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Related Concept Videos

Protein-protein Interfaces02:04

Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Protein Networks02:26

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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.
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Conserved Binding Sites01:49

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Cell Adhesion Molecules - Types and Functions01:20

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Cell adhesion molecules (CAMs) are pivotal to multicellularity and the coordinated functioning of tissues and organ systems. They enable physical interactions between cells and provide mechanical strength to tissues. They also function as receptors for signal transmission across the plasma membrane. The CAMs are broadly classified into four families - integrins, cadherins, selectins, and immunoglobulin-like CAMs (IgCAMs).
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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Overview of Cell-Matrix Interactions01:24

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The extracellular matrix or ECM holds cells together to form a tissue and allows the cells within the tissue to communicate. ECM comprises proteins such as fibronectin, collagen, laminin, etc. The most abundant protein in this space is collagen. Collagen fibers are interwoven with carbohydrate-containing protein molecules called proteoglycans. ECM allows cell migration and provides a structural scaffold at cell adhesion that anchors the cell when the extracellular matrix proteins interact with...
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In-vivo Detection of Protein-protein Interactions on Micro-patterned Surfaces
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Prediction of Interactions between Cell Surface Proteins by Machine Learning.

Zhaoqian Su1, Brian Griffin2, Scott Emmons2

  • 1Department of Systems and Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461.

Biorxiv : the Preprint Server for Biology
|September 25, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a computational framework to predict cell surface protein interactions, focusing on immunoglobulin (Ig) domains. The machine learning models achieve over 70% accuracy, aiding in the discovery of novel protein-protein interactions (PPIs).

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Area of Science:

  • Computational biology and bioinformatics
  • Molecular and cell biology
  • Machine learning in protein science

Background:

  • Cell surface proteins mediate crucial cellular functions through complex interaction networks.
  • The dynamic nature of these protein-protein interactions (PPIs) poses challenges for traditional experimental methods.
  • Immunoglobulin (Ig) folds are the most abundant domain family in cell surface proteins, mediating diverse interactions.

Approach:

  • Developed a computational framework to identify interactions between Ig domains in cell surface proteins.
  • Created an interface fragment pair library from structural data of Ig domain interactions.
  • Utilized machine learning models to predict the probability of interaction based on high-dimensional profiles derived from protein sequences.

Key Points:

  • Achieved over 70% accuracy in predicting PPIs on an experimentally derived dataset of 564 human cell surface proteins.
  • Successfully screened all possible interactions among 46 cell surface proteins in C. elegans, identifying many literature-confirmed interactions.
  • The computational platform is freely accessible to the scientific community for identifying potential new cell surface protein interactions.

Conclusions:

  • The developed computational platform offers a valuable tool for discovering novel cell surface protein interactions, complementing experimental techniques.
  • The machine learning framework is adaptable for studying interactions within other protein domain superfamilies.
  • This approach enhances our understanding of cell communication and environmental sensing mechanisms.