Water Networks in Complexes between Proteins and FDA-Approved Drugs.

Water molecules at protein-ligand interfaces are often of significant pharmaceutical interest, owing in part to the entropy which can be released upon the displacement of an ordered water by a therapeutic compound. Protein structures may not, however, completely resolve all critical bound water molecules, or there may be no experimental data available. As such, predicting the location of water molecules in the absence of a crystal structure is important in the context of rational drug design. Grand canonical Monte Carlo (GCMC) is a computational technique that is gaining popularity for the simulation of buried water sites. In this work, we assess the ability of GCMC to accurately predict water binding locations, using a dataset that we have curated, containing 108 unique structures of complexes between proteins and Food and Drug Administration (FDA)-approved small-molecule drugs. We show that GCMC correctly predicts 81.4% of nonbulk crystallographic water sites to within 1.4 Å. However, our analysis demonstrates that the reported performance of water prediction methods is highly sensitive to the way in which the performance is measured. We also find that crystallographic water sites with more protein/ligand hydrogen bonds and stronger electron density are more reliably predicted by GCMC. An analysis of water networks revealed that more than half of the structures contain at least one ligand-contacting water network. In these cases, displacement of a water site by a ligand modification might yield unexpected results if the larger network is destabilized. Cooperative effects between waters should therefore be explicitly considered in structure-based drug design.

Water molecules at protein-drug interfaces: computational prediction and analysis methods.

The fundamental importance of water molecules at drug-protein interfaces is now widely recognised and a significant feature in structure-based drug design. Experimental methods for analysing the role of water in drug binding have many challenges, including the accurate location of bound water molecules in crystal structures, and problems in resolving specific water contributions to binding thermodynamics. Computational analyses of binding site water molecules provide an alternative, and in principle complete, structural and thermodynamic picture, and their use is now commonplace in the pharmaceutical industry. In this review, we describe the computational methodologies that are available and discuss their strengths and weaknesses. Additionally, we provide a critical analysis of the experimental data used to validate the methods, regarding the type and quality of experimental structural data. We also discuss some of the fundamental difficulties of each method and suggest directions for future study.

Rimantadine Binds to and Inhibits the Influenza A M2 Proton Channel without Enantiomeric Specificity.

The influenza A M2 wild-type (WT) proton channel is the target of the anti-influenza drug rimantadine. Rimantadine has two enantiomers, though most investigations into drug binding and inhibition have used a racemic mixture. Solid-state NMR experiments using the full length-M2 WT have shown significant spectral differences that were interpreted to indicate tighter binding for (R)- vs (S)-rimantadine. However, it was unclear if this correlates with a functional difference in drug binding and inhibition. Using X-ray crystallography, we have determined that both (R)- and (S)-rimantadine bind to the M2 WT pore with slight differences in the hydration of each enantiomer. However, this does not result in a difference in potency or binding kinetics, as shown by similar values for kon, koff, and Kd in electrophysiological assays and for EC50 values in cellular assays. We concluded that the slight differences in hydration for the (R)- and (S)-rimantadine enantiomers are not relevant to drug binding or channel inhibition. To further explore the effect of the hydration of the M2 pore on binding affinity, the water structure was evaluated by grand canonical ensemble molecular dynamics simulations as a function of the chemical potential of the water. Initially, the two layers of ordered water molecules between the bound drug and the channel's gating His37 residues mask the drug's chirality. As the chemical potential becomes more unfavorable, the drug translocates down to the lower water layer, and the interaction becomes more sensitive to chirality. These studies suggest the feasibility of displacing the upper water layer and specifically recognizing the lower water layers in novel drugs.

grand: A Python Module for Grand Canonical Water Sampling in OpenMM.

Networks of water molecules can play a critical role at the protein-ligand interface and can directly influence drug-target interactions. Grand canonical methods aid in the sampling of these water molecules, where conventional molecular dynamics equilibration times are often long, by allowing waters to be inserted and deleted from the system, according to the chemical potential. Here, we present our open source Python module, grand (https://github.com/essex-lab/grand), which allows molecular dynamics simulations to be performed in conjunction with grand canonical Monte Carlo sampling, using the OpenMM simulation engine. We demonstrate the accuracy of this module by reproducing the density of bulk water observed from constant pressure simulations. Application of this code to the bovine pancreatic trypsin inhibitor protein reproduces three buried crystallographic water sites that are poorly sampled using conventional molecular dynamics.

Prediction of the Closed Conformation and Insights into the Mechanism of the Membrane Enzyme LpxR.

Covalent modification of outer membrane lipids of Gram-negative bacteria can impact the ability of the bacterium to develop resistance to antibiotics as well as modulating the immune response of the host. The enzyme LpxR from Salmonella typhimurium is known to deacylate lipopolysaccharide molecules of the outer membrane; however, the mechanism of action is unknown. Here, we employ molecular dynamics and Monte Carlo simulations to study the conformational dynamics and substrate binding of LpxR in representative outer membrane models as well as detergent micelles. We examine the roles of conserved residues and provide an understanding of how LpxR binds its substrate. Our simulations predict that the catalytic H122 must be Nε-protonated for a single water molecule to occupy the space between it and the scissile bond, with a free binding energy of -8.5 kcal mol-1. Furthermore, simulations of the protein within a micelle enable us to predict the structure of the putative "closed" protein. Our results highlight the need for including dynamics, a representative environment, and the consideration of multiple tautomeric and rotameric states of key residues in mechanistic studies; static structures alone do not tell the full story.

End-to-end differentiable construction of molecular mechanics force fields.

Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety of applications in biomolecular modeling and drug discovery, from rapid virtual screening to detailed free energy calculations. Traditionally, MM potentials have relied on human-curated, inflexible, and poorly extensible discrete chemical perception rules (atom types) for applying parameters to small molecules or biopolymers, making it difficult to optimize both types and parameters to fit quantum chemical or physical property data. Here, we propose an alternative approach that uses graph neural networks to perceive chemical environments, producing continuous atom embeddings from which valence and nonbonded parameters can be predicted using invariance-preserving layers. Since all stages are built from smooth neural functions, the entire process-spanning chemical perception to parameter assignment-is modular and end-to-end differentiable with respect to model parameters, allowing new force fields to be easily constructed, extended, and applied to arbitrary molecules. We show that this approach is not only sufficiently expressive to reproduce legacy atom types, but that it can learn to accurately reproduce and extend existing molecular mechanics force fields. Trained with arbitrary loss functions, it can construct entirely new force fields self-consistently applicable to both biopolymers and small molecules directly from quantum chemical calculations, with superior fidelity than traditional atom or parameter typing schemes. When adapted to simultaneously fit partial charge models, espaloma delivers high-quality partial atomic charges orders of magnitude faster than current best-practices with low inaccuracy. When trained on the same quantum chemical small molecule dataset used to parameterize the Open Force Field ("Parsley") openff-1.2.0 small molecule force field augmented with a peptide dataset, the resulting espaloma model shows superior accuracy vis-á-vis experiments in computing relative alchemical free energy calculations for a popular benchmark. This approach is implemented in the free and open source package espaloma, available at https://github.com/choderalab/espaloma.

Ligand Binding Free Energies with Adaptive Water Networks: Two-Dimensional Grand Canonical Alchemical Perturbations.

Computational methods to calculate ligand binding affinities are increasing in popularity, due to improvements in simulation algorithms, computational resources, and easy-to-use software. However, issues can arise in relative ligand binding free energy simulations if the ligands considered have different active site water networks, as simulations are typically performed with a predetermined number of water molecules (fixed N ensembles) in preassigned locations. If an alchemical perturbation is attempted where the change should result in a different active site water network, the water molecules may not be able to adapt appropriately within the time scales of the simulations-particularly if the active site is occluded. By combining the grand canonical ensemble (µVT) to sample active site water molecules, with conventional alchemical free energy methods, the water network is able to dynamically adapt to the changing ligand. We refer to this approach as grand canonical alchemical perturbation (GCAP). In this work we demonstrate GCAP for two systems; Scytalone Dehydratase (SD) and Adenosine A2 A receptor. For both systems, GCAP is shown to perform well at reproducing experimental binding affinities. Calculating the relative binding affinities with a naïve, conventional attempt to solvate the active site illustrates how poor results can be if proper consideration of water molecules in occluded pockets is neglected. GCAP results are shown to be consistent with time-consuming double decoupling simulations. In addition, by obtaining the free energy surface for ligand perturbations, as a function of both the free energy coupling parameter and water chemical potential, it is possible to directly deconvolute the binding energetics in terms of protein-ligand direct interactions and protein binding site hydration.

Replica-Exchange and Standard State Binding Free Energies with Grand Canonical Monte Carlo.

The ability of grand canonical Monte Carlo (GCMC) to create and annihilate molecules in a given region greatly aids the identification of water sites and water binding free energies in protein cavities. However, acceptance rates without the application of biased moves can be low, resulting in large variations in the observed water occupancies. Here, we show that replica-exchange of the chemical potential significantly reduces the variance of the GCMC data. This improvement comes at a negligible increase in computational expense when simulations comprise of runs at different chemical potentials. Replica-exchange GCMC is also found to substantially increase the precision of water binding free energies as calculated with grand canonical integration, which has allowed us to address a missing standard state correction.