Improving Protein-peptide Interface Predictions in the Low Data Regime
Justin Diamond Markus Lill
We propose a novel approach for predicting protein-peptide interactions using
a bi-modal transformer architecture that learns an inter-facial joint
distribution of residual contacts. The current data sets for crystallized
protein-peptide complexes are limited, making it difficult to accurately
predict interactions between proteins and peptides. To address this issue, we
propose augmenting the existing data from PepBDB with pseudo protein-peptide
complexes derived from the PDB. The augmented data set acts as a method to
transfer physics-based contextdependent intra-residue (within a domain)
interactions to the inter-residual (between) domains. We show that the
distributions of inter-facial residue-residue interactions share overlap with
inter residue-residue interactions, enough to increase predictive power of our
bi-modal transformer architecture. In addition, this dataaugmentation allows us
to leverage the vast amount of protein-only data available in the PDB to train
neural networks, in contrast to template-based modeling that acts as a prior