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A novel bioinformatics pipeline for the identification of immune inhibitory receptors as potential therapeutic targets
Open accessPreprint — published as 10.7554/elife.92870.3 · Paper page
Abstract
Blocking inhibitory receptors like PD-1 and CTLA-4 has revolutionized cancer treatment in recent years. However, despite major successes in melanoma and lung cancer, the majority of cancer types are not responsive to these immunotherapies. As such, there is an ongoing need for the identification of novel inhibitory receptors as drug targets. Most inhibitory receptors signal via immunoreceptor tyrosine-based inhibitory motifs (ITIMs) and previous studies have estimated that our genome contains over 1600 ITIM- bearing transmembrane proteins. However, further testing and development of this large number of candidates requires increased understanding of their expression patterns and likelihood to function as inhibitory receptor. To assist in the selection of novel inhibitory receptor as therapeutic targets, we designed a novel bioinformatics pipeline integrating machine learning-guided structural predictions and sequence-based likelihood models to identify 51 known and 390 putative inhibitory receptors. Using publicly available transcriptomics data of immune cells, we determined the expression of these novel inhibitory receptors, and classified them into previously proposed functional categories. Known and putative inhibitory receptors were expressed across a wide variety of immune cells, and we found cell type-specific patterns in expression of these receptors. We used our pipeline to study inhibitory receptor expression patterns in single cell transcriptomics data of tumour infiltrating T cells. We determined that putative immune inhibitory receptors were expressed differentially in CD4 + and CD8 + T cell subsets, including exhausted CD8 + T cells and CD4 + memory T cells, which could allow for subset-specific targeting. In conclusion, we present an inhibitory receptor pipeline that identifies 51 known and 390 novel inhibitory receptors. This pipeline will support future drug target selection across diseases where therapeutic targeting of immune inhibitory receptors is warranted.
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Cites
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
Highly accurate protein structure prediction for the human proteome
Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03828-1 · Cited by 3,025 · cited 2×
“…out of 101 ITIM/ITSMs of the 51 known receptors had low confidence scores, i.e., less than 80 pLDDT, with an average confidence score of 49.3 pLDDT. 64 of the ITIMs even had pLDDT values below 50, which means they are particularly likely to be intrinsically disordered[18] (Supplementary Figure 1C).”
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Metadata
- Authors
- Singh, A., Bedate, A. M., von Richthofen, H. J., Vijver, S. V., van der Vlist, M., Kuhn, R., Yermanos, A., Kuball, J., Keşmir, C., Ramos, M. I. P., & Meyaard, L.
- Affiliations
- Utrecht University (NL)
- ETH Zurich (CH)
- Corresponding author
- Meyaard, Linde
- ORCID
- Venue
- eLife
- Publisher
- openRxiv
- Published
- 2023-10-28
- Volume
- 13
- ISSN
- 2050-084X (ISSN-L)
- OpenAlex
- W4403217777
- Cited by
- 11
- References
- 2
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 2
- FWCI
- 1.89
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- eLife (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- ETH Zürich Research Collection
- Funders
- Argenx
- NGM Biopharmaceuticals
- Branco Weiss Fellowship – Society in Science
- KWF Kankerbestrijding (2021-14339)
- Oncode Institute
- Topics
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Life Sciences › Immunology and Microbiology › Immunology › Immune Cell Function and Interaction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Keywords
- immune inhibitory receptors, ITIM, drug target identification, machine learning prediction, structure prediction, transcriptomics, tumor-infiltrating T cells, cancer immunotherapy, treatment resistance
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