scholarly journals Rapid decomposition and visualisation of protein–ligand binding free energies by residue and by water

2014 ◽  
Vol 169 ◽  
pp. 477-499 ◽  
Author(s):  
Christopher J. Woods ◽  
Maturos Malaisree ◽  
Julien Michel ◽  
Ben Long ◽  
Simon McIntosh-Smith ◽  
...  

Recent advances in computational hardware, software and algorithms enable simulations of protein–ligand complexes to achieve timescales during which complete ligand binding and unbinding pathways can be observed. While observation of such events can promote understanding of binding and unbinding pathways, it does not alone provide information about the molecular drivers for protein–ligand association, nor guidance on how a ligand could be optimised to better bind to the protein. We have developed the waterswap (C. J. Woods et al., J. Chem. Phys., 2011, 134, 054114) absolute binding free energy method that calculates binding affinities by exchanging the ligand with an equivalent volume of water. A significant advantage of this method is that the binding free energy is calculated using a single reaction coordinate from a single simulation. This has enabled the development of new visualisations of binding affinities based on free energy decompositions to per-residue and per-water molecule components. These provide a clear picture of which protein–ligand interactions are strong, and which active site water molecules are stabilised or destabilised upon binding. Optimisation of the algorithms underlying the decomposition enables near-real-time visualisation, allowing these calculations to be used either to provide interactive feedback to a ligand designer, or to provide run-time analysis of protein–ligand molecular dynamics simulations.


2017 ◽  
Vol 57 (4) ◽  
pp. 846-863 ◽  
Author(s):  
Juan Pablo Arcon ◽  
Lucas A. Defelipe ◽  
Carlos P. Modenutti ◽  
Elias D. López ◽  
Daniel Alvarez-Garcia ◽  
...  


2021 ◽  
Author(s):  
Yunhui Ge ◽  
Vincent Voelz

Accurate and efficient simulation of the thermodynamics and kinetics of protein-ligand interactions is crucial for computational drug discovery. Multiensemble Markov Model (MEMM) estimators can provide estimates of both binding rates and affinities from collections of short trajectories, but have not been systematically explored for situations when a ligand is decoupled through scaling of non-bonded interactions. In this work, we compare the performance of two MEMM approaches for estimating ligand binding affinities and rates: (1) the transition-based reweighting analysis method (TRAM) and (2) a Maximum Caliber (MaxCal) based method. As a test system, we construct a small host-guest system where the ligand is a single uncharged Lennard-Jones (LJ) particle, and the receptor is an 11-particle icosahedral pocket made from the same atom type. To realistically mimic a protein-ligand binding system, the LJ ε parameter was tuned, and the system placed in a periodic box with 860 TIP3P water molecules. A benchmark was performed using over 80 μs of unbiased simulation, and an 18-state Markov state model used to estimate reference binding affinities and rates. We then tested the performance of TRAM and MaxCal when challenged with limited data. Both TRAM and MaxCal approaches perform better than conventional MSMs, with TRAM showing better convergence and accuracy. We find that subsampling of trajectories to remove time correlation improves the accuracy of both TRAM and MaxCal, and that in most cases only a single biased ensemble to enhance sampled transitions is required to make accurate estimates.



2021 ◽  
Author(s):  
Yunhui Ge ◽  
Vincent Voelz

Accurate and efficient simulation of the thermodynamics and kinetics of protein-ligand interactions is crucial for computational drug discovery. Multiensemble Markov Model (MEMM) estimators can provide estimates of both binding rates and affinities from collections of short trajectories, but have not been systematically explored for situations when a ligand is decoupled through scaling of non-bonded interactions. In this work, we compare the performance of two MEMM approaches for estimating ligand binding affinities and rates: (1) the transition-based reweighting analysis method (TRAM) and (2) a Maximum Caliber (MaxCal) based method. As a test system, we construct a small host-guest system where the ligand is a single uncharged Lennard-Jones (LJ) particle, and the receptor is an 11-particle icosahedral pocket made from the same atom type. To realistically mimic a protein-ligand binding system, the LJ ε parameter was tuned, and the system placed in a periodic box with 860 TIP3P water molecules. A benchmark was performed using over 80 μs of unbiased simulation, and an 18-state Markov state model used to estimate reference binding affinities and rates. We then tested the performance of TRAM and MaxCal when challenged with limited data. Both TRAM and MaxCal approaches perform better than conventional MSMs, with TRAM showing better convergence and accuracy. We find that subsampling of trajectories to remove time correlation improves the accuracy of both TRAM and MaxCal, and that in most cases only a single biased ensemble to enhance sampled transitions is required to make accurate estimates.



2018 ◽  
Vol 58 (6) ◽  
pp. 1312-1312
Author(s):  
Juan Pablo Arcon ◽  
Lucas A. Defelipe ◽  
Carlos P. Modenutti ◽  
Elias D. López ◽  
Daniel Alvarez Garcia ◽  
...  


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