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Related Concept Videos

Proteomics01:33

Proteomics

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 proteomics...
Signal Sequences and Sorting Receptors01:41

Signal Sequences and Sorting Receptors

Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...

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Related Experiment Video

Updated: May 23, 2026

Separation and Fractionation of Culture Filtrate Proteins (CFPs) from Mycobacterium tuberculosis
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Separation and Fractionation of Culture Filtrate Proteins (CFPs) from Mycobacterium tuberculosis

Published on: July 11, 2025

Computational comparative study of tuberculosis proteomes using a model learned from signal peptide structures.

Jhih-Siang Lai1, Cheng-Wei Cheng, Ting-Yi Sung

  • 1Institute of Information Science, Academia Sinica, Taipei, Taiwan.

Plos One
|April 13, 2012
PubMed
Summary

SVMSignal, a new machine learning tool, accurately predicts signal peptides for improved secretome analysis. This method aids in identifying potential secreted and transmembrane proteins, crucial for pathogen studies and drug discovery.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Proteomics

Background:

  • Secretome analysis is vital for understanding pathogen behavior and identifying potential drug targets.
  • Accurate prediction of signal peptides is essential for identifying secreted proteins, but they are often confused with transmembrane domains.
  • The lack of experimentally determined transmembrane protein structures necessitates reliable prediction methods.

Purpose of the Study:

  • To develop a general signal peptide predictor that does not require prior knowledge of the organism.
  • To improve the accuracy of signal peptide prediction by directly learning their structures.
  • To facilitate secretome analysis and the identification of secreted and transmembrane proteins.

Main Methods:

  • Developed SVMSignal, a machine learning method utilizing biochemical properties and a novel encoding scheme.
  • Trained and tested SVMSignal on benchmark datasets from SPdb and UniProt/Swiss-Prot databases.
  • Applied SVMSignal to analyze proteomes in the HAMAP microbial database and compare secretomes of tuberculosis-related strains.

Main Results:

  • SVMSignal demonstrated strong performance in signal peptide prediction, outperforming existing methods.
  • Analysis of the HAMAP database identified ten potential secreted proteins, including two drug-resistant and four potential transmembrane proteins.
  • Directly learning signal peptide structures proved to be a promising approach for prediction.

Conclusions:

  • SVMSignal offers an effective and generalizable solution for signal peptide prediction, aiding secretome analysis.
  • The tool facilitates the discovery of novel secreted and transmembrane proteins, relevant for pathogen studies and drug development.
  • SVMSignal is publicly available, providing user-friendly interfaces and downloadable prediction results.