A multi-agent AI system for automated curation and functional annotation of enzymes in human gut microbiome

About This Grant

PROJECT SUMMARY Human gut microbiomes influence health by producing metabolites and enzymes that modulate immunity, transform drugs, and digest nutrients. However, most of these enzymes remain functionally unknown. Current annotation tools rely mainly on sequence similarity searches, which can only assign meaningful functions to less than 30% of microbial proteins. Although recent approaches incorporate protein language models and structural comparison, they still rely on predefined pipelines, manual literature or database searches, and specialized expertise in microbial research. This makes the annotations time-consuming without intelligent automation for context-aware insights and limits their scalability across diverse microbial ecosystems. Large language models (LLMs) have emerged as powerful tools in scientific research by analyzing data, answering complex questions, and generating new hypotheses. Building on these strengths, Artificial Intelligence (AI) agents, which combine LLMs with external resources like databases, tools and APIs, can automate tasks and workflows, mimicking human expert decision-making. Although they are widely used in industry, their potential in bioinformatics has only recently been explored. The overall objective of our project is to develop GENZ-AI (Gut ENZyme AI), a multi-agent AI system for automated curation and functional annotation of gut microbial enzymes. GENZ-AI will leverage LLM and advanced AI agents to autonomously delegate tasks, integrate diverse data sources, and deliver enriched annotations with relevant references. We will use advanced techniques, such as prompt optimization and imitation learning, to continuously refine its performance based on real-world annotation sample workflows and user feedback. The significance of GENZ-AI lies in leveraging these cutting-edge technologies to automate and enhance the data curation and workflow organization for enhanced enzyme annotation. This achievement will also improve gut microbiome-based diagnostics and therapeutics (e.g., dietary interventions, drug enhancement, immune modulation) while substantially reducing the time and effort required. The outcome will be a set of novel computational approaches implemented as user- friendly, reusable, open-source tools, including specialized applications for CAZymes, a class of glycan- metabolism enzymes critical to gut microbiome functions. The CAZyme annotation results and software tools will be integrated into dbCAN-PUL and dbCAN-sub databases. The key innovations of this project include a structure-informed protein language model for generalized EC number prediction, the application of CrewAI framework to build a multi-agent system optimized for enzyme annotation in microbiome, and the in-depth investigation of CAZyme and its glycan substrate utilization through GENZ-AI. The broader impact extends beyond the human gut microbiome, as GENZ-AI can be applied to any microbes, providing a scalable solution for diverse microbial ecosystems and pioneering the adaptation of LLM-powered AI agents in bioinformatics.