# Protein Detection and Quantitation by Mass Spectrometry

> **NIH NIH P41** · UNIVERSITY OF WASHINGTON · 2021 · $286,356

## Abstract

Project Summary
Proteomics is dependent on the collection of tandem mass spectra of peptides and proteins to make
identifications in database searches. Large-scale proteome analyses have become more comprehensive as
instrument scan speeds have increased and ion dissociation methods have improved. Additionally, protein
quantitation benefits from the creation of internal standards and data collection methods to acquire sufficient
data for accurate quantification. The YRC invented small window data independent acquisition (DIA) for
proteomics more than a decade ago and we have innovated and improved these methods. To improve the
comprehensiveness and reproducibility of peptide identification and quantitation, DIA methods will be
advanced for bottom-up proteomics. Reagents will be developed for general enrichment of peptides for
targeted quantitation of peptides. To determine proteoforms of proteins in complexes, strategies employing
capillary electrophoresis to separate intact proteins for top down analysis using new DIA data collection
strategies coupled to emerging ion dissociation methods for intact proteins will be developed. To advance
multiplexed quantitation of peptides in bottom up proteomics and to multiplex “molecular painting” new
reagents will be developed.

## Key facts

- **NIH application ID:** 10141250
- **Project number:** 5P41GM103533-25
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** John R Yates III
- **Activity code:** P41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $286,356
- **Award type:** 5
- **Project period:** 1997-09-30 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10141250

## Citation

> US National Institutes of Health, RePORTER application 10141250, Protein Detection and Quantitation by Mass Spectrometry (5P41GM103533-25). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10141250. Licensed CC0.

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