Advancing MALDI-TOF-Based Microbial and Metabolite Identification Through Machine Learning and Multi-Omics Approaches
NIAID - National Institute of Allergy and Infectious Diseases
About This Grant
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.
Grant Summary
Advancing MALDI-TOF-Based Microbial and Metabolite Identification Through Machine Learning and Multi-Omics Approaches is a NIAID - National Institute of Allergy and Infectious Diseases grant providing up to $47K for university, nonprofit, healthcare org. Applications are due 2028-06-21 (open). Check eligibility and apply with FindGrants.
Not quite the right fit?
Search 9,000+ open grants, or get matches ranked for your organization — free.
Focus Areas
Eligibility
How to Apply
Up to $47K
2028-06-21
- 1Confirm your organization is eligible for Advancing MALDI-TOF-Based Microbial and Metabolite Identification Through Machine Learning and Multi-Omics Approaches from NIAID - National Institute of Allergy and Infectious Diseases, checking organization type, location, and any population or project requirements.
- 2Gather the required documents and information, including your organization details, project plan, and budget figures.
- 3Draft your application narrative and budget addressing the funder's priorities and review criteria. FindGrants can draft each section for you to review and edit.
- 4Review every section against the requirements checklist, then export a submission-ready application pack and submit it to NIAID - National Institute of Allergy and Infectious Diseases before the deadline.
Don't want to draft it yourself?
We'll draft the complete application against NIAID - National Institute of Allergy and Infectious Diseases's requirements, run a quality review, and email you a submission-ready PDF plus an editable Word doc within 5 business days. Most orders deliver in 24-48 hours. Flat $399, any grant size.
AI Requirement Analysis
Detailed requirements not yet analyzed
Have the NOFO? Paste it below for AI-powered requirement analysis.
Advancing MALDI-TOF-Based Microbial and Metabolite Identification Through Machine Learning and Multi-Omics Approaches: Frequently Asked Questions
Who is eligible for the Advancing MALDI-TOF-Based Microbial and Metabolite Identification Through Machine Learning and Multi-Omics Approaches?
Advancing MALDI-TOF-Based Microbial and Metabolite Identification Through Machine Learning and Multi-Omics Approaches is offered by NIAID - National Institute of Allergy and Infectious Diseases and is generally open to university, nonprofit, healthcare org. It is open to organizations nationwide unless the funder specifies otherwise. Review the specific eligibility terms before applying, since funders set their own requirements around organization type, location, and the population or project being served.
How much funding does the Advancing MALDI-TOF-Based Microbial and Metabolite Identification Through Machine Learning and Multi-Omics Approaches provide?
Advancing MALDI-TOF-Based Microbial and Metabolite Identification Through Machine Learning and Multi-Omics Approaches provides up to $47K per award from NIAID - National Institute of Allergy and Infectious Diseases. Actual award sizes depend on the scope of your project, available program funds, and the number of applicants, so build a budget that reflects realistic, allowable costs rather than the maximum figure.
When is the Advancing MALDI-TOF-Based Microbial and Metabolite Identification Through Machine Learning and Multi-Omics Approaches deadline?
Applications for Advancing MALDI-TOF-Based Microbial and Metabolite Identification Through Machine Learning and Multi-Omics Approaches are due 2028-06-21 (open). Because deadlines can change, verify the date with the funder, NIAID - National Institute of Allergy and Infectious Diseases, and give yourself enough time to prepare a complete, competitive application before the close date.
How do you apply for the Advancing MALDI-TOF-Based Microbial and Metabolite Identification Through Machine Learning and Multi-Omics Approaches?
To apply for Advancing MALDI-TOF-Based Microbial and Metabolite Identification Through Machine Learning and Multi-Omics Approaches, confirm your eligibility, gather the required documents, and prepare a narrative and budget that address the funder's priorities. FindGrants guides you step by step and can draft each section, then exports a submission-ready application pack for this grant from NIAID - National Institute of Allergy and Infectious Diseases.