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Related Concept Videos

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...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein Networks02:26

Protein Networks

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NF-κB-dependent Signaling Pathway02:26

NF-κB-dependent Signaling Pathway

The transcription factor NF-κB was discovered in 1986 in the lab of Nobel laureate Professor David Baltimore, for its interaction with the immunoglobulin light chain enhancer in B-cells. After more than three decades of study, it is now evident that NF-κB regulates the expression of over 100 genes. Most of these genes play an essential role in the innate and adaptive immune responses as well as the inflammatory responses of animals.
NF-κB-dependent Signaling Mechanism
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Tagging and Fusion Proteins01:24

Tagging and Fusion Proteins

Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
Synaptic Signaling01:12

Synaptic Signaling

Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.

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Related Experiment Video

Updated: May 17, 2026

An Integrated Approach for Microprotein Identification and Sequence Analysis
09:37

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

Signal-BNF: a Bayesian network fusing approach to predict signal peptides.

Zhi Zheng1, Youying Chen, Liping Chen

  • 1Key Laboratory of Network Security and Cryptology, Fujian Normal University, Fuzhou 350007, China.

Journal of Biomedicine & Biotechnology
|November 3, 2012
PubMed
Summary

We developed Signal-BNF, a novel Bayesian network tool, to accurately predict N-terminal signal peptides and their cleavage sites. This advancement aids drug discovery and gene therapy by improving protein targeting identification.

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Peptide-based Identification of Functional Motifs and their Binding Partners
14:28

Peptide-based Identification of Functional Motifs and their Binding Partners

Published on: June 30, 2013

Area of Science:

  • Bioinformatics
  • Molecular Biology
  • Computational Biology

Background:

  • Signal peptides are essential for protein transport and are critical targets for drug discovery and gene therapy.
  • Identifying signal peptides is increasingly vital due to the large volume of protein sequences generated post-genomics.

Purpose of the Study:

  • To introduce Signal-BNF, a novel computational tool for predicting N-terminal signal peptides and their cleavage sites.
  • To leverage Bayesian reasoning networks for enhanced prediction accuracy in signal peptide identification.

Main Methods:

  • Signal-BNF employs a Bayesian reasoning network, integrating multiple Bayesian classifiers with diverse feature datasets.
  • A weighted voting system fuses the outputs of individual classifiers for robust prediction.

Main Results:

  • Signal-BNF demonstrates superior performance compared to existing predictors like Signal-3L and PrediSi.
  • The predictor achieves high accuracy in identifying signal peptides and their cleavage sites.

Conclusions:

  • Signal-BNF offers a highly accurate and valuable tool for signal peptide prediction.
  • This tool can facilitate further research into the molecular mechanisms of cellular protein-sorting systems.