# New Algorithms and Software for Mass Spectrometric Analysis of Intact Proteins and Complexes

> **NIH NIH R44** · PROTEIN METRICS, LLC · 2022 · $795,816

## Abstract

Intact proteins and multi-protein complexes represent two of the most important levels of
organization in biology, yet these levels are also some of the most difficult to study. Native
mass spectrometry provides a means to observe proteins in complex with ligands, other
proteins, and nucleic acids. The proposed project will build new algorithms and software for
intact and native mass spectrometry. Specifically, the project will further develop Protein
Metrics Intact Mass charge-deconvolution software, incorporating multiple information channels
(isotope spacing, co-elution, and charge states) into the core algorithm, so that the algorithm
can handle complex, low signal-to-noise mass spectra over a wide range of masses and
abundances. The project will also build automation software for analyzing data as it is produced
by mass spectrometry instruments, naming and organizing files, and producing reports showing
results at several levels of detail. The project will enable a number of interesting applications,
including small-molecule screening, large-molecule drug comparability, pharmacokinetics, and
protein / protein and protein / nucleic acid binding.

## Key facts

- **NIH application ID:** 10333416
- **Project number:** 5R44GM133239-03
- **Recipient organization:** PROTEIN METRICS, LLC
- **Principal Investigator:** MARSHALL Wayne BERN
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $795,816
- **Award type:** 5
- **Project period:** 2019-04-01 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10333416

## Citation

> US National Institutes of Health, RePORTER application 10333416, New Algorithms and Software for Mass Spectrometric Analysis of Intact Proteins and Complexes (5R44GM133239-03). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10333416. Licensed CC0.

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