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Related Concept Videos

Protein Networks02:26

Protein Networks

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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.
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 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.
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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.
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Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key...
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A deep learning framework for identifying essential proteins based on multiple biological information.

Yi Yue1,2,3,4, Chen Ye5,6, Pei-Yun Peng5,6

  • 1Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, 230036, China. yyyue@ahau.edu.cn.

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|August 4, 2022
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Summary

This study introduces a deep learning framework to identify essential proteins using protein-protein interaction networks, subcellular localization, and gene expression. The model significantly improves essential protein prediction accuracy.

Keywords:
Deep learningEssential proteinGene expressionProtein–protein interaction networkSubcellular localization

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Essential proteins are critical for cellular functions and organism survival.
  • Traditional methods struggle with complex protein-protein interaction (PPI) networks.
  • Existing machine learning methods overlook temporal and spatial biological data.

Purpose of the Study:

  • To develop a deep learning framework for accurate essential protein prediction.
  • To integrate diverse biological data, including PPI networks, subcellular localization, and gene expression.
  • To overcome limitations of traditional and existing machine learning approaches.

Main Methods:

  • Utilized node2vec for continuous protein feature representation in PPI networks.
  • Applied depthwise separable convolution to gene expression profiles for temporal analysis.
  • Integrated subcellular localization information as a one-dimensional vector.
  • Employed a sampling method to address imbalanced learning challenges.

Main Results:

  • The proposed deep learning model outperformed traditional centrality and other machine learning methods.
  • Experiments on Saccharomyces cerevisiae data demonstrated superior performance.
  • Integration of multiple biological data sources proved advantageous.

Conclusions:

  • The deep learning framework effectively identifies essential proteins by integrating diverse biological data.
  • Enhanced subcellular localization information significantly improves prediction accuracy.
  • Depthwise separable convolution on gene expression data boosts model performance.