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Advancing MALDI-TOF-Based Microbial and Metabolite Identification Through Machine Learning and Multi-Omics Approaches

Award Number: F31AI200270
ORGANIZATION: NATIONAL INSTITUTE OF ALLERGY & INFECTIOUS DISEASES
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: FELLOWSHIP/SCHOLARSHIP/STUDENT LOANS
PERIOD OF PERFORMANCE START DATE: 11/21/2026
PERIOD OF PERFORMANCE END DATE: 11/20/2027

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Advancing MALDI-TOF-Based Microbial and Metabolite Identification Through Machine Learning and Multi-Omics Approaches - Project Abstract The successful identification of bacteria and the specialized metabolites (SMs) they produce are key challenges in understanding the ecology of the microbiome, and its interaction with its host. Matrix-assisted laser desorption-ionization mass spectrometry (MALDI MS) has enabled low-cost, high throughput identification of bacteria. However, the ability to annotate bacteria remains limited to pathogenic-focused libraries and locked within vendor-specific software. Our recent introduction of the IDBac-Knowledgebase (IDBac-KB) has facilitated the creation of a crowd-sourced library of MALDI proteomic fingerprints, enabling the identification of microbes in a broader setting but has exposed new challenges in cross-platform and cross- laboratory identification. Therefore, the development of a parameter- and platform-conscious approach for identifying bacteria based on proteomic fingerprints is essential for the broad annotation of the organisms in the microbiome. This advancement will accelerate the discovery of bioactive, health- promoting supplements and the isolation of natural product scaffolds for therapeutic development. We propose to develop a novel approach for bacterial comparison and identification based on state-of-the-art machine learning methods, enhancing the utility of this new resource. Further we propose extending IDBac to enable the identification of SM by integrating cutting-edge tandem MS (MS/MS) workflows on MALDI-Time-of- Flight (TOF) instruments with machine-learning-based methods for annotating MS/MS spectra with compound libraries. These aims will enable the characterization of SMs at scale, allowing for the identification of metabolites associated with up to 384 bacterial colonies within four hours. In doing so, we will illuminate the SMs responsible for mediating microbial interactions and creating stable communities. These discoveries will inform research in the production of bio-active supplements that improve microbiome health and allow for the isolation of compounds that enable the growth of otherwise unculturable bacteria thereby expanding the space of bacteria accessible in isolation. Downstream, this will enable the study of novel natural product space, unlocking access to novel natural product chemical space and new therapeutic scaffolds. Successful completion of these aims will constitute a major advance in microbial ecology and our understanding of the human microbiome. It will enable researchers to explore new chemical space and identify compounds that promote microbiome-host symbiosis or contribute to dysbiosis and disease.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $46,788 )
2026	2026	REGENTS OF THE UNIVERSITY OF CALIFORNIA AT RIVERSIDE	200 UNIVERSTY OFC BUILDING	RIVERSIDE	CA	92521	RIVERSIDE	USA	Allergy and Infectious Diseases Research	000	1	5/21/2026	NEW	$46,788
														Subtotal = $46,788

Grand Total All Awards = $46,788

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Advancing MALDI-TOF-Based Microbial and Metabolite Identification Through Machine Learning and Multi-Omics Approaches

Award Number: F31AI200270

ORGANIZATION: NATIONAL INSTITUTE OF ALLERGY & INFECTIOUS DISEASES

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: FELLOWSHIP/SCHOLARSHIP/STUDENT LOANS

PERIOD OF PERFORMANCE START DATE: 11/21/2026

PERIOD OF PERFORMANCE END DATE: 11/20/2027

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