STAR Protocols (Mar 2022)

Statistical machine learning for comparative protein dynamics with the DROIDS/maxDemon software pipeline

  • Gregory A. Babbitt,
  • Ernest P. Fokoue,
  • Harsh R. Srivastava,
  • Breanna Callahan,
  • Madhusudan Rajendran

Journal volume & issue
Vol. 3, no. 1
p. 101194

Abstract

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Summary: Comparative analysis of protein structure or sequence alignments often ignores the protein dynamics and function. We offer a graphical user interface to a computing pipeline, complete with molecular visualization, enabling the biophysical simulation and statistical comparison of two-state functional protein dynamics (i.e., single unbound state vs. complex with a ligand, DNA, or protein). We utilize multi-agent machine learning classifiers to identify functionally conserved dynamic motions and compare them in genetic or drug-class variants.For complete details on the use and execution of this profile, please refer to Babbitt et al. (2020b, 2020a, 2018) and Rynkiewicz et al. (2021).

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