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Guide tree optimization with genetic algorithm to improve multiple protein 3D-structure alignment.

Maksim V Shegay1, Vytas K Švedas2,3, Vladimir V Voevodin1,4

  • 1Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Vorobjev Hills, Moscow 119991, Russia.

Bioinformatics (Oxford, England)
|December 1, 2021
PubMed
Summary
This summary is machine-generated.

Genetic algorithms optimize protein 3D-structure alignments, significantly improving accuracy for comparative bioinformatics. This tool enhances the study of protein structure-function relationships.

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Area of Science:

  • Bioinformatics
  • Structural Biology
  • Computational Biology

Background:

  • Comparative bioinformatic analysis is increasingly utilizing 3D structural data over sequence alignments.
  • Improving the accuracy of 3D-superimposition is crucial for advanced structural bioinformatics.

Purpose of the Study:

  • To develop a universal tool for systematically improving multiple protein 3D-structure alignment (M3DSA) quality.
  • To enhance the accuracy of 3D-alignments using genetic algorithms (GA).

Main Methods:

  • Proposed guide tree optimization using a genetic algorithm (GA).
  • Implemented the GA-based approach in Matt and Caretta M3DSA algorithms.
  • Validated performance on SABmark Superfamilies and Twilight datasets.

Main Results:

  • Statistically significant improvement in TM-score quality indicator by up to 220-1523%.
  • Improvements observed in 49-77% of Superfamilies and 59-80% of Twilight cases.
  • Demonstrated GA's potential for optimizing diverse protein superfamily 3D-alignments.

Conclusions:

  • GA-based guide tree optimization is an effective method for enhancing M3DSA.
  • This approach aids in studying structure-function relationships in proteins.
  • The optimized algorithms (gaMatt and gaCaretta) are publicly available.