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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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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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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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DeepEP: a deep learning framework for identifying essential proteins.

Min Zeng1, Min Li2, Fang-Xiang Wu3

  • 1School of Computer Science and Engineering, Central South University, Changsha, 410083, People's Republic of China.

BMC Bioinformatics
|December 3, 2019
PubMed
Summary
This summary is machine-generated.

DeepEP, a deep learning framework, accurately identifies essential proteins by learning topological and semantic features from protein-protein interaction networks. This method effectively addresses the challenges of imbalanced data in essential protein identification.

Keywords:
Deep learningIdentifying essential proteinsImbalanced learningMulti-scale convolutional neural networksProtein-protein interaction networknode2vec

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Identifying essential proteins is critical for understanding cellular life but remains a significant computational challenge.
  • Traditional network analysis methods struggle to capture complex topological features of biological networks.
  • Essential protein identification is an imbalanced learning problem, often unaddressed by current machine learning approaches.

Purpose of the Study:

  • To develop a novel deep learning framework, DeepEP, for accurate identification of essential proteins.
  • To leverage node2vec and multi-scale convolutional neural networks for feature extraction from protein-protein interaction (PPI) networks and gene expression data.
  • To address data imbalance in essential protein identification using a specialized sampling technique.

Main Methods:

  • DeepEP integrates the node2vec technique to learn topological and semantic features from PPI networks.
  • Multi-scale convolutional neural networks are employed to extract patterns from gene expression profiles, treated as image data.
  • A novel sampling method is utilized during training to mitigate the effects of imbalanced datasets by balancing majority and minority class samples.

Main Results:

  • DeepEP significantly outperforms traditional centrality methods in essential protein identification.
  • The proposed framework demonstrates superior performance compared to existing shallow machine learning-based methods.
  • Analysis confirms that node2vec-generated dense vectors effectively capture crucial PPI network properties, enhancing prediction accuracy.
  • The implemented sampling method demonstrably improves the performance of essential protein identification.

Conclusions:

  • DeepEP enhances essential protein prediction by synergistically combining deep learning techniques and a data balancing sampling method.
  • The study validates DeepEP's superior effectiveness over existing computational approaches for identifying essential proteins.