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

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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).

Original languageEnglish
Article number101194
JournalSTAR Protocols
Volume3
Issue number1
DOIs
StatePublished - 18 Mar 2022
Externally publishedYes

Keywords

  • Bioinformatics
  • Biophysics
  • Computer sciences
  • Evolutionary biology
  • Protein Biochemistry
  • Structural Biology
  • Systems biology

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