In silico design of novel probes for the atypical opioid receptor MRGPRX2

The primate-exclusive MRGPRX2 G protein-coupled receptor (GPCR) has been suggested to modulate pain and itch. Despite putative peptide and small molecule MRGPRX2 agonists, selective nanomolar potency probes have not yet been reported. To identify a MRGPRX2 probe, we first screened 5,695 small molecules and found many opioid compounds activated MRGPRX2, including (−)- and (+)-morphine, hydrocodone, sinomenine, dextromethorphan and the prodynorphin-derived peptides, dynorphin A, dynorphin B, and α- and β-neoendorphin. We used these to select for mutagenesis-validated homology models and docked almost 4 million small molecules. From this docking, we predicted ZINC-3573, which represents a potent MRGPRX2-selective agonist, showing little activity against 315 other GPCRs and 97 representative kinases, and an essentially inactive enantiomer. ZINC-3573 activates endogenous MRGPRX2 in a human mast cell line inducing degranulation and calcium release. MRGPRX2 is a unique atypical opioid-like receptor important for modulating mast cell degranulation, which can now be specifically modulated with ZINC-3573

The application of molecular modelling in the safety assessment of chemicals: A case study on ligand-dependent PPARγ dysregulation.

The aim of this paper was to provide a proof of concept demonstrating that molecular modelling methodologies can be employed as a part of an integrated strategy to support toxicity prediction consistent with the mode of action/adverse outcome pathway (MoA/AOP) framework. To illustrate the role of molecular modelling in predictive toxicology, a case study was undertaken in which molecular modelling methodologies were employed to predict the activation of the peroxisome proliferator-activated nuclear receptor γ (PPARγ) as a potential molecular initiating event (MIE) for liver steatosis. A stepwise procedure combining different in silico approaches (virtual screening based on docking and pharmacophore filtering, and molecular field analysis) was developed to screen for PPARγ full agonists and to predict their transactivation activity (EC50). The performance metrics of the classification model to predict PPARγ full agonists were balanced accuracy=81%, sensitivity=85% and specificity=76%. The 3D QSAR model developed to predict EC50 of PPARγ full agonists had the following statistical parameters: q(2)cv=0.610, Nopt=7, SEPcv=0.505, r(2)pr=0.552. To support the linkage of PPARγ agonism predictions to prosteatotic potential, molecular modelling was combined with independently performed mechanistic mining of available in vivo toxicity data followed by ToxPrint chemotypes analysis. The approaches investigated demonstrated a potential to predict the MIE, to facilitate the process of MoA/AOP elaboration, to increase the scientific confidence in AOP, and to become a basis for 3D chemotype development

Status of GPCR modeling and docking as reflected by community-wide GPCR Dock 2010 assessment

The community-wide GPCR Dock assessment is conducted to evaluate the status of molecular modeling and ligand docking for human G protein-coupled receptors. The present round of the assessment was based on the recent structures of dopamine D3 and CXCR4 chemokine receptors bound to small molecule antagonists and CXCR4 with a synthetic cyclopeptide. Thirty-five groups submitted their receptor-ligand complex structure predictions prior to the release of the crystallographic coordinates. With closely related homology modeling templates, as for dopamine D3 receptor, and with incorporation of biochemical and QSAR data, modern computational techniques predicted complex details with accuracy approaching experimental. In contrast, CXCR4 complexes that had less-characterized interactions and only distant homology to the known GPCR structures still remained very challenging. The assessment results provide guidance for modeling and crystallographic communities in method development and target selection for further expansion of the structural coverage of the GPCR universe. © 2011 Elsevier Ltd. All rights reserved

Structure of the D2 dopamine receptor bound to the atypical antipsychotic drug risperidone

Dopamine is a neurotransmitter that has been implicated in processes as diverse as reward, addiction, control of coordinated movement, metabolism and hormonal secretion. Correspondingly, dysregulation of the dopaminergic system has been implicated in diseases such as schizophrenia, Parkinson's disease, depression, attention deficit hyperactivity disorder, and nausea and vomiting. The actions of dopamine are mediated by a family of five G-protein-coupled receptors. The D2 dopamine receptor (DRD2) is the primary target for both typical and atypical antipsychotic drugs, and for drugs used to treat Parkinson's disease. Unfortunately, many drugs that target DRD2 cause serious and potentially life-threatening side effects due to promiscuous activities against related receptors. Accordingly, a molecular understanding of the structure and function of DRD2 could provide a template for the design of safer and more effective medications. Here we report the crystal structure of DRD2 in complex with the widely prescribed atypical antipsychotic drug risperidone. The DRD2-risperidone structure reveals an unexpected mode of antipsychotic drug binding to dopamine receptors, and highlights structural determinants that are essential for the actions of risperidone and related drugs at DRD2. © 2018 Macmillan Publishers Limited, part of Springer Nature. All rights reserved

UFSRAT:Ultra-fast shape recognition with atom types -The discovery of novel bioactive small molecular scaffolds for FKBP12 and 11βHSD1

MOTIVATION:Using molecular similarity to discover bioactive small molecules with novel chemical scaffolds can be computationally demanding. We describe Ultra-fast Shape Recognition with Atom Types (UFSRAT), an efficient algorithm that considers both the 3D distribution (shape) and electrostatics of atoms to score and retrieve molecules capable of making similar interactions to those of the supplied query. RESULTS:Computational optimization and pre-calculation of molecular descriptors enables a query molecule to be run against a database containing 3.8 million molecules and results returned in under 10 seconds on modest hardware. UFSRAT has been used in pipelines to identify bioactive molecules for two clinically relevant drug targets; FK506-Binding Protein 12 and 11β-hydroxysteroid dehydrogenase type 1. In the case of FK506-Binding Protein 12, UFSRAT was used as the first step in a structure-based virtual screening pipeline, yielding many actives, of which the most active shows a KD, app of 281 µM and contains a substructure present in the query compound. Success was also achieved running solely the UFSRAT technique to identify new actives for 11β-hydroxysteroid dehydrogenase type 1, for which the most active displays an IC50 of 67 nM in a cell based assay and contains a substructure radically different to the query. This demonstrates the valuable ability of the UFSRAT algorithm to perform scaffold hops. AVAILABILITY AND IMPLEMENTATION:A web-based implementation of the algorithm is freely available at http://opus.bch.ed.ac.uk/ufsrat/

CAR is Not Requred for Adenovirus Infection: Integrin alpha v beta 5 Mediates Binding to CAR-Negative Cells

Adenovirus (Ad) is the most commonly used vector in gene therapy trials worldwide. Therefore, understanding the interaction between the virus and the cell surface and how this interaction impacts cell infection is of great importance to both the analysis of current trials using Ad vectors and the design of next generation Ad vectors. Cancer is in particular an important target of Ad-based therapeutics. Therefore, we have measured the ability of a replication incompetent subgroup C adenovirus (Ad5-GFP) to infect a panel of cancer cell lines. Infection across this panel was highly variable. Coxsackie and Adenovirus Receptor (CAR) is the known cellular receptor for subgroup C adenoviruses so we hypothesized that varying levels of CAR on these cell lines would explain the varying infections. However, neither CAR mRNA levels, as measured by both Affymetrix array and QT-PCR, nor CAR protein levels, as measured by both FACS and western, correlated with infection. One cell line, MDA MB 435, is CAR negative by all criteria that we have measured, but is one of the most infectible cell lines on the panel. Additionally, MCF7 cells and WM278 cells have minimal surface CAR but are infectible. Ad5 binds to these cell lines via a high affinity (0.16 nM) interaction. Surprisingly, the infection of CAR-negative cells is fiber-independent, as determined by competition experiments using soluble fiber. Because the penton base of the Ad virion is known to interact with RGD-binding integrins, we examined the ability of an RGD peptide to block the binding of Ad5 to CAR-negative cells. We found that Ad5 is using an RGD-binding integrin as a primary receptor for binding and infection. We then utilized blocking antibodies to determine which integrins are involved. We found that a blocking antibody to integrin αvβ5 blocks Ad5 from binding to CAR-negative cells lines. We conclude that integrin αvβ5 is an alternate attachment receptor for Ad5, representing a previously unidentified entry pathway for Ad5 that is both CAR and fiber-independent

Ligand Desolvation in Molecular Docking (How, Why, and With What?)

Ultimately, our bodies are biochemical factories of diabolical complexity. As scaffolds, reactors, engines, and signals, proteins are our essential building-blocks. Drugs, with their potential to alleviate symptoms or cure disease, are often small molecules that amplify or extinguish protein function in just the right way. Molecular docking attempts to understand and predict how those small molecule drugs interact with their protein targets inside us. In isolation, both ligand and protein are bathed in water - to bind one another, some water must necessarily depart. At its core, this dissertation is about how to account for desolvation of the ligand upon protein binding. To highlight why ligand desolvation is important, we discover new chemical probes for CXCR4, a protein target implicated in cancer and HIV. En route, we create the LogAUC metric and the DUD-E benchmarking dataset to better assess retrospective docking performance.Our rapid context-dependent ligand desolvation scoring term relates the Generalized-Born effective Born radii for every ligand atom to a fractional desolvation, and then uses this fraction to scale an atom-by-atom decomposition of the full transfer free energy. In a test that fails with no desolvation, our method properly discriminates ligands from highly charges molecules. The method is also flexible, performing well whether the protein binding site is charged or neutral, open or closed. We first retrospectively test ligand desolvation on the 40 original DUD targets, but discover many ways to improve that benchmark. So we construct DUD-E, an improved set with more diverse and biomedically relevant targets, totaling 102 proteins with 22,886 clustered ligands, each with 50 property-matched decoys. To ensure chemotype diversity we cluster the ligands by Bemis-Murcko frameworks. To improve decoys, we add net charge as an additional matched physico-chemical property, and only include the most dissimilar decoys by topology. To test our method prospectively, we screen both a homology model and then a crystal structure of CXCR4. Several of our novel scaffolds are potent and relatively small, with IC50 values as low as 306 nM, ligand efficiencies as high as 0.36, and substantial efficacy in blocking cellular chemotaxis