DepoScope: accurate phage depolymerase annotation and domain delineation using large language models

Abstract

AbstractBacteriophages (phages) are viruses that infect bacteria. Many of them produce specific enzymes called depolymerases to break down external polysaccharide structures. Accurate annotation and domain identification of these depolymerases are challenging due to their inherent sequence diversity. Hence, we present DepoScope, a machine learning tool that combines a fine-tuned ESM-2 model with a convolutional neural network to precisely identify depolymerase sequences and their enzymatic domains. To accomplish this, we curated a dataset from the INPHARED phage genome database, created a polysaccharide-degrading domain database, and applied sequential filters to construct a high-quality dataset, which are subsequently used to train DepoScope. Our work is the first approach that combines sequence-level predictions with amino-acid-level predictions for an accurate depolymerase detection and functional domain identification. In that way, we believe that DepoScope can enhance our understanding of phage-host interactions at the level of depolymerases.Summary with Key MessagesPhage depolymerases are proteins that play a crucial role in the first step of a phage replication cycle. As a result, they are both important from a biological perspective and a therapeutical perspective.Current methods to accurately annotate phage depolymerases and their associated enzymatic domains remains challenging due to their inherent high sequence diversity.We have developed DepoScope, a language-based artificial intelligence model that can accurately identify phage depolymerases and their specific enzymatic domains.We provide full public access to the DepoScope code and database to give broad access to the research community and promote further research.