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Related Concept Videos

Overview of Protein Sorting and Transport01:45

Overview of Protein Sorting and Transport

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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...
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Cotranslational Protein Translocation01:20

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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
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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...
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Nuclear Protein Sorting01:34

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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.
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Regulated mRNA Transport02:22

Regulated mRNA Transport

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In eukaryotes, transcription and translation are compartmentalized; an mRNA is first synthesized in the nucleus and then selectively transported to the cytoplasm for protein synthesis. Before transport, a pre-mRNA undergoes several steps of post-transcriptional modifications including splicing, 5' capping, and the addition of a poly-adenine tail. Various proteins bind to the pre-mRNA during these modifications. The mRNA transport takes place with the help of multiple proteins playing...
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Protein Translocation Machinery on the ER Membrane01:28

Protein Translocation Machinery on the ER Membrane

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The translocon complex situated on the ER membrane is the main gateway for the protein secretory pathway. It facilitates the transport of nascent peptides into the ER lumen and their insertion into the ER membrane.
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Related Experiment Video

Updated: Oct 23, 2025

Localizing Protein in 3D Neural Stem Cell Culture: a Hybrid Visualization Methodology
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Translation of Cellular Protein Localization Using Convolutional Networks.

Kei Shigene1, Yuta Hiasa2, Yoshito Otake2

  • 1Division of Biological Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan.

Frontiers in Cell and Developmental Biology
|August 23, 2021
PubMed
Summary

Artificial intelligence predicts protein localization. Convolutional networks accurately map related cytoskeletal proteins like WAVE2, IRSp53, and vinculin from actin images, revealing functional relationships.

Keywords:
Pix2pixWAVE2image conversionlamellipodiamachine learning

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Area of Science:

  • Cell Biology
  • Bioinformatics
  • Artificial Intelligence

Background:

  • Protein localization is crucial for cellular function and is typically studied using fluorescent labeling.
  • Analyzing the relationships between the localization of different proteins has been a challenge.
  • Artificial intelligence (AI) offers novel approaches to study these complex biological questions.

Purpose of the Study:

  • To apply convolutional neural networks (CNNs) for predicting protein localization based on images of other proteins.
  • To investigate the ability of AI to infer functional relationships between proteins through their subcellular localization patterns.
  • To determine if actin filament images contain sufficient information to predict the localization of associated proteins.

Main Methods:

  • Utilized a convolutional neural network architecture for image-to-image translation.
  • Trained the CNN using paired microscopic images of actin filaments and proteins such as WAVE2, IRSp53, vinculin, and microtubules.
  • Evaluated the accuracy of predicted protein localization images generated by the AI model.

Main Results:

  • The CNN successfully generated highly similar images for WAVE2, IRSp53, and vinculin localization when trained on actin filament images.
  • Prediction of microtubule localization from actin filament images was less accurate, lacking characteristic filamentous structures.
  • This indicates that actin-related protein localization is more predictable from actin images than non-related structures.

Conclusions:

  • Image translation using CNNs can effectively predict the localization of functionally related proteins.
  • AI models, specifically CNNs, show potential for elucidating protein-protein relationships based on their spatial distribution within cells.
  • Microscopic images of actin filaments provide rich information for predicting the localization of actin-associated proteins.