Discovery of beta-lactamase CMY-10 inhibitors for combination therapy against multi-drug resistant Enterobacteriaceae.

ß-lactam antibiotics are the most widely used antimicrobial agents since the discovery of benzylpenicillin in the 1920s. Unfortunately, these life-saving antibiotics are vulnerable to inactivation by continuously evolving ß-lactamase enzymes that are primary resistance determinants in multi-drug resistant pathogens. The current study exploits the strategy of combination therapeutics and aims at identifying novel ß-lactamase inhibitors that can inactivate the ß-lactamase enzyme of the pathogen while allowing the ß-lactam antibiotic to act against its penicillin-binding protein target. Inhibitor discovery applied the Site-Identification by Ligand Competitive Saturation (SILCS) technology to map the functional group requirements of the ß-lactamase CMY-10 and generate pharmacophore models of active site. SILCS-MC, Ligand-grid Free Energy (LGFE) analysis and Machine-learning based random-forest (RF) scoring methods were then used to screen and filter a library of 700,000 compounds. From the computational screens 74 compounds were subjected to experimental validation in which ß-lactamase activity assay, in vitro susceptibility testing, and Scanning Electron Microscope (SEM) analysis were conducted to explore their antibacterial potential. Eleven compounds were identified as enhancers while 7 compounds were recognized as inhibitors of CMY-10. Of these, compound 11 showed promising activity in ß-lactamase activity assay, in vitro susceptibility testing against ATCC strains (E. coli, E. cloacae, E. agglomerans, E. alvei) and MDR clinical isolates (E. cloacae, E. alvei and E. agglomerans), with synergistic assay indicating its potential as a ß-lactam enhancer and ß-lactamase inhibitor. Structural similarity search against the active compound 11 yielded 28 more compounds. The majority of these compounds also exhibited ß-lactamase inhibition potential and antibacterial activity. The non-ß-lactam-based ß-lactamase inhibitors identified in the current study have the potential to be used in combination therapy with lactam-based antibiotics against MDR clinical isolates that have been found resistant against last-line antibiotics.

Prediction of Membrane Permeation of Drug Molecules by Combining an Implicit Membrane Model with Machine Learning.

Lipid membrane permeation of drug molecules was investigated with Heterogeneous Dielectric Generalized Born (HDGB)-based models using solubility-diffusion theory and machine learning. Free energy profiles were obtained for neutral molecules by the standard HDGB and Dynamic HDGB (DHDGB) to account for the membrane deformation upon insertion of drugs. We also obtained hybrid free energy profiles where the neutralization of charged molecules was taken into account upon membrane insertion. The evaluation of the predictions was done against experimental permeability coefficients from Parallel Artificial Membrane Permeability Assays (PAMPA), and effects of partial charge sets, CGenFF, AM1-BCC, and OPLS, on the performance of the predictions were discussed. (D)HDGB-based models improved the predictions over the two-state implicit membrane models, and partial charge sets seemed to have a strong impact on the predictions. Machine learning increased the accuracy of the predictions, although it could not outperform the physics-based approach in terms of correlations.

Computer-Aided Drug Design Methods.

Computational approaches are useful tools to interpret and guide experiments to expedite the antibiotic drug design process. Structure-based drug design (SBDD) and ligand-based drug design (LBDD) are the two general types of computer-aided drug design (CADD) approaches in existence. SBDD methods analyze macromolecular target 3-dimensional structural information, typically of proteins or RNA, to identify key sites and interactions that are important for their respective biological functions. Such information can then be utilized to design antibiotic drugs that can compete with essential interactions involving the target and thus interrupt the biological pathways essential for survival of the microorganism(s). LBDD methods focus on known antibiotic ligands for a target to establish a relationship between their physiochemical properties and antibiotic activities, referred to as a structure-activity relationship (SAR), information that can be used for optimization of known drugs or guide the design of new drugs with improved activity. In this chapter, standard CADD protocols for both SBDD and LBDD will be presented with a special focus on methodologies and targets routinely studied in our laboratory for antibiotic drug discoveries.

Inhibition of TLR2 signaling by small molecule inhibitors targeting a pocket within the TLR2 TIR domain.

Toll-like receptor (TLR) signaling is initiated by dimerization of intracellular Toll/IL-1 receptor resistance (TIR) domains. For all TLRs except TLR3, recruitment of the adapter, myeloid differentiation primary response gene 88 (MyD88), to TLR TIR domains results in downstream signaling culminating in proinflammatory cytokine production. Therefore, blocking TLR TIR dimerization may ameliorate TLR2-mediated hyperinflammatory states. The BB loop within the TLR TIR domain is critical for mediating certain protein-protein interactions. Examination of the human TLR2 TIR domain crystal structure revealed a pocket adjacent to the highly conserved P681 and G682 BB loop residues. Using computer-aided drug design (CADD), we sought to identify a small molecule inhibitor(s) that would fit within this pocket and potentially disrupt TLR2 signaling. In silico screening identified 149 compounds and 20 US Food and Drug Administration-approved drugs based on their predicted ability to bind in the BB loop pocket. These compounds were screened in HEK293T-TLR2 transfectants for the ability to inhibit TLR2-mediated IL-8 mRNA. C16H15NO4 (C29) was identified as a potential TLR2 inhibitor. C29, and its derivative, ortho-vanillin (o-vanillin), inhibited TLR2/1 and TLR2/6 signaling induced by synthetic and bacterial TLR2 agonists in human HEK-TLR2 and THP-1 cells, but only TLR2/1 signaling in murine macrophages. C29 failed to inhibit signaling induced by other TLR agonists and TNF-α. Mutagenesis of BB loop pocket residues revealed an indispensable role for TLR2/1, but not TLR2/6, signaling, suggesting divergent roles. Mice treated with o-vanillin exhibited reduced TLR2-induced inflammation. Our data provide proof of principle that targeting the BB loop pocket is an effective approach for identification of TLR2 signaling inhibitors.

Mapping functional group free energy patterns at protein occluded sites: nuclear receptors and G-protein coupled receptors.

Occluded ligand-binding pockets (LBP) such as those found in nuclear receptors (NR) and G-protein coupled receptors (GPCR) represent a significant opportunity and challenge for computer-aided drug design. To determine free energies maps of functional groups of these LBPs, a Grand-Canonical Monte Carlo/Molecular Dynamics (GCMC/MD) strategy is combined with the Site Identification by Ligand Competitive Saturation (SILCS) methodology. SILCS-GCMC/MD is shown to map functional group affinity patterns that recapitulate locations of functional groups across diverse classes of ligands in the LBPs of the androgen (AR) and peroxisome proliferator-activated-γ (PPARγ) NRs and the metabotropic glutamate (mGluR) and ß2-adreneric (ß2AR) GPCRs. Inclusion of protein flexibility identifies regions of the binding pockets not accessible in crystal conformations and allows for better quantitative estimates of relative ligand binding affinities in all the proteins tested. Differences in functional group requirements of the active and inactive states of the ß2AR LBP were used in virtual screening to identify high efficacy agonists targeting ß2AR in Airway Smooth Muscle (ASM) cells. Seven of the 15 selected ligands were found to effect ASM relaxation representing a 46% hit rate. Hence, the method will be of use for the rational design of ligands in the context of chemical biology and the development of therapeutic agents.

Targeting protein tyrosine phosphatase SHP2 for the treatment of PTPN11-associated malignancies.

Activating mutations in PTPN11 (encoding SHP2), a protein tyrosine phosphatase (PTP) that plays an overall positive role in growth factor and cytokine signaling, are directly associated with the pathogenesis of Noonan syndrome and childhood leukemias. Identification of SHP2-selective inhibitors could lead to the development of new drugs that ultimately serve as treatments for PTPN11-associated diseases. As the catalytic core of SHP2 shares extremely high homology to those of SHP1 and other PTPs that play negative roles in cell signaling, to identify selective inhibitors of SHP2 using computer-aided drug design, we targeted a protein surface pocket that is adjacent to the catalytic site, is predicted to be important for binding to phosphopeptide substrates, and has structural features unique to SHP2. From computationally selected candidate compounds, #220-324 effectively inhibited SHP2 activity with an IC50 of 14 µmol/L. Fluorescence titration experiments confirmed its direct binding to SHP2. This active compound was further verified for its ability to inhibit SHP2-mediated cell signaling and cellular function with minimal off-target effects. Furthermore, mouse myeloid progenitors with the activating mutation (E76K) in PTPN11 and patient leukemic cells with the same mutation were more sensitive to this inhibitor than wild-type cells. This study provides evidence that SHP2 is a "druggable" target for the treatment of PTPN11-associated diseases. As the small-molecule SHP2 inhibitor identified has a simple chemical structure, it represents an ideal lead compound for the development of novel anti-SHP2 drugs. Mol Cancer Ther; 12(9); 1738-48. ©2013 AACR.

Synthesis, modeling, and pharmacological evaluation of UMB 425, a mixed µ agonist/δ antagonist opioid analgesic with reduced tolerance liabilities.

Opioid narcotics are used for the treatment of moderate-to-severe pain and primarily exert their analgesic effects through µ receptors. Although traditional µ agonists can cause undesired side effects, including tolerance, addition of δ antagonists can attenuate said side effects. Herein, we report 4a,9-dihydroxy-7a-(hydroxymethyl)-3-methyl-2,3,4,4a,5,6-hexahydro-1H-4,12-methanobenzofuro[3,2-e]isoquinolin-7(7aH)-one (UMB 425) a 5,14-bridged morphinan-based orvinol precursor synthesized from thebaine. Although UMB 425 lacks δ-specific motifs, conformationally sampled pharmacophore models for µ and δ receptors predict it to have efficacy similar to morphine at µ receptors and similar to naltrexone at δ receptors, due to the compound sampling conformations in which the hydroxyl moiety interacts with the receptors similar to orvinols. As predicted, UMB 425 exhibits a mixed µ agonist/δ antagonist profile as determined in receptor binding and [(35)S]GTPγS functional assays in CHO cells. In vivo studies in mice show that UMB 425 displays potent antinociception in the hot plate and tail-flick assays. The antinociceptive effects of UMB 425 are blocked by naloxone, but not by the κ-selective antagonist norbinaltorphimine. During a 6-day tolerance paradigm, UMB 425 maintains significantly greater antinociception compared to morphine. These studies thus indicate that, even in the absence of δ-specific motifs fused to the C-ring, UMB 425 has mixed µ agonist/δ antagonist properties in vitro that translate to reduced tolerance liabilities in vivo.

CHARMM Additive All-Atom Force Field for Phosphate and Sulfate Linked to Carbohydrates.

Presented is an extension of the CHARMM additive all-atom carbohydrate force field to enable the modeling of phosphate and sulfate linked to carbohydrates. The parameters are developed in a hierarchical fashion using model compounds containing the key atoms in the full carbohydrates. Target data for parameter optimization included full two-dimensional energy surfaces defined by the glycosidic dihedral angle pairs in the phosphate/sulfate model compound analogs of hexopyranose monosaccharide phosphates and sulfates, as determined by quantum mechanical (QM) MP2/cc-pVTZ single point energies on MP2/6-31+G(d) optimized structures. In order to achieve balanced, transferable dihedral parameters for the dihedral angles, surfaces for all possible anomeric and conformational states were included during the parametrization process. In addition, to model physiologically relevant systems both the mono- and di-anionic charged states were studied for the phosphates. This resulted in over 7000 MP2/cc-pVTZ//MP2/6-31G+(d) model compound conformational energies which, supplemented with QM geometries, were the main target data for the parametrization. Parameters were validated against crystals of relevant monosaccharide derivatives obtained from the Cambridge Structural Database (CSD) and larger systems, namely inositol-(tri/tetra/penta) phosphates non-covalently bound to the pleckstrin homology (PH) domain and oligomeric chondroitin sulfate in solution and in complex with cathepsin K protein.

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.

Impact of arsenic/phosphorus substitution on the intrinsic conformational properties of the phosphodiester backbone of DNA investigated using ab initio quantum mechanical calculations.

Deoxyribonucleic acid (DNA) is composed of five major elements carbon, hydrogen, nitrogen, oxygen, and phosphorus. The substitution of any of these elements in DNA would be anticipated to have major biological implications. However, recent studies have suggested that the substitution of arsenic into DNA (As-DNA) in bacteria may be possible. To help evaluate this possibility, ab initio quantum mechanical calculations are used to show that arsenodiester and phosphodiester linkages have similar geometric and conformational properties. Based on these results, it is suggested that the As-DNA will have similar conformational properties to phosphorus-based DNA, including the maintenance of base stacking.

Automated selection of compounds with physicochemical properties to maximize bioavailability and druglikeness.

Adequate bioavailability is one of the essential properties for an orally administered drug. Lipinski and others have formulated simplified rules in which compounds that satisfy selected physiochemical properties, for example, molecular weight (MW) ≤ 500 or the logarithm of the octanol-water partition coefficient, log P(o/w) < 5, are anticipated to likely have pharmacokinetic properties appropriate for oral administration. However, these schemes do not simultaneously consider the combination of the physiochemical properties, complicating their application in a more automated fashion. To overcome this, we present a novel method to select compounds with a combination of physicochemical properties that maximize bioavailability and druglikeness based on compounds in the World Drug Index database. In the study four properties, MW, log P(o/w), number of hydrogen bond donors, and number of hydrogen acceptors, were combined into a 4-dimensional (4D) histogram, from which a scoring function was defined on the basis of a 4D dependent multivariate Gaussian model. The resulting equation allows for assigning compounds a bioavailability score, termed 4D-BA, such that chemicals with higher 4D-BA scores are more likely to have oral druglike characteristics. The descriptor is validated by applying the function to drugs previously categorized in the Biopharmaceutics Classification System, and examples of application of the descriptor are given in the context of previously published studies targeting heme oxygenase and SHP2 phosphatase. The approach is anticipated to be useful in early lead identification studies in combination with clustering methods to maximize chemical and structural diversity when selecting compounds for biological assays from large database screens. It may also be applied to prioritize synthetically feasible chemical modifications during lead compound optimization.

Identification of small molecular weight inhibitors of Src homology 2 domain-containing tyrosine phosphatase 2 (SHP-2) via in silico database screening combined with experimental assay.

Virtual screening methods combined with experimental assays were used to identify low molecular weight inhibitors for Src homology 2 domain-containing phosphatase 2 (SHP-2) that is mutated and hyperactivated in Noonan syndrome and a significant portion of childhood leukemias. Virtual screening included multiple conformations of the protein, score normalization procedures, and chemical similarity considerations. As the catalytic core of SHP-2 shares extremely high homology to those of the related SHP-1 phosphatase and other tyrosine phosphatases, in order to identify selective inhibitors, we chose to target an adjacent protein surface pocket that is predicted to be important for binding to phosphopeptides and that has structural features unique to SHP-2. From a database of 1.3 million compounds, 9 out of 165 computationally selected compounds were shown to inhibit SHP-2 activity with IC(50) values of approximately 100 microM. Two of the active compounds were further verified for their ability to inhibit SHP-2-mediated cellular functions. Fluorescence titration experiments confirmed their direct binding to SHP-2. Because of their simple chemical structures, these small organic compounds have the potential to act as lead compounds for the development of novel anti-SHP-2 drugs.

Lead validation and SAR development via chemical similarity searching; application to compounds targeting the pY+3 site of the SH2 domain of p56lck.

Compound selection based on chemical similarity has been used to validate active "parent" compounds identified via database searching as viable lead compounds and to obtain initial structure-activity relationships for those leads. Twelve parent compounds that have inhibitory activity against the SH2 domain of the p56 T-cell tyrosine kinase (Lck) are the focus of this study. Lck is involved in the T-cell mediated immune response, and inhibitors of Lck protein-protein interactions could potentially be used to develop novel immunosuppressants. Similarity searches for each parent compound were performed using 2D structural fingerprints on a database containing 1,300,000 commercially available compounds. The inhibitory activity of the selected compounds was assessed using enzyme immunoassay (EIA). In general, the most active parent compounds yield the most high activity similar compounds; however, in two cases low activity parent compounds (i.e. inhibitory activity < 25% at 100 microM) yielded multiple similar compounds with activities > 60%. Such compounds may, therefore, be considered as viable lead compounds for optimization. Structure-activity relationships were explored by examining both ligand structures and their computed bound conformations to the protein. Functional groups common to the active compounds as well as key amino acid residues that form hydrogen bonds with the active compounds were identified. This information will act as the basis for the rational optimization of the lead compounds.

Consideration of molecular weight during compound selection in virtual target-based database screening.

Virtual database screening allows for millions of chemical compounds to be computationally selected based on structural complimentary to known inhibitors or to a target binding site on a biological macromolecule. Compound selection in virtual database screening when targeting a biological macromolecule is typically based on the interaction energy between the chemical compound and the target macromolecule. In the present study it is shown that this approach is biased toward the selection of high molecular weight compounds due to the contribution of the compound size to the energy score. To account for molecular weight during energy based screening, we propose normalization strategies based on the total number of heavy atoms in the chemical compounds being screened. This approach is computationally efficient and produces molecular weight distributions of selected compounds that can be selected to be (1) lower than that of the original database used in the virtual screening, which may be desirable for selection of leadlike compounds or (2) similar to that of the original database, which may be desirable for the selection of drug-like compounds. By eliminating the bias in target-based database screening toward higher molecular weight compounds it is anticipated that the proposed procedure will enhance the success rate of computer-aided drug design.