Can AlphaFold2 predict protein-peptide complex structures accurately?
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A bstract In this preprint, we investigated whether AlphaFold2, AF2, can predict protein-peptide complex structures only with sequence information. We modeled the structures of 203 protein-peptide complexes from the PepBDB DB and 183 from the PepSet. The structures were modeling with concatenated sequences of receptors and peptides via poly-glycine linker. We found that for more than half of the test cases, AF2 predicted the bound structures of peptides with good accuracy, C α -RMSD of a peptide < 3.0 Å. For about 40% of cases, the peptide structures were modeled with an accuracy of C α -RMSD < 2.0 Å. Our benchmark results clearly show that AF2 has a great potential to be applied to various higher-order structure prediction tasks.
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Contact predictions can be used to simultaneously fold and dock proteins, and AF2 can predict the structure of complexes.
“it has been shown recently that contact predictions can be used to simultaneously fold and dock proteins17, and early reports have indicated that AF2 can predict the structure of complexes18–20, which it was not initially trained to handle.”
From A structural biology community assessment of AlphaFold2 applications (2022) · cites this paper as [18]
An early study adapted AF2 to complex prediction by connecting two protein sequences with a poly-glycine linker.
“One early such study involved simply connecting two protein sequences with a poly-glycine linker, thus converting it into a single-chain structure prediction problem19.”
From AF2Complex predicts direct physical interactions in multimeric proteins with deep learning (2022) · cites this paper as [19]
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Correia, C., Ung, C. Y., Grenko, C. M., Zhu, S., Billadeau, D. D., & Li, H. (2026) · Challenges and advances in computational chemistry and physics · doi:10.1007/978-3-032-07880-3_1
Singh, D., Tripathi, A., Taunk, J., Gaikwad, K., & Pal, M. (2026) · BMC Genomics · doi:10.1186/s12864-026-12711-0
Structural basis for Kisspeptin-10 selectivity toward KISS1R: computational insights
Netesa, M. A., Pyurveev, S. S., Лебедев, А. А., Бычков, Э. Р., Skorb, E. V., Shityakov, S., & Shabanov, P. D. (2026) · In Silico Pharmacology · doi:10.1007/s40203-026-00726-0
Linear antibody epitope prediction using AlphaFold2
DeRoo, J., Terry, J. S., Zhao, N., Stasevich, T. J., Snow, C. D., & Geiss, B. J. (2026) · eLife · doi:10.7554/elife.98369.3
Harteveld, Z., Rigoldi, F., Toniolo, C., Granger, A., Gensous, C., Blum, T., Jean-Baptiste, R., Kobayashi, M., Siegal, G., Habeshian, S., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.24.754019
Yao, X., Ya, Y., Li, H., & Huang, S. (2026) · Nature Computational Science · doi:10.1038/s43588-026-01062-1
- Authors
- Ko, J., & Lee, J.
- Affiliations
- Kangwon National University (KR)
- Corresponding author
- Ko, Junsu
- Lee, Juyong
- ORCID
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2021-07-27
- OpenAlex
- W3186381913
- Cited by
- 83
- References
- 14
- Citations by year
- 2026: 8 · 2025: 12 · 2024: 22 · 2023: 17 · 2022: 18 · 2021: 6
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- 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 › vaccines and immunoinformatics approaches
- Keywords
- AlphaFold2, protein-peptide complex, structure prediction, peptide binding, benchmarking
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