Distance-AF improves predicted protein structure models by AlphaFold2 with user-specified distance constraints
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Abstract
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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Zhu, J., Li, Z., Zheng, Z., Zhang, B., Zhong, B., Bai, J., Hong, X., Wang, T., Wei, T., Yang, J., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5178914 · Cited by 2 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Machine learning in NMR spectroscopy
Klukowski, P., Riek, R., & Güntert, P. (2025) · Progress in Nuclear Magnetic Resonance Spectroscopy · doi:10.1016/j.pnmrs.2025.101575 · Cited by 22 · see citing works
Improving AlphaFold 3 structural modeling by incorporating explicit crosslinksPreprint
Gilep, K., Obarska-Kosińska, A., & Kosiński, J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.03.626671 · Cited by 19 · see citing works
Fadini, A., Li, M., McCoy, A. J., Banjara, S., Okumura, H., Napier, E., Fontana, P., Khan, A. R., Jovine, L., Terwilliger, T. C., et al. (2026) · Nature Methods · doi:10.1038/s41592-026-03047-4 · Cited by 22 · see citing works
“Recent efforts to adapt ML-based structure prediction to incorporate experimental constraints have done so either through fine-tuning of weights from existing architectures 26,27 or full training of a novel architecture, as exemplified by the ModelAngelo method for cryo-EM data 28 .”
Frontiers in integrative structural modeling of macromolecular assemblies
Majila, K., Arvindekar, S. M., Jindal, M., & Viswanath, S. (2025) · QRB Discovery · doi:10.1017/qrd.2024.15 · Cited by 9 · see citing works
ESMDynamic: Fast and accurate prediction of protein dynamic contact maps from single sequences
Kleiman, D. E., Feng, J., Xue, Z., & Shukla, D. (2026) · Nature Communications · doi:10.1038/s41467-026-76361-2 · Cited by 7 · see citing works
“These approaches may construct energy restrictions based on pairwise residue distances 29 .”
iMAX FRET (Information Maximized FRET) for Multipoint Single-Molecule Structural Analysis
Joshi, B. S., de Lannoy, C. V., Howarth, M. R., Kim, S. H., & Joo, C. (2024) · Nano Letters · doi:10.1021/acs.nanolett.4c00447 · Cited by 5 · see citing works
Li, J., Zhu, Z., & Song, C. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.09.598121 · Cited by 6 · see citing works
Assembly of Protein Complexes in and on the Membrane with Predicted Spatial Arrangement Constraints
Christoffer, C., Harini, K., Archit, G., & Kihara, D. (2024) · Journal of Molecular Biology · doi:10.1016/j.jmb.2024.168486 · Cited by 4 · see citing works
Self-citationNajafi, N. N., Karbassian, R., Hajihassani, H., & Irani, M. A. (2025) · Journal of Molecular Modeling · doi:10.1007/s00894-025-06392-x · Cited by 3 · see citing works
Integrative Protein Assembly With LZerD and Deep Learning in CAPRI 47–55
Christoffer, C. W., Kagaya, Y., Verburgt, J. C., Terashi, G., Shin, W., Jain, A., Sarkar, D., Aderinwale, T. W., Subramaniya, S. R. M. V., Wang, X., et al. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26818 · Cited by 3 · see citing works
Self-citationPradeep, B. K., Chen, S., Dima, R., Hassan, M., Joachimiak, A., Kihara, D., Kloczkowski, A., Law, J., Liwo, A., Meller, J., et al. (2026) · Journal of Molecular Biology · doi:10.1016/j.jmb.2026.169949 · Cited by 2 · see citing works
Self-citationSehsah, A. I., Mousa, A., & Elhady, G. F. (2025) · Scientific Reports · doi:10.1038/s41598-025-94747-y · Cited by 2 · see citing works
Integrative modeling with AlphaFold
Majila, K., Golatkar, O., & Viswanath, S. (2026) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2026.103239 · Cited by 1 · see citing works
Bayesian-Steered Structure Prediction of Mechanical Biomolecules Using Twisted DiffusionPreprint
Klaus, C., & Sotomayor, M. M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.11.724187
SteerAF: Distogram-based Steering of AlphaFold2 toward Alternative ConformationsPreprint
Tang, J., Zhu, Z., Yang, S., & Song, C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.19.733296
A generalisable framework to inject distance information into Alphafold-like structure predictorsPreprint
Mirabello, C., Wallner, B., Orekhov, V., Nystedt, B., & Pearce, N. M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.02.736010
Sharma, P., Yadav, D., Faustino, A. M., Manriquez-Sandoval, E., Wagner, M., Carell, T., & Fried, S. D. (2026) · Journal of Proteome Research · doi:10.1021/acs.jproteome.6c00388
Reconstructing hidden GPCR conformations and ligand binding modes through integrative DEER modeling.
Belyaeva, J., & Elgeti, M. (2026) · Structure · doi:10.1016/j.str.2026.09.003
Weyer, E., Wang, Y., Tomita, T., Madrid-Aliste, C. J., Nandigrami, P., Fiser, A., Sidoli, S., Aguilan, J. T., Han, B., & Weiss, L. M. (2026) · mBio · doi:10.1128/mbio.02261-26
RNA Tertiary Structure Prediction and Analysis Using NuFold and the NuFold Database
Liu, B., Kagaya, Y., Platt, D., & Kihara, D. (2026) · Methods in molecular biology · doi:10.1007/978-1-0716-5583-2_13
Self-citation
- 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
- Published
- 2025-09-30
- Volume
- 8
- Issue
- 1
- Pages
- 1392-1392
- ISSN
- 2399-3642 (ISSN-L)
- PMC
- PMC12484874
- OpenAlex
- W4414646132
- Cited by
- 19
- References
- 76
- Citations by year
- 2026: 10 · 2025: 5 · 2024: 3 · 2023: 1
- FWCI
- 4.51
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- 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
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (R01GM133840)
- NSF | BIO | Division of Biological Infrastructure (DBI2146026, DBI2003635)
- NSF | BIO | Division of Molecular and Cellular Biosciences (MCB1925643)
- NSF | Directorate for Computer & Information Science & Engineering | Division of Information and Intelligent Systems (IIS2211598)
- NSF | Directorate for Mathematical & Physical Sciences | Division of Mathematical Sciences (DMS2151678)
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- AlphaFold2, protein structure prediction, distance constraints, conformational ensembles, multidomain proteins
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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