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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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GPU-based detection of protein cavities using Gaussian surfaces.

Sérgio E D Dias1,2, Ana Mafalda Martins1, Quoc T Nguyen1,2

  • 1Universidade da Beira Interior, Av. Marques D'Ávila e Bolama, Covilhã, 6200-001, Portugal.

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|November 18, 2017
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Summary
This summary is machine-generated.

This study introduces a novel geometric algorithm to accurately detect protein cavities by resolving ambiguities in grid-based methods. It uses two implicit isosurfaces to define cavities, improving drug discovery and design.

Keywords:
Cavity detectionGaussian kernel functionGaussianFinderPocket detection

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Area of Science:

  • Structural Biology
  • Computational Chemistry
  • Drug Discovery

Background:

  • Protein cavities are crucial for molecular recognition and interactions, particularly in drug discovery.
  • Existing grid-based methods for cavity detection suffer from sensitivity to scanning directions (cavity ground-and-walls ambiguity) and difficulty distinguishing cavity nodes (cavity ceiling ambiguity).

Purpose of the Study:

  • To address the limitations of current grid-based protein cavity detection methods.
  • To develop a novel geometric algorithm for robust and accurate protein cavity identification.

Main Methods:

  • The study employs two implicit isosurfaces of a protein: an inner isosurface (protein surface) and an outer isosurface.
  • Cavities are identified as grid nodes located between these two analytically defined isosurfaces.
  • The method avoids the need for explicit surface evaluation, triangulation, or rendering.

Main Results:

  • The proposed algorithm effectively resolves the cavity ground-and-walls and cavity ceiling ambiguities inherent in previous methods.
  • It accurately identifies protein cavities by defining them as regions between two implicit Gaussian surfaces.
  • The method's reliance on analytic functions simplifies the detection process.

Conclusions:

  • A novel geometric algorithm utilizing analytic functions of two Gaussian surfaces has been developed for protein cavity detection.
  • This approach offers a more robust and less ambiguous method for identifying cavities on protein surfaces.
  • The findings have significant implications for advancing drug discovery and design through improved protein-ligand interaction analysis.