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Related Concept Videos

Protein Networks02:26

Protein Networks

4.1K
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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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-Protein Interfaces

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Protein Organization01:24

Protein Organization

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Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....
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Proteomics01:33

Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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Ligand Binding Sites02:40

Ligand Binding Sites

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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.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
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Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells
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Computational Methods and Deep Learning for Elucidating Protein Interaction Networks.

Dhvani Sandip Vora1, Yogesh Kalakoti1, Durai Sundar2,3

  • 1Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India.

Methods in Molecular Biology (Clifton, N.J.)
|October 13, 2022
PubMed
Summary
This summary is machine-generated.

Predicting protein interactions using machine learning accelerates biological discovery. This review covers methods, applications, and challenges in computational protein interaction prediction, highlighting future directions.

Keywords:
Deep learningInteractionMachine learningNeural networksPPIProtein networks

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Molecular biology

Background:

  • Protein interactions are fundamental to all cellular functions.
  • Experimental methods for identifying protein interactions are laborious and costly.
  • Advances in omics technologies enable large-scale interaction prediction.

Purpose of the Study:

  • To review computational methods for predicting protein interactions.
  • To discuss applications and challenges in the field.
  • To outline future prospects in protein interaction biology.

Main Methods:

  • Machine learning approaches for predicting protein-protein, protein-nucleic acid, and protein-drug interactions.
  • Leveraging next-generation sequencing and multi-omics data.
  • Analysis of existing prediction tools and their methodologies.

Main Results:

  • Machine learning significantly enhances the scale and efficiency of protein interaction prediction.
  • Diverse computational tools are available for various interaction types.
  • Key challenges include data quality, model interpretability, and experimental validation.

Conclusions:

  • Computational methods are crucial for advancing protein interaction studies.
  • Overcoming current challenges will drive future innovations.
  • The field holds significant promise for understanding complex biological systems.