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  3. Clinically Relevant Post-translational Modification Analyses-maturing Workflows And Bioinformatics Tools.
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  3. Clinically Relevant Post-translational Modification Analyses-maturing Workflows And Bioinformatics Tools.

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Clinically Relevant Post-Translational Modification Analyses-Maturing Workflows and Bioinformatics Tools.

Dana Pascovici1,2, Jemma X Wu3,4, Matthew J McKay5,6

  • 1Department of Molecular Sciences, Macquarie University, Sydney, NSW 2109, Australia. dana.pascovici@mq.edu.au.

International Journal of Molecular Sciences
|December 23, 2018

View abstract on PubMed

Summary
This summary is machine-generated.

Post-translational modifications (PTMs) regulate protein functions. Advanced mass spectrometry and bioinformatics tools enhance PTM analysis in health and disease, with a focus on clinical samples.

Keywords:
PTMbody fluidsclinical samplespost translational modificationquantitative proteomics

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Area of Science:

  • Biochemistry
  • Proteomics
  • Bioinformatics

Background:

  • Post-translational modifications (PTMs) are critical for protein function, influencing folding, stability, localization, and interactions.
  • Quantitative mass spectrometry (MS) and bioinformatics are advancing the study of PTMs in human health and disease.
  • Data Independent Acquisition (DIA) offers re-mining capabilities for comprehensive PTM analysis.

Purpose of the Study:

  • To review current options and progress in analyzing PTMs from large quantitative studies.
  • To emphasize PTM analysis in clinically relevant samples like plasma and other body fluids.
  • To discuss platform selection, data analysis, and interpretation tools for PTM studies.

Main Methods:

  • Utilizing quantitative mass spectrometry (MS) techniques, including labeled multiplexed and label-free approaches.
  • Employing advanced bioinformatics workflows for PTM site assignment and analysis of unassigned spectra.
  • Leveraging Data Independent Acquisition (DIA) for enhanced PTM detection and re-mining.

    • Main Results:

    • Maturing MS and bioinformatics workflows provide increasing coverage for PTM studies.
    • Development of tools for PTM prediction, site assignment, and association analysis.
    • Emerging strategies for extracting functional insights from PTM data in clinical contexts.

    Conclusions:

    • Extracting functional information from clinically relevant PTM studies remains a key challenge.
    • This review provides an overview of PTM analysis platforms and data interpretation tools for large-scale quantitative studies.
    • Emphasis is placed on the application of these methods to clinical samples for understanding human health and disease.