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Residence time calculation from metadynamics

Postprocessing analysis of metadynamics simulation data to estimate protein-ligand complex residence times

Goals:

  • To identify patterns underlying protein-ligand interactions, binding and dissociation processes
  • To expedite the dissociation process of a ligand from a protein
  • To estimate dissociation kinetic parameter (residence time) which has been found implicated in the duration of pharmacological action and efficacy of drugs.

Workflows:

  1. Running multiple metadynamics simulations (before this analysis)
  2. Post-processing
  3. Obtaining unbiased dissociation times from each trajectory using PLUMED
  4. Fitting for residence times based on cummulative Poisson distribution image

Required software:
-VMD and NAMD (https://www.ks.uiuc.edu)
-PLUMED (https://www.plumed.org)
-Python (pandas and numpy)

Required files:
-PDB (coordinates)
-PSF (structural info)
-Force field parameter files for MD simulations (i.e., CHARMM) (https://mackerell.umaryland.edu/charmm_ff.shtml)
-MD simulation trajectories (e.g., dcd, nc, or xtc formats)

Examples of metadynamics simulations files:
simulation_files --> prep
simulation_files --> metad

Notes: This are just examples from one of simulation sets. You need to go into each individual files to change or specify paths to data or trajectories yourself.

Coordination numbers between the ligand and the binding pocket residues were used in the examples, calculated based on this formula (https://www.ks.uiuc.edu/Research/namd/2.14/ug/node53.html):

img294

Postprocessing analysis steps (after completing metadynamics simulations):

  1. Get the first time frame when the ligand reached the protein surface: python exittime.py
  2. (optional) Generate new trajectory files with reduced size by stripping out water and lipid molecules: ./trajnowaterlipid (
  3. Write new trajectory files for analysis (ended at the frame when ligand exited): vmd -dispdev text -e loadframe.tcl
  4. Unbiasing and reweighting using PLUMED (accelerated time --> real time; reweighted free energy profiles): ./runplumed (need to edit "accel0.txt")
  5. Fitting for residence time: python residencetime.py

References:

  • Tiwary, P.; Parrinello, M. From Metadynamics to Dynamics. Phys. Rev. Lett. 2013, 111, 230602.
  • Barducci, A.; Bussi, G.; Parrinello, M. Well-Tempered Metadynamics: A Smoothly Converging and Tunable Free-Energy Method. Phys. Rev. Lett. 2008, 100, 020603.
  • Tiwary, P.; Parinnello, M. A Time-Independent Free Energy Estimator from Metadynamics. J. Phys. Chem. B. 2015, 119, 736-742.
  • Ray, D.; Parrinello, M. Kinetics from Metadynamics: Principles, Applications, and Outlook. J. Chem. Theory. Comput. 2023, 19, 5649-5670.
  • Salvalaglio, M.; Tiwary, P.; Parrinello, M. Assessing the Reliability of the Dynamics Reconstructed from Metadynamics. J. Chem. Theory Comput. 2014, 10, 1420-1425.
  • Tribello, G. A.; Bonomi, M.; Branduardi, D.; Camilloni, C.; Bussi, G. PLUMED 2: New Feathers for an Old Bird. Comput. Phys. Commun. 2014, 185, 604-613.
  • Fiorin, G.; Klein, M. L.; Hénin, J. Using Collective Variables to Drive Molecular Dynamics Simulations. Mol. Phys. 2013, 111, 3345-3362.
  • P. Mahinthichaichan, R. Liu, Q. N. Vo., C. R. Ellis, L. Stavitskaya, J. Shen. Structure-kinetics relationships of opioids from molecular dynamics simulations and machine learning. J. Chem. Info. Model. 2023, 63, 2196-2206.

Acknowledgements:

The work is currently a part of projects in Dr. Yanxin Liu's group at the University of Maryland, College Park (https://blog.umd.edu/liu), and was started during my ORISE fellowship (2020-2023) with the FDA and University of Maryland, Baltimore (Dr. Jana Shen's group).