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Related Concept Videos

Overview of Protein Sorting and Transport01:45

Overview of Protein Sorting and Transport

Eukaryotic cells have different membrane-bound organelles with distinct protein requirements. The process by which proteins are targeted to a specific organelle is called protein sorting.
Protein sorting can be of two types: signal-based sorting and vesicle-based trafficking. In signal-based sorting, specific amino acid sequences called sorting signals target proteins to the proper location inside the cell either via gated transport or by protein translocation.  In gated transport, folded...
Cotranslational Protein Translocation01:20

Cotranslational Protein Translocation

Translocation of proteins across membranes is an ancient process that occurs even in bacteria and archaebacteria. In fact, the components of the translocation machinery are still conserved between prokaryotes and eukaryotes.
Sec61 channel partners for cotranslational translocation
During cotranslational translocation, the Sec61 channel partners with the signal recognition particle (SRP), the signal recognition particle receptor (SR), and the ribosomes to transport the nascent polypeptide chain...
Nuclear Protein Sorting01:34

Nuclear Protein Sorting

Nuclear protein sorting is the selective trafficking of histones, polymerases, gene regulatory proteins into the nucleus and exporting RNAs and ribosomes to the cytosol. It is a tightly controlled process that regulates gene expression within a cell.
Proteins targeted to the nucleus carry nuclear localization signals or NLS recognized by import receptors in the cytosol. Similarly, proteins with nuclear export signals are recognized by export receptors. Import and export receptors are...
Nuclear Localization Signals and Import01:46

Nuclear Localization Signals and Import

Proteins targeted to the nucleus carry short stretches of amino acid sequences called the nuclear localization signal or NLS. Classical nuclear localization signals are of two types: monopartite and bipartite NLS. Monopartite classical NLS (cNLS) consists of a single cluster of 4-8 amino acids. Bipartite cNLS consists of two clusters of  2-3 amino acids and a 9-12 residue long proline-rich linker bridging the two clusters. Signal clusters are rich in positively charged amino acids such as...
Translocation of Proteins into the Mitochondria01:19

Translocation of Proteins into the Mitochondria

Mitochondrial precursors are translocated to the internal subcompartments via independent mechanisms involving distinct protein machineries called translocases.
Sorting of outer membrane proteins:
Mitochondrial outer membrane proteins are of two types: the transmembrane, beta-barrel porins, and the membrane-anchored, alpha-helical proteins. Beta-barrel porin precursors are translocated by the TOM complex and inserted into the outer mitochondrial membrane by the SAM complex. In contrast,...
Post-translational Translocation of Proteins to the RER01:27

Post-translational Translocation of Proteins to the RER

A sizable fraction of proteins destined for ER are first synthesized in the cell cytosol and then transported across the ER membrane–a process called post-translational translocation. Similar to cotranslationally translocated proteins, these proteins also use the Sec translocon complex to enter the ER lumen.
Targeting proteins to the ER
Hsp40 and Hsp70 chaperone molecules bind the translated proteins in the cytosol to prevent their folding. The chaperone binding helps to keep the signal...

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Gene ontology based transfer learning for protein subcellular localization.

Suyu Mei1, Wang Fei, Shuigeng Zhou

  • 1Software College, Shenyang Normal University, Shenyang, PR China. 061021053@fudan.edu.cn

BMC Bioinformatics
|February 3, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a Gene Ontology Based Transfer Learning Model (GO-TLM) to improve protein subcellular localization prediction by integrating multiple data sources. GO-TLM effectively transfers homologous gene ontology information, significantly enhancing prediction accuracy over existing models.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Genomics

Background:

  • Protein subcellular localization prediction is complex, often requiring integrated multi-source data.
  • Gene Ontology (GO) provides a standardized vocabulary for protein functions and cellular components, useful for predictive modeling.
  • Existing models struggle to estimate the discriminative power of individual GO aspects (biological process, molecular function, cellular component).

Purpose of the Study:

  • To develop an improved model for large-scale protein subcellular localization prediction.
  • To leverage heterogeneous data sources, specifically Gene Ontology (GO) terms, for enhanced prediction accuracy.
  • To address limitations in existing methods by estimating the discriminative abilities of different GO aspects.

Main Methods:

  • Proposed a Gene Ontology Based Transfer Learning Model (GO-TLM) integrating homologous GO terms and protein sequence information.
  • Derived three GO kernels and two spectrum kernels to measure protein similarities.
  • Employed non-parametric cross-validation to explicitly weigh kernel discriminative abilities, merging them into a single kernel for prediction.

Main Results:

  • GO-TLM demonstrated substantial accuracy improvements over baseline models like Euk-mPLoc and MultiLoc-GO across multiple benchmark datasets.
  • Achieved accuracy increases of up to 12.98% against Euk-mPLoc and over 6.67% against MultiLoc-GO on various plant, animal, and fungal datasets.
  • Validated performance on independent test sets, showing significant accuracy gains compared to MultiLoc-GO.

Conclusions:

  • The GO-TLM effectively transfers homologous GO knowledge while mitigating noise from false terms, reducing outliers.
  • Explicitly weighting GO kernels and utilizing homology-based transfer significantly enhances protein subcellular localization prediction performance.
  • The model offers a reliable learning system for improved predictive accuracy in computational biology.