Cysteine is a building block for several biomolecules that are crucial for living organisms. The last step of cysteine biosynthesis is catalyzed by O-acetylserine sulfydrylase (OASS), a highly conserved pyridoxal 5'-phosphate (PLP)-dependent enzyme, present in different isoforms in bacteria, plants, and nematodes, but absent in mammals. Beside the biosynthesis of cysteine, OASS exerts a series of "moonlighting" activities in bacteria, such as transcriptional regulation, contact-dependent growth inhibition, swarming motility, and induction of antibiotic resistance. Therefore, the discovery of molecules capable of inhibiting OASS would be a valuable tool to unravel how this protein affects the physiology of unicellular organisms. As a continuation of our efforts toward the synthesis of OASS inhibitors, in this work we have used a combination of computational and spectroscopic approaches to rationally design, synthesize, and test a series of substituted 2-phenylcyclopropane carboxylic acids that bind to the two S. typhymurium OASS isoforms at nanomolar concentrations.

Tools for molecular de novo design are actively sought incorporating sets of chemical rules for fast and efficient identification of structurally new chemotypes endowed with a desired set of biological properties. In this paper, we present LiGen, a suite of programs which can be used sequentially or as stand-alone tools for specific purposes. In its standard application, LiGen modules are used to define input constraints, either structure-based, through active site identification, or ligand-based, through pharmacophore definition, to docking and to de novo generation. Alternatively, individual modules can be combined in a user-defined manner to generate project-centric workflows. Specific features of LiGen are the use of a pharmacophore-based docking procedure which allows flexible docking without conformer enumeration and accurate and flexible reactant mapping coupled with reactant tagging through substructure searching. The full description of LiGen functionalities is presented.

On route toward a novel de novo design program, called LiGen, we developed a docking program, LiGenDock, based on pharmacophore models of binding sites, including a non-enumerative docking algorithm. In this paper, we present the functionalities of LiGenDock and its accompanying module LiGenPocket, aimed at the binding site analysis and structure-based pharmacophore definition. We also report the optimization procedure we have carried out to improve the cognate docking and virtual screening performance of LiGenDock. In particular, we applied the design of experiments (DoE) methodology to screen the set of user-adjustable parameters to identify those having the largest influence on the accuracy of the results (which ensure the best performance in pose prediction and in virtual screening approaches) and then to choose their optimal values. The results are also compared with those obtained by two popular docking programs, namely, Glide and AutoDock for pose prediction, and Glide and DOCK6 for Virtual Screening.

Pathogenic variants in the leucine-rich repeat kinase 2 (LRRK2) gene have been identified that increase the risk for developing Parkinson's disease in a dominantly inherited fashion. These pathogenic variants, of which G2019S is the most common, cause abnormally high kinase activity, and compounds that inhibit this activity are being pursued as potentially disease-modifying therapeutics. Because LRRK2 regulates important cellular processes, developing inhibitors that can selectively target the pathogenic variant while sparing normal LRRK2 activity could offer potential advantages in heterozygous carriers. We conducted a high-throughput screen and identified a single selective compound that preferentially inhibited G2019S-LRRK2. Optimization of this scaffold led to a series of novel, potent, and highly selective G2019S-LRRK2 inhibitors.

No abstract

3-Hydroxyanthranilic acid 3,4-dioxygenase (3-HAO) is the enzyme responsible for the production of the neurotoxic tryptophan metabolite quinolinic acid (QUIN). Elevated brain levels of QUIN are observed in several neurodegenerative diseases, but pharmacological investigation on its role in the pathogenesis of these conditions is difficult because only one class of substrate-analogue 3-HAO inhibitors, with poor chemical stability, has been reported so far. Here we describe the design, synthesis, and biological evaluation of a novel class of chemically stable inhibitors based on the 2-aminonicotinic acid 1-oxide nucleus. After the preliminary in vitro evaluation of newly synthesized compounds using brain tissue homogenate, we selected the most active inhibitor and showed its ability to acutely reduce the production of QUIN in the rat brain in vivo. These findings provide a novel pharmacological tool for the study of the mechanisms underlying the onset and propagation of neurodegenerative diseases.

Malaria eradication is a global health priority, but current therapies are not always suitable for providing a radical cure. Artemisinin has paved the way for the current malaria treatment, the so-called Artemisinin-based Combination Therapy (ACT). However, with the detection of resistance to ACT, innovative compounds active against multiple parasite species and at multiple life stages are needed. GlaxoSmithKline has recently disclosed the results of a phenotypic screening of an internal library, publishing a collection of 400 antimalarial chemotypes, termed the "Malaria Box". After analysis of the data set, we have carried out a medicinal chemistry campaign in order to define the structure-activity relationships for one of the released compounds, which embodies a benzothiophene-2-carboxamide core. Thirty-five compounds were prepared, and a description of the structural features responsible for the in vitro activity against different strains of P. falciparum, the toxicity, and the metabolic stability is herein reported.

This study clearly indicates that the presence of a dimerization interface must explicitly be considered when studying G-protein coupled receptors known to exist as dimers. Molecular dynamics simulation and cluster analysis are appropriate tools to study the phenomenon.