Data-Driven Software to Automate Top-Down Mass Spectrometry of Large Molecules

NIH RePORTER · NIH · R44 · $955,646 · view on reporter.nih.gov ↗

Abstract

PROJECT SUMMARY Analysis of intact proteins by top-down mass spectrometry enables direct measurement of the specific sequences and posttranslational modifications that drive biological processes, disease, and protein therapeutic attributes. Despite the potential that top-down mass spectrometry has for protein analytics, acquiring high- quality data and then interpreting the data requires significant expertise and time, producing a sizable barrier to both experts and those new to the field. This project aims to develop the first commercial software to ease this acquisition burden, with workflows that automate the process of acquiring optimal top-down data, searching this data in real-time, and updating the instrument with new parameters to maximize protein characterization. In this project, new processes for detecting and identifying mass features will be implemented. Strategies will also be designed to direct the instrument in collecting high-quality fragmentation data with data-driven updates to fragmentation settings. Strategies will also be specially developed for biologics and other large molecules. Lastly, data will be presented to users via interactive visuals intended to simplify and ease data interpretation. Collectively, the new software platform will provide great improvements to how top-down mass spectrometry data can be acquired, leading to more efficient and effective protein characterization.

Key facts

NIH application ID
10761429
Project number
2R44GM136046-02
Recipient
PROTEINACEOUS, INC.
Principal Investigator
Kenneth Durbin
Activity code
R44
Funding institute
NIH
Fiscal year
2023
Award amount
$955,646
Award type
2
Project period
2020-04-01 → 2025-08-31