Nitro-benzylideneoxymorphone, a bifunctional mu and delta opioid receptor ligand with high mu opioid receptor efficacy.

Introduction: There is a major societal need for analgesics with less tolerance, dependence, and abuse liability. Preclinical rodent studies suggest that bifunctional ligands with both mu (MOPr) and delta (DOPr) opioid peptide receptor activity may produce analgesia with reduced tolerance and other side effects. This study explores the structure-activity relationships (SAR) of our previously reported MOPr/DOPr lead, benzylideneoxymorphone (BOM) with C7-methylene-substituted analogs. Methods: Analogs were synthesized and tested in vitro for opioid receptor binding and efficacy. One compound, nitro-BOM (NBOM, 12) was evaluated for antinociceptive effects in the warm water tail withdrawal assay in C57BL/6 mice. Acute and chronic antinociception was determined, as was toxicologic effects on chronic administration. Molecular modeling experiments were performed using the Site Identification by Ligand Competitive Saturation (SILCS) method. Results: NBOM was found to be a potent MOPr agonist/DOPr partial agonist that produces high-efficacy antinociception. Antinociceptive tolerance was observed, as was weight loss; this toxicity was only observed with NBOM and not with BOM. Modeling supports the hypothesis that the increased MOPr efficacy of NBOM is due to the substituted benzylidene ring occupying a nonpolar region within the MOPr agonist state. Discussion: Though antinociceptive tolerance and non-specific toxicity was observed on repeated administration, NBOM provides an important new tool for understanding MOPr/DOPr pharmacology.

Drude Polarizable Lipid Force Field with Explicit Treatment of Long-Range Dispersion: Parametrization and Validation for Saturated and Monounsaturated Zwitterionic Lipids.

Accurate empirical force fields of lipid molecules are a critical component of molecular dynamics simulation studies aimed at investigating properties of monolayers, bilayers, micelles, vesicles, and liposomes, as well as heterogeneous systems, such as protein-membrane complexes, bacterial cell walls, and more. While the majority of lipid force field-based simulations have been performed using pairwise-additive nonpolarizable models, advances have been made in the development of the polarizable force field based on the classical Drude oscillator model. In the present study, we undertake further optimization of the Drude lipid force field, termed Drude2023, including improved treatment of the phosphate and glycerol linker region of PC and PE headgroups, additional optimization of the alkene group in monounsaturated lipids, and inclusion of long-range Lennard-Jones interactions using the particle-mesh Ewald method. Initial optimization targeted quantum mechanical (QM) data on small model compounds representative of the linker region. Subsequent optimization targeted QM data on larger model compounds, experimental data, and dihedral potentials of mean force from the CHARMM36 additive lipid force field using a parameter reweighting protocol. The use of both experimental and QM target data during the reweighting protocol is shown to produce physically reasonable parameters that reproduce a collection of experimental observables. Target data for optimization included surface area/lipid for DPPC, DSPC, DMPC, and DLPC bilayers and nuclear magnetic resonance (NMR) order parameters for DPPC bilayers. Validation data include prediction of membrane thickness, scattering form factors, electrostatic potential profiles, compressibility moduli, surface area per lipid, water permeability, NMR T1 relaxation times, diffusion constants, and monolayer surface tensions for a variety of saturated and unsaturated lipid mono- and bilayers. Overall, the agreement with experimental data is quite good, though the results are less satisfactory for the NMR T1 relaxation times for carbons near the ester groups. Notable improvements compared to the additive C36 force field were obtained for membrane dipole potentials, lipid diffusion coefficients, and water permeability with the exception of monounsaturated lipid bilayers. It is anticipated that the optimized polarizable Drude2023 force field will help generate more accurate molecular simulations of pure bilayers and heterogeneous systems containing membranes, advancing our understanding of the role of electronic polarization in these systems.

Harnessing Deep Learning for Optimization of Lennard-Jones Parameters for the Polarizable Classical Drude Oscillator Force Field.

The outcomes of computational chemistry and biology research, including drug design, are significantly influenced by the underlying force field (FF) used in molecular simulations. While improved FF accuracy may be achieved via inclusion of explicit treatment of electronic polarization, such an extension must be accompanied by optimization of van der Waals (vdW) interactions, in the context of the Lennard-Jones (LJ) formalism in the present study. This is particularly challenging due to the extensive nature of chemical space combined with the correlated nature of LJ parameters. To address this challenge, a deep learning (DL)-based parametrization framework is developed, allowing for sampling of wide ranges of LJ parameters targeting experimental condensed phase thermodynamic properties. The present work utilizes this framework to develop the LJ parameters for atoms associated with four distinct groups covering 10 different atom types. Final parameter selection was facilitated by quantum mechanical data on rare-gas interactions with the training set molecules. The chosen parameters were then validated through experimental hydration free energies and condensed phase thermodynamic properties of validation set molecules to confirm transferability. The ultimate outcome of utilizing this framework is a set of LJ parameters in the context of the polarizable Drude FF, which demonstrated improvement in the reproduction of both experimental pure solvent and crystal properties and hydration free energies of the molecules compared to the additive CHARMM General FF (CGenFF) including the ability of the Drude FF to accurately reproduce both experimental pure solvent properties and hydration free energies. The study also shows how correlations between difference in the reproduction of condensed phase data between model compounds may be used to direct the selection of new atom types and training set molecules during FF development.

Deep Neural Network Model to Predict the Electrostatic Parameters in the Polarizable Classical Drude Oscillator Force Field.

The Drude polarizable force field (FF) captures electronic polarization effects via auxiliary Drude particles that are attached to non-hydrogen atoms, distinguishing it from commonly used additive FFs that rely on fixed charges. The Drude FF currently includes parameters for biomolecules such as proteins, nucleic acids, lipids, and carbohydrates and small-molecule representative of those classes of molecules as well as a range of atomic ions. Extension of the Drude FF to novel small druglike molecules is challenging as it requires the assignment of partial charges, atomic polarizabilities, and Thole scaling factors. In the present article, deep neural network (DNN) models are trained on quantum mechanical (QM)-based partial charges and atomic polarizabilities along with Thole scale factors trained to target QM molecular dipole moments and polarizabilities. Training of the DNN model used a collection of 39 421 molecules with molecular weights up to 200 Da and containing H, C, N, O, P, S, F, Cl, Br, or I atoms. The DNN model utilizes bond connectivity, including 1,2, 1,3, 1,4, and 1,5 terms and distances of Drude FF atom types as the feature vector to build the model, allowing it to capture both local and nonlocal effects in the molecules. Novel methods have been developed to determine restrained electrostatic potential (RESP) charges on atoms and external points representing lone pairs and to determine Thole scale factors, which have no QM analogue. A penalty scheme is devised as a performance predictor of the trained model. Validation studies show that these DNN models can precisely predict molecular dipole and polarizabilities of Food and Drug Administration (FDA)-approved drugs compared to reference MP2 calculations. The availability of the DNN model allowing for the rapid estimation of the Drude electrostatic parameters will facilitate its applicability to a wider range of molecular species.

CHARMM-GUI Drude prepper for molecular dynamics simulation using the classical Drude polarizable force field.

Explicit treatment of electronic polarizability in empirical force fields (FFs) represents an extension over a traditional additive or pairwise FF and provides a more realistic model of the variations in electronic structure in condensed phase, macromolecular simulations. To facilitate utilization of the polarizable FF based on the classical Drude oscillator model, Drude Prepper has been developed in CHARMM-GUI. Drude Prepper ingests additive CHARMM protein structures file (PSF) and pre-equilibrated coordinates in CHARMM, PDB, or NAMD format, from which the molecular components of the system are identified. These include all residues and patches connecting those residues along with water, ions, and other solute molecules. This information is then used to construct the Drude FF-based PSF using molecular generation capabilities in CHARMM, followed by minimization and equilibration. In addition, inputs are generated for molecular dynamics (MD) simulations using CHARMM, GROMACS, NAMD, and OpenMM. Validation of the Drude Prepper protocol and inputs is performed through conversion and MD simulations of various heterogeneous systems that include proteins, nucleic acids, lipids, polysaccharides, and atomic ions using the aforementioned simulation packages. Stable simulations are obtained in all studied systems, including 5 µs simulation of ubiquitin, verifying the integrity of the generated Drude PSFs. In addition, the ability of the Drude FF to model variations in electronic structure is shown through dipole moment analysis in selected systems. The capabilities and availability of Drude Prepper in CHARMM-GUI is anticipated to greatly facilitate the application of the Drude FF to a range of condensed phase, macromolecular systems.

Cancer Cell Metabolism Featuring Nrf2.

Although the major role of Nrf2 has long been established as a transcription factor for providing cellular protection against oxidative stress, multiple pieces of research and reviews now claim exactly the opposite. The dilemma - "to activate or inhibit" the protein requires an immediate answer, which evidently links cellular metabolism to the causes and purpose of cancer. Profusely growing cancerous cells have prolific energy requirements, which can only be fulfilled by modulating cellular metabolism. This review highlights the cause and effect of Nrf2 modulation in cancer that in turn channelize cellular metabolism, thereby fulfilling the energy requirements of cancer cells. The present work also highlights the purpose of genetic mutations in Nrf2, in relation to cellular metabolism in cancer cells, thus pointing out a newer approach where parallel mutations may be the key factor to decide whether to activate or inhibit Nrf2.

Solvation dynamics: improved reproduction of the time-dependent Stokes shift with polarizable empirical force field chromophore models.

The inclusion of explicit polarization in molecular dynamics simulation has gained increasing interest during the last several years. An understudied area is the role of polarizability in computer simulations of solvation dynamics around chromophores, particularly for the large solutes used in experimental studies. In this work, we present fully polarizable ground and excited state force fields for the common fluorophores N-methyl-6-oxyquinolium betaine and Coumarin 153. While analyzing the solvation responses in water, methanol, and the highly viscous ionic liquid 1-ethyl-3-methylimidazolium trifluoromethanesulfonate we found that the inclusion of solute polarizability considerably increases the agreement of the obtained Stokes shift relaxation functions with experimental data. Solute polarizability slows down the inertial solvation response in the femtosecond time regime and enables the chromophore to adapt its dipole moment to the environment. Furthermore, the developed chromophore force field reproduces the solute dipole moments in both the electronic ground and excited state in environments ranging from gas phase to highly polar media correctly. Based on these studies it is anticipated that polarizable models of chromophores will lead to an improved understanding of the relationship of their environment to their spectroscopic properties.

Toward Prediction of Electrostatic Parameters for Force Fields That Explicitly Treat Electronic Polarization.

The derivation of atomic polarizabilities for polarizable force field development has been a long-standing problem. Atomic polarizabilities were often refined manually starting from tabulated values, rendering an automated assignment of parameters difficult and hampering reproducibility and transferability of the obtained values. To overcome this, we trained both a linear increment scheme and a multilayer perceptron neural network on a large number of high-quality quantum mechanical atomic polarizabilities and partial atomic charges, where only the type of each atom and its connectivity were used as input. The predicted atomic polarizabilities and charges had average errors of 0.023 Å3 and 0.019 e using the neural net and 0.063 Å3 and 0.069 e using the simple increment scheme. As the algorithm relies only on the connectivities of the atoms within a molecule, thus omitting dependencies on the three-dimensional conformation, the approach naturally assigns like charges and polarizabilities to symmetrical groups. Accordingly, a convenient utility is presented for generating the partial atomic charges and atomic polarizabilities for organic molecules as needed in polarizable force field development.

Ranking Reversible Covalent Drugs: From Free Energy Perturbation to Fragment Docking.

Reversible covalent inhibitors have drawn increasing attention in drug design, as they are likely more potent than noncovalent inhibitors and less toxic than covalent inhibitors. Despite those advantages, the computational prediction of reversible covalent binding presents a formidable challenge because the binding process consists of multiple steps and quantum mechanics (QM) level calculation is needed to estimate the covalent binding free energy. It has been shown that the dissociation rates and the equilibrium dissociation constants vary significantly even with similar warheads, due to noncovalent interactions. We have previously used a simplistic two-state model for predicting the relative binding selectivity of reversible covalent inhibitors ( J. Am. Chem. Soc. 2017, 139 , 17945 ). Here we go beyond binding selectivity and demonstrate that it is possible to use free energy perturbation (FEP) molecular dynamics (MD) to calculate the overall reversible covalent binding using a specially designed thermodynamic cycle. We show that FEP can predict the varying binding free energies of the analogs sharing a common warhead. More importantly, our results revealed that the chemical modification away from warhead alters the binding affinity at both noncovalent and covalent binding states, and the computational prediction can be improved by considering the binding free energy of both states. Furthermore, we explored the possibility of using a more rapid computational method, site-identification by ligand competitive saturation (SILCS), to rank the same set of reversible covalent inhibitors. We found that the fragment docking to a set of precomputed fragment maps produces a reasonable ranking. In conclusion, two independent approaches provided consistent results that the covalent binding state is suitable for the initial ranking of the reversible covalent drug candidates. For lead-optimization, the FEP approach designed here can provide more rigorous and detailed information regarding how much the covalent and noncovalent binding states are contributing to the overall binding affinity, thus offering a new avenue for fine-tuning the noncovalent interactions for optimizing reversible covalent drugs.

The connexin26 human mutation N14K disrupts cytosolic intersubunit interactions and promotes channel opening.

A group of human mutations within the N-terminal (NT) domain of connexin 26 (Cx26) hemichannels produce aberrant channel activity, which gives rise to deafness and skin disorders, including keratitis-ichthyosis-deafness (KID) syndrome. Structural and functional studies indicate that the NT of connexin hemichannels is folded into the pore, where it plays important roles in permeability and gating. In this study, we explore the molecular basis by which N14K, an NT KID mutant, promotes gain of function. In macroscopic and single-channel recordings, we find that the N14K mutant favors the open conformation of hemichannels, shifts calcium and voltage sensitivity, and slows deactivation kinetics. Multiple copies of MD simulations of WT and N14K hemichannels, followed by the Kolmogorov-Smirnov significance test (KS test) of the distributions of interaction energies, reveal that the N14K mutation significantly disrupts pairwise interactions that occur in WT hemichannels between residue K15 of one subunit and residue E101 of the adjacent subunit (E101 being located at the transition between transmembrane segment 2 [TM2] and the cytoplasmic loop [CL]). Double mutant cycle analysis supports coupling between the NT and the TM2/CL transition in WT hemichannels, which is disrupted in N14K mutant hemichannels. KS tests of the α carbon correlation coefficients calculated over MD trajectories suggest that the effects of the N14K mutation are not confined to the K15-E101 pairs but extend to essentially all pairwise residue correlations between the NT and TM2/CL interface. Together, our data indicate that the N14K mutation increases hemichannel open probability by disrupting interactions between the NT and the TM2/CL region of the adjacent connexin subunit. This suggests that NT-TM2/CL interactions facilitate Cx26 hemichannel closure.

A Nexus model of cellular transition in cancer.

The exact cause of cancer is one of the most immutable medical questions of the century. Cancer as an evolutionary disease must have a purpose and understanding the purpose is more important than decoding the cause. The model of cancer proposed herein, provides a link between the cellular biochemistry and cellular genetics of cancer evolution. We thus call this model as the "Nexus model" of cancer. The Nexus model is an effort to identify the most apparent route to the disease. We have tried to utilize existing cancer literature to identify the most plausible causes of cellular transition in cancer, where the primary cancer-causing agents (physical, chemical or biological) act as inducing factors to produce cellular impeders. These cellular impeders are further linked to the Nexus. The Nexus then generates codes for epigenetics and genetics in cancer development.

Effects of bioactive constituents in the Traditional Chinese Medicinal formula Si-Wu-Tang on Nrf2 signaling and neoplastic cellular transformation.

BACKGROUND: The nuclear factor erythroid 2-related factor 2 (Nrf2) is a potential molecular target for cancer chemoprevention. Si-Wu-Tang (SWT), a popular traditional Chinese medicine for women's health, was reported with a novel activity of cancer prevention. PURPOSE: The present study was aimed to identify the bioactive constituents in SWT responsible for the Nrf2 activating and cancer preventive activity and explore the pharmacological mechanisms. METHODS: Nine compounds detectable from various batches of SWT were ranked using in silico molecular docking based on their ability to interfere the forming of Nrf2-Keap1 complex. The predicted Nrf2 activating effect was validated using the antioxidant response element (ARE) luciferase reporter assay and quantitative RT-PCR analysis for select Nrf2 regulated genes Hmox1, Nqo1 and Slc7a11. The antimutagenic activity of the compounds were determined by the Ames test. The chemopreventive activity of these compounds were assessed on EGF-induced neoplastic transformation of JB6 P+ cells, an established non-cancerous murine epidermal model for studying tumor promotion and identifying cancer preventive agents. These compounds were further characterized using luciferase reporter assay on EGF-induced activation of AP-1, a known transcription factor mediating carcinogenesis. RESULTS: Three of the nine compounds predicted as Nrf2 activators by molecular docking, gallic acid (GA), Z-liguistilide (LIG), and senkyunolide A (SA), were confirmed with highest potency of increasing the Nrf2/ARE promoter activity and upregulating the expression of Hmox1, Nqo1 and Slc7a11. In addition, GA, LIG and SA exhibited an antimutagenic activity against the direct mutagen 2-nitrofluorene while no mutagenic effects were observed at the same time in Ames test. At nontoxic concentrations, GA, LIG, and SA inhibited EGF-induced neoplastic transformation of JB6 P+ cells. Combined treatment of GA, LIG and SA, in the same ratio as detected in SWT, showed enhanced effect against JB6 transformation compared with that of the single compound alone. GA, LIG and SA, alone or in combination, suppressed EGF-induced activation of AP-1. CONCLUSION: We identified three bioactive constituents in SWT responsible for the Nrf2 activating and cancer preventive activity. This study provides evidence supporting novel molecular basis of SWT in cancer prevention.

Molecular Mechanism of Resveratrol's Lipid Membrane Protection.

Resveratrol, a natural compound found in red wine and various vegetables, has drawn increasing interest due to its reported benefit in cardiovascular protection, neurodegenerative disorders, and cancer therapy. The mechanism by which resveratrol exerts such pleiotropic effects remains unclear. It remains as one of the most discussed polyphenol compounds in the debating "French Paradox". In this study, using molecular dynamics simulations of dipalmitoyl phosphatidylcholine (DPPC) bilayer with resveratrol, we generated a free energy map of resveratrol's location and orientation of inside the lipid bilayer. We found that resveratrol increases the surface area per lipid and decreases membrane thickness, which is the opposite effect of the well-studied cholesterol on liquid phase DPPC. Most importantly, based on the simulation observation that resveratrol has a high probability of forming hydrogen bonds with sn-1 and sn-2 ester groups, we discovered a new mechanism using experimental approach, in which resveratrol protects both sn-1 and sn-2 ester bonds of DPPC and distearoyl phosphatidylcholine (DSPC) from phospholipase A1 (PLA1) and phospholipase A2 (PLA2) cleavage. Our study elucidates the new molecular mechanism of potential health benefits of resveratrol and possibly other similar polyphenols and provides a new paradigm for drug design based on resveratrol and its analogs.

A Nexus model of cellular transition in cancer

The exact cause of cancer is one of the most immutable medical questions of the century. Cancer as an evolutionary disease must have a purpose and understanding the purpose is more important than decoding the cause. The model of cancer proposed herein, provides a link between the cellular biochemistry and cellular genetics of cancer evolution. We thus call this model as the "Nexus model" of cancer. The Nexus model is an effort to identify the most apparent route to the disease. We have tried to utilize existing cancer literature to identify the most plausible causes of cellular transition in cancer, where the primary cancer-causing agents (physical, chemical or biological) act as inducing factors to produce cellular impeders. These cellular impeders are further linked to the Nexus. The Nexus then generates codes for epigenetics and genetics in cancer development.

Can Relative Binding Free Energy Predict Selectivity of Reversible Covalent Inhibitors?

Reversible covalent inhibitors have many clinical advantages over noncovalent or irreversible covalent drugs. However, apart from selecting a warhead, substantial efforts in design and synthesis are needed to optimize noncovalent interactions to improve target-selective binding. Computational prediction of binding affinity for reversible covalent inhibitors presents a unique challenge since the binding process consists of multiple steps, which are not necessarily independent of each other. In this study, we lay out the relation between relative binding free energy and the overall reversible covalent binding affinity using a two-state binding model. To prove the concept, we employed free energy perturbation (FEP) coupled with λ-exchange molecular dynamics method to calculate the binding free energy of a series of α-ketoamide analogues relative to a common warhead scaffold, in both noncovalent and covalent binding states, and for two highly homologous proteases, calpain-1 and calpain-2. We conclude that covalent binding state alone, in general, can be used to predict reversible covalent binding selectivity. However, exceptions may exist. Therefore, we also discuss the conditions under which the noncovalent binding step is no longer negligible and propose to combine the relative FEP calculations with a single QM/MM calculation of warhead to predict the binding affinity and binding kinetics. Our FEP calculations also revealed that covalent and noncovalent binding states of an inhibitor do not necessarily exhibit the same selectivity. Thus, investigating both binding states, as well as the kinetics will provide extremely useful information for optimizing reversible covalent inhibitors.

Polymodal allosteric regulation of Type 1 Serine/Threonine Kinase Receptors via a conserved electrostatic lock.

Type 1 Serine/Threonine Kinase Receptors (STKR1) transduce a wide spectrum of biological signals mediated by TGF-ß superfamily members. The STKR1 activity is tightly controlled by their regulatory glycine-serine rich (GS) domain adjacent to the kinase domain. Despite decades of studies, it remains unknown how physiological or pathological GS domain modifications are coupled to STKR1 kinase activity. Here, by performing molecular dynamics simulations and free energy calculation of Activin-Like Kinase 2 (ALK2), we found that GS domain phosphorylation, FKBP12 dissociation, and disease mutations all destabilize a D354-R375 salt-bridge, which normally acts as an electrostatic lock to prevent coordination of adenosine triphosphate (ATP) to the catalytic site. We developed a WAFEX-guided principal analysis and unraveled how phosphorylation destabilizes this highly conserved salt-bridge in temporal and physical space. Using current-flow betweenness scores, we identified an allosteric network of residue-residue contacts between the GS domain and the catalytic site that controls the formation and disruption of this salt bridge. Importantly, our novel network analysis approach revealed how certain disease-causing mutations bypass FKBP12-mediated kinase inhibition to produce leaky signaling. We further provide experimental evidence that this salt-bridge lock exists in other STKR1s, and acts as a general safety mechanism in STKR1 to prevent pathological leaky signaling. In summary, our study provides a compelling and unifying allosteric activation mechanism in STKR1 kinases that reconciles a large number of experimental studies and sheds light on a novel therapeutic avenue to target disease-related STKR1 mutants.