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Fusion of Secretory Vesicles with the Plasma Membrane01:26

Fusion of Secretory Vesicles with the Plasma Membrane

11.0K
Proteins and neurotransmitters in secretory vesicles can be released from a cell upon vesicle docking, priming, and fusion with the plasma membrane. Vesicles are docked and primed in preparation for the quick exocytosis of their contents in response to a stimulus. The fusion process is mainly carried out by a SNAP Receptor or SNARE complex, consisting of synaptobrevin, syntaxin-1, and SNAP-25.
In 1993, Jim Rothman proposed that the antiparallel pairing of vesicular and transmembrane SNAREs, or...
11.0K
SNAREs and Membrane Fusion01:43

SNAREs and Membrane Fusion

10.9K
Once a transport vesicle has recognized its target organelle, the vesicular membrane needs to fuse with the target membrane to unload the cargo. Transmembrane proteins called SNAREs present on organelle membranes and their vesicles, mediate vesicle fusion.
SNAREs exist in pairs that symmetrically interact and catalyze the fusion of the lipid bilayers in vesicle and target organelle. v-SNARE in the vesicle membrane are single polypeptide chains that bind to a complementary t-SNARE, composed of 2...
10.9K
Overview of Secretory Vesicles01:33

Overview of Secretory Vesicles

8.5K
Secretory vesicles, also known as dense core vesicles (DCVs), are membrane-bound vesicles that transport secretory proteins, such as hormones or neurotransmitters. Regulated secretory vesicles transport proteins from the trans-Golgi network to the exterior of the cell. Proteins present in regulated secretory vesicles are required to be rapidly exocytosed in large amounts upon a specific stimulus.
Various proteins regulate the aggregation of molecules inside the secretory vesicles. Chromogranins...
8.5K
Pinching-off of Coated Vesicles01:32

Pinching-off of Coated Vesicles

3.1K
Vesicle budding is orchestrated by distinct cytosolic proteins such as adaptor proteins, coat proteins, and GTPases. To initiate vesicle budding, membrane-bending proteins containing crescent-shaped BAR domains bind to the lipid heads in the bilayer and distort the membrane to form a protein-coated vesicle bud. Adaptors proteins such as AP2 for clathrin-coated vesicles can nucleate on the deformed membrane. Finally, coat proteins such as clathrin or COPI and COPII assemble into a coat forming...
3.1K
Vesicular Tubular Clusters01:45

Vesicular Tubular Clusters

2.5K
After budding out from the ER membrane, some COPII vesicles lose their coat and fuse with one another to form larger vesicles and interconnected tubules called vesicular tubular clusters or VTCs. These clusters constitute a compartment at the ER-Golgi interface known as ERGIC (Endoplasmic Reticulum Golgi Intermediate Compartment). The ERGIC is a mobile membrane-bound cargo transport system that sorts proteins secreted from ER and delivers them to the Golgi.
With the help of motor proteins such...
2.5K
COP Coated Vesicles00:59

COP Coated Vesicles

7.8K
Membrane-enclosed structures called vesicles transport proteins and lipids across the cell. The vesicles derive their cargo from the plasma membrane, Golgi, ER, or endosome. Coated vesicles are spherical, protein-coated carriers with a 50–100 nm diameter that mediate bidirectional transport between the ER and the Golgi. The distribution of proteins between the ER and Golgi complex is dynamic and is maintained by different coated vesicles. Their formation is driven by the assembly of...
7.8K

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Related Experiment Video

Updated: Jun 23, 2025

In Vesiculo Synthesis of Peptide Membrane Precursors for Autonomous Vesicle Growth
07:10

In Vesiculo Synthesis of Peptide Membrane Precursors for Autonomous Vesicle Growth

Published on: June 28, 2019

5.7K

Neural-network-based solver for vesicle shapes predicted by the Helfrich model.

Yousef Rohanizadegan1, Hong Li2, Jeff Z Y Chen1

  • 1Department of Physics and Astronomy, University of Waterloo, Ontario, N2L3G1, Canada. yrohaniz@uwaterloo.ca.

Soft Matter
|June 24, 2024
PubMed
Summary
This summary is machine-generated.

Artificial neural networks can model three-dimensional vesicle shapes. This machine learning approach simplifies representing deformable membrane surfaces and calculating their energy minimization for various shapes.

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Area of Science:

  • Biophysics
  • Computational Biology
  • Materials Science

Background:

  • Modeling three-dimensional vesicle morphology is crucial for understanding biological and synthetic membrane systems.
  • Traditional methods for representing deformable membrane surfaces can be complex and computationally intensive.

Purpose of the Study:

  • To propose and demonstrate a novel method for modeling three-dimensional vesicle morphology using artificial neural networks.
  • To adapt the Helfrich bending energy into a field-based representation for direct surface modeling.
  • To utilize machine learning for efficient energy minimization in vesicle shape computation.

Main Methods:

  • Phase-field representation of membrane energy.
  • Equivalence of Helfrich bending energy to field-based energy.
  • Application of artificial neural networks for energy minimization.
  • Computation of both axisymmetric and nonsymmetric vesicle shapes.

Main Results:

  • Demonstration that artificial neural networks can effectively model three-dimensional vesicle morphology.
  • Successful adaptation of Helfrich energy into a field-based representation.
  • Efficient computation of vesicle shapes using machine learning techniques.

Conclusions:

  • Artificial neural networks provide a powerful and versatile tool for modeling complex three-dimensional vesicle morphologies.
  • The proposed method offers a more direct and efficient approach to representing and analyzing deformable membrane surfaces.
  • This approach has the potential to advance research in biophysics, materials science, and drug delivery systems.