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Relative Principal Components Analysis: Application to Analyzing Biomolecular Conformational Changes

View ORCID ProfileMazen Ahmad, Volkhard Helms, Olga V. Kalinina, Thomas Lengauer
doi: https://doi.org/10.1101/409474
Mazen Ahmad
aComputational Biology Research Group, Max Planck Institute for Informatics, Saarland Informatics Campus, Campus E1 4, 66123 Saarbrücken, Germany
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  • ORCID record for Mazen Ahmad
  • For correspondence: mahmad@mpi-inf.mpg.de
Volkhard Helms
bCenter for Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
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Olga V. Kalinina
aComputational Biology Research Group, Max Planck Institute for Informatics, Saarland Informatics Campus, Campus E1 4, 66123 Saarbrücken, Germany
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Thomas Lengauer
aComputational Biology Research Group, Max Planck Institute for Informatics, Saarland Informatics Campus, Campus E1 4, 66123 Saarbrücken, Germany
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Abstract

A new method termed “Relative Principal Components analysis” (RPCA) is introduced that extracts optimal relevant principal components to describe the change between two data samples representing two macroscopic states. The method is widely applicable in data-driven science. Calculating the components is based on a unified physical framework which introduces the objective function, namely the Kullback-Leibler divergence, appropriate for quantifying the change of the macroscopic state as it is effected by the microscopic features. To demonstrate the applicability of RPCA, we analyze the thermodynamically relevant conformational changes of the protein HIV-1 protease upon binding to different drug molecules. In this case, the RPCA method provides a sound thermodynamic foundation for the analysis of the binding process. The relevant collective (global) conformational changes can be reconstructed from the informative latent variables to exhibit both the enhanced and the restricted conformational fluctuations upon ligand association. Moreover, RPCA characterizes the locally relevant conformational changes which can be presented on the structure of the protein.

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Posted October 20, 2018.
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Relative Principal Components Analysis: Application to Analyzing Biomolecular Conformational Changes
Mazen Ahmad, Volkhard Helms, Olga V. Kalinina, Thomas Lengauer
bioRxiv 409474; doi: https://doi.org/10.1101/409474
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Relative Principal Components Analysis: Application to Analyzing Biomolecular Conformational Changes
Mazen Ahmad, Volkhard Helms, Olga V. Kalinina, Thomas Lengauer
bioRxiv 409474; doi: https://doi.org/10.1101/409474

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