Abstract:Improper reaction coordinates can pose significant problems for path-based binding free energy calculations. Particularly, omission of long timescale motions can lead to over-estimation of the energetic barriers between the bound and unbound states. Many methods exist to construct the optimal reaction coordinate using a pre-defined basis set of features. Although simulations are typically conducted in explicit solvent, the solvent atoms are often excluded by these feature sets—resulting in little being known a… Show more
“…Following ref. 54 , we also investigated the role of the ionic density and found that it does not play a relevant role in this system (see Supplementary Information ( SI )). Fig.…”
The process of ligand-protein unbinding is crucial in biophysics. Water is an essential part of any biological system and yet, many aspects of its role remain elusive. Here, we simulate with state-of-the-art enhanced sampling techniques the binding of Benzamidine to Trypsin which is a much studied and paradigmatic ligand-protein system. We use machine learning methods to determine efficient collective coordinates for the complex non-local network of water. These coordinates are used to perform On-the-fly Probability Enhanced Sampling simulations, which we adapt to calculate also the ligand residence time. Our results, both static and dynamic, are in good agreement with experiments. We find that the presence of a water molecule located at the bottom of the binding pocket allows via a network of hydrogen bonds the ligand to be released into the solution. On a finer scale, even when unbinding is allowed, another water molecule further modulates the exit time.
“…Following ref. 54 , we also investigated the role of the ionic density and found that it does not play a relevant role in this system (see Supplementary Information ( SI )). Fig.…”
The process of ligand-protein unbinding is crucial in biophysics. Water is an essential part of any biological system and yet, many aspects of its role remain elusive. Here, we simulate with state-of-the-art enhanced sampling techniques the binding of Benzamidine to Trypsin which is a much studied and paradigmatic ligand-protein system. We use machine learning methods to determine efficient collective coordinates for the complex non-local network of water. These coordinates are used to perform On-the-fly Probability Enhanced Sampling simulations, which we adapt to calculate also the ligand residence time. Our results, both static and dynamic, are in good agreement with experiments. We find that the presence of a water molecule located at the bottom of the binding pocket allows via a network of hydrogen bonds the ligand to be released into the solution. On a finer scale, even when unbinding is allowed, another water molecule further modulates the exit time.
“…While it appears that the cutoff COM distance chosen could have been reduced for the OA‐G6 and OA‐G3 systems, it was found in previous work (Ref. 39) that for these systems, rebinding can often occur below a 0.7 nm d COM distance. The tutorial repository associated with this article 40 contains everything needed to reproduce the results here, including: code for the custom distance metric and boundary condition, scripts to run the binding and unbinding simulations, scripts to plot the d COM for reactive trajectories, and scripts to extract the rebinding simulation starting poses.…”
Section: Methodsmentioning
confidence: 91%
“…This is caused by the rare occurance of high probability walkers crossing into the unbound state in unbinding simulations, which introduces large jumps in transition fluxes and unbinding rates. Interestingly, our previous work sought to identify microscopic determinants of these differences in probability and found that there was a correlation between guest‐ion interactions and the unbinding weight of a given trajectory for several SAMPL systems 39 . Specifically, rare high‐probability trajectories that contributed most significantly to the unbinding rate had a lower amount of guest‐ion interaction than the far more numerous low‐probability trajectories.…”
Section: Introductionmentioning
confidence: 99%
“…Interestingly, our previous work sought to identify microscopic determinants of these differences in probability and found that there was a correlation between guest-ion interactions and the unbinding weight of a given trajectory for several SAMPL systems. 39 Specifically, rare highprobability trajectories that contributed most significantly to the unbinding rate had a lower amount of guest-ion interaction than the far more numerous low-probability trajectories. By tracking the center of mass (COM)-to-COM distance (d COM ), we also found that the high-and low-probability trajectories could be distinguished even relatively early on in the unbinding pathway (d COM = 7 Å) based on their guest-ion interaction, prior to there being a discernible difference in the trajectory weight.…”
The prediction of (un)binding rates and free energies is of great significance to the drug design process. Although many enhanced sampling algorithms and approaches have been developed, there is not yet a reliable workflow to predict these quantities. Previously we have shown that free energies and transition rates can be calculated by directly simulating the binding and unbinding processes with our variant of the WE algorithm “Resampling of Ensembles by Variation Optimization”, or “REVO”. Here, we calculate binding free energies retrospectively for three SAMPL6 host‐guest systems and prospectively for a SAMPL9 system to test a modification of REVO that restricts its cloning behavior in quasi‐unbound states. Specifically, trajectories cannot clone if they meet a physical requirement that represents a high likelihood of unbinding, which in the case of this work is a center‐of‐mass to center‐of‐mass distance. The overall effect of this change was difficult to predict, as it results in fewer unbinding events each of which with a much higher statistical weight. For all four systems tested, this new strategy produced either more accurate unbinding free energies or more consistent results between simulations than the standard REVO algorithm. This approach is highly flexible, and any feature of interest for a system can be used to determine cloning eligibility. These findings thus constitute an important improvement in the calculation of transition rates and binding free energies with the weighted ensemble method.
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