A virtual library of small molecules mimicking dipeptides.

Virtual combinatorial libraries are prevalent in drug discovery due to improvements in the prediction of synthetic reactions that can be performed. This has gone hand in hand with the development of virtual screening capabilities to effectively screen the large chemical spaces spanned by exhaustive enumeration of reaction products. In this study, we generated a small-molecule dipeptide mimic library to target proteins binding small peptides. The library was created based on the general idea of peptide synthesis, that is, amino acid mimics were reacted in silico to form the dipeptide mimics, yielding 2,036,819 unique compounds. After docking calculations, two compounds from the library were synthesized and tested against WD repeat-containing protein 5 (WDR5) and histamine receptors H1-H4 to evaluate whether these molecules are viable in assays. The compounds showed the highest potency at the histamine H3 receptor, with Ki values in the two-digit micromolar range.

Differential interaction patterns of opioid analgesics with µ opioid receptors correlate with ligand-specific voltage sensitivity.

The µ opioid receptor (MOR) is the key target for analgesia, but the application of opioids is accompanied by several issues. There is a wide range of opioid analgesics, differing in their chemical structure and their properties of receptor activation and subsequent effects. A better understanding of ligand-receptor interactions and the resulting effects is important. Here, we calculated the respective binding poses for several opioids and analyzed interaction fingerprints between ligand and receptor. We further corroborated the interactions experimentally by cellular assays. As MOR was observed to display ligand-induced modulation of activity due to changes in membrane potential, we further analyzed the effects of voltage sensitivity on this receptor. Combining in silico and in vitro approaches, we defined discriminating interaction patterns responsible for ligand-specific voltage sensitivity and present new insights into their specific effects on activation of the MOR.

The Isoleucine at Position 118 in Transmembrane 2 Is Responsible for the Selectivity of Xamoterol, Nebivolol, and ICI89406 for the Human ß1-Adrenoceptor.

Known off-target interactions frequently cause predictable drug side-effects (e.g., ß1-antagonists used for heart disease, risk ß2-mediated bronchospasm). Computer-aided drug design would improve if the structural basis of existing drug selectivity was understood. A mutagenesis approach determined the ligand-amino acid interactions required for ß1-selective affinity of xamoterol and nebivolol, followed by computer-based modeling to provide possible structural explanations. 3H-CGP12177 whole cell binding was conducted in Chinese hamster ovary cells stably expressing human ß1, ß2, and chimeric ß1/ß2-adrenoceptors (ARs). Single point mutations were investigated in transiently transfected cells. Modeling studies involved docking ligands into three-dimensional receptor structures and performing molecular dynamics simulations, comparing interaction frequencies between apo and holo structures of ß1 and ß2-ARs. From these observations, an ICI89406 derivative was investigated that gave further insights into selectivity. Stable cell line studies determined that transmembrane 2 was crucial for the ß1-selective affinity of xamoterol and nebivolol. Single point mutations determined that the ß1-AR isoleucine (I118) rather than the ß2 histidine (H93) explained selectivity. Studies of other ß1-ligands found I118 was important for ICI89406 selective affinity but not that for betaxolol, bisoprolol, or esmolol. Modeling studies suggested that the interaction energies and solvation of ß1-I118 and ß2-H93 are factors determining selectivity of xamoterol and ICI89406. ICI89406 without its phenyl group loses its high ß1-AR affinity, resulting in the same affinity as for the ß2-AR. The human ß1-AR residue I118 is crucial for the ß1-selective affinity of xamoterol, nebivolol, and ICI89406 but not all ß1-selective compounds. SIGNIFICANCE STATEMENT: Some ligands have selective binding affinity for the human ß1 versus the ß2-adrenoceptor; however, the molecular/structural reason for this is not known. The transmembrane 2 residue isoleucine I118 is responsible for the selective ß1-binding of xamoterol, nebivolol, and ICI89406 but does not explain the selective ß1-binding of betaxolol, bisoprolol, or esmolol. Understanding the structural basis of selectivity is important to improve computer-aided ligand design, and targeting I118 in ß1-adrenoceptors is likely to increase ß1-selectivity of drugs.

How Carvedilol activates ß2-adrenoceptors.

Carvedilol is among the most effective ß-blockers for improving survival after myocardial infarction. Yet the mechanisms by which carvedilol achieves this superior clinical profile are still unclear. Beyond blockade of ß1-adrenoceptors, arrestin-biased signalling via ß2-adrenoceptors is a molecular mechanism proposed to explain the survival benefits. Here, we offer an alternative mechanism to rationalize carvedilol's cellular signalling. Using primary and immortalized cells genome-edited by CRISPR/Cas9 to lack either G proteins or arrestins; and combining biological, biochemical, and signalling assays with molecular dynamics simulations, we demonstrate that G proteins drive all detectable carvedilol signalling through ß2ARs. Because a clear understanding of how drugs act is imperative to data interpretation in basic and clinical research, to the stratification of clinical trials or to the monitoring of drug effects on the target pathway, the mechanistic insight gained here provides a foundation for the rational development of signalling prototypes that target the ß-adrenoceptor system.

Genome-Wide Association for HbA1c in Malay Identified Deletion on SLC4A1 that Influences HbA1c Independent of Glycemia.

CONTEXT: Glycated hemoglobin A1c (HbA1c) level is used to screen and diagnose diabetes. Genetic determinants of HbA1c can vary across populations and many of the genetic variants influencing HbA1c level were specific to populations. OBJECTIVE: To discover genetic variants associated with HbA1c level in nondiabetic Malay individuals. DESIGN AND PARTICIPANTS: We conducted a genome-wide association study (GWAS) analysis for HbA1c using 2 Malay studies, the Singapore Malay Eye Study (SiMES, N = 1721 on GWAS array) and the Living Biobank study (N = 983 on GWAS array and whole-exome sequenced). We built a Malay-specific reference panel to impute ethnic-specific variants and validate the associations with HbA1c at ethnic-specific variants. RESULTS: Meta-analysis of the 1000 Genomes imputed array data identified 4 loci at genome-wide significance (P < 5 × 10-8). Of the 4 loci, 3 (ADAM15, LINC02226, JUP) were novel for HbA1c associations. At the previously reported HbA1c locus ATXN7L3-G6PC3, association analysis using the exome data fine-mapped the HbA1c associations to a 27-bp deletion (rs769664228) at SLC4A1 that reduced HbA1c by 0.38 ±â€…0.06% (P = 3.5 × 10-10). Further imputation of this variant in SiMES confirmed the association with HbA1c at SLC4A1. We also showed that these genetic variants influence HbA1c level independent of glucose level. CONCLUSION: We identified a deletion at SLC4A1 associated with HbA1c in Malay. The nonglycemic lowering of HbA1c at rs769664228 might cause individuals carrying this variant to be underdiagnosed for diabetes or prediabetes when HbA1c is used as the only diagnostic test for diabetes.

Association of G6PD variants with hemoglobin A1c and impact on diabetes diagnosis in East Asian individuals.

OBJECTIVE: Hemoglobin A1c (HbA1c) accuracy is important for diabetes diagnosis and estimation of overall glycemia. The G6PD-Asahi variant which causes glucose-6-phosphate dehydrogenase (G6PD) deficiency has been shown to lower HbA1c independently of glycemia in African ancestry populations. As different G6PD variants occur in Asian ancestry, we sought to identify Asian-specific G6PD variants associated with HbA1c. RESEARCH DESIGN AND METHODS: In eight Asian population-based cohorts, we performed imputation on the X chromosome using the 1000 Genomes reference panel and tested for association with HbA1c (10 005 East Asians and 2051 South Asians). Results were meta-analyzed across studies. We compared the proportion of individuals classified as having diabetes/pre-diabetes by fasting glucose ≥100 mg/dL or HbA1c ≥5.7% units among carriers and non-carriers of HbA1c-associated variants. RESULTS: The strongest association was a missense variant (G6PD-Canton, rs72554665, minor allele frequency=2.2%, effect in men=-0.76% unit, 95% CI -0.88 to -0.64, p=1.25×10-27, n=2844). Conditional analyses identified a secondary distinct signal, missense variant (G6PD-Kaiping, rs72554664, minor allele frequency=1.6%, effect in men=-1.12 % unit, 95% CI -1.32 to -0.92, p=3.12×10-15, pconditional_Canton=7.57×10-11). Adjusting for glucose did not attenuate their effects. The proportion of individuals with fasting glucose ≥100 mg/dL did not differ by carrier status of G6PD-Canton (p=0.21). Whereas the proportion of individuals with HbA1c ≥5.7% units was lower in carriers (5%) compared with non-carriers of G6PD-Canton (30%, p=0.03). CONCLUSIONS: We identified two G6PD variants in East Asian men associated with non-glycemic lowering of HbA1c. Carriers of these variants are more likely to be underdiagnosed for diabetes or pre-diabetes than non-carriers if screened by HbA1c without confirmation by direct glucose measurements.

A benchmarking study on virtual ligand screening against homology models of human GPCRs.

G-protein-coupled receptor (GPCR) is an important target class of proteins for drug discovery, with over 27% of FDA-approved drugs targeting GPCRs. However, being a membrane protein, it is difficult to obtain the 3D crystal structures of GPCRs for virtual screening of ligands by molecular docking. Thus, we evaluated the virtual screening performance of homology models of human GPCRs with respect to the corresponding crystal structures. Among the 19 GPCRs involved in this study, we observed that 10 GPCRs have homology models that have better or comparable performance with respect to the corresponding X-ray structures, making homology models a viable choice for virtual screening. For a small subset of GPCRs, we also explored how certain methods like consensus enrichment and sidechain perturbation affect the utility of homology models in virtual screening, as well as the selectivity between agonists and antagonists. Most notably, consensus enrichment across multiple homology models often yields results comparable to the best performing model, suggesting that ligand candidates predicted with consensus scores from multiple models can be the optimal option in practical applications where the performance of each model cannot be estimated.