DeepBCR: Deep learning framework for cancer-type classification and binding affinity estimation using B cell receptor repertoires.

Abstract

We developed a deep learning framework to model the binding specificity of B-cell receptors (BCRs). The DeepBCR framework can predict the cancer type from a repertoire of BCRs and estimate the binding affinity of a single BCR. We designed a peptide encoding network that includes an amino acid encoding layer, k-mer motif layer, and immunoglobulin isotype layer, and used transfer learning to reduce parameters and over-fitting. When we applied the framework to evaluate the three commercial anti-PD1 drugs (Opdivo, Keytruda, and Libtayo), the predicted binding affinities correlate with the real affinities measured in kD values. This validates the prediction and indicates that we can use the framework to select strong antigen-specific binders.