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Improvements to ProSightPD nodes in the Thermo Scientific Proteome Discoverer Software Framework

Reputable Mentor II
Reputable Mentor II
David M. Horn,1 Tara L. Schroeder,1 Ioanna Ntai,1 Richard D. Leduc,2 Ryan T. Fellers, 2 Joseph B. Greer, 2 and Neil L. Kelleher 2
ASMS 2018
Purpose: Here we demonstrate the use of the ProSightPD™ nodes for the Thermo Scientific™ Proteome Discoverer™ software framework to analyze complex top down proteomics data. We also demonstrate how sliding window deconvolution results from Thermo Scientific™ Biopharma Finder™ can be used to determine differentially expressed proteoforms. Methods: For this work, Study 1 data from Ntai et al1 were used for the analysis. Both the “High/High” and “Low/High” GELFrEE fractions were analyzed using ProSightPD Base and ProSightPD High Mass, respectively. The High/High data were analyzed using a five-step search to maximize the number of identifications, while the Low/High data were analyzed using a three-step search. PrSMs were filtered by a minimum C score of 3. Sliding window deconvolution in the Biopharma Finder software was used to process the High/High data using Xtract deconvolution. The quantitative results were integrated with the filtered human PrSMs from ProSightPD via a Perl script. Normalization and p-value calculations were produced using InfernoRDN and Microsoft® Excel. Results: The High/High runs identified 2,374 proteoforms total with 735 producing a C-score of 3 or better. The Low/High runs identified 254 proteoforms with 38 proteoforms having a C-score of 3 or better. Many of the proteoforms that were identified had masses greater than 40 kDa. Integration of the sliding window deconvolution results with the High/High data produced over 400 proteoforms that differ significantly between the WHIM2 and WHIM16 samples.

1 Thermo Fisher Scientific, San Jose, CA 2 Northwestern University, Evanston, IL
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Last update:
‎10-15-2021 06:09 AM
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