Decoding the signaling of a GPCR heteromeric complex reveals a unifying mechanism of action of antipsychotic drugs.

Atypical antipsychotic drugs, such as clozapine and risperidone, have a high affinity for the serotonin 5-HT(2A) G protein-coupled receptor (GPCR), the 2AR, which signals via a G(q) heterotrimeric G protein. The closely related non-antipsychotic drugs, such as ritanserin and methysergide, also block 2AR function, but they lack comparable neuropsychological effects. Why some but not all 2AR inhibitors exhibit antipsychotic properties remains unresolved. We now show that a heteromeric complex between the 2AR and the G(i)-linked GPCR, metabotropic glutamate 2 receptor (mGluR2), integrates ligand input, modulating signaling output and behavioral changes. Serotonergic and glutamatergic drugs bind the mGluR2/2AR heterocomplex, which then balances Gi- and Gq-dependent signaling. We find that the mGluR2/2AR-mediated changes in Gi and Gq activity predict the psychoactive behavioral effects of a variety of pharmocological compounds. These observations provide mechanistic insight into antipsychotic action that may advance therapeutic strategies for disorders including schizophrenia and dementia.

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.

First-in-Class Mitogen-Activated Protein Kinase p38α: MAPK-Activated Protein Kinase-2 (MK2) Dual Signal Modulator with Anti-inflammatory and Endothelial-stabilizing Properties.

We previously identified a small molecule, UM101, predicted to bind to the substrate-binding groove of p38aMitogen-activated Protein Kinase (MAPK) near the binding site of its proinflammatory substrate, MAPK-activated protein kinase (MK2). UM101 exhibited anti-inflammatory, endothelial-stabilizing, and lung-protective effects. To overcome its limited aqueous solubility and p38a binding affinity, we designed an analog of UM101, GEn-1124, with improved aqueous solubility, stability, and p38a binding affinity. Compared with UM101, GEn-1124 has 18-fold greater p38a-binding affinity as measured by Surface Plasmon Resonance (SPR), 11-fold greater aqueous solubility, enhanced barrier-stabilizing activity in thrombin-stimulated human pulmonary artery endothelial cells (hPAEC) in vitro, and greater lung protection in vivo GEn-1124 improved survival from 10% to 40% in murine acute lung injury (ALI) induced by combined exposure to intratracheal bacterial endotoxin lipopolysaccharide (LPS) instillation and febrile-range hyperthermia (FRH) and from 0% to 50% in a mouse influenza pneumonia model. Gene expression analysis by RNASeq in TNFa-treated hPAEC showed that the gene-modifying effects of GEn-1124 were much more restricted to TNFa-inducible genes than the catalytic site p38 inhibitor, SB203580. Gene expression pathway analysis, confocal immunofluorescence analysis of p38aand MK2 subcellular trafficking, and SPR analysis of phosphorylated p38a:MK2 binding affinity supports a novel mechanism of action. GEn-1124 destabilizes the activated p38a:MK2 complex, dissociates nuclear export of MK2 and p38a, thereby promoting intranuclear retention and enhanced intranuclear signaling by phosphorylated p38a retention, and accelerated inactivation of p38-free cytosolic MK2 by unopposed phosphatases. Significance Statement We describe an analog of our first-in-class small molecule modulator of p38a/MK2 signaling targeted to a pocket near the ED substrate binding domain of p38a, which destabilizes the p38a:MK2 complex without blocking p38 catalytic activity or ablating downstream signaling. The result is a rebalancing of downstream pro- and anti-inflammatory signaling, yielding anti-inflammatory, endothelial-stabilizing, and lung-protective effects with therapeutic potential in ARDS.

Combined Physics- and Machine-Learning-Based Method to Identify Druggable Binding Sites Using SILCS-Hotspots.

Identifying druggable binding sites on proteins is an important and challenging problem, particularly for cryptic, allosteric binding sites that may not be obvious from X-ray, cryo-EM, or predicted structures. The Site-Identification by Ligand Competitive Saturation (SILCS) method accounts for the flexibility of the target protein using all-atom molecular simulations that include various small molecule solutes in aqueous solution. During the simulations, the combination of protein flexibility and comprehensive sampling of the water and solute spatial distributions can identify buried binding pockets absent in experimentally determined structures. Previously, we reported a method for leveraging the information in the SILCS sampling to identify binding sites (termed Hotspots) of small mono- or bicyclic compounds, a subset of which coincide with known binding sites of drug-like molecules. Here, we build on that physics-based approach and present a ML model for ranking the Hotspots according to the likelihood they can accommodate drug-like molecules (e.g., molecular weight >200 Da). In the independent validation set, which includes various enzymes and receptors, our model recalls 67% and 89% of experimentally validated ligand binding sites in the top 10 and 20 ranked Hotspots, respectively. Furthermore, we show that the model's output Decision Function is a useful metric to predict binding sites and their potential druggability in new targets. Given the utility the SILCS method for ligand discovery and optimization, the tools presented represent an important advancement in the identification of orthosteric and allosteric binding sites and the discovery of drug-like molecules targeting those sites.

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.

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.

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.

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.

Small molecules inhibitors of the heterogeneous ribonuclear protein A18 (hnRNP A18): a regulator of protein translation and an immune checkpoint.

We have identified chemical probes that simultaneously inhibit cancer cell progression and an immune checkpoint. Using the computational Site Identification by Ligand Competitive Saturation (SILCS) technology, structural biology and cell-based assays, we identify small molecules that directly and selectively bind to the RNA Recognition Motif (RRM) of hnRNP A18, a regulator of protein translation in cancer cells. hnRNP A18 recognizes a specific RNA signature motif in the 3'UTR of transcripts associated with cancer cell progression (Trx, VEGF, RPA) and, as shown here, a tumor immune checkpoint (CTLA-4). Post-transcriptional regulation of immune checkpoints is a potential therapeutic strategy that remains to be exploited. The probes target hnRNP A18 RRM in vitro and in cells as evaluated by cellular target engagement. As single agents, the probes specifically disrupt hnRNP A18-RNA interactions, downregulate Trx and CTLA-4 protein levels and inhibit proliferation of several cancer cell lines without affecting the viability of normal epithelial cells. These first-in-class chemical probes will greatly facilitate the elucidation of the underexplored biological function of RNA Binding Proteins (RBPs) in cancer cells, including their effects on proliferation and immune checkpoint activation.

The SKI complex is a broad-spectrum, host-directed antiviral drug target for coronaviruses, influenza, and filoviruses.

The SARS-CoV-2 pandemic has made it clear that we have a desperate need for antivirals. We present work that the mammalian SKI complex is a broad-spectrum, host-directed, antiviral drug target. Yeast suppressor screening was utilized to find a functional genetic interaction between proteins from influenza A virus (IAV) and Middle East respiratory syndrome coronavirus (MERS-CoV) with eukaryotic proteins that may be potential host factors involved in replication. This screening identified the SKI complex as a potential host factor for both viruses. In mammalian systems siRNA-mediated knockdown of SKI genes inhibited replication of IAV and MERS-CoV. In silico modeling and database screening identified a binding pocket on the SKI complex and compounds predicted to bind. Experimental assays of those compounds identified three chemical structures that were antiviral against IAV and MERS-CoV along with the filoviruses Ebola and Marburg and two further coronaviruses, SARS-CoV and SARS-CoV-2. The mechanism of antiviral activity is through inhibition of viral RNA production. This work defines the mammalian SKI complex as a broad-spectrum antiviral drug target and identifies lead compounds for further development.

Structure of the cell-binding component of the Clostridium difficile binary toxin reveals a di-heptamer macromolecular assembly.

Targeting Clostridium difficile infection is challenging because treatment options are limited, and high recurrence rates are common. One reason for this is that hypervirulent C. difficile strains often have a binary toxin termed the C. difficile toxin, in addition to the enterotoxins TsdA and TsdB. The C. difficile toxin has an enzymatic component, termed CDTa, and a pore-forming or delivery subunit termed CDTb. CDTb was characterized here using a combination of single-particle cryoelectron microscopy, X-ray crystallography, NMR, and other biophysical methods. In the absence of CDTa, 2 di-heptamer structures for activated CDTb (1.0 MDa) were solved at atomic resolution, including a symmetric (SymCDTb; 3.14 Å) and an asymmetric form (AsymCDTb; 2.84 Å). Roles played by 2 receptor-binding domains of activated CDTb were of particular interest since the receptor-binding domain 1 lacks sequence homology to any other known toxin, and the receptor-binding domain 2 is completely absent in other well-studied heptameric toxins (i.e., anthrax). For AsymCDTb, a Ca2+ binding site was discovered in the first receptor-binding domain that is important for its stability, and the second receptor-binding domain was found to be critical for host cell toxicity and the di-heptamer fold for both forms of activated CDTb. Together, these studies represent a starting point for developing structure-based drug-design strategies to target the most severe strains of C. difficile.

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.

Contributions and competition of Mg2+ and K+ in folding and stabilization of the Twister ribozyme.

Native folded and compact intermediate states of RNA typically involve tertiary structures in the presence of divalent ions such as Mg2+ in a background of monovalent ions. In a recent study, we have shown how the presence of Mg2+ impacts the transition from partially unfolded to folded states through a "push-pull" mechanism where the ion both favors and disfavors the sampling of specific phosphate-phosphate interactions. To further understand the ion atmosphere of RNA in folded and partially folded states results from atomistic umbrella sampling and oscillating chemical potential grand canonical Monte Carlo/molecular dynamics (GCMC/MD) simulations are used to obtain atomic-level details of the distributions of Mg2+ and K+ ions around Twister RNA. Results show the presence of 100 mM Mg2+ to lead to increased charge neutralization over that predicted by counterion condensation theory. Upon going from partially unfolded to folded states, overall charge neutralization increases at all studied ion concentrations that, while associated with an increase in the number of direct ion-phosphate interactions, is fully accounted for by the monovalent K+ ions. Furthermore, K+ preferentially interacts with purine N7 atoms of helical regions in partially unfolded states, thereby potentially stabilizing the helical regions. Thus, both secondary helical structures and formation of tertiary structures leads to increased counterion condensation, thereby stabilizing those structural features of Twister. Notably, it is shown that K+ can act as a surrogate for Mg2+ by participating in specific interactions with nonsequential phosphate pairs that occur in the folded state, explaining the ability of Twister to self-cleave at submillimolar Mg2+ concentrations.

Insights into substrate recognition and specificity for IgG by Endoglycosidase S2.

Antibodies bind foreign antigens with high affinity and specificity leading to their neutralization and/or clearance by the immune system. The conserved N-glycan on IgG has significant impact on antibody effector function, with the endoglycosidases of Streptococcus pyogenes deglycosylating the IgG to evade the immune system, a process catalyzed by the endoglycosidase EndoS2. Studies have shown that two of the four domains of EndoS2, the carbohydrate binding module (CBM) and the glycoside hydrolase (GH) domain are critical for catalytic activity. To yield structural insights into contributions of the CBM and the GH domains as well as the overall flexibility of EndoS2 to the proteins' catalytic activity, models of EndoS2-Fc complexes were generated through enhanced-sampling molecular-dynamics (MD) simulations and site-identification by ligand competitive saturation (SILCS) docking followed by reconstruction and multi-microsecond MD simulations. Modeling results predict that EndoS2 initially interacts with the IgG through its CBM followed by interactions with the GH yielding catalytically competent states. These may involve the CBM and GH of EndoS2 simultaneously interacting with either the same Fc CH2/CH3 domain or individually with the two Fc CH2/CH3 domains, with EndoS2 predicted to assume closed conformations in the former case and open conformations in the latter. Apo EndoS2 is predicted to sample both the open and closed states, suggesting that either complex can directly form following initial IgG-EndoS2 encounter. Interactions of the CBM and GH domains with the IgG are predicted to occur through both its glycan and protein regions. Simulations also predict that the Fc glycan can directly transfer from the CBM to the GH, facilitating formation of catalytically competent complexes and how the 734 to 751 loop on the CBM can facilitate extraction of the glycan away from the Fc CH2/CH3 domain. The predicted models are compared and consistent with Hydrogen/Deuterium Exchange data. In addition, the complex models are consistent with the high specificity of EndoS2 for the glycans on IgG supporting the validity of the predicted models.

Preserving the Integrity of Empirical Force Fields.

Generalized force fields (FFs) act as extensions to biomolecular FFs to provide a wide coverage of organic molecules. However, their precise application to an arbitrary molecule presents a separate challenge. We show that MATCH assigns different atom types and bonded and nonbonded parameters than CGenFF, and the AM1-BCC charge model, commonly used with GAFF/GAFF2, does not exactly reproduce the performance of the RESP charge model. The results indicate the need for caution when employing FFs to ensure their integrity with respect to their implementation and validation.

Second harmonic generation detection of Ras conformational changes and discovery of a small molecule binder.

Second harmonic generation (SHG) is an emergent biophysical method that sensitively measures real-time conformational change of biomolecules in the presence of biological ligands and small molecules. This study describes the successful implementation of SHG as a primary screening platform to identify fragment ligands to oncogenic Kirsten rat sarcoma (KRas). KRas is the most frequently mutated driver of pancreatic, colon, and lung cancers; however, there are few well-characterized small molecule ligands due to a lack of deep binding pockets. Using SHG, we identified a fragment binder to KRasG12D and used 1H 15N transverse relaxation optimized spectroscopy (TROSY) heteronuclear single-quantum coherence (HSQC) NMR to characterize its binding site as a pocket adjacent to the switch 2 region. The unique sensitivity of SHG furthered our study by revealing distinct conformations induced by our hit fragment compared with 4,6-dichloro-2-methyl-3-aminoethyl-indole (DCAI), a Ras ligand previously described to bind the same pocket. This study highlights SHG as a high-throughput screening platform that reveals structural insights in addition to ligand binding.

Cholecalciferol complexation with hydroxypropyl-ß-cyclodextrin (HPBCD) and its molecular dynamics simulation.

The focus of the current study is to investigate cholecalciferol (vitamin D3) solubilization by hydroxypropyl-ß-cyclodextrin (HPBCD) complexation through experimental and computational studies. Phase solubility diagram of vitamin D3 (completely insoluble in water) has an AP profile revealing a deviation from a linear regression with HPBCD concentration increase. Differential scanning calorimetry (DSC) is the best tool to confirm complex formation by disappearance of cholecalciferol exothermic peak in cholecalciferol-HPBCD complex thermogram, due to its amorphous state by entering HPBCD inner hydrophobic cavity, similarly validated by Fourier-transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD). AP solubility diagram profile can be associated with cholecalciferol-HPBCD complex instability in liquid phase requiring spray drying to bring it to a solid dispersion state (always more stable) illustrated by scanning electron microscopy (SEM). Computational studies led to a deeper understanding and clarification, at molecular level, of the interactions within cholecalciferol-HPBCD complex. Thermodynamics and geometry of the complex were investigated by molecular dynamics (MD) simulation.

Site-Selective Chemoenzymatic Modification on the Core Fucose of an Antibody Enhances Its Fcγ Receptor Affinity and ADCC Activity.

Fc glycosylation profoundly impacts the effector functions of antibodies and often dictates an antibody's pro- or anti-inflammatory activities. It is well established that core fucosylation of the Fc domain N-glycans of an antibody significantly reduces its affinity for FcγRIIIa receptors and antibody-dependent cellular cytotoxicity (ADCC). Previous structural studies have suggested that the presence of a core fucose remarkably decreases the unique and favorable carbohydrate-carbohydrate interactions between the Fc and the receptor N-glycans, leading to reduced affinity. We report here that in contrast to natural core fucose, special site-specific modification on the core fucose could dramatically enhance the affinity of an antibody for FcγRIIIa. The site-selective modification was achieved through an enzymatic transfucosylation with a novel fucosidase mutant, which was shown to be able to use modified α-fucosyl fluoride as the donor substrate. We found that replacement of the core l-fucose with 6-azide- or 6-hydroxy-l-fucose (l-galactose) significantly enhanced the antibody's affinity for FcγRIIIa receptors and substantially increased the ADCC activity. To understand the mechanism of the modified fucose-mediated affinity enhancement, we performed molecular dynamics simulations. Our data revealed that the number of glycan contacts between the Fc and the Fc receptor was increased by the selective core-fucose modifications, showing the importance of unique carbohydrate-carbohydrate interactions in achieving high FcγRIIIa affinity and ADCC activity of antibodies. Thus, the direct site-selective modification turns the adverse effect of the core fucose into a favorable force to promote the carbohydrate-carbohydrate interactions.

Simple Synthesis of a Heterocyclophane Exhibiting Anti-c-Met Activity by Acting as a Hatch Blocking Access to the Active Site*.

A simple approach to the synthesis of heterocyclophane consisting of two 4,4'-bithiazoles has been developed in mild conditions. The heterocyclophane with two short chains was conveniently prepared by Hantzsch thiazoles synthesis using the reaction of 3-tert-butoxycarbonyl-3-azapentanethiocarboxamide with 1,4-dibromobutane-2,3-dione in methanol under reflux for only 15 min. Amino groups at the linkers of this heterocyclophane can be functionalized to give acylated and carbamate derivatives. Their properties as protein kinase inhibitors were investigated, and one of the heterocyclophanes exhibited specific anti-activity for c-mesenchymal epithelial transition factor (IC50 =603 nm), among seven types of protein kinases investigated. The computational site identification by ligand competitive saturation method was used to determine why the one heterocyclophane exhibited strong anti-activity for c-mesenchymal epithelial transition factor.