# An Automated Proteoform Characterization Control System

> **NIH NIH R43** · PROTEINACEOUS, INC. · 2020 · $202,005

## Abstract

ABSTRACT
Current mass spectrometers have benefited from a technological revolution over recent years, with great
increases to speed and resolution as well as significant improvements in fragmentation capabilities for
biomolecule analyses. Proteomics results that were previously unthinkable only a few years ago are now routine
and being performed by hundreds of laboratories across the world. However, despite these advancements and
successes, the software systems guiding mass spectrometry data acquisition have not seen technology
improvements on the same scale. As such, these new mass spectrometers have not yet reached their full potential,
particularly for intact protein characterization where analytes are more varied than peptides and acquisition
parameters are tougher to generalize. A software named AutoProt is being developed to automate the targeted
characterization of proteoforms from a proteoform family. AutoProt controls the acquisition of mass
spectrometry data, initiating a data-driven fragmentation routine when proteoforms-of-interest from a
proteoform family are detected in survey scans. As each fragmentation data scan is collected, AutoProt matches
experimental data to theoretical proteoform fragments and uses the corresponding results to make an informed
decision on how to further characterize the proteoform. By using instant feedback, fragmentation settings are
customized specifically and in real-time for each proteoform being analyzed, leading to better characterization
results in less overall time. In Phase I, AutoProt will be further developed to handle the characterization of
multiple proteoforms such as modified and truncated forms from the same proteoform family in the same
acquisition run, enabling a wider assessment of the proteoforms present in a sample (e.g., all the proteoforms
from the same UniProt gene accession after immunoprecipitation). A database will be integrated into AutoProt
to store run data and allow sample characterization to be resumed for additional acquisition runs. Furthermore,
the characterization routine will be made more robust in this grant through the collection of a large amount of
fragmentation data on many proteoforms and then the distillation of that data into a general fragmentation
model. AutoProt will then be able to broadly characterize proteoforms with different properties (e.g., molecular
weight and fragmentation propensities). Lastly, AutoProt will be outfitted with report generation to
automatically output experimental report files compatible with the proteoform data visualization tool,
TDCollider. With these features in place, AutoProt will be a first-of-its-kind automated characterization software
with first-class proteoform characterization abilities and acquisition-to-report capabilities, eliminating time-
consuming set up of acquisition and processing for proteoform characterization data.

## Key facts

- **NIH application ID:** 10010454
- **Project number:** 1R43GM136046-01A1
- **Recipient organization:** PROTEINACEOUS, INC.
- **Principal Investigator:** Kenneth Durbin
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $202,005
- **Award type:** 1
- **Project period:** 2020-04-01 → 2021-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10010454

## Citation

> US National Institutes of Health, RePORTER application 10010454, An Automated Proteoform Characterization Control System (1R43GM136046-01A1). Retrieved via AI Analytics 2026-06-04 from https://api.ai-analytics.org/grant/nih/10010454. Licensed CC0.

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