Project Details
Description
Summary
Mass spectrometry-based top-down proteomics has become one of the most informative
approaches in protein analysis because it provides the bird's-eye view of intact
proteoforms (protein forms) generated from post-translational modifications and
sequence variations. Data dependent acquisition and data independent acquisition are
the two main methods in top-down mass spectrometry. The former has been the
dominant one, but it has two main challenges in proteome-wide studies: low protein
coverage: a regular experiment of human cells can identify only 200 – 400 proteins, and
low reproducibility: a technical triplet shares only about one third of identified
proteoforms. Top-down data independent acquisition mass spectrometry (TD-DIA-MS)
has the potential to significantly increase protein coverage and improve reproducibility in
proteome-wide studies. However, its application has been hampered by the complexity
of the data and the lack of efficient software tools. To address this problem, we will
propose new algorithms and machine learning models and develop the first software
package for proteoform identification by TD-DIA-MS. The proposed research will be
conducted by a group of researchers with complementary expertise. All the proposed
algorithms will be implemented as user-friendly open source software tools.
Status | Active |
---|---|
Effective start/end date | 06/1/16 → 08/31/24 |
Funding
- National Institute of General Medical Sciences: $294,904.00
- National Institute of General Medical Sciences: $335,435.00
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