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Generative Topographic Mapping of Conformational Space.

Dragos Horvath1, Igor Baskin2, Gilles Marcou1

  • 1Laboratoire de Chémoinformatique, UMR 7140 CNRS-Univ. Strasbourg, 1 rue Blaise Pascal, Strasbourg, 67000, France.

Molecular Informatics
|April 20, 2017
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Summary

Generative Topographic Mapping effectively projected complex molecular structures, enabling accurate property predictions. This method reveals distinct functional and folding regions within molecular conformational spaces.

Keywords:
Conformational SamplingConformational Space MappingGenerative Topographic Maps

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Area of Science:

  • Computational Chemistry
  • Molecular Modeling
  • Cheminformatics

Background:

  • High-dimensional conformational spaces of complex molecules pose challenges for visualization and analysis.
  • Generative Topographic Mapping (GTM) is a probability-based strategy for dimensionality reduction and data visualization.
  • Accurate property prediction models require robust and informative representations of molecular conformations.

Purpose of the Study:

  • To evaluate Generative Topographic Mapping's capability in generating planar projections of high-dimensional conformational spaces.
  • To assess the quality of GTM maps by using them to support property prediction models.
  • To explore novel conformational descriptors for enhanced map building.

Main Methods:

  • Generative Topographic Mapping (GTM) was applied to the conformational space of the 1LE1 peptide.
  • An evolutionary strategy was employed for selecting optimal conformational descriptors, including atom-centric autocorrellograms.
  • Property prediction models were built and cross-validated using a subset of 20K conformers from a pool of 2M generated geometries.
  • The properties predicted included total, non-bonded, and contact energies, surface area, and fingerprint darkness.

Main Results:

  • GTM achieved robust three-fold cross-validated determination coefficients (Q^2 = 0.7-0.8) for all modeled properties.
  • Mapping the full conformer set yielded intuitive and information-rich property landscapes.
  • Distinct zones corresponding to functional and folding subspaces were identified, without using RMSD to PDB structure as a criterion.

Conclusions:

  • Generative Topographic Mapping is a powerful tool for visualizing and analyzing high-dimensional molecular conformational spaces.
  • The use of novel descriptors like atom-centric autocorrellograms enhances the precision of GTM in capturing subtle conformational differences.
  • GTM-based property prediction provides an objective measure of map quality and reveals meaningful biological insights into molecular behavior.