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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.
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A Protocol for Computer-Based Protein Structure and Function Prediction
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Comprehensive Study on Enhancing Low-Quality Position-Specific Scoring Matrix with Deep Learning for Accurate Protein

Yuzhi Guo1,2, Jiaxiang Wu2, Hehuan Ma1

  • 1Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, Texas, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|February 22, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an unsupervised method to enhance protein Position-Specific Scoring Matrix (PSSM) features, improving protein structure property predictions. The enhanced features lead to more accurate predictions, especially for proteins with limited homologous sequences.

Keywords:
deep learningenhancing PSSMproteinprotein solvent accessibilitysecondary structureunsupervised learning

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Structural biology

Background:

  • Accurate prediction of protein structure properties like secondary structure and solvent accessibility is crucial for understanding protein function.
  • Position-Specific Scoring Matrix (PSSM) features are commonly used but can be of low quality due to insufficient homologous sequences, limiting prediction accuracy.

Purpose of the Study:

  • To propose an unsupervised scheme for enhancing Position-Specific Scoring Matrix (PSSM) features.
  • To improve the accuracy of protein structure property prediction by utilizing enhanced PSSM features.

Main Methods:

  • Introduced the "Bagging Multiple Sequence Alignment" (MSA) method to calculate PSSM features.
  • Employed a convolutional network for local context features and bidirectional long short-term memory for long-term dependencies.
  • Integrated these components within an unsupervised framework to generate enhanced PSSM features.

Main Results:

  • Developed structure property prediction models based on the enhanced PSSM features.
  • Evaluated the effectiveness of the enhanced PSSM features using two frameworks and real-world scenarios.
  • Empirical evaluations on CB513, CASP11, and CASP12 datasets demonstrated that the unsupervised enhancing scheme yields more informative PSSM features.

Conclusions:

  • The proposed unsupervised enhancing scheme effectively generates more informative PSSM features.
  • Enhanced PSSM features significantly improve the accuracy of protein structure property predictions.
  • The method addresses limitations of low-quality PSSM features in proteins with insufficient homologous sequences.