  An accurate binding affinity prediction between T-cell receptors and epitopes
contributes decisively to develop successful immunotherapy strategies. Some
state-of-the-art computational methods implement deep learning techniques by
integrating evolutionary features to convert the amino acid residues of cell
receptors and epitope sequences into numerical values, while some other methods
employ pre-trained language models to summarize the embedding vectors at the
amino acid residue level to obtain sequence-wise representations.
  Here, we propose a highly reliable novel method, MATE-Pred, that performs
multi-modal attention-based prediction of T-cell receptors and epitopes binding
affinity. The MATE-Pred is compared and benchmarked with other deep learning
models that leverage multi-modal representations of T-cell receptors and
epitopes. In the proposed method, the textual representation of proteins is
embedded with a pre-trained bi-directional encoder model and combined with two
additional modalities: a) a comprehensive set of selected physicochemical
properties; b) predicted contact maps that estimate the 3D distances between
amino acid residues in the sequences.
  The MATE-Pred demonstrates the potential of multi-modal model in achieving
state-of-the-art performance (+8.4\% MCC, +5.5\% AUC compared to baselines) and
efficiently capturing contextual, physicochemical, and structural information
from amino acid residues. The performance of MATE-Pred projects its potential
application in various drug discovery regimes.
