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Protein Organization01:24

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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 Multi-Objective Approach for Protein Structure Prediction Based on an Energy Model and Backbone Angle Preferences.

Jyh-Jong Tsay1, Shih-Chieh Su2, Chin-Sheng Yu3

  • 1Department of Computer Science and Information Engineering, National Chung Cheng University, Min-Hsiung Township, Chia-yi County 62102, Taiwan. tsay@cs.ccu.edu.tw.

International Journal of Molecular Sciences
|July 8, 2015
PubMed
Summary

This study introduces a new backbone angle preference factor to improve protein structure prediction (PSP). The novel multiobjective optimization approach enhances accuracy in ab initio PSP compared to traditional energy models.

Keywords:
backbone angle preferencesface-centered cubicmultiobjective optimizationprotein structure

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Area of Science:

  • Computational biology
  • Biophysics
  • Structural bioinformatics

Background:

  • Protein structure prediction (PSP) aims to determine a protein's 3D structure from its amino acid sequence.
  • Existing optimization methods based on energy models require further improvement for accuracy and structural similarity.
  • Understanding protein folding mechanisms is crucial for advancing PSP.

Purpose of the Study:

  • To introduce a novel backbone angle preference factor to enhance ab initio protein structure prediction.
  • To develop and evaluate a multiobjective optimization approach integrating energy models and backbone angle preferences.
  • To improve the accuracy and similarity of predicted protein structures.

Main Methods:

  • Developed a multiobjective optimization approach for ab initio PSP.
  • Incorporated a novel backbone angle preference factor alongside traditional energy models.
  • Validated the approach using 75 diverse amino acid sequences from the CB513 dataset (22-88 amino acids).

Main Results:

  • The multiobjective optimization approach demonstrated superior performance compared to typical energy models.
  • Achieved lower root-mean-square deviation (RMSD), indicating higher accuracy in structure prediction.
  • The method proved effective on a highly dissimilar and meaningful benchmark dataset.

Conclusions:

  • The proposed multiobjective optimization approach, incorporating backbone angle preferences, significantly facilitates ab initio protein structure prediction.
  • This novel method offers improved accuracy and structural similarity over existing energy-based models.
  • The findings contribute to advancing computational methods for understanding protein folding and structure.