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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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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
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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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Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
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A Protocol for Computer-Based Protein Structure and Function Prediction
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Reduction strategies for hierarchical multi-label classification in protein function prediction.

Ricardo Cerri1, Rodrigo C Barros2, André C P L F de Carvalho3

  • 1Department of Computer Science, UFSCar Federal University of São Carlos, Rodovia Washington Luís, Km 235, São Carlos, 13565-905, SP, Brazil. cerri@dc.ufscar.br.

BMC Bioinformatics
|September 16, 2016
PubMed
Summary

This study introduces a novel hierarchical multi-label classification method using multiple neural networks for protein function prediction. The approach enhances accuracy by feeding outputs from one network level as inputs to the next, improving protein function prediction performance.

Keywords:
Hierarchical multi-label classificationMachine learningNeural networksProtein function prediction

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Hierarchical Multi-Label Classification (HMLC) involves predicting classes organized in a hierarchy, where instances can belong to multiple paths.
  • This is common in protein function prediction, as proteins can have multiple specialized functions.
  • Existing methods face challenges in effectively modeling these complex hierarchical relationships.

Purpose of the Study:

  • To develop a novel HMLC method for accurate protein function prediction.
  • To leverage a multi-neural network architecture for improved classification performance.
  • To enhance feature representation by incorporating outputs from preceding network levels.

Main Methods:

  • A novel HMLC method employing an ensemble of incrementally trained neural networks.
  • Each neural network is responsible for predicting classes at a specific hierarchical level.
  • Outputs from a neural network at one level are used as complementary input features for the next level's network.

Main Results:

  • The proposed method demonstrated superior predictive performance compared to various reduction strategies.
  • It achieved competitive or better results than state-of-the-art HMLC methods in protein function prediction.
  • Empirical evidence confirmed the benefit of using sequential level outputs as input features.

Conclusions:

  • Integrating outputs from one level to the next significantly improves classification accuracy.
  • The method effectively learns relationships between protein functions during training.
  • Specific functional classes where the method excels were identified, providing insights for future research.