The maximal and current accuracy of rigorous protein-ligand binding free energy calculations.

Computational techniques can speed up the identification of hits and accelerate the development of candidate molecules for drug discovery. Among techniques for predicting relative binding affinities, the most consistently accurate is free energy perturbation (FEP), a class of rigorous physics-based methods. However, uncertainty remains about how accurate FEP is and can ever be. Here, we present what we believe to be the largest publicly available dataset of proteins and congeneric series of small molecules, and assess the accuracy of the leading FEP workflow. To ascertain the limit of achievable accuracy, we also survey the reproducibility of experimental relative affinity measurements. We find a wide variability in experimental accuracy and a correspondence between binding and functional assays. When careful preparation of protein and ligand structures is undertaken, FEP can achieve accuracy comparable to experimental reproducibility. Throughout, we highlight reliable protocols that can help maximize the accuracy of FEP in prospective studies.

OPLS4: Improving Force Field Accuracy on Challenging Regimes of Chemical Space.

We report on the development and validation of the OPLS4 force field. OPLS4 builds upon our previous work with OPLS3e to improve model accuracy on challenging regimes of drug-like chemical space that includes molecular ions and sulfur-containing moieties. A novel parametrization strategy for charged species, which can be extended to other systems, is introduced. OPLS4 leads to improved accuracy on benchmarks that assess small-molecule solvation and protein-ligand binding.

Enhancing Water Sampling in Free Energy Calculations with Grand Canonical Monte Carlo.

The prediction of protein-ligand binding affinities using free energy perturbation (FEP) is becoming increasingly routine in structure-based drug discovery. Most FEP packages use molecular dynamics (MD) to sample the configurations of proteins and ligands, as MD is well-suited to capturing coupled motion. However, MD can be prohibitively inefficient at sampling water molecules that are buried within binding sites, which has severely limited the domain of applicability of FEP and its prospective usage in drug discovery. In this paper, we present an advancement of FEP that augments MD with grand canonical Monte Carlo (GCMC), an enhanced sampling method, to overcome the problem of sampling water. We accomplished this without degrading computational performance. On both old and newly assembled data sets of protein-ligand complexes, we show that the use of GCMC in FEP is essential for accurate and robust predictions for ligand perturbations that disrupt buried water.

An intra-aortic guide wire: what "knot" to do.

We report a rare complication of central venous catheter placement in a 5-month-old baby.

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.

Large-scale analysis of water stability in bromodomain binding pockets with grand canonical Monte Carlo.

Conserved water molecules are of interest in drug design, as displacement of such waters can lead to higher affinity ligands and in some cases, contribute towards selectivity. Bromodomains, small protein domains involved in the epigenetic regulation of gene transcription, display a network of four conserved water molecules in their binding pockets and have recently been the focus of intense medicinal chemistry efforts. Understanding why certain bromodomains have displaceable water molecules and others do not is extremely challenging, and it remains unclear which water molecules in a given bromodomain can be targeted for displacement. Here we estimate the stability of the conserved water molecules in 35 bromodomains via binding free energy calculations using all-atom grand canonical Monte Carlo simulations. Encouraging quantitative agreement to the available experimental evidence is found. We thus discuss the expected ease of water displacement in different bromodomains and the implications for ligand selectivity.

Biomolecular Simulations under Realistic Macroscopic Salt Conditions.

Biomolecular simulations are typically performed in an aqueous environment where the number of ions remains fixed for the duration of the simulation, generally with either a minimally neutralizing ion environment or a number of salt pairs intended to match the macroscopic salt concentration. In contrast, real biomolecules experience local ion environments where the salt concentration is dynamic and may differ from bulk. The degree of salt concentration variability and average deviation from the macroscopic concentration remains, as yet, unknown. Here, we describe the theory and implementation of a Monte Carlo osmostat that can be added to explicit solvent molecular dynamics or Monte Carlo simulations to sample from a semigrand canonical ensemble in which the number of salt pairs fluctuates dynamically during the simulation. The osmostat reproduces the correct equilibrium statistics for a simulation volume that can exchange ions with a large reservoir at a defined macroscopic salt concentration. To achieve useful Monte Carlo acceptance rates, the method makes use of nonequilibrium candidate Monte Carlo (NCMC) moves in which monovalent ions and water molecules are alchemically transmuted using short nonequilibrium trajectories, with a modified Metropolis-Hastings criterion ensuring correct equilibrium statistics for an ( Δµ, N, p, T) ensemble to achieve a ∼1046× boost in acceptance rates. We demonstrate how typical protein (DHFR and the tyrosine kinase Src) and nucleic acid (Drew-Dickerson B-DNA dodecamer) systems exhibit salt concentration distributions that significantly differ from fixed-salt bulk simulations and display fluctuations that are on the same order of magnitude as the average.

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes via Nonequilibrium Candidate Monte Carlo.

Accurately predicting protein-ligand binding affinities and binding modes is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragment-like ligand in a receptor, and the populations of the multiple binding modes which may be relevant. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation time scales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes. In this technique, the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over 2 orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step toward applying this methodology to pharmaceutically relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding modes of ligands using enhanced sampling (BLUES) package which is freely available on GitHub.

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.

Waterdock 2.0: Water placement prediction for Holo-structures with a pymol plugin.

Water is often found to mediate interactions between a ligand and a protein. It can play a significant role in orientating the ligand within a binding pocket and contribute to the free energy of binding. It would thus be extremely useful to be able to accurately predict the position and orientation of water molecules within a binding pocket. Recently, we developed the WaterDock protocol that was able to predict 97% of the water molecules in a test set. However, this approach generated false positives at a rate of over 20% in most cases and whilst this might be acceptable for some applications, in high throughput scenarios this is not desirable. Here we tackle this problem via the inclusion of knowledge regarding the solvation structure of ligand functional groups. We call this new protocol WaterDock2 and demonstrate that this protocol maintains a similar true positive rate to the original implementation but is capable of reducing the false-positive rate by over 50%. To improve the usability of the method, we have also developed a plugin for the popular graphics program PyMOL. The plugin also contains an implementation of the original WaterDock.

Water Sites, Networks, And Free Energies with Grand Canonical Monte Carlo.

Water molecules play integral roles in the formation of many protein-ligand complexes, and recent computational efforts have been focused on predicting the thermodynamic properties of individual waters and how they may be exploited in rational drug design. However, when water molecules form highly coupled hydrogen-bonding networks, there is, as yet, no method that can rigorously calculate the free energy to bind the entire network or assess the degree of cooperativity between waters. In this work, we report theoretical and methodological developments to the grand canonical Monte Carlo simulation technique. Central to our results is a rigorous equation that can be used to calculate efficiently the binding free energies of water networks of arbitrary size and complexity. Using a single set of simulations, our methods can locate waters, estimate their binding affinities, capture the cooperativity of the water network, and evaluate the hydration free energy of entire protein binding sites. Our techniques have been applied to multiple test systems and compare favorably to thermodynamic integration simulations and experimental data. The implications of these methods in drug design are discussed.

One Size Does Not Fit All: The Limits of Structure-Based Models in Drug Discovery.

A major goal in computational chemistry has been to discover the set of rules that can accurately predict the binding affinity of any protein-drug complex, using only a single snapshot of its three-dimensional structure. Despite the continual development of structure-based models, predictive accuracy remains low, and the fundamental factors that inhibit the inference of all-encompassing rules have yet to be fully explored. Using statistical learning theory and information theory, here we prove that even the very best generalized structure-based model is inherently limited in its accuracy, and protein-specific models are always likely to be better. Our results refute the prevailing assumption that large data sets and advanced machine learning techniques will yield accurate, universally applicable models. We anticipate that the results will aid the development of more robust virtual screening strategies and scoring function error estimations.

Oseltamivir-resistant 2009 H1N1 influenza pneumonia during therapy in a renal transplant recipient.

The emergence of oseltamivir-resistant 2009 H1N1 influenza virus (conferred by the H275Y substitution in NA) during therapy or prophylaxis in immunocompromised patients is a serious concern. The optimal therapy for immunosuppressed patients with oseltamivir-resistant 2009 H1N1 influenza virus is unknown and few options exist. We report a 10-yr-old recipient of kidney transplant who was hospitalized with oseltamivir-resistant 2009 H1N1 influenza pneumonia complicated by severe respiratory failure, ARDS, and renal failure requiring institution of ECMO and CRRT. On presentation, treatment with oseltamivir (second course) and broad-spectrum antibiotics was initiated. Immunosuppressive agents were stopped on hospital day (d) 2. On hospital d 7, given his critical status, immunocompromised state, and difficulty in obtaining intravenous zanamivir, after obtaining ethical approval and parental consent, he was treated with intravenous peramivir (through an Emergency Investigational New Drug Application) for two wk. He tolerated the regimen well and his clinical status improved gradually. Several factors may have contributed to virus clearance and survival including recovery of the immune system, aggressive critical care support, and administration of peramivir. Ongoing surveillance is essential to monitor how oseltamivir-resistant H275Y mutant viruses may evolve in the future.

Rapid and accurate prediction and scoring of water molecules in protein binding sites.

Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules. WaterDock was validated using data from X-ray crystallography, neutron diffraction and molecular dynamics simulations and correctly predicted 97% of the water molecules in the test set. In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. When applied to WaterDock predictions in the Astex Diverse Set of protein ligand complexes, we could identify whether a water molecule was conserved or displaced to an accuracy of 75%. A second model predicted whether water molecules were displaced by polar groups or by non-polar groups to an accuracy of 80%. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity.

Purpura fulminans caused by community-associated methicillin-resistant Staphylococcus aureus.

Sepsis-induced purpura fulminans is a rare but life-threatening condition characterized by rapidly progressive hemorrhagic infarction of the skin due to dermal vascular thrombosis resulting in tissue loss and severe scarring. Although most commonly related to meningococcal or invasive group A streptococcal disease, it may also be caused by several other bacterial or viral pathogens including Pneumococcus and Varicella. Purpura fulminans associated with Staphylococcus aureus sepsis is rare but has been reported in adults. However, the syndrome is very unusual in children, and to our knowledge, only 2 cases of staphylococcal purpura fulminans have been reported in children, both due to methicillin-susceptible S aureus in the United Kingdom. We report the first well-described case of purpura fulminans due to community-associated methicillin-resistant S aureus in a child.

Inhaled nitric oxide induced NOS inhibition and rebound pulmonary hypertension: a role for superoxide and peroxynitrite in the intact lamb.

Previous in vivo studies indicate that inhaled nitric oxide (NO) decreases nitric oxide synthase (NOS) activity and that this decrease is associated with significant increases in pulmonary vascular resistance (PVR) upon the acute withdrawal of inhaled NO (rebound pulmonary hypertension). In vitro studies suggest that superoxide and peroxynitrite production during inhaled NO therapy may mediate these effects, but in vivo data are lacking. The objective of this study was to determine the role of superoxide in the decrease in NOS activity and rebound pulmonary hypertension associated with inhaled NO therapy in vivo. In control lambs, 24 h of inhaled NO (40 ppm) decreased NOS activity by 40% (P<0.05) and increased endothelin-1 levels by 64% (P<0.05). Withdrawal of NO resulted in an acute increase in PVR (60.7%, P<0.05). Associated with these changes, superoxide and peroxynitrite levels increased more than twofold (P<0.05) following 24 h of inhaled NO therapy. However, in lambs treated with polyethylene glycol-conjugated superoxide dismutase (PEG-SOD) during inhaled NO therapy, there was no change in NOS activity, no increase in superoxide or peroxynitrite levels, and no increase in PVR upon the withdrawal of inhaled NO. In addition, endothelial NOS nitration was 18-fold higher (P<0.05) in control lambs than in PEG-SOD-treated lambs following 24 h of inhaled NO. These data suggest that superoxide and peroxynitrite participate in the decrease in NOS activity and rebound pulmonary hypertension associated with inhaled NO therapy. Reactive oxygen species scavenging may be a useful therapeutic strategy to ameliorate alterations in endogenous NO signaling during inhaled NO therapy.

Endothelial alterations during inhaled NO in lambs with pulmonary hypertension: implications for rebound hypertension.

Clinically significant increases in pulmonary vascular resistance (PVR) have been noted upon acute withdrawal of inhaled nitric oxide (iNO). Previous studies in the normal pulmonary circulation demonstrate that iNO increases endothelin-1 (ET-1) levels and decreases endogenous nitric oxide synthase (NOS) activity, implicating an endothelial etiology for the increase in resistance upon iNO withdrawal. However, the effect of iNO on endogenous endothelial function in the clinically relevant pulmonary hypertensive circulation is unknown. The objective of this study was to determine the effects of iNO on endogenous NO-cGMP and ET-1 signaling in lambs with preexisting pulmonary hypertension secondary to increased pulmonary blood flow. Eight fetal lambs underwent in utero placement of an aortopulmonary vascular graft (shunt lambs). After delivery (4 wk), the shunt lambs were mechanically ventilated with iNO (40 ppm) for 24 h. After 24 h of inhaled NO, plasma ET-1 levels increased by 34.8% independently of changes in protein levels (P < 0.05). Contrary to findings in normal lambs, total NOS activity did not decrease during iNO. In fact, Western blot analysis demonstrated that tissue endothelial NOS protein levels decreased by 43% such that NOS activity relative to protein levels actually increased during iNO (P < 0.05). In addition, the beta-subunit of soluble guanylate cyclase decreased by 70%, whereas phosphodiesterase 5 levels were unchanged (P < 0.05). Withdrawal of iNO was associated with an acute increase in PVR, which exceeded baseline PVR by 45%, and a decrease in cGMP concentrations to levels that were below baseline. These data suggest that the endothelial response to iNO and the potential mechanisms of rebound pulmonary hypertension are dependent upon the underlying pulmonary vasculature.

Tezosentan, a combined parenteral endothelin receptor antagonist, produces pulmonary vasodilation in lambs with acute and chronic pulmonary hypertension.

OBJECTIVE: To investigate the hemodynamic effects of tezosentan in the intact lamb both at rest and during acute and chronic pulmonary hypertension. DESIGN: Prospective, randomized experimental study. SETTING: University-based research laboratory. SUBJECTS: Lambs with and without pulmonary hypertension. INTERVENTIONS: Six newborn lambs were instrumented to measure vascular pressures and left pulmonary blood flow. The hemodynamic effects of tezosentan (0.5, 1.0, 5.0 mg/kg, intravenously) were studied at rest and during U46619-induced pulmonary hypertension. Following in utero placement of an aortopulmonary vascular graft, nine additional lambs with increased pulmonary blood flow and chronic pulmonary hypertension (shunt) were also studied at 1 wk (n = 5) and 8 wks (n = 4) of age. MEASUREMENTS AND MAIN RESULTS: At rest, tezosentan had no significant effect on any of the variables. During acute U46619-induced pulmonary hypertension, tezosentan caused a dose-dependent decrease in pulmonary arterial pressure (from 5.9% +/- 4.7 to 16.0% +/- 10.7; p < .05) and pulmonary vascular resistance (from 6.2% +/- 8.0 to 21% +/- 8.8; p < .05). Mean systemic arterial pressure was unchanged. In 1- and 8-wk-old shunt lambs with increased pulmonary blood flow, tezosentan (1 mg/kg) produced potent nonselective pulmonary vasodilation. CONCLUSIONS: Tezosentan, a combined endothelin receptor antagonist optimized for parenteral use, induces potent selective pulmonary vasodilation during acute U46619-induced pulmonary hypertension and potent nonselective vasodilation in chronic pulmonary hypertension secondary to increased pulmonary blood flow. In general, the hemodynamic effects of bolus doses of tezosentan occurred within 60 secs of administration and lasted approximately 5-10 mins. The hemodynamic profile of intravenous tezosentan may make it a useful adjunct therapy for acute pulmonary hypertensive disorders and warrants further study.

Emergence of smooth muscle cell endothelin B-mediated vasoconstriction in lambs with experimental congenital heart disease and increased pulmonary blood flow.

BACKGROUND: Endothelin-1 (ET-1) has been implicated in the pathophysiology of pulmonary hypertension. In 1-month-old lambs with increased pulmonary blood flow, we have demonstrated early alterations in the ET-1 cascade. The objective of this study was to investigate the role of potential later alterations of the ET cascade in the pathophysiology of pulmonary hypertension secondary to increased pulmonary blood flow. METHODS AND RESULTS: Eighteen fetal lambs underwent in utero placement of an aortopulmonary vascular graft (shunt) and were studied 8 weeks after spontaneous delivery. Compared with age-matched control lambs, lung tissue ET-1 levels were increased in shunt lambs (317.2+/-113.8 versus 209.8+/-61.8 pg/g, P<0.05). In shunt lambs (n=9), exogenous ET-1 induced potent pulmonary vasoconstriction, which was blocked by the ETA receptor antagonist PD 156707 (n=3). This pulmonary vasoconstriction was mimicked by exogenous Ala1,3,11,15 ET-1 (4 Ala ET-1), the ETB receptor agonist, and was blocked by the ETB receptor antagonist BQ 788 (n=3). However, in control lambs (n=7), ET-1 and 4 Ala ET-1 did not change pulmonary vascular tone. In contrast to 4-week-old shunt lambs, immunohistochemistry revealed the emergence of ETB receptors on smooth muscle cells in the vasculature of 8-week-old shunt lambs. CONCLUSIONS: Over time, increased pulmonary blood flow and/or pressure results in the emergence of ETB-mediated vasoconstriction, which coincides with the emergence of ETB receptors on smooth muscle cells. These data suggest an important role for ETB receptors in the pathophysiology of pulmonary hypertension in this animal model of increased pulmonary blood flow.