structure prediction, structural classification, neural network retrieval, protein folding
- MeSH terms
- Models, Molecular, Proteins, Software, Neural Networks, Computer
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