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Related Concept Videos

Proteomics01:33

Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples
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Probing the Missing Human Proteome: A Computational Perspective.

Dhirendra Kumar1, Aradhya Jain1, Debasis Dash1

  • 1G. N. Ramachandran Knowledge Centre for Genome Informatics, CSIR-Institute of Genomics and Integrative Biology , South Campus, Sukhdev Vihar, Mathura Road, Delhi 110025, India.

Journal of Proteome Research
|September 26, 2015
PubMed
Summary
This summary is machine-generated.

Researchers identified 24 missing human proteins using advanced mass spectrometry (MS) search strategies. Optimizing computational parameters and databases significantly aids in discovering proteins previously undetected at the protein level.

Keywords:
missing proteinsneXtProtpost-translational modificationsproteomicssemitryptic searches

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Human Genome Research

Background:

  • The human proteome is incomplete, with approximately 18% of predicted protein-coding genes lacking experimental evidence.
  • These 'missing proteins' may hold pharmacological significance, particularly membrane receptors, necessitating comprehensive characterization.

Purpose of the Study:

  • To investigate the impact of various computational parameters on identifying missing human proteins from tandem mass spectrometry (MS) data.
  • To explore strategies for enhancing the detection of proteins currently considered part of the missing proteome.

Main Methods:

  • Utilized a multialgorithmic approach for protein detection from publicly available MS datasets across diverse human tissues and cell types.
  • Evaluated parameters including search database composition, shared peptides, semitryptic searches, post-translational modifications (PTMs), and transcriptome-guided proteogenomic searches.

Main Results:

  • Successfully identified 24 missing proteins (22-PE2, 1-PE4, 1-PE5) using the explored computational strategies.
  • Identifications were often linked to variations in reference proteome databases, highlighting the importance of standardized databases.
  • Modified search strategies, including PTMs and comprehensive databases, proved effective for missing protein discovery.

Conclusions:

  • Optimized computational search strategies and the use of comprehensive, compact databases are crucial for identifying missing human proteins.
  • Complementary spectral data searches incorporating diverse parameters can significantly advance the characterization of the human proteome.