An integrated AI knowledge graph framework of bacterial enzymology and metabolism

Abstract

The study of bacterial metabolism holds immense significance for improving human health and advancing agricultural practices. The prospective applications of genomically encoded bacterial metabolism present a compelling opportunity, particularly in the light of the rapid expansion of genomic sequencing data. Current metabolic inference tools face challenges in scaling with large datasets, leading to increased computational demands, and often exhibit limited inter-relatability and interoperability. Here, we introduce the Integrated Biosynthetic Inference Suite (IBIS), which employs deep learning models and a knowledge graph to facilitate rapid, scalable bacterial metabolic inference. This system leverages a series of Transformer based models to generate high quality, meaningful embeddings for individual enzymes, biosynthetic domains, and metabolic pathways. These embedded representations enable rapid, large-scale comparisons of metabolic proteins and pathways, surpassing the capabilities of conventional methodologies. The examination of evolutionary and functionally conserved metabolites across diverse bacterial species is facilitated by integrating the predictive capabilities of IBIS into a graph database enriched with comprehensive metadata. The consideration of both primary and specialized metabolism, combined with an embedding logic for enzyme discovery, uniquely positions IBIS to identify potential novel metabolic pathways. With the expansion of genomic data necessitating transformative approaches to advance molecular metabolism research, IBIS delivers an AI-driven holistic investigation of bacterial metabolism.