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Related Concept Videos

Protein Transport to the Inner Chloroplast Membrane01:18

Protein Transport to the Inner Chloroplast Membrane

Proteins targeted to the inner chloroplast membrane, or plastid proteins, are transported by two general pathways: the stop-transfer and the re-insertion or post-import pathways. Most plastid proteins carry N-terminal transit sequences and internal import sequences targeting it to the specific chloroplast subcompartment. Proteins targeted by the stop-transfer pathway have internal hydrophobic sequences that inhibit their translocation into the stroma. As a result, these precursors are arrested...
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
Protein Transport to the Outer Chloroplast Membrane01:11

Protein Transport to the Outer Chloroplast Membrane

Chloroplast outer membrane proteins encoded by the nucleus are synthesized in the cytosol. Soon after synthesis, they bind cytosolic factors such as 14-3-3 protein and the Hsp70 chaperones that keep these precursors in an unfolded state until their translocation.
Two models describe the mechanism of precursor recognition and entry across the outer membrane through the TOC complex. Model 1 suggests the newly synthesized precursor binds to the TOC receptor 159 and forms a complex.
Protein Transport to the Stroma01:24

Protein Transport to the Stroma

Chloroplasts are triple membrane structures with an outer membrane, an inner membrane, and a thylakoid membrane, each containing distinct metabolite transporters, membrane translocons, and enzymes. Appropriate sorting and translocating these proteins to their correct membrane systems is essential for chloroplast function.
Protein complexes called the translocon of the outer chloroplast membrane or TOC complex, and the translocon of the inner chloroplast membrane or TIC complex mediate the...
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,...

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Updated: May 20, 2026

TurboID-Based Proximity Labeling for In Planta Identification of Protein-Protein Interaction Networks
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TurboID-Based Proximity Labeling for In Planta Identification of Protein-Protein Interaction Networks

Published on: May 17, 2020

Predicting plant protein subcellular multi-localization by Chou's PseAAC formulation based multi-label homolog

Suyu Mei1

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

Journal of Theoretical Biology
|July 4, 2012
PubMed
Summary

This study introduces a new computational model, MLMK-TLM, for predicting multiple plant protein locations. This advanced method improves accuracy and understanding of plant protein subcellular localization.

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Co-expression of Multiple Chimeric Fluorescent Fusion Proteins in an Efficient Way in Plants

Published on: July 1, 2018

Area of Science:

  • Computational Biology
  • Plant Molecular Biology
  • Bioinformatics

Background:

  • Computational models for protein subcellular localization have advanced significantly.
  • Predicting multiple subcellular locations for plant proteins remains a challenge.
  • Existing models often lack the ability to handle multi-localization predictions effectively.

Purpose of the Study:

  • To develop a novel computational model for predicting multiple subcellular locations of plant proteins.
  • To extend existing multi-kernel transfer learning approaches for plant protein multi-localization.
  • To provide a comprehensive assessment of model performance, including its limitations.

Main Methods:

  • Proposed a multi-label multi-kernel transfer learning model (MLMK-TLM).
  • Introduced a multi-label confusion matrix and adapted probabilistic outputs for multi-label learning.
  • Extended the previously developed MK-TLM (multi-kernel transfer learning) method.
  • Utilized homolog knowledge transfer for predicting novel protein localizations.

Main Results:

  • MLMK-TLM demonstrated superior performance compared to baseline models on a plant protein benchmark dataset.
  • The model effectively handles multi-label learning scenarios for plant protein subcellular localization.
  • The study analyzed the model's misleading tendencies, offering insights into its performance limitations.

Conclusions:

  • MLMK-TLM is an effective computational tool for predicting plant protein subcellular multi-localization.
  • The model's ability to transfer homolog knowledge enhances its applicability to novel proteins.
  • Reporting misleading tendencies provides a more comprehensive evaluation of multi-label prediction models.