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Peer-reviewedLicence not statedCell Systems

Predicting specificity of TCR-pMHC interactions using machine-learning and biophysical models

  • Martin Culka1
  • Nicolas Lounsbury2
  • William John Thrift2
  • Santrupti Nerli2
  • Andrew Wallace2
  • Gergő Nikolényi1
  • Darya Yu. Orlova2
  • Kiran Mukhyala3
  • Mohammed AlQuraishi4
4 affiliations
  1. 1Department of Systems Biology, Columbia University, New York, NY 10032, USA
  2. 2Genentech, South San Francisco, CA 94080, USA
  3. 3Genentech, South San Francisco, CA 94080, USA. Electronic address: [email protected]
  4. 4Department of Systems Biology, Columbia University, New York, NY 10032, USA. Electronic address: [email protected]
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Cite

BibTeXRIS
BibTeX
@article{culka2026predicting,
  title = {{Predicting specificity of TCR-pMHC interactions using machine-learning and biophysical models}},
  author = {Martin Culka and Nicolas Lounsbury and William John Thrift and Santrupti Nerli and Andrew Wallace and Gergő Nikolényi and Darya Yu. Orlova and Kiran Mukhyala and Mohammed AlQuraishi},
  journal = {Cell Systems},
  year = {2026},
  month = aug,
  doi = {10.1016/j.cels.2026.101700},
  url = {https://doi.org/10.1101/2025.04.04.647165},
}
RIS
TY  - JOUR
TI  - Predicting specificity of TCR-pMHC interactions using machine-learning and biophysical models
AU  - Martin Culka
AU  - Nicolas Lounsbury
AU  - William John Thrift
AU  - Santrupti Nerli
AU  - Andrew Wallace
AU  - Gergő Nikolényi
AU  - Darya Yu. Orlova
AU  - Kiran Mukhyala
AU  - Mohammed AlQuraishi
PY  - 2026
DA  - 2026/08/13
JO  - Cell Systems
DO  - 10.1016/j.cels.2026.101700
UR  - https://doi.org/10.1101/2025.04.04.647165
SN  - 2405-4712
SN  - 2405-4720
SN  - 2639-5460
ER  -