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Publications

Publications From the Birnbaum Lab

The latest publications are found below. To review older publications from Michael Birnbaum, access them here:

Publication Archive

Library-based single-cell analysis of CAR signaling reveals drivers of in vivo persistence. Perez, C. R., Garmilla, A., Nilsson, A., Baghdassarian, H. M., Gordon, K. S., Lima, L. G., Smith, B. E., Maus, M. V., Lauffenburger, D. A., & Birnbaum, M. E. bioRxiv, (2024).

DOI: https://doi.org/10.1101/2024.04.29.591541%20

Pooled screening for CAR function identifies novel IL13Rα2-targeted CARs for treatment of glioblastoma. Gordon, K. S., Perez, C. R., Garmilla, A., Lam, M. S. Y., Aw, J. J., Datta, A., Lauffenburger, D. A., Pavesi, A., & Birnbaum, M. E. bioRxiv, (2024).

DOI: https://doi.org/10.1101/2024.04.04.586240%20

High-throughput characterization of HLA-E-presented CD94/NKG2x ligands reveals peptides which modulate NK cell activation. Huisman, B. D., Guan, N., Rückert, T., Garner, L., Singh, N. K., McMichael, A. J., Gillespie, G. M., Romagnani, C., & Birnbaum, M. E. Nature Communications, 14(1) (2023).

DOI: https://doi.org/10.1038/s41467-023-40220-1%20

Yeast display platform with expression of linear peptide epitopes for high-throughput assessment of peptide-MHC-II binding. Huisman, B. D., Balivada, P. A., & Birnbaum, M. E. Journal of Biological Chemistry, 299(3), 102913 (2023).

DOI: https://doi.org/10.1016/j.jbc.2023.102913%20

Identification of highly cross-reactive mimotopes for a public T cell response in murine melanoma. Grace, B. E., Backlund, C. M., Morgan, D. M., Kang, B. H., Singh, N. K., Huisman, B. D., Rappazzo, C. G., Moynihan, K. D., Maiorino, L., Dobson, C. S., Kyung, T., Gordon, K. S., Holec, P. V., Mbah, O. C., Garafola, D., Wu, S., Love, J. C., Wittrup, K. D., Irvine, D. J., & Birnbaum, M. E. Frontiers in Immunology, 13 (2022).

DOI: https://doi.org/10.3389/fimmu.2022.886683

A high-throughput yeast display approach to profile pathogen proteomes for MHC-II binding. Huisman, B. D., Dai, Z., Gifford, D. K., & Birnbaum, M. E. ELife, 11 (2022).

DOI: https://doi.org/10.7554/elife.78589

Screening for CD19-specific chimaeric antigen receptors with enhanced signalling via a barcoded library of intracellular domains.Gordon, K. S., Kyung, T., Perez, C. R., Holec, P. V., Ramos, A., Zhang, A. Q., Agarwal, Y., Liu, Y., Koch, C. E., Starchenko, A., Joughin, B. A., Lauffenburger, D. A., Irvine, D. J., Hemann, M. T., & Birnbaum, M. E. Nature Biomedical Engineering, 6(7), 855–866 (2022).

DOI: https://doi.org/10.1038/s41551-022-00896-0

Proteome-scale screening to identify high-expression signal peptides with minimal N-terminus biases via yeast display. Holec, P. V., Camacho, K. V., Breuckman, K. C., Mou, J., & Birnbaum, M. E. ACS Synthetic Biology, 11(7), 2405–2416 (2022).

DOI: https://doi.org/10.1021/acssynbio.2c00101

Yeast display for the identification of peptide-MHC ligands of immune receptors. Huisman, B. D., Grace, B. E., Holec, P. V., & Birnbaum, M. E. Methods in Molecular Biology, 263–291 (2022).

DOI: https://doi.org/10.1007/978-1-0716-2285-8_15

Antigen identification and high-throughput interaction mapping by reprogramming viral entry. Dobson, C. S., Reich, A. N., Gaglione, S., Smith, B. E., Kim, E. J., Dong, J., Ronsard, L., Okonkwo, V., Lingwood, D., Dougan, M., Dougan, S. K., & Birnbaum, M. E. Nature Methods, 19(4), 449–460 (2022).

DOI: https://doi.org/10.1038/s41592-022-01436-z

Machine learning optimization of peptides for presentation by class II MHCs. Dai, Z., Huisman, B. D., Zeng, H., Carter, B., Jain, S., Birnbaum, M. E., & Gifford, D. K. Bioinformatics, 37(19), 3160–3167 (2021).

DOI: https://doi.org/10.1093/bioinformatics/btab131

Repertoire-scale determination of class II MHC peptide binding via yeast display improves antigen prediction. Rappazzo, C. G., Huisman, B. D., & Birnbaum, M. E. Nature Communications, 11(1) (2020).

DOI: https://doi.org/10.1038/s41467-020-18204-2

A Bayesian framework for high-throughput T cell receptor pairing. Holec, P. V., Berleant, J., Bathe, M., & Birnbaum, M. E. Bioinformatics, 35(8), 1318–1325 (2018).

DOI: https://doi.org/10.1093/bioinformatics/bty801