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Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
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20D-dynamic representation of protein sequences.

Agata Czerniecka1, Dorota Bielińska-Wąż1, Piotr Wąż2

  • 1Department of Radiological Informatics and Statistics, Medical University of Gdańsk, Tuwima 15, 80-210 Gdańsk, Poland.

Genomics
|December 27, 2015
PubMed
Summary

A novel method represents protein sequences as point masses in a 20D space. This approach utilizes a 20D walk to analyze amino acid distribution and introduces 20D moments of inertia as new protein sequence descriptors.

Keywords:
Alignment-free methodsDescriptorsMoments of inertiaSimilarity/dissimilarity analysis of protein sequences

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Bioinformatics

Background:

  • Protein sequence comparison is crucial for understanding protein function and evolution.
  • Existing methods may not fully capture the complex distribution of amino acids within sequences.

Purpose of the Study:

  • To introduce a new method for comparing protein sequences.
  • To represent protein sequences in a high-dimensional space for analysis.

Main Methods:

  • Representing amino acid sequences as point masses in a 20-dimensional space.
  • Applying a 'walk' method in the 20D space to analyze point distributions.
  • Utilizing projections into 2D or 3D spaces to visualize amino acid distribution.
  • Proposing 20D moments of inertia as novel sequence descriptors.

Main Results:

  • The 20D representation effectively illustrates amino acid distribution patterns.
  • Projections provide insights into the sequential arrangement of specific amino acids.
  • 20D moments of inertia offer a new quantitative descriptor for protein sequences.

Conclusions:

  • The formulated method offers a novel perspective on protein sequence comparison.
  • The 20D representation and moments of inertia can enhance bioinformatics analyses.
  • This approach has potential applications in protein structure and function prediction.