Agent_K: An Agent-Orchestrated Pipeline for Mapping Genes to Biosynthetic Pathway towards Metabolite Discovery.

Abstract

The identification of biosynthetic gene clusters (BGCs) and their associated metabolic pathways is central to genome-guided natural product discovery. However, existing computational tools depend on fragmented workflows for data processing, annotation, and analysis, which require substantial manual intervention and lack seamless integration across heterogeneous platforms and tools. Here, we present, Agent_K, an agent-orchestrated framework that automates the end-to-end process from bacterial whole genome sequence analysis to biosynthetic pathway mapping and metabolite discovery as candidate gene products for functional insights. The system employs a large-language-model (LLM) agent to coordinate multiple bioinformatics tools, enabling dynamic execution, data integration, result interpretation, and flexible and intuitive visualization. By integrating genomic sequences to functional annotations and pathway-level reconstructions, the framework facilitates the identification of candidate gene products, their biosynthetic pathways associated with individual BGCs, and the putative metabolites produced by those pathways. The developed novel agentic framework provides a user-friendly scalable and extensible solution for automated high-throughput analysis and mapping of bacterial secondary metabolism pathways for interactive exploration of candidate potential.

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