Cross-modality deep learning-based prediction of TAP binding and naturally processed peptide

Hanan Besser, Yoram Louzoun

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Epitopes presented on MHC class I molecules pass multiple processing stages before their presentation on MHC molecules, the main ones being proteasomal cleavage and TAP binding. Transporter associated with antigen processing (TAP) binding is a necessary stage for most, but not all, MHC-I-binding peptides. The molecular determinants of TAP-binding peptides can be experimentally estimated from binding experiments and from the properties of peptides inducing a CD8 T cell response. We here propose novel optimization formalisms to combine binding and activation experimental results to produce a classifier for TAP binding using dual-output kernel and deep learning approaches. The application of these algorithms to the human and murine TAP binding leads to predictors that are much more precise than current state of the art methods. Moreover, the computed score is highly correlated with the observed binding energy. The new predictors show that TAP binding may be much more selective than previously assumed in humans and mice and sensitive to the properties of most positions of the peptides. Beyond the improved precision for TAP binding, we propose that the same approach holds in most molecular binding problems, where functional and binding measures are simultaneously available, and can be used to significantly improve the precision of binding prediction algorithms in general and immune system molecules specifically.

Original languageEnglish
Pages (from-to)419-428
Number of pages10
Issue number7
StatePublished - 1 Jul 2018

Bibliographical note

Publisher Copyright:
© 2018, Springer-Verlag GmbH Germany, part of Springer Nature.


  • Deep learning
  • Dual output
  • Prediction
  • TAP


Dive into the research topics of 'Cross-modality deep learning-based prediction of TAP binding and naturally processed peptide'. Together they form a unique fingerprint.

Cite this