Improving Protein-peptide Interface Predictions in the Low Data Regime

• URL: http://arxiv.org/abs/2306.00557v1
• Date: Wed, 31 May 2023 17:04:27 GMT
• Title: Improving Protein-peptide Interface Predictions in the Low Data Regime
• Authors: Justin Diamond, Markus Lill
• Abstract summary: We propose a novel approach for predicting protein-peptide interactions using a bi-modal transformer architecture. We show that the distributions of inter-facial residue-residue interactions share overlap with inter residue-residue interactions. This dataaugmentation allows us to leverage the vast amount of protein-only data available in the PepBDB to train neural networks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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