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Ligand Binding and Linkage00:49

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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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The hemoglobin in the blood, the chlorophyll in green plants, vitamin B-12, and the catalyst used in the manufacture of polyethylene all contain coordination compounds. Ions of the metals, especially the transition metals, are likely to form complexes.
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Predicting ligand binding affinity using on- and off-rates for the SAMPL6 SAMPLing challenge.

Tom Dixon1,2, Samuel D Lotz1, Alex Dickson3,4

  • 1Department of Biochemistry & Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA.

Journal of Computer-Aided Molecular Design
|August 25, 2018
PubMed
Summary

Ligand binding kinetics, crucial for drug efficacy, are predicted using the novel REVO enhanced sampling method. REVO shows improved consistency in predicting binding and unbinding rates compared to previous methods.

Keywords:
Binding affinityKineticsMolecular dynamicsREVORare eventsSAMPL6WExploreWeighted ensemble

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Area of Science:

  • Computational chemistry and molecular dynamics simulations.
  • Pharmacology and drug discovery.
  • Biophysics and statistical mechanics.

Background:

  • Ligand residence time is critical for drug efficacy, driving interest in ligand binding kinetics.
  • Enhanced sampling methods aim to predict ligand binding (on-rates) and unbinding (off-rates) via simulations.
  • Existing methods vary in mechanism and accuracy for kinetic predictions.

Purpose of the Study:

  • To evaluate a new enhanced sampling method, Reweighting of Ensembles by Variance Optimization (REVO), for predicting ligand binding kinetics.
  • To compare REVO's performance against experimental measurements and benchmark calculations for host-guest systems.
  • To analyze simulation trajectories and identify factors influencing kinetic predictions.

Main Methods:

  • Development and application of the REVO method, a variant of WExplore, utilizing variance optimization for trajectory cloning/merging.
  • Calculation of ligand binding on-rates (k_on) and off-rates (k_off) using REVO.
  • Comparison of predicted dissociation equilibrium constants (K_d) derived from k_on/k_off ratios with reference data.
  • Analysis of simulation results using tree network visualizations and conformation space networks.

Main Results:

  • REVO produced consistent estimates for on-rates and off-rates across multiple simulations (log10-scale std dev of 0.28 and 0.56, respectively).
  • The rank ordering of host-guest pairs matched reference calculations.
  • Predicted K_d values were systematically lower than reference values by an average of 4.2 kcal/mol.

Conclusions:

  • REVO demonstrates improved consistency and reliability in predicting ligand binding kinetics compared to prior methods.
  • Discrepancies in predicted K_d values warrant further investigation into simulation parameters and analysis techniques.
  • Direct inclusion of on-rate and off-rate challenges in future SAMPL iterations is recommended to advance kinetic prediction methodologies.