Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Mitochondrial Protein Sorting

Mitochondrial Protein Sorting

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.
Most of these mitochondrial proteins are encoded by the nucleus and imported to the mitochondria as unfolded or loosely folded precursors. Mitochondrial precursors...

Translocation of Proteins into the Mitochondria

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,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

[Association between genetic polymorphism of tumor necrosis factor and chronic severe hepatitis B in patients].

Zhonghua yi xue za zhi·2007

Same author

In vivo translational inaccuracy in Escherichia coli: missense reporting using extremely low activity mutants of Vibrio harveyi luciferase.

Biochemistry·2007

Same author

[Construction of recombinant adenovirus vector expressing extracellular domain of TbetaR-II-RANTES fusion gene and its anti-tumor effects].

Zhonghua zhong liu za zhi [Chinese journal of oncology]·2007

Same author

[Characteristics, evolution and variation of M genes of human avian H5N1 strains in Guangdong].

Bing du xue bao = Chinese journal of virology·2007

Same author

Dynamic changes in microbial activity and community structure during biodegradation of petroleum compounds: a laboratory experiment.

Journal of environmental sciences (China)·2007

Same author

Differences in optical transport properties between human meridian and non-meridian.

The American journal of Chinese medicine·2007

Search research articles

Related Experiment Video

Updated: Oct 26, 2025

Assessment of Submitochondrial Protein Localization in Budding Yeast Saccharomyces cerevisiae

Assessment of Submitochondrial Protein Localization in Budding Yeast Saccharomyces cerevisiae

Published on: July 19, 2021

iDeepSubMito: identification of protein submitochondrial localization with deep learning.

Zilong Hou¹, Yuning Yang², Hui Li³

¹School of Artificial Intelligence, Jilin University, Jilin, China.

Briefings in Bioinformatics

|August 2, 2021

Summary

This summary is machine-generated.

We developed iDeepSubMito, a novel computational method for predicting mitochondrial protein locations. This deep learning approach accurately identifies protein destinations within mitochondria, improving upon existing methods.

Keywords:

deep learning protein sequences protein submitochondrial localization

More Related Videos

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Published on: June 30, 2018

Related Experiment Videos

Last Updated: Oct 26, 2025

Assessment of Submitochondrial Protein Localization in Budding Yeast Saccharomyces cerevisiae

Assessment of Submitochondrial Protein Localization in Budding Yeast Saccharomyces cerevisiae

Published on: July 19, 2021

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Published on: June 30, 2018

Area of Science:

Mitochondrial biology
Computational biology
Bioinformatics

Background:

Mitochondria contain over 1000 proteins crucial for function, gene expression, and metabolism.
Accurate prediction of protein localization within mitochondrial compartments is essential for understanding their roles.
Existing computational methods for predicting protein localization have limitations in feature extraction and efficiency.

Purpose of the Study:

To propose and validate a novel computational method, iDeepSubMito, for predicting the submitochondrial localization of mitochondrial proteins.
To address the limitations of current methods by incorporating advanced deep learning techniques.
To enhance the accuracy and efficiency of predicting protein localization within mitochondria.

Main Methods:

Utilized ProteinELMo for a coding scheme to model protein sequence probability distributions and represent sequences as continuous vectors.
Developed a convolutional neural network architecture integrating bidirectional LSTM with a self-attention mechanism.
Explored contextual information and semantic features within protein sequences for improved prediction.

Main Results:

iDeepSubMito demonstrated superior performance compared to existing computational methods in cross-validation tests on datasets of 424 and 570 proteins.
The method was further validated on M187, M983, and MitoCarta3.0 datasets, confirming its efficiency.
Motif and interpretability analyses provided novel insights into the subcellular functions of mitochondrial proteins.

Conclusions:

iDeepSubMito offers a highly effective and accurate computational approach for predicting mitochondrial protein localization.
The deep learning architecture effectively captures sequence features and contextual information for improved identification.
The method advances our understanding of mitochondrial protein function and localization, with potential applications in biological research.