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Related Concept Videos

Translocation of Proteins into the Mitochondria01:19

Translocation of Proteins into the Mitochondria

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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,...
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Mitochondrial Precursor Proteins01:39

Mitochondrial Precursor Proteins

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Mitochondrial precursors are partially unfolded or loosely folded polypeptide chains. Newly synthesized precursors are inhibited from spontaneously folding into their native conformation by the cytosolic chaperones, heat shock proteins 70 (Hsp70), and mitochondrial import stimulation factors (MSFs). Precursors bound to MSFs are guided to the TOM70-TOM37 receptors, while precursors bound to Hsp70  chaperones are targetted to TOM20-TOM22 receptor complexes.
Most of the mitochondrial...
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Mitochondrial Protein Sorting01:39

Mitochondrial Protein Sorting

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Mitochondria are double-membrane organelles of the eukaryotes involved in cellular metabolism, signaling, ATP synthesis, and programmed cell death.  Each of these processes requires specific proteins and enzymes that must be correctly sorted to the right mitochondrial subcompartment for the proper functioning of the organelle.
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Protein Transport into the Inner Mitochondrial Membrane01:34

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Nuclear encoded mitochondrial precursors are imported to the inner membrane in a multistep process involving two separate translocons, TIM22 and TIM23. TIM23 is a cation-selective pore that remains closed by the N terminal segment of the protein. Negative charges on the TIM23 act as a receptor for the incoming precursor, pulling the positively charged matrix-targeting sequence for peptide insertion and translocation.
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Ribosome Profiling02:24

Ribosome Profiling

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
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Related Experiment Video

Updated: Apr 21, 2026

Assessment of Submitochondrial Protein Localization in Budding Yeast Saccharomyces cerevisiae
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Protein submitochondrial localization from integrated sequence representation and SVM-based backward feature

Liqi Li1, Sanjiu Yu, Weidong Xiao

  • 1Department of General Surgery, Xinqiao Hospital, Third Military Medical University, Chongqing 400037, China. yanghuaxq@163.com.

Molecular Biosystems
|October 22, 2014
PubMed
Summary

Predicting protein submitochondrial locations is crucial for understanding human diseases. A new computational method using support vector machines (SVM) and integrated features achieves high accuracy, offering an efficient alternative to experimental techniques.

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Area of Science:

  • Cell Biology
  • Biochemistry
  • Bioinformatics

Background:

  • Mitochondria are vital organelles in eukaryotic cells, essential for energy production.
  • Mitochondrial dysfunction is linked to numerous human diseases.
  • Determining protein location within mitochondria is key to understanding cellular functions and disease mechanisms.

Purpose of the Study:

  • To develop a fast, reliable, and computational method for predicting protein submitochondrial locations.
  • To overcome the limitations of costly and time-consuming experimental methods.

Main Methods:

  • A support vector machine (SVM) based computational approach was developed.
  • Integrated features from Position-Specific Score Matrix (PSSM), Gene Ontology (GO), and protein features (PROFEAT) were utilized.
  • Recursive feature selection was employed to identify optimal predictive features.

Main Results:

  • The proposed SVM method achieved high prediction accuracy: 99.37% on dataset M317 and 100% on datasets M1105 and T86.
  • The method demonstrated effectiveness through jackknife cross-validation tests.
  • The integrated feature set and SVM model proved highly predictive.

Conclusions:

  • The developed computational method provides an economic and effective solution for accurate protein submitochondrial location prediction.
  • This approach can accelerate research into mitochondrial functions and associated diseases.
  • The findings highlight the potential of machine learning in biological research.