Harnessing Genome Representation Learning for Decoding Phage-Host Interactions

Abstract

Accurate prediction of the phages that target a bacterial host plays an important role in combating anti-microbial resistance. Our work explores the power of deep neural networks, convolutional neural networks, and pre-trained large DNA/protein language models to predict the host for a given phage. This work mainly uses the data provided by Gonzales et al. that contains receptor-binding protein sequences of the phages and the target host genus. We used pre-trained language models to obtain the dense representations of protein/nucleotide sequences to train a deep neural network to predict the target host genus. Additionally, convolutional neural networks were trained on one-hot encoding of nucleotide sequences to predict the target host genus. We achieved a weighted F1-score of 73.76% outperforming state-of-the-art models with an improvement of around 11% by using the protein language model ESM-1b. The data and the source code are available at https://github.com/sumanth2002629/Bacteriophage-Research.