PMIpred: a physics-informed web server for quantitative protein–membrane interaction prediction
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Abstract
Motivation: Many membrane peripheral proteins have evolved to transiently interact with the surface of (curved) lipid bilayers. Currently, methods to quantitatively predict sensing and binding free energies for protein sequences or structures are lacking, and such tools could greatly benefit the discovery of membrane-interacting motifs, as well as their de novo design. Results: Here, we trained a transformer neural network model on molecular dynamics data for >50 000 peptides that is able to accurately predict the (relative) membrane-binding free energy for any given amino acid sequence. Using this information, our physics-informed model is able to classify a peptide’s membrane-associative activity as either non-binding, curvature sensing, or membrane binding. Moreover, this method can be applied to detect membrane-interaction regions in a wide variety of proteins, with comparable predictive performance as state-of-the-art data-driven tools like DREAMM, PPM3, and MODA, but with a wider applicability regarding protein diversity, and the added feature to distinguish curvature sensing from general membrane binding. Availability and implementation: We made these tools available as a web server, coined Protein-Membrane Interaction predictor (PMIpred), which can be accessed at https://pmipred.fkt.physik.tu-dortmund.de.
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Dong, R., Awang, T., Cao, Q., Kang, K., Wang, L., Zhu, Z., & Song, C. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag453 · Cited by 1 · see citing works
Santana, P., Tamayo, L., Stambuk, F., Aguilar, L. F., Cortés, M., Guzmán, F., Forero, J. C., Romero, M. S., & Álvarez, C. (2024) · Journal of Fish Diseases · doi:10.1111/jfd.14056 · Cited by 1 · see citing works
Revilla, J., Guzmán, F., Cárdenas, C., Oyanedel, D., Araya, M., Álvarez, C., Schmitt, P., & Brokordt, K. (2026) · Fish & Shellfish Immunology · doi:10.1016/j.fsi.2026.111488
Maurya, A. K., Cadena, L. R., Ehret, G., & Nowack, E. C. M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.22.649992
Selection inference in a complex genomic landscape: the impact of polymorphic inversionsPreprint
Morales-García, J., Babik, W., Zieliński, P., & Nadachowska‐Brzyska, K. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.01.662526
Ferguson, N., Desai, M., Silva, J. F., Nguyen, H., Rodenborn, B., Shindell, O., & Healy, F. G. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.06.686970
Krebs, M., & Risselada, H. J. (2026) · Advanced Science · doi:10.1002/advs.76899 · Cited by 1 · see citing works
Self-citationKesmen, Z., Batman, S. G., Canpolat, M., & Büyükkiraz, M. E. (2026) · Probiotics and Antimicrobial Proteins · doi:10.1007/s12602-025-10875-x
SNX-mediated biogenesis of a plant-unique vesicle derived from the multivesicular body
Li, Y., Tao, R., Zhang, H., Wen, X., Leung, S. K. P., Qi, Q., Zheng, X., Guo, H., Wu, C., Fu, Z., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-71067-x
Kinase-gated coincidence detection controls kinesin-driven lysosome transportPreprint
O’Regan, L., Bristow, S., Antón, Z., Bodzęta, A., Cross, J. A., Woolfson, D. N., & Dodding, M. P. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.27.714776
van Hilten, N., von Maltitz, P., Aschmann, D., Hoernke, M., Krebs, M., Methorst, J., Kros, A., Münch, J., & Risselada, H. J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.20.719566
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Expansion of Olfactory Receptor Family 14 Across Fossorial and Insectivorous MammalsPreprint
Ryan, L., & Hughes, G. M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.15.738794
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Screening of amphipathic helices identifies features linked to inner nuclear membrane properties
Lee, S., Roux, A. L., Goudge, M. C., Mors, M., Vanni, S., Roca‐Cusachs, P., & Bahmanyar, S. (2026) · Nature Communications · doi:10.1038/s41467-026-75561-0
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A minimal Asgard archaeal ESCRT-III system remodels membranes across scalesPreprint
Schwille, P., Zivkovic, H., Hrebík, D., Frohn, B. P., Basquin, J., Riggi, M., & Briggs, J. A. G. (2026) · Research Square · doi:10.21203/rs.3.rs-10742307/v1
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An artificially developed peptide with anti-oomycete activity against Saprolegnia
Tandel, R. S., Chanu, K. V., Dash, P., Bhat, R. A. H., & Thakuria, D. (2026) · Fisheries Science · doi:10.1007/s12562-026-02021-5
Mehraj, I., Pandit, R. K., Qureshi, S., Ganai, N. A., Dar, T. U. H., Najar, M. A., Shafiq, M., & Bhat, S. S. (2026) · Probiotics and Antimicrobial Proteins · doi:10.1007/s12602-026-11226-0
Reading the Lipid Code: Cooperative Domain Functions in Perilipin Targeting to Lipid Droplets
Stříbný, J., & Schneiter, R. (2026) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2026.113619
- Authors
- van Hilten, N., Verwei, N., Methorst, J., Nase, C., Bernatavicius, A., & Risselada, H. J.
- Affiliations
- Leiden Institute of Chemistry, Leiden University , Leiden 2333 CC, Netherlands
- Leiden University (NL)
- TU Dortmund University (DE)
- Corresponding author
- Risselada, Herre Jelger
- ORCID
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-02-01
- Volume
- 40
- Issue
- 2
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC11212490
- OpenAlex
- W4391575167
- Cited by
- 41
- References
- 77
- Citations by year
- 2026: 23 · 2025: 8 · 2024: 10
- FWCI
- 5.58
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Dutch Research Organisation
- Deutsche Forschungsgemeinschaft
- NWO Vidi scheme
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Lipid Membrane Structure and Behavior
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- protein-membrane interactions, curvature sensing, peptide binding prediction, physics-informed machine learning, transformer models, molecular dynamics, web server
- MeSH terms
- Amino Acid Sequence, Membrane Proteins, Peptides, Physics, Neural Networks, Computer
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