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

doi:10.1016/j.xpro.2022.101194

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

STAR Protoc. 2022 Feb 24;3(1):101194. doi: 10.1016/j.xpro.2022.101194. eCollection 2022 Mar 18.

Authors

Gregory A Babbitt¹, Ernest P Fokoue², Harsh R Srivastava¹, Breanna Callahan¹, Madhusudan Rajendran¹

Affiliations

¹ Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, NY 14623, USA.
² School of Mathematical Sciences, Rochester Institute of Technology, Rochester, NY 14623, USA.

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).

Keywords: Bioinformatics; Biophysics; Computer sciences; Evolutionary biology; Protein Biochemistry; Structural Biology; Systems biology.

MeSH terms

Computer Simulation
Machine Learning
Proteins* / chemistry
Sequence Alignment
Software*

Substances

Proteins