Fragment-based lead discovery on G-protein-coupled receptors

Syntheses and Biological Activities of triazole-based Sulfonamides

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The triazole and sulfonamide compounds are known as biologically active agents that were employed for medicinal applications. These compounds were obtained in different forms by a variety of techniques to vast ranges of applications. The broad biological properties of these compounds have encouraged researchers to design and synthesize triazole-based sulfonamide derivatives as compounds with potential biological activity. In this review, we summarized the synthetic procedures of triazole-based sulfonamide compounds together with their biological activities during the last two decades.

Fragment Hits: What do They Look Like and How do They Bind?

A “fragment hit”, a molecule of low molecular weight that has been validated to bind to a target protein, can be an effective chemical starting point for a drug discovery project. Our ability to find and progress fragment hits could potentially be improved by enhancing our understanding of their binding properties, which to date has largely been based on tacit knowledge and reports from individual projects. In the work reported here, we systematically analyzed the molecular and binding properties of fragment hits using 489 published protein–fragment complexes. We identified a number of notable features that these hits tend to have in common, including preferences in buried surface area upon binding, hydrogen bonding and other directional interactions with the protein targets, structural topology, functional-group occurrence, and degree of carbon saturation. In the future, taking account of these preferences in designing and selecting fragments to screen against protein targets may increase the chances of success in fragment screening campaigns.

Exploring G Protein-Coupled Receptors (GPCRs) Ligand Space via Cheminformatics Approaches: Impact on Rational Drug Design

The primary goal of rational drug discovery is the identification of selective ligands which act on single or multiple drug targets to achieve the desired clinical outcome through the exploration of total chemical space. To identify such desired compounds, computational approaches are necessary in predicting their drug-like properties. G Protein-Coupled Receptors (GPCRs) represent one of the largest and most important integral membrane protein families. These receptors serve as increasingly attractive drug targets due to their relevance in the treatment of various diseases, such as inflammatory disorders, metabolic imbalances, cardiac disorders, cancer, monogenic disorders, etc. In the last decade, multitudes of three-dimensional (3D) structures were solved for diverse GPCRs, thus referring to this period as the "golden age for GPCR structural biology." Moreover, accumulation of data about the chemical properties of GPCR ligands has garnered much interest toward the exploration of GPCR chemical space. Due to the steady increase in the structural, ligand, and functional data of GPCRs, several cheminformatics approaches have been implemented in its drug discovery pipeline. In this review, we mainly focus on the cheminformatics-based paradigms in GPCR drug discovery. We provide a comprehensive view on the ligand- and structure-based cheminformatics approaches which are best illustrated via GPCR case studies. Furthermore, an appropriate combination of ligand-based knowledge with structure-based ones, i.e., integrated approach, which is emerging as a promising strategy for cheminformatics-based GPCR drug design is also discussed.

High-Throughput Screening (HTS) by NMR Guided Identification of Novel Agents Targeting the Protein Docking Domain of YopH

Recently we described a novel approach, named high-throughput screening (HTS) by NMR that allows the identification, from large combinatorial peptide libraries, of potent and selective peptide mimetics against a given target. Here, we deployed the "HTS by NMR" approach for the design of novel peptoid sequences targeting the N-terminal domain of Yersinia outer protein H (YopH-NT), a bacterial toxin essential for the virulence of Yersinia pestis. We aimed at disrupting the protein-protein interactions between YopH-NT and its cellular substrates, with the goal of inhibiting indirectly YopH enzymatic function. These studies resulted in a novel agent of sequence Ac-F-pY-cPG-d-P-NH2 (pY=phosphotyrosine; cPG=cyclopentyl glycine) with a Kd value against YopH-NT of 310 nm. We demonstrated that such a pharmacological inhibitor of YopH-NT results in the inhibition of the dephosphorylation by full-length YopH of a cellular substrate. Hence, potentially this agent represents a valuable stepping stone for the development of novel therapeutics against Yersinia infections. The data reported further demonstrate the utility of the HTS by NMR approach in deriving novel peptide mimetics targeting protein-protein interactions.

Combinatorial Consensus Scoring for Ligand-Based Virtual Fragment Screening: A Comparative Case Study for Serotonin 5-HT(3)A, Histamine H(1), and Histamine H(4) Receptors

In the current study we have evaluated the applicability of ligand-based virtual screening (LBVS) methods for the identification of small fragment-like biologically active molecules using different similarity descriptors and different consensus scoring approaches. For this purpose, we have evaluated the performance of 14 chemical similarity descriptors in retrospective virtual screening studies to discriminate fragment-like ligands of three membrane-bound receptors from fragments that are experimentally determined to have no affinity for these proteins (true inactives). We used a complete fragment affinity data set of experimentally determined ligands and inactives for two G protein-coupled receptors (GPCRs), the histamine H1 receptor (H1R) and the histamine H4 receptor (H4R), and one ligand-gated ion channel (LGIC), the serotonin receptor (5-HT3AR), to validate our retrospective virtual screening studies. We have exhaustively tested consensus scoring strategies that combine the results of multiple actives (group fusion) or combine different similarity descriptors (similarity fusion), and for the first time systematically evaluated different combinations of group fusion and similarity fusion approaches. Our studies show that for these three case study protein targets both consensus scoring approaches can increase virtual screening enrichments compared to single chemical similarity search methods. Our cheminformatics analyses recommend to use a combination of both group fusion and similarity fusion for prospective ligand-based virtual fragment screening.

Structure-based virtual screening for fragment-like ligands of the G protein-coupled histamine H4 receptor

Structure-based virtual screening using H₁R- and β₂R-based histamine H₄R homology models identified 9 fragments with an affinity ranging from 0.14 to 6.3 μm for H₄R.

What can we learn from molecular dynamics simulations for GPCR drug design?

Recent years have seen a tremendous progress in the elucidation of experimental structural information for G-protein coupled receptors (GPCRs). Although for the vast majority of pharmaceutically relevant GPCRs structural information is still accessible only by homology models the steadily increasing amount of structural information fosters the application of structure-based drug design tools for this important class of drug targets. In this article we focus on the application of molecular dynamics (MD) simulations in GPCR drug discovery programs. Typical application scenarios of MD simulations and their scope and limitations will be described on the basis of two selected case studies, namely the binding of small molecule antagonists to the human CC chemokine receptor 3 (CCR3) and a detailed investigation of the interplay between receptor dynamics and solvation for the binding of small molecules to the human muscarinic acetylcholine receptor 3 (hM3R).

Multiple fragment docking and linking in primary and secondary pockets of dopamine receptors

A sequential docking methodology was applied to computationally predict starting points for fragment linking using the human dopamine D3 receptor crystal structure and a human dopamine D2 receptor homology model. Two focused fragment libraries were docked in the primary and secondary binding sites, and best fragment combinations were enumerated. Similar top scoring fragments were found for the primary site, while secondary site fragments were predicted to convey selectivity. Three linked compounds were synthesized that had 9-, 39-, and 55-fold selectivity in favor of D3 and the subtype selectivity of the compounds was assessed on a structural basis.