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Distance-AF: Modifying Predicted Protein Structure Models by Alphafold2 with User-Specified Distance Constraints
Open accessPreprint — published as 10.1038/s42003-025-08783-5 · Paper page
Abstract
The three-dimensional structure of a protein plays a fundamental role in determining its function and has an essential impact on understanding biological processes. Despite significant progress in protein structure prediction, such as AlphaFold2, challenges remain on those hard targets that Alphafold2 does not often perform well due to the complex folding of protein and a large number of possible conformations. Here we present a modified version of the AlphaFold2, called Distance-AF, which aims to improve the performance of AlphaFold2 by including distance constraints as input information. Distance-AF uses AlphaFold2’s predicted structure as a starting point and incorporates distance constraints between amino acids to adjust folding of the protein structure until it meets the constraints. Distance-AF can correct the domain orientation on challenging targets, leading to more accurate structures with a lower root mean square deviation (RMSD). The ability of Distance-AF is also useful in fitting protein structures into cryo-electron microscopy maps.
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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
High‐accuracy protein structure prediction in CASP14
Pereira, J., Simpkin, A. J., Hartmann, M. D., Rigden, D. J., Keegan, R. M., & Lupas, A. N. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26171 · Cited by 314
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Metadata
- Authors
- Zhang, Y., Zhang, Z., Kagaya, Y., Terashi, G., Zhao, B., Xiong, Y., & Kihara, D.
- Affiliations
- Purdue University West Lafayette (US)
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Kihara, Daisuke
- Venue
- Communications Biology
- Publisher
- openRxiv
- Published
- 2023-12-04
- Volume
- 8
- Issue
- 1
- Pages
- 1392-1392
- ISSN
- 2399-3642 (ISSN-L)
- PMC
- PMC12484874
- OpenAlex
- W4414646132
- Cited by
- 20
- References
- 2
- Citations by year
- 2026: 11 · 2025: 5 · 2024: 3 · 2023: 1
- FWCI
- 4.78
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Communications Biology (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed Central (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (1925643, 2003635, 1825941, 2151678, 2146026)
- U.S. Department of Health and Human Services (R01GM133840)
- National Institutes of Health (IIS2211598)
- Directorate for Computer and Information Science and Engineering (IIS2211598)
- Directorate for Engineering (CMMI1825941)
- Directorate for Mathematical and Physical Sciences (DMS2151678)
- National Institute of General Medical Sciences (R01GM133840)
- Division of Mathematical Sciences (DMS2151678)
- Division of Information and Intelligent Systems (IIS2211598)
- Division of Civil, Mechanical and Manufacturing Innovation (CMMI1825941)
- Division of Molecular and Cellular Biosciences (MCB1925643)
- Division of Biological Infrastructure (DBI2146026, DBI2146026, DBI2003635, DBI2003635)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- AlphaFold2, protein structure prediction, distance constraints, conformational ensembles, multidomain proteins
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