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AI-assisted design of ligands for lipocalin-2
Open accessPublished version of a preprint
Abstract
Introduction: Lipocalin-2 (LCN2) is an acute-phase glycoprotein whose upregulation is associated with blood–brain-barrier breakdown and neuroinflammation, making it a potential diagnostic and therapeutic target. Methods: We developed an end-to-end, AI-guided workflow to design de-novo miniproteins targeting LCN2. Backbone scaffolds were generated using RFdiffusion, sequences optimized with ProteinMPNN, and candidates filtered in silico based on AlphaFold2 confidence metrics (mean interface pAE < 10) and binding free energy predicted by Prodigy. From an initial library of 10,000 designs, five candidates were expressed and purified from E. coli. Binding affinities were assessed using biolayer interferometry (BLI), and structural interactions were analyzed via computational modeling. Results: BLI identified MiniP-2 as the lead construct, exhibiting a dissociation constant (Kd) of 4.2 nM. Structural modeling indicated that binding is primarily mediated by backbone hydrogen bonds, complemented by a stabilizing salt bridge between Arg37 of MiniP-2 and Asp97 of LCN2. Surface plasmon resonance (SPR) competition experiments demonstrated that MiniP-2 inhibits LCN2 binding to MMP-9, highlighting its potential to interfere with pathological LCN2 interactions. Discussion: These results demonstrate that a fully computational generative workflow can yield nanomolar LCN2 binders in a single design–build–test cycle. MiniP-2 represents a promising starting point for affinity maturation, structural studies, and in vivo evaluation as an imaging probe or antagonist of LCN2-mediated signaling.
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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
De novo design of protein structure and function with RFdiffusion
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al. (2023) · Nature · doi:10.1038/s41586-023-06415-8 · Cited by 2,325
Robust deep learning–based protein sequence design using ProteinMPNN
Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Courbet, A., de Haas, R. J., Bethel, N., et al. (2022) · Science · doi:10.1126/science.add2187 · Cited by 2,210
Robust deep learning based protein sequence design using ProteinMPNN
Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I. M., Courbet, A., de Haas, R. J., Bethel, N., et al. (2022) · doi:10.1101/2022.06.03.494563 · Cited by 2,107
De novo design of picomolar SARS-CoV-2 miniprotein inhibitors
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Metadata
- Authors
- Sgrignani, J., Buscarini, S., Locatelli, P., Guerra, C., Furlan, A., Chen, Y., Zoppi, G., & Cavalli, A.
- Affiliations
- Institute for Research in Biomedicine (ES)
- Institute for Research in Biomedicine (CH)
- SIB Swiss Institute of Bioinformatics (CH)
- Corresponding author
- Cavalli, Andrea
- Venue
- Frontiers in Immunology
- Publisher
- Frontiers Media SA
- Published
- 2025-09-12
- Volume
- 16
- Pages
- 1631868-1631868
- ISSN
- 1664-3224 (ISSN-L)
- PMC
- PMC12463876
- OpenAlex
- W4414261835
- Cited by
- 4
- References
- 6
- Citations by year
- 2026: 4
- FWCI
- 1.20
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Immunology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Keywords
- neutrophil gelatinase-associated lipocalin, RFdiffusion, ProteinMPNN, biolayer interferometry, surface plasmon resonance, neuroinflammation, blood-brain barrier disruption, affinity maturation, imaging agents
- MeSH terms
- Lipocalin-2, Humans, Ligands, Models, Molecular, Protein Binding, Surface Plasmon Resonance
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