FFParam-v2.0: A Comprehensive Tool for CHARMM Additive and Drude Polarizable Force-Field Parameter Optimization and Validation.

Developing production quality CHARMM force-field (FF) parameters is a very detailed process involving a variety of calculations, many of which are specific for the molecule of interest. The first version of FFParam was developed as a standalone Python package designed for the optimization of electrostatic and bonded parameters of the CHARMM additive and polarizable Drude FFs by using quantum mechanical (QM) target data. The new version of FFParam has multiple new capabilities for FF parameter optimization and validation, with an emphasis on the ability to use condensed-phase target data in optimization. FFParam-v2 allows optimization of Lennard-Jones (LJ) parameters using potential energy scans of interactions between selected atoms in a molecule and noble gases, viz., He and Ne, and through condensed-phase calculations, from which experimental observables such as heats of vaporization and free energies of solvation may be obtained. This functionality serves as a gold standard for both optimizing parameters and validating the performance of the final parameters. A new bonded parameter optimization algorithm has been introduced to account for simultaneously optimizing multiple molecules sharing parameters. FFParam-v2 also supports the comparison of normal modes and the potential energy distribution of internal coordinates towards each normal mode obtained from QM and molecular mechanics calculations. Such comparison capability is vital to validate the balance among various bonded parameters that contribute to the complex normal modes of molecules. User interaction has been extended beyond the original graphical user interface to include command-line interface capabilities that allow for integration of FFParam in workflows, thereby facilitating the automation of parameter optimization. With these new functionalities, FFParam is a more comprehensive parameter optimization tool for both beginners and advanced users.

Identifying and Assessing Putative Allosteric Sites and Modulators for CXCR4 Predicted through Network Modeling and Site Identification by Ligand Competitive Saturation.

The chemokine receptor CXCR4 is a critical target for the treatment of several cancer types and HIV-1 infections. While orthosteric and allosteric modulators have been developed targeting its extracellular or transmembrane regions, the intramembrane region of CXCR4 may also include allosteric binding sites suitable for the development of allosteric drugs. To investigate this, we apply the Gaussian Network Model (GNM) to the monomeric and dimeric forms of CXCR4 to identify residues essential for its local and global motions located in the hinge regions of the protein. Residue interaction network (RIN) analysis suggests hub residues that participate in allosteric communication throughout the receptor. Mutual residues from the network models reside in regions with a high capacity to alter receptor dynamics upon ligand binding. We then investigate the druggability of these potential allosteric regions using the site identification by ligand competitive saturation (SILCS) approach, revealing two putative allosteric sites on the monomer and three on the homodimer. Two screening campaigns with Glide and SILCS-Monte Carlo docking using FDA-approved drugs suggest 20 putative hit compounds including antifungal drugs, anticancer agents, HIV protease inhibitors, and antimalarial drugs. In vitro assays considering mAB 12G5 and CXCL12 demonstrate both positive and negative allosteric activities of these compounds, supporting our computational approach. However, in vivo functional assays based on the recruitment of ß-arrestin to CXCR4 do not show significant agonism and antagonism at a single compound concentration. The present computational pipeline brings a new perspective to computer-aided drug design by combining conformational dynamics based on network analysis and cosolvent analysis based on the SILCS technology to identify putative allosteric binding sites using CXCR4 as a showcase.

Balancing Group I Monatomic Ion-Polar Compound Interactions for Condensed Phase Simulation in the Polarizable Drude Force Field.

Molecular dynamics (MD) simulations are a commonly used method for investigating molecular behavior at the atomic level. Achieving reliable MD simulation results necessitates the use of an accurate force field. In the present work, we present a protocol to enhance the quality of group 1 monatomic ions (specifically Li+, Na+, K+, Rb+, and Cs+) with respect to their interactions with common polar model compounds in biomolecules in condensed phases in the context of the Drude polarizable force field. Instead of adjusting preexisting individual parameters for ions, model compounds, and water, we employ atom-pair specific Lennard-Jones (LJ) (known as NBFIX in CHARMM) and through-space Thole dipole screening (NBTHOLE) terms to fine-tune the balance of ion-model compound, ion-water, and model compound-water interactions. This involved establishing a protocol for the optimization of NBFIX and NBTHOLE parameters targeting the difference between molecular mechanical (MM) and quantum mechanical (QM) potential energy scans (PES). It is shown that targeting PES involving complexes that include multiple model compounds and/or ions as trimers and tetramers yields parameters that produce condensed phase properties in agreement with experimental data. Validation of this protocol involved the reproduction of experimental thermodynamic benchmarks, including solvation free energies of ions in methanol and N-methylacetamide, osmotic pressures, ionic conductivities, and diffusion coefficients within the condensed phase. These results show the importance of including more complex ion-model compound complexes beyond dimers in the QM target data to account for many-body effects during parameter fitting. The presented parameters represent a significant refinement of the Drude polarizable force field, which will lead to improved accuracy for modeling ion-biomolecular interactions.

Biomolecular dynamics in the 21st century.

The relevance of motions in biological macromolecules has been clear since the early structural analyses of proteins by X-ray crystallography. Computer simulations have been applied to provide a deeper understanding of the dynamics of biological macromolecules since 1976, and are now a standard tool in many labs working on the structure and function of biomolecules. In this mini-review we highlight some areas of current interest and active development for simulations, in particular all-atom molecular dynamics simulations.

Non-ß Lactam Inhibitors of the Serine ß-Lactamase blaCTX-M15 in Drug-Resistant Salmonella typhi.

Antibiotic resistance by bacterial pathogens against widely used ß-lactam drugs is a major concern to public health worldwide, resulting in high healthcare cost. The present study aimed to extend previous research by investigating the potential activity of reported compounds against the S. typhi ß-lactamase protein. 74 compounds from computational screening reported in our previous study against ß-lactamase CMY-10 were subjected to docking studies against blaCTX-M15. Site-Identification by Ligand Competitive Saturation (SILCS)-Monte Carlo (SILCS-MC) was applied to the top two ligands selected from molecular docking studies to predict and refine their conformations for binding conformations against blaCTX-M15. The SILCS-MC method predicted affinities of -8.6 and -10.7 kcal/mol for Top1 and Top2, respectively, indicating low micromolar binding to the blaCTX-M15 active site. MD simulations initiated from SILCS-MC docked orientations were carried out to better characterize the dynamics and stability of the complexes. Important interactions anchoring the ligand within the active site include pi-pi stacked, amide-pi, and pi-alkyl interactions. Simulations of the Top2-blaCTX-M15 complex exhibited stability associated with a wide range of hydrogen-bond and aromatic interactions between the protein and the ligand. Experimental ß-lactamase (BL) activity assays showed that Top1 has 0.1 u/mg BL activity, and Top2 has a BL activity of 0.038 u/mg with a minimum inhibitory concentration of 1 mg/mL. The inhibitors proposed in this study are non-ß-lactam-based ß-lactamase inhibitors that exhibit the potential to be used in combination with ß-lactam antibiotics against multidrug-resistant clinical isolates. Thus, Top1 and Top2 represent lead compounds that increase the efficacy of ß-lactam antibiotics with a low dose concentration.

Dendritic Cell-Mediated Cross-Priming by a Bispecific Neutralizing Antibody Boosts Cytotoxic T Cell Responses and Protects Mice against SARS-CoV-2.

Administration of neutralizing antibodies (nAbs) has proved to be effective by providing immediate protection against SARS-CoV-2. However, dual strategies combining virus neutralization and immune response stimulation to enhance specific cytotoxic T cell responses, such as dendritic cell (DC) cross-priming, represent a promising field but have not yet been explored. Here, a broadly nAb, TNT , are first generated by grafting an anti-RBD biparatopic tandem nanobody onto a trimerbody scaffold. Cryo-EM data show that the TNT structure allows simultaneous binding to all six RBD epitopes, demonstrating a high-avidity neutralizing interaction. Then, by C-terminal fusion of an anti-DNGR-1 scFv to TNT , the bispecific trimerbody TNT DNGR-1 is generated to target neutralized virions to type 1 conventional DCs (cDC1s) and promote T cell cross-priming. Therapeutic administration of TNT DNGR-1, but not TNT , protects K18-hACE2 mice from a lethal SARS-CoV-2 infection, boosting virus-specific humoral responses and CD8+ T cell responses. These results further strengthen the central role of interactions with immune cells in the virus-neutralizing antibody activity and demonstrate the therapeutic potential of the Fc-free strategy that can be used advantageously to provide both immediate and long-term protection against SARS-CoV-2 and other viral infections.

Combining SILCS and Artificial Intelligence for High-Throughput Prediction of the Passive Permeability of Drug Molecules.

Membrane permeability of drug molecules plays a significant role in the development of new therapeutic agents. Accordingly, methods to predict the passive permeability of drug candidates during a medicinal chemistry campaign offer the potential to accelerate the drug design process. In this work, we combine the physics-based site identification by ligand competitive saturation (SILCS) method and data-driven artificial intelligence (AI) to create a high-throughput predictive model for the passive permeability of druglike molecules. In this study, we present a comparative analysis of four regression models to predict membrane permeabilities of small druglike molecules; of the tested models, Random Forest was the most predictive yielding an R2 of 0.81 for the independent data set. The input feature vector used to train the developed prediction model includes absolute free energy profiles of ligands through a POPC-cholesterol bilayer based on ligand grid free energy (LGFE) profiles obtained from the SILCS approach. The use of the membrane free energy profiles from SILCS offers information on the physical forces contributing to ligand permeability, while the use of AI yields a more predictive model trained on experimental PAMPA permeability data for a collection of 229 molecules. This combination allows for rapid estimations of ligand permeability at a level of accuracy beyond currently available predictive models while offering insights into the contributions of the functional groups in the ligands to the permeability barrier, thereby offering quantitative information to facilitate rational ligand design.

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.

Influence of Mg2+ Distribution on the Stability of Folded States of the Twister Ribozyme Revealed Using Grand Canonical Monte Carlo and Generative Deep Learning Enhanced Sampling.

Metal ions, particularly magnesium ions (Mg2+), play a role in stabilizing the tertiary structures of RNA molecules. Theoretical models and experimental techniques show that metal ions can change RNA dynamics and how it transitions through different stages of folding. However, the specific ways in which metal ions contribute to the formation and stabilization of RNA's tertiary structure are not fully understood at the atomic level. Here, we combined oscillating excess chemical potential Grand Canonical Monte Carlo (GCMC) and metadynamics to bias toward the sampling of unfolded states using reaction coordinates generated by machine learning allowing for examination of Mg2+-RNA interactions that contribute to stabilizing folded states of the pseudoknot found in the Twister ribozyme. GCMC is used to sample diverse ion distributions around the RNA with deep learning applied to iteratively generate system-specific reaction coordinates to maximize conformational sampling during metadynamics simulations. Results from 6 µs simulations performed on 9 individual systems indicate that Mg2+ ions play a crucial role in stabilizing the three-dimensional (3D) structure of the RNA by stabilizing specific interactions of phosphate groups or phosphate groups and bases of neighboring nucleotides. While many phosphates are accessible to interactions with Mg2+, it is observed that multiple, specific interactions are required to sample conformations close to the folded state; coordination of Mg2+ at individual specific sites facilitates sampling of folded conformations though unfolding ultimately occurs. It is only when multiple specific interactions occur, including the presence of specific inner-shell cation interactions linking two nucleotides, that conformations close to the folded state are stable. While many of the identified Mg2+ interactions are observed in the X-ray crystal structure of Twister, the present study suggests two new Mg2+ ion sites in the Twister ribozyme that contribute to stabilization. In addition, specific interactions with Mg2+ are observed that destabilize the local RNA structure, a process that may facilitate the folding of RNA into its correct structure.

Identification of a novel transport system in Borrelia burgdorferi that links the inner and outer membranes.

Borrelia burgdorferi, the spirochete that causes Lyme disease, is a diderm organism that is similar to Gram-negative organisms in that it contains both an inner and outer membrane. Unlike typical Gram-negative organisms, however, B. burgdorferi lacks lipopolysaccharide (LPS). Using computational genome analyses and structural modeling, we identified a transport system containing six proteins in B. burgdorferi that are all orthologs to proteins found in the lipopolysaccharide transport (LPT) system that links the inner and outer membranes of Gram-negative organisms and is responsible for placing LPS on the surface of these organisms. While B. burgdorferi does not contain LPS, it does encode over 100 different surface-exposed lipoproteins and several major glycolipids, which like LPS are also highly amphiphilic molecules, though no system to transport these molecules to the borrelial surface is known. Accordingly, experiments supplemented by molecular modeling were undertaken to determine whether the orthologous LPT system identified in B. burgdorferi could transport lipoproteins and/or glycolipids to the borrelial outer membrane. Our combined observations strongly suggest that the LPT transport system does not transport lipoproteins to the surface. Molecular dynamic modeling, however, suggests that the borrelial LPT system could transport borrelial glycolipids to the outer membrane.

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.

Integrated Covalent Drug Design Workflow Using Site Identification by Ligand Competitive Saturation.

Covalent drug design is an important component in drug discovery. Traditional drugs interact with their target in a reversible equilibrium, while irreversible covalent drugs increase the drug-target interaction duration by forming a covalent bond with targeted residues and thus may offer a more effective therapeutic approach. To facilitate the design of this class of ligands, computational methods can be used to help identify reactive nucleophilic residues, frequently cysteines, on a target protein for covalent binding, to test various warhead groups for their potential reactivities, and to predict noncovalent contributions to binding that can facilitate drug-target interactions that are important for binding specificity. To further aid covalent drug design, we extended a functional group mapping approach based on explicit solvent all-atom molecular simulations (SILCS: site identification by ligand competitive saturation) that intrinsically considers protein flexibility, functional group, and protein desolvation along with functional group-protein interactions. Through docking of a library of representative warhead fragments using SILCS-Monte Carlo (SILCS-MC), reactive cysteines can be correctly identified for proteins being tested. Furthermore, a machine learning model was trained to quantify the effectiveness of various warhead groups for proteins using metrics from SILCS-MC as well as experimental model compound warhead reactivity data. The ability to rank covalent molecular binders with similar warheads using SILCS ligand grid free energy (LGFE) ranking was also tested for several proteins. Based on these tools, an integrated SILCS-based workflow was developed, named SILCS-Covalent, which can both qualitatively and quantitatively inform covalent drug discovery.

GPU-specific algorithms for improved solute sampling in grand canonical Monte Carlo simulations.

The Grand Canonical Monte Carlo (GCMC) ensemble defined by the excess chemical potential, µex , volume, and temperature, in the context of molecular simulations allows for variations in the number of particles in the system. In practice, GCMC simulations have been widely applied for the sampling of rare gasses and water, but limited in the context of larger molecules. To overcome this limitation, the oscillating µex GCMC method was introduced and shown to be of utility for sampling small solutes, such as formamide, propane, and benzene, as well as for ionic species such as monocations, acetate, and methylammonium. However, the acceptance of GCMC insertions is low, and the method is computationally demanding. In the present study, we improved the sampling efficiency of the GCMC method using known cavity-bias and configurational-bias algorithms in the context of GPU architecture. Specifically, for GCMC simulations of aqueous solution systems, the configurational-bias algorithm was extended by applying system partitioning in conjunction with a random interval extraction algorithm, thereby improving the efficiency in a highly parallel computing environment. The method is parallelized on the GPU using CUDA and OpenCL, allowing for the code to run on both Nvidia and AMD GPUs, respectively. Notably, the method is particularly well suited for GPU computing as the large number of threads allows for simultaneous sampling of a large number of configurations during insertion attempts without additional computational overhead. In addition, the partitioning scheme allows for simultaneous insertion attempts for large systems, offering considerable efficiency. Calculations on the BK Channel, a transporter, including a lipid bilayer with over 760,000 atoms, show a speed up of ~53-fold through the use of system partitioning. The improved algorithm is then combined with an enhanced µex oscillation protocol and shown to be of utility in the context of the site-identification by ligand competitive saturation (SILCS) co-solvent sampling approach as illustrated through application to the protein CDK2.

Site Identification by Ligand Competitive Saturation-Biologics Approach for Structure-Based Protein Charge Prediction.

Protein-based therapeutics typically require high concentrations of the active protein, which can lead to protein aggregation and high solution viscosity. Such solution behaviors can limit the stability, bioavailability, and manufacturability of protein-based therapeutics and are directly influenced by the charge of a protein. Protein charge is a system property affected by its environment, including the buffer composition, pH, and temperature. Thus, the charge calculated by summing the charges of each residue in a protein, as is commonly done in computational methods, may significantly differ from the effective charge of the protein as these calculations do not account for contributions from bound ions. Here, we present an extension of the structure-based approach termed site identification by ligand competitive saturation-biologics (SILCS-Biologics) to predict the effective charge of proteins. The SILCS-Biologics approach was applied on a range of protein targets in different salt environments for which membrane-confined electrophoresis-determined charges were previously reported. SILCS-Biologics maps the 3D distribution and predicted occupancy of ions, buffer molecules, and excipient molecules bound to the protein surface in a given salt environment. Using this information, the effective charge of the protein is predicted such that the concentrations of the ions and the presence of excipients or buffers are accounted for. Additionally, SILCS-Biologics also produces 3D structures of the binding sites of ions on the proteins, which enable further analyses such as the characterization of protein surface charge distribution and dipole moments in different environments. Notable is the capability of the method to account for competition between salts, excipients, and buffers on the calculated electrostatic properties in different protein formulations. Our study demonstrates the ability of the SILCS-Biologics approach to predict the effective charge of proteins and its applicability in uncovering protein-ion interactions and their contributions to protein solubility and function.

Structure-Based Design of Potent Iminosugar Inhibitors of Endoplasmic Reticulum α-Glucosidase I with Anti-SARS-CoV-2 Activity.

Enveloped viruses depend on the host endoplasmic reticulum (ER) quality control (QC) machinery for proper glycoprotein folding. The endoplasmic reticulum quality control (ERQC) enzyme α-glucosidase I (α-GluI) is an attractive target for developing broad-spectrum antivirals. We synthesized 28 inhibitors designed to interact with all four subsites of the α-GluI active site. These inhibitors are derivatives of the iminosugars 1-deoxynojirimycin (1-DNJ) and valiolamine. Crystal structures of ER α-GluI bound to 25 1-DNJ and three valiolamine derivatives revealed the basis for inhibitory potency. We established the structure-activity relationship (SAR) and used the Site Identification by Ligand Competitive Saturation (SILCS) method to develop a model for predicting α-GluI inhibition. We screened the compounds against SARS-CoV-2 in vitro to identify those with greater antiviral activity than the benchmark α-glucosidase inhibitor UV-4. These host-targeting compounds are candidates for investigation in animal models of SARS-CoV-2 and for testing against other viruses that rely on ERQC for correct glycoprotein folding.

Glycosidic α-linked mannopyranose disaccharides: an NMR spectroscopy and molecular dynamics simulation study employing additive and Drude polarizable force fields.

D-Mannose is a structural component in N-linked glycoproteins from viruses and mammals as well as in polysaccharides from fungi and bacteria. Structural components often consist of D-Manp residues joined via α-(1→2)-, α-(1→3)-, α-(1→4)- or α-(1→6)-linkages. As models for these oligo- and polysaccharides, a series of mannose-containing disaccharides have been investigated with respect to conformation and dynamics. Translational diffusion NMR experiments were performed to deduce rotational correlation times for the molecules, 1D 1H,1H-NOESY and 1D 1H,1H-T-ROESY NMR experiments were carried out to obtain inter-residue proton-proton distances and one-dimensional long-range and 2D J-HMBC experiments were acquired to gain information about conformationally dependent heteronuclear coupling constants across glycosidic linkages. To attain further spectroscopic data, the doubly 13C-isotope labeled α-D-[1,2-13C2]Manp-(1→4)-α-D-Manp-OMe was synthesized thereby facilitating conformational analysis based on 13C,13C coupling constants as interpreted by Karplus-type relationships. Molecular dynamics simulations were carried out for the disaccharides with explicit water as solvent using the additive CHARMM36 and Drude polarizable force fields for carbohydrates, where the latter showed broader population distributions. Both simulations sampled conformational space in such a way that inter-glycosidic proton-proton distances were very well described whereas in some cases deviations were observed between calculated conformationally dependent NMR scalar coupling constants and those determined from experiment, with closely similar root-mean-square differences for the two force fields. However, analyses of dipole moments and radial distribution functions with water of the hydroxyl groups indicate differences in the underlying physical forces dictating the wider conformational sampling with the Drude polarizable versus additive C36 force field and indicate the improved utility of the Drude polarizable model in investigating complex carbohydrates.

Computer-Aided Drug Design: An Update.

Computer-aided drug design (CADD) approaches are playing an increasingly important role in understanding the fundamentals of ligand-receptor interactions and helping medicinal chemists design therapeutics. About 5 years ago, we presented a chapter devoted to an overview of CADD methods and covered typical CADD protocols including structure-based drug design (SBDD) and ligand-based drug design (LBDD) approaches that were frequently used in the antibiotic drug design process. Advances in computational hardware and algorithms and emerging CADD methods are enhancing the accuracy and ability of CADD in drug design and development. In this chapter, an update to our previous chapter is provided with a focus on new CADD approaches from our laboratory and other peers that can be employed to facilitate the development of antibiotic therapeutics.

In silico identification of a ß2-adrenoceptor allosteric site that selectively augments canonical ß2AR-Gs signaling and function.

Activation of ß2-adrenoceptors (ß2ARs) causes airway smooth muscle (ASM) relaxation and bronchodilation, and ß2AR agonists (ß-agonists) are front-line treatments for asthma and other obstructive lung diseases. However, the therapeutic efficacy of ß-agonists is limited by agonist-induced ß2AR desensitization and noncanonical ß2AR signaling involving ß-arrestin that is shown to promote asthma pathophysiology. Accordingly, we undertook the identification of an allosteric site on ß2AR that could modulate the activity of ß-agonists to overcome these limitations. We employed the site identification by ligand competitive saturation (SILCS) computational method to comprehensively map the entire 3D structure of in silico-generated ß2AR intermediate conformations and identified a putative allosteric binding site. Subsequent database screening using SILCS identified drug-like molecules with the potential to bind to the site. Experimental assays in HEK293 cells (expressing recombinant wild-type human ß2AR) and human ASM cells (expressing endogenous ß2AR) identified positive and negative allosteric modulators (PAMs and NAMs) of ß2AR as assessed by regulation of ß-agonist-stimulation of cyclic AMP generation. PAMs/NAMs had no effect on ß-agonist-induced recruitment of ß-arrestin to ß2AR- or ß-agonist-induced loss of cell surface expression in HEK293 cells expressing ß2AR. Mutagenesis analysis of ß2AR confirmed the SILCS identified site based on mutants of amino acids R131, Y219, and F282. Finally, functional studies revealed augmentation of ß-agonist-induced relaxation of contracted human ASM cells and bronchodilation of contracted airways. These findings identify a allosteric binding site on the ß2AR, whose activation selectively augments ß-agonist-induced Gs signaling, and increases relaxation of ASM cells, the principal therapeutic effect of ß-agonists.

Scaffold hopping from indoles to indazoles yields dual MCL-1/BCL-2 inhibitors from MCL-1 selective leads.

Overexpression of the anti-apoptotic BCL-2 proteins is associated with the development and progression of a range of cancers. Venetoclax, an FDA-approved BCL-2 inhibitor, is fast becoming the standard-of-care for acute myeloid leukemia and chronic lymphocytic leukemia. However, the median survival offered by venetoclax is only 18 months (as part of a combination therapy regimen), and one of the primary culprits for this is the concomitant upregulation of sister anti-apoptotic proteins, in particular MCL-1 (and BCL-xL), which provides an escape route that manifests as venetoclax resistance. Since inhibition of BCL-xL leads to thrombocytopenia, we believe that a dual MCL-1/BCL-2 inhibitor may provide an enhanced therapeutic effect relative to a selective BCL-2 inhibitor. Beginning with a carboxylic acid-containing literature compound that is a potent inhibitor of MCL-1 and a moderate inhibitor of BCL-2, we herein describe our efforts to develop dual inhibitors of MCL-1 and BCL-2 by scaffold hopping from an indole core to an indazole framework. Subsequently, further elaboration of our novel N2-substituted, indazole-3-carboxylic acid lead into a family of indazole-3-acylsulfonamides resulted in improved inhibition of both MCL-1 and BCL-2, possibly through occupation of the p4 pocket, with minimal or no inhibition of BCL-xL.

Extension of the CHARMM Classical Drude Polarizable Force Field to N- and O-Linked Glycopeptides and Glycoproteins.

Molecular dynamic simulations are an effective tool to study complex molecular systems and are contingent upon the availability of an accurate and reliable molecular mechanics force field. The Drude polarizable force field, which allows for the explicit treatment of electronic polarization in a computationally efficient fashion, has been shown to reproduce experimental properties that were difficult or impossible to reproduce with the CHARMM additive force field, including peptide folding cooperativity, RNA hairpin structures, and DNA base flipping. Glycoproteins are essential components of glycoconjugate vaccines, antibodies, and many pharmaceutically important molecules, and an accurate polarizable force field that includes compatibility between the protein and carbohydrate aspect of the force field is essential to study these types of systems. In this work, we present an extension of the Drude polarizable force field to glycoproteins, including both N- and O-linked species. Parameter optimization focused on the dihedral terms using a reweighting protocol targeting NMR solution J-coupling data for model glycopeptides. Validation of the model include eight model glycopeptides and four glycoproteins with multiple N- and O-linked glycosylations. The new glycoprotein carbohydrate force field can be used in conjunction with the remainder of Drude polarizable force field through a variety of MD simulation programs including GROMACS, OPENMM, NAMD, and CHARMM and may be accessed through the Drude Prepper module in the CHARMM-GUI.