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Interactive Interface for Graph-Based Analyses of Dynamic H-Bond Networks: Application to Spike Protein S.

Malte SiemersAna-Nicoleta Bondar
Published in: Journal of chemical information and modeling (2021)
Dynamic hydrogen-bond networks are key determinants of protein conformational dynamics. In the case of macromolecular protein complexes, which can have a large number of hydrogen bonds giving rise to extensive hydrogen-bond networks, efficient algorithms are required to analyze interactions that could be important for the dynamics and biological function of the complex. We present here a highly efficient, standalone interface designed for analyses of dynamical hydrogen-bond networks of biomolecules and macromolecular complexes. To facilitate a comprehensive description of protein dynamics, the interface includes analyses of hydrophobic interactions. We illustrate the usefulness and workflow of the interface by dissecting the dynamics of the ectodomain of SARS-CoV-2 protein S in its closed conformation. We find that protein S contains numerous local clusters of dynamic hydrogen bonds and identify hydrogen bonds that are sampled persistently. The receptor binding domain of the spike protein hosts only a handful of persistent hydrogen-bond clusters, suggesting structural plasticity. Our data analysis interface is released here for open use.
Keyphrases
  • sars cov
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  • machine learning
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  • deep learning
  • transition metal
  • respiratory syndrome coronavirus