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Histone Modification02:32

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The histone proteins have a flexible N-terminal tail extending out from the nucleosome. These histone tails are often subjected to post-translational modifications such as acetylation, methylation, phosphorylation, and ubiquitination. Particular combinations of these modifications form “histone codes” that influence the chromatin folding and tissue-specific gene expression.
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The histone proteins in the nucleosomes are post-translationally modified (PTM) to increase or decrease access to DNA. The commonly observed PTMs are methylation, acetylation, phosphorylation, and ubiquitination of lysine amino acids in the histone H3 tail region. These histone modifications have specific meaning for the cell. Hence, they are called "histone code". The protein complex involved in histone modification is termed as "reader-writer" complex.
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Detecting Modifications in Proteomics Experiments with Param-Medic.

Damon H May1, Kaipo Tamura1, William S Noble1,2

  • 1Department of Genome Sciences , University of Washington , Seattle , Washington 98195 , United States.

Journal of Proteome Research
|February 5, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a method to automatically detect experimental modifications in mass spectrometry data, even when metadata is missing. Param-Medic software infers labeling and phosphorylation directly from spectra, improving data analysis accuracy.

Keywords:
database searchisobaric labelingmass spectrometryphosphorylationproteomicstandem mass tags

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Area of Science:

  • Proteomics
  • Mass Spectrometry Data Analysis

Background:

  • Accurate experimental parameters are crucial for searching tandem mass spectra against peptide databases.
  • Missing or inaccurate metadata, common in reanalyzing public datasets, hinders reliable analysis.

Purpose of the Study:

  • To develop a method for automatically inferring experimental parameters directly from mass spectra.
  • To address the challenge of missing or inaccurate metadata in mass spectrometry data analysis.

Main Methods:

  • Developed a novel approach to automatically infer various post-translational modifications and labeling strategies from raw mass spectra.
  • Implemented the method in open-source Python and C++ within the Param-Medic software tool.

Main Results:

  • Demonstrated high sensitivity and specificity in inferring modifications such as stable-isotope labeling, isobaric labeling, tandem mass tags, and phosphopeptide enrichment.
  • Successfully validated the method's ability to recover experimental details directly from spectral data.

Conclusions:

  • The automated inference method significantly enhances the reliability of mass spectrometry data analysis, especially for public datasets.
  • Param-Medic provides a valuable open-source tool for researchers to improve the accuracy and reproducibility of proteomic studies.