Combined receptor and ligand-based approach to the universal pharmacophore model development for studies of drug blockade to the hERG1 pore domain

Long QT syndrome, LQTS, results in serious cardiovascular disorders, such as tachyarrhythmia and sudden cardiac death. A promiscuous binding of different drugs to the intracavitary binding site in the pore domain (PD) of human ether-a-go-go related gene (hERG) channels leads to a similar dysfunction, known as a drug-induced LQTS. Therefore, an assessment of the blocking ability for potent drugs is of great pragmatic value for molecular pharmacology and medicinal chemistry of hERGs. Thus, we attempted to create an in silico model aimed at blinded drug screening for their blocking ability to the hERG1 PD. Two distinct approaches to the drug blockage, ligand-based QSAR and receptor-based molecular docking methods, are combined for development of a universal pharmacophore model, which provides rapid assessment of drug blocking ability to the hERG1 channel. The best 3D-QSAR model (AAADR.7) from PHASE modeling was selected from a pool consisting of 44 initial candidates. The constructed model using 31 hERG blockers was validated with 9 test set compounds. The resulting model correctly predicted the pIC(50) values of test set compounds as true unknowns. To further evaluate the pharmacophore model, 14 hERG blockers with diverse hERG blocking potencies were selected from literature and they were used as additional external blind test sets. The resulting average deviation between in vitro and predicted pIC(50) values of external test set blockers is found as 0.29 suggesting that the model is able to accuretely predict the pIC(50) values as true unknowns. These pharmacophore models were merged with a previously developed atomistic receptor model for the hERG1 PD and exhibited a high consistency between ligand-based and receptor-based models. Therefore, the developed 3D-QSAR model provides a predictive tool for profiling candidate compounds before their synthesis. This model also indicated the key functional groups determining a high-affinity blockade of the hERG1 channel. To cross-validate consistency between the constructed hERG1 pore domain and the pharmacophore models, we performed docking studies using the homology model of hERG1. To understand how polar or nonpolar moieties of inhibitors stimulate channel inhibition, critical amino acid replacement (i.e., T623, S624, S649, Y652 and F656) at the hERG cavity was examined by in silico mutagenesis. The average docking score differences between wild type and mutated hERG channels was found to have the following order: F656A > Y652A > S624A > T623A > S649A. These results are in agreement with experimental data.     

Insights into docking and scoring neuronal α4β2 nicotinic receptor agonists using molecular dynamics simulations and QM/MM calculations

A combined quantum mechanical (QM)‐polarized docking and molecular dynamics approach to study the binding mode and to predict the binding affinity of ligands acting at the α4β2‐nAChR is presented. The results obtained in this study indicate that the quantum mechanical/molecular mechanics docking protocol well describes the charge‐driven interactions occurring in the binding of nicotinic agonists, and it is able to represent the polarization effects on the ligand exerted by the surrounding atoms of the receptor at the binding site. This makes it possible to properly score agonists of α4β2‐nAChR and to reproduce the experimental binding affinity data with good accuracy, within a mean error of 2.2 kcal/mol. Moreover, applying the QM‐polarized docking to an ensemble of nAChR conformations obtained from MD simulations enabled us to accurately capture nAChR‐ligand induced‐fit effects. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2009     

Role of bridging water molecules in GSK3β-inhibitor complexes: insights from QM/MM, MD, and molecular docking studies

The role of water molecules is increasingly gaining interest in drug design, and several studies have highlighted their paramount contributions to the specificity and the affinity of ligand binding. In this study, we employ the two-layer ONIOM-based quantum mechanics/molecular mechanics (QM/MM) calculations, molecular dynamics (MD) simulations, and molecular docking studies to investigate the effect of bridging water molecules at the GSK3β-inhibitors interfaces. The results obtained from the ONIOM geometry optimization and AIM analysis corroborated the presence of bridging water molecules that form hydrogen bonds with protein side chain of Thr138 and/or backbone of Gln185, and mediate interactions with inhibitors in the 10 selected GSK3β-inhibitor complexes. Subsequently, MD simulations carried out on a representative system of 1R0E demonstrated that the bridging water molecule is stable at the GSK3β-inhibitor interface and appears to contribute to the stability of the protein-inhibitor interactions. Furthermore, molecular docking studies of GSK3β-inhibitor complexes indicated that the inhibitors can increase binding affinities and the better docked conformation of inhibitors can be obtained by inclusion of the bridging water molecules, especially for the flexible inhibitors, in docking experiments into individual protein conformations. Our results elucidate the importance of bridging water molecules at the GSK3β-inhibitor interfaces and suggest that they might prove useful in rational drug design.     

Computational investigation of the binding mode of bis(hydroxylphenyl)arenes in 17β-HSD1: molecular dynamics simulations,MM-PBSA free energy calculations,and molecular electrostatic potential maps

17β-Hydroxysteroid dehydrogenase type 1 (17β-HSD1) catalyzes the last step of the estrogen biosynthesis, namely the reduction of estrone to the biologically potent estradiol. As such it is a potentially attractive drug target for the treatment of estrogen-dependent diseases like breast cancer and endometriosis. 17β-HSD1 belongs to the bisubstrate enzymes and exists as an ensemble of conformations. These principally differ in the region of the βFαG′-loop, suggesting a prominent role in substrate and inhibitor binding. Although several classes of potent non-steroidal 17β-HSD1 inhibitors currently exist, their binding mode is still unclear. We aimed to elucidate the binding mode of bis(hydroxyphenyl)arenes, a highly potent class of 17β-HSD1 inhibitors, and to rank these compounds correctly with respect to their inhibitory potency, two essential aspects in drug design. Ensemble docking experiments resulted in a steroidal binding mode for the closed enzyme conformations and in an alternative mode for the opened and occluded conformers with the inhibitors placed below the NADPH interacting with it synergically via π–π stacking and H-bond formation. Both binding modes were investigated by MD simulations and MM-PBSA binding free energy estimations using as representative member for this class compound 1 (50 nM). Notably, only the alternative binding mode proved stable and was energetically more favorable, while when simulated in the steroidal binding mode compound 1 was displaced from the active site. In parallel, ab initio studies of small NADPH-inhibitor complexes were performed, which supported the importance of the synergistic interaction between inhibitors and cofactor.     

Significant enhancement of docking sensitivity using implicit ligand sampling

The efficient and accurate quantification of protein-ligand interactions using computational methods is still a challenging task. Two factors strongly contribute to the failure of docking methods to predict free energies of binding accurately: the insufficient incorporation of protein flexibility coupled to ligand binding and the neglected dynamics of the protein-ligand complex in current scoring schemes. We have developed a new methodology, named the 'ligand-model' concept, to sample protein conformations that are relevant for binding structurally diverse sets of ligands. In the ligand-model concept, molecular-dynamics (MD) simulations are performed with a virtual ligand, represented by a collection of functional groups that binds to the protein and dynamically changes its shape and properties during the simulation. The ligand model essentially represents a large ensemble of different chemical species binding to the same target protein. Representative protein structures were obtained from the MD simulation, and docking was performed into this ensemble of protein conformation. Similar binding poses were clustered, and the averaged score was utilized to rerank the poses. We demonstrate that the ligand-model approach yields significant improvements in predicting native-like binding poses and quantifying binding affinities compared to static docking and ensemble docking simulations into protein structures generated from an apo MD simulation.     

Blocking of the nicotinic acetylcholine receptor ion channel by chlorpromazine, a noncompetitive inhibitor: A molecular dynamics simulation study

A large series of pharmacological agents, distinct from the typical competitive antagonists, block in a noncompetitive manner the permeability response of the nicotinic acetylcholine receptor (nAChR) to the neurotransmitter acetylcholine. Taking the neuroleptic chlorpromazine (CPZ) as an example of such agents, the blocking mechanism of noncompetitive inhibitors to the ion channel pore of the nAChR has been explored at the atomic level using both conventional and steered molecular dynamics (MD) simulations. Repeated steered MD simulations have permitted calculation of the free energy (approximately 36 kJ/mol) of CPZ binding and identification of the optimal site in the region of the serine and leucine rings, at approximately 4 A from the pore entrance. Coulomb and the Lennard-Jones interactions between CPZ and the ion channel as well as the conformational fluctuations of CPZ were examined to assess the contribution of each to the binding of CPZ to the nAChR. The MD simulations disclose a dynamic interaction of CPZ binding to the nAChR ionic channel. The cationic ammonium head of CPZ forms strong hydrogen bonds with Glu262 (alpha), Asp268 (beta), Glu272 (beta), Ser276 (beta), Glu280 (delta), Gln271 (gamma), Glu275 (gamma), and Asn279 (gamma) nAChR residues. Finally, the conventional MD simulation of CPZ at its identified binding site demonstrates that the binding of CPZ not only blocks ion transport through the channel but also markedly inhibits the conformational transitions of the channel, necessary for nAChR to carry out its biological function.     

Predicting bioactive conformations and binding modes of macrocycles

Macrocyclic compounds experience increasing interest in drug discovery. It is often thought that these large and chemically complex molecules provide promising candidates to address difficult targets and interfere with protein–protein interactions. From a computational viewpoint, these molecules are difficult to treat. For example, flexible docking of macrocyclic compounds is hindered by the limited ability of current docking approaches to optimize conformations of extended ring systems for pose prediction. Herein, we report predictions of bioactive conformations of macrocycles using conformational search and binding modes using docking. Conformational ensembles generated using specialized search technique of about 70 % of the tested macrocycles contained accurate bioactive conformations. However, these conformations were difficult to identify on the basis of conformational energies. Moreover, docking calculations with limited ligand flexibility starting from individual low energy conformations rarely yielded highly accurate binding modes. In about 40 % of the test cases, binding modes were approximated with reasonable accuracy. However, when conformational ensembles were subjected to rigid body docking, an increase in meaningful binding mode predictions to more than 50 % of the test cases was observed. Electrostatic effects did not contribute to these predictions in a positive or negative manner. Rather, achieving shape complementarity at macrocycle-target interfaces was a decisive factor. In summary, a combined computational protocol using pre-computed conformational ensembles of macrocycles as a starting point for docking shows promise in modeling binding modes of macrocyclic compounds.     

Structural Identification of Spectroscopic Substates in Neuroglobin

The structural origins of infrared absorptions of photodissociated CO in murine neuroglobin (Ngb) are determined by combining Fourier transform infrared (FTIR) spectroscopy and molecular dynamics (MD) simulations. Such an approach allows to identify and characterize both the different conformations of the Ngb active site and the transient ligand docking sites. To capture the influence of the protein environment on the spectroscopy and dynamics, experiments and simulations are carried out for the wild type protein and its F28L and F28W mutants. It is found that a voluminous side chain at position 28 divides site B into two subsites, B’ and B”. At low temperatures, CO in wt Ngb only migrates to site B’ from where it can rebind, and B” is not populated. The spectra of CO in site B’ for wt Ngb from simulations and experiments are very similar in spectral shift and shape. They both show doublets, red‐shifted with respect to gas‐phase CO and split by≈8 cm^?1. The FTIR spectra of the F28L mutant show additional bands which are also found in the simulations and can be attributed to CO located in substate B”. The different bands are mainly related to different orientations of the His64 side chain with respect to the CO ligand. Large red‐shifts arise from strong interactions between the Histidine? NH and the CO oxygen. After dissociation from the heme iron, the CO ligand visits multiple docking sites. The locations of the primary docking site B and a secondary site C, which corresponds to the Mb Xe4 cavity, could be identified unambiguously. Finally, by comparing experiment and simulations it is also possible to identify protonation of its ε position (His_ε64 NgbCO) as the preferred heme‐bound conformation in the wild type protein with a signal at 1935 cm^?1.     

Predictive power of molecular dynamics receptor structures in virtual screening

Molecular dynamics (MD) simulation is a well-established method for understanding protein dynamics. Conformations from unrestrained MD simulations have yet to be assessed for blind virtual screening (VS) by docking. This study presents a critical analysis of the predictive power of MD snapshots to this regard, evaluating two well-characterized systems of varying flexibility in ligand-bound and unbound configurations. Results from such VS predictions are discussed with respect to experimentally determined structures. In all cases, MD simulations provide snapshots that improve VS predictive power over known crystal structures, possibly due to sampling more relevant receptor conformations. Additionally, MD can move conformations previously not amenable to docking into the predictive range.     

Identification of the quinolinedione inhibitor binding site in Cdc25 phosphatase B through docking and molecular dynamics simulations

Cdc25 phosphatase B, a potential target for cancer therapy, is inhibited by a series of quinones. The binding site and mode of quinone inhibitors to Cdc25B remains unclear, whereas this information is important for structure-based drug design. We investigated the potential binding site of NSC663284 [DA3003-1 or 6-chloro-7-(2-morpholin-4-yl-ethylamino)-quinoline-5, 8-dione] through docking and molecular dynamics simulations. Of the two main binding sites suggested by docking, the molecular dynamics simulations only support one site for stable binding of the inhibitor. Binding sites in and near the Cdc25B catalytic site that have been suggested previously do not lead to stable binding in 50 ns molecular dynamics (MD) simulations. In contrast, a shallow pocket between the C-terminal helix and the catalytic site provides a favourable binding site that shows high stability. Two similar binding modes featuring protein-inhibitor interactions involving Tyr428, Arg482, Thr547 and Ser549 are identified by clustering analysis of all stable MD trajectories. The relatively flexible C-terminal region of Cdc25B contributes to inhibitor binding. The binding mode of NSC663284, identified through MD simulation, likely prevents the binding of protein substrates to Cdc25B. The present results provide useful information for the design of quinone inhibitors and their mechanism of inhibition.     

Extra precision docking,free energy calculation and molecular dynamics studies on glutamic acid derivatives as MurD inhibitors

The binding modes of well known MurD inhibitors have been studied using molecular docking and molecular dynamics (MD) simulations. The docking results of inhibitors 1-30 revealed similar mode of interaction with Escherichia coli-MurD. Further, residues Thr36, Arg37, His183, Lys319, Lys348, Thr321, Ser415 and Phe422 are found to be important for inhibitors and E. coli-MurD interactions. Our docking procedure precisely predicted crystallographic bound inhibitor 7 as evident from root mean square deviation (0.96 Å). In addition inhibitors 2 and 3 have been successfully cross-docked within the MurD active site, which was pre-organized for the inhibitor 7. Induced fit best docked poses of 2, 3, 7 and 15/2Y1O complexes were subjected to 10 ns MD simulations to determine the stability of the predicted binding conformations. Induce fit derived docked complexes were found to be in a state of near equilibrium as evident by the low root mean square deviations between the starting complex structure and the energy minimized final average MD complex structures. The results of molecular docking and MD simulations described in this study will be useful for the development of new MurD inhibitors with high potency.     

HMGB1–Carbenoxolone Interactions: Dynamics Insights from Combined Nuclear Magnetic Resonance and Molecular Dynamics

The interplay of protein dynamics and molecular recognition is of fundamental importance in biological processes. Atomic‐resolution insights into these phenomena may provide new opportunities for drug discovery. Herein, we have combined NMR relaxation experiments and residual dipolar coupling (RDC) measurements with molecular dynamics (MD) simulations to study the effects of the anti‐inflammatory drug carbenoxolone (CBNX) on the conformational properties and on the internal dynamics of a subdomain (box A) of high‐mobility group B protein (HMGB1). ¹⁵N relaxation data show that CBNX binding enhances the fast pico‐ to nanosecond motions of a loop and partially removes the internal motional anisotropy of the first two helices of box A. Dipolar wave analysis of amide RDC data shows that ligand binding induces helical distortions. In parallel, increased mobility of the loop upon ligand binding is highlighted by the essential dynamics analysis (EDA) of MD simulations. Moreover, simulations detect two possible orientations for CBNX, which induces two possible conformations of helix H3, one being similar to the free form and the second one causing a partial helical distortion. Finally, we introduce a new approach for the analysis of the internal coordination of protein residues that is consistent with experimental data and allows us to pinpoint which substructures of box A are dynamically affected by CBNX. The observations reported here may be useful for understanding the role of protein dynamics in binding at atomic resolution.     

Assessing the performance of the molecular mechanics/Poisson Boltzmann surface area and molecular mechanics/generalized Born surface area methods. II. The accuracy of ranking poses generated from docking

In molecular docking, it is challenging to develop a scoring function that is accurate to conduct high-throughput screenings. Most scoring functions implemented in popular docking software packages were developed with many approximations for computational efficiency, which sacrifices the accuracy of prediction. With advanced technology and powerful computational hardware nowadays, it is feasible to use rigorous scoring functions, such as molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) and molecular mechanics/generalized Born surface area (MM/GBSA) in molecular docking studies. Here, we systematically investigated the performance of MM/PBSA and MM/GBSA to identify the correct binding conformations and predict the binding free energies for 98 protein-ligand complexes. Comparison studies showed that MM/GBSA (69.4%) outperformed MM/PBSA (45.5%) and many popular scoring functions to identify the correct binding conformations. Moreover, we found that molecular dynamics simulations are necessary for some systems to identify the correct binding conformations. Based on our results, we proposed the guideline for MM/GBSA to predict the binding conformations. We then tested the performance of MM/GBSA and MM/PBSA to reproduce the binding free energies of the 98 protein-ligand complexes. The best prediction of MM/GBSA model with internal dielectric constant 2.0, produced a Spearman's correlation coefficient of 0.66, which is better than MM/PBSA (0.49) and almost all scoring functions used in molecular docking. In summary, MM/GBSA performs well for both binding pose predictions and binding free-energy estimations and is efficient to re-score the top-hit poses produced by other less-accurate scoring functions.     

A critical assessment of combined ligand- and structure-based approaches to HERG channel blocker modeling

Blockade of human ether-à-go-go related gene (hERG) channel prolongs the duration of the cardiac action potential and is a common reason for drug failure in preclinical safety trials. Therefore, it is of great importance to develop robust in silico tools to predict potential hERG blockers in the early stages of drug discovery and development. Herein we described comprehensive approaches to assess the discrimination of hERG-active and -inactive compounds by combining quantitative structure-activity relationship (QSAR) modeling, pharmacophore analysis, and molecular docking. Our consensus models demonstrated high-predictive capacity and improved enrichment and could correctly classify 91.8% of 147 hERG blockers from 351 inactives. To further enhance our modeling effort, hERG homology models were constructed, and molecular docking studies were conducted, resulting in high correlations (R2 = 0.81) between predicted and experimental pIC??s. We expect our unique models can be applied to efficient screening for hERG blockades, and our extensive understanding of the hERG-inhibitor interactions will facilitate the rational design of drugs devoid of hERG channel activity and hence with reduced cardiac toxicities.     

Dynamic mechanism for the autophosphorylation of CheA histidine kinase: molecular dynamics simulations

The two-component system (TCS) is an important signal transduction component for most bacteria. This signaling pathway is mediated by histidine kinases via autophosphorylation between P1 and P4 domains. Taking chemotaxis protein CheA as a model of TCS, the autophosphorylation mechanism of the TCS histidine kinases has been investigated in this study by using a computational approach integrated homology modeling, ligand-protein docking, protein-protein docking, and molecular dynamics (MD) simulations. Four nanosecond-scale MD simulations were performed on the free P4 domain, P4-ATP, P4-TNPATP, and P1-P4-ATP complexes, respectively. Upon its binding to the binding pocket of P4 with a folded conformation, ATP gradually extends to an open state with help from a water molecule. Meanwhile, ATP forms two hydrogen bonds with His413 and Lys494 at this state. Because of the lower energy of the folded conformations, ATP shrinks back to its folded conformations, leading to the rupture of the hydrogen bond between ATP and Lys494. Consequently, Lys494 moves away from the pocket entrance, resulting in an open of the ATP lid of P4. It is the open state of P4 that can bind tightly to P1, where the His45 of P1 occupies a favorable position for its autophosphorylation from ATP. This indicates that ATP is not only a phosphoryl group donor but also an activator for CheA phosphorylation. Accordingly, a mechanism of the autophosphorylation of CheA is proposed as that the ATP conformational switch triggers the opening of the ATP lid of P4, leading to P1 binding tightly, and subsequently autophosphorylation from ATP to P1.     

Molecular dynamics simulation reveals structural and thermodynamic features of kinase activation by cancer mutations within the epidermal growth factor receptor

The epidermal growth factor receptor (EGFR) is a major target for drugs in treating lung carcinoma as it promotes cell growth and tumor progression. Structural studies have demonstrated that EGFR exists in an equilibrium between catalytically active and inactive forms, and dramatic conformational transitions occur during its activation. It is known that EGFR mutations promote such conformational changes that affect its activation and drug efficacy. The most common point mutation in lung cancer patients is a leucine to arginine substitution at amino acid 834 (L834R). In a recent article, we have studied changes in drug binding affinities due to cancer mutations of EGFR using ensemble molecular dynamics (MD) simulations. Here, we address an enhanced activation mechanism thought to be associated with this mutation. Using extended timescale MD simulations, the structural and energetic properties are studied for both active and inactive conformations of EGFR. The thermodynamic stabilities of these two conformations are characterized by free energy landscapes estimated from molecular mechanics/Poisson-Boltzmann solvent area calculations. Our study reveals that the L834R mutation introduces conformational changes in both states, adjusting the relative stabilities of active and inactive conformations and hence the activation of the EGFR kinase.     

Microscopic binding of butyrylcholinesterase with quinazolinimine derivatives and the structure–activity correlation

Butyrylcholinesterase (BChE) is not only an important protein for development of anti-cocaine medication but also an established drug target to develop new treatment for Alzheimer’s disease (AD). The molecular basis of interaction of a new series of quinazolinimine derivatives as BChE inhibitors has been studied by molecular docking and molecular dynamics (MD) simulations. The molecular docking and MD simulations revealed that all of these inhibitors bind with BChE in similar binding mode. Based on the similar binding mode, we have carried out three-dimensional quantitative structure–activity relationship (3D-QSAR) studies on these inhibitors using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), to understand the structure–activity correlation of this series of inhibitors and to develop predictive models that could be used in the design of new inhibitors of BChE. The study has resulted in satisfactory 3D-QSAR models. We have also developed ligand-based 3D-QSAR models. The contour maps obtained from the 3D-QSAR models in combination with the simulated binding structures help to better interpret the structure–activity relationship and is consistent with available experimental activity data. The satisfactory 3D-QSAR models strongly suggest that the determined BChE-inhibitor binding modes are reasonable. The identified binding modes and developed 3D-QSAR models for these BChE inhibitors are expected to be valuable for rational design of new BChE inhibitors that may be valuable in the treatment of Alzheimer’s disease.