NU 2016-068

INVENTORS

• Neil Kelleher*
• Kenneth Clevenger
• Paul Thomas

SHORT DESCRIPTION

This platform uses fungal artificial chromosomes and ultra‐high mass accuracy LC-MS to profile secondary metabolites. It offers a rapid, scalable approach for natural product discovery in drug and agricultural research.

BACKGROUND

Current natural product discovery methods often rely on time-consuming extraction and screening processes. High costs and low throughput limit the ability to uncover novel bioactive compounds, leaving a significant gap in timely therapeutic and agricultural innovations.

ABSTRACT

The invention leverages untargeted LC-MS with ultrahigh mass accuracy to analyze secondary metabolite products from heterologously expressed fungal biosynthetic gene clusters. It captures full-length gene clusters via fungal artificial chromosomes and screens host transformants using a robust scoring system to link spectral features with specific clusters. The results demonstrate that this method systematically identifies novel metabolites and lays the groundwork for scalable natural product discovery.

MARKET OPPORTUNITY

The demand for natural products is surging across both the pharmaceutical and agricultural industries, creating a powerful dual-market opportunity. The global drug discovery market, valued at $78.3 billion in 2023, is increasingly turning to natural sources for novel therapeutic leads to combat complex diseases (Source: Grand View Research, 2024).

DEVELOPMENT STAGE

TRL-4 - Prototype Validated in Lab: Key functions have been demonstrated in a laboratory-scale prototype using LC-MS to profile fungal metabolites.

APPLICATIONS

• Natural product drug discovery: Rapid identification of bioactive compounds.
• Functional genomics research: Linking biosynthetic gene clusters with metabolite production.
• Bioprospecting for agricultural chemicals: Screening fungal strains for novel metabolites.
• Industrial fermentation quality control: Profiling secondary metabolites during production.

ADVANTAGES

• Increases discovery speed: Streamlines metabolite identification with a robust scoring system.
• Enhances accuracy: Uses ultrahigh mass accuracy LC-MS for precise profiling.
• Lowers research costs: Reduces manual extraction and screening efforts.
• Scalable workflow: Supports high-throughput screening across large genomic libraries.

PUBLICATIONS

• Neil Kelleher et al, A Scalable Platform to Identify Fungal Secondary Metabolites and Their Gene Clusters, Nat Chem Biol. 2017 Aug;13(8):895-901.

IP STATUS

Issued US Patent 10,808,256.