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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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ProSIMSIt: The Best of Both Worlds in Data-Driven Rescoring and Identification Transfer.

Firas Hamood1, Wassim Gabriel2, Pia Pfeiffer2

  • 1Chair of Proteomics and Bioanalytics, School of Life Sciences, Technical University of Munich, 85354 Freising, Germany.

Journal of Proteome Research
|March 22, 2025
PubMed
Summary

ProSIMSIt enhances proteomic data analysis by combining spectrum clustering and rescoring, significantly improving peptide spectrum matches (PSMs) and revealing new drug-response insights in large clinical studies.

Keywords:
data-driven rescoringdrug-response profilingidentification transferisobaric labelingmissing valuespeptide-spectrum matchphosphoproteomicsspectrum clustering

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Mass Spectrometry

Background:

  • Multibatch isobaric labeling is crucial for large-scale clinical and pharmaceutical studies.
  • Missing values present a significant challenge in proteomic data analysis.

Purpose of the Study:

  • Introduce the ProSIMSIt pipeline to address missing values in proteomic data.
  • Improve the identification and quantification of peptides in complex biological samples.

Main Methods:

  • Combine SIMSI-Transfer for tandem mass spectrum clustering.
  • Integrate Prosit and Oktoberfest for data-driven rescoring.
  • Develop an open-source Python package for easy integration with MaxQuant results.

Main Results:

  • Achieved a 40% increase in peptide spectrum matches (PSMs) on large-scale cancer cohort data.
  • Enhanced both global and phosphoproteome datasets.
  • Substantially increased drug-post-translational modification (PTM) relations in drug-response profiling.
  • Revealed previously undetected downstream effects of drug target inhibition.

Conclusions:

  • ProSIMSIt effectively tackles missing values in proteomic studies.
  • The pipeline offers complementary and mutually beneficial integration of existing tools.
  • ProSIMSIt facilitates deeper insights into drug mechanisms and biological pathways.