In this study, we report a ligand-centric data mining approach that guided the identification of suitable target profiles for treating obesity. The newly developed method is based on identifying target pairs for synergistic positive effects and also encompasses the exclusion of compounds showing a detrimental effect on obesity treatment (off-targets). Ligands with known activity against obesity-relevant targets were compared using fingerprint representations. Similar compounds with activities to different targets were evaluated for the mechanism of action since activation or deactivation of drug targets determines the pharmacological effect. In vitro validation of the modeling results revealed that three known modulators of melanin-concentrating hormone receptor 1 (MCHR1) show a previously unknown submicromolar affinity to the histamine H3 receptor (H3R). This synergistic activity may present a novel therapeutic option against obesity.Keywords: fingerprints; histamine H3 receptor; melanin-concentrating hormone receptor 1; Multitarget drugs; obesity;

G protein-coupled receptors transmit extracellular signals across cell membranes via different G protein classes and β-arrestins. Some pathways may be therapeutically beneficial, whereas others may be detrimental under certain pathophysiological conditions. For many GPCRs, biased agonists are available, which preferentially signal through one pathway or a subset of pathways, and harnessing biased agonism could be a potential novel therapeutic strategy. However, the incomplete mechanistic understanding of biased agonism hampers rational design of biased ligands. Using the muscarinic M2 receptor as a model system, we have analyzed the relationship between ligand-dependent conformational changes as revealed in all-atom MD simulations and the activation of specific G proteins. We find that the extent of closure of the extracellular, allosteric binding site interferes with the activation of certain G proteins. Our data allow the rational design of Gi-biased agonists at the M2 receptor and delineate a simple principle which may be translated to other GPRCs.

Starch catabolism leading to high glucose level in blood is highly problematic in chronic metabolic diseases, such as type II diabetes and obesity. α-Amylase catalyzes the hydrolysis of starch, increasing blood sugar concentration. Its inhibition represents a promising therapeutic approach to control hyperglycaemia. However, only few drug-like molecule inhibitors without sugar moieties have been discovered so far, and little information on the enzymatic mechanism is available. This work aims at the discovery of novel small α-amylase binders using a systematic in silico methodology. 3D-pharmacophore-based high throughput virtual screening of small compounds libraries was performed to identify compounds with high α-amylase affinity. Twenty-seven compounds were selected and biologically tested, revealing IC50 values in the micromolar range and ligand efficiency higher than the one of the bound form of acarbose, which is used as a reference for α-amylase inhibition.

The constant risk of emerging new influenza virus strains that are resistant to established inhibitors like oseltamivir leaves influenza neuraminidase (NA) a prominent target for drug design. The inhibitory activity of several flavonoid derivatives was experimentally tested in comparison to oseltamivir for the NA expressed by the seasonal influenza virus strains A/California/7/09 (A(H1N1)pdm09), A/Perth/16/09 (A(H3N2)), and B/Brisbane/60/08. IC50 values of polyphenols confirmed moderate inhibition in the μM range. Structurally, the amount and site of glycosylation of tested flavonoids have no significant influence on their inhibitory potency. In a pharmacophore-based docking approach the structure–activity relationship was evaluated. Molecular dynamics simulations revealed highly flexible parts of the enzyme and the contribution of salt bridges to the structural stability of NA. The findings of this study elucidate the impact of flavonoids on viral neuraminidase activity and the analysis of their modes of action provide valuable information about the mechanism of NA inhibition.

•Which molecular dynamics (MD) techniques are available for drug design?•How is MD used to investigate ligand–macromolecule complexes?•How are MD studies applied to human and non-human therapeutic targets?•What are the latest advances in the field of MD?Among all tools available to design new drugs, molecular dynamics (MD) simulations have become an essential technique. Initially developed to investigate molecular models with a limited number of atoms, computers now enable investigations of large macromolecular systems with a simulation time reaching the microsecond range. The reviewed articles cover four years of research to give an overview on the actual impact of MD on the current medicinal chemistry landscape with a particular emphasis on studies of ligand–protein interactions. With a special focus on studies combining computational approaches with data gained from other techniques, this review shows how deeply embedded MD simulations are in drug design strategies and articulates what the future of this technique could be.Download high-res image (214KB)Download full-size image

As essential proteins of the innate immune system, Toll-like receptors (TLRs) are involved in a plethora of physiological pathologies and their modulation is an ongoing quest in the field of drug discovery. Although TLRs recognize an unusually broad range of different molecular patterns, only a few small-molecule TLR modulators have been reported to date. Recent advances in crystallography and in silico techniques provide promising opportunities for TLR investigations and drug design. Here, three application areas for computational approaches are considered: (i) exploration of TLR structure and activation; (ii) understanding TLR modulation; and (iii) TLR drug discovery. By providing an overview on state-of-the-art computational methods, we highlight the value of molecular modeling in mechanistically understanding TLR function and guiding drug design.

Despite the considerable advances in medical and pharmaceutical research during the past years, diseases caused by viruses have remained a major burden to public health. Virtual in silico screening has repeatedly proven to be useful to meet the special challenges of antiviral drug discovery. Large virtual compound libraries are filtered by different computational screening methods such as docking, ligand-based similarity searches or pharmacophore-based screening, reducing the number of candidate molecules to a smaller set of promising candidates that are then tested biologically. This rational approach makes the drug discovery process more goal-oriented and saves resources in terms of time and money. In this review we discuss how different virtual screening techniques can be applied to antiviral drug discovery, present recent success stories in this field and finally address the main differences between the methods.