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Natural products have been proven to be important starting points for the development of new drugs. Bacteria in the genera Photorhabdus and Xenorhabdus produce antimicrobial compounds as secondary metabolites to compete with other organisms. Our study is the first comprehensive study screening the anti-protozoal activity of supernatants containing secondary metabolites produced by 5 Photorhabdus and 22 Xenorhabdus species against human parasitic protozoa, Acanthamoeba castellanii, Entamoeba histolytica, Trichomonas vaginalis, Leishmania tropica and Trypanosoma cruzi, and the identification of novel bioactive antiprotozoal compounds using the easyPACId approach (easy Promoter Activated Compound Identification) method. Though not in all species, both bacterial genera produce antiprotozoal compounds effective on human pathogenic protozoa. The promoter exchange mutants revealed that antiprotozoal bioactive compounds produced by Xenorhabdus bacteria were fabclavines, xenocoumacins, xenorhabdins and PAX peptides. Among the bacteria assessed, only P. namnaoensis appears to have acquired amoebicidal property which is effective on E. histolytica trophozoites. These discovered antiprotozoal compounds might serve as starting points for the development of alternative and novel pharmaceutical agents against human parasitic protozoa in the future.
Fragment-based screening has evolved as a remarkable approach within the drug discovery process both in the industry and academia. Fragment screening has become a more structure-based approach to inhibitor development, but also towards development of pathway-specific clinical probes. However, it is often witnessed that the availability, immediate and long-term, of a high quality fragment-screening library is still beyond the reach of most academic laboratories. Within iNEXT (Infrastructure for NMR, EM and X-rays for Translational research), a EU-funded Horizon 2020 program, a collection of 782 fragments were assembled utilizing the concept of “poised fragments” with the aim to facilitate downstream synthesis of ligands with high affinity by fragment ligation. Herein, we describe the analytical procedure to assess the quality of this purchased and assembled fragment library by NMR spectroscopy. This quality assessment requires buffer solubility screening, comparison with LC/MS quality control and is supported by state-of-the-art software for high throughput data acquisition and on-the-fly data analysis. Results from the analysis of the library are presented as a prototype of fragment progression through the quality control process.
Bromodomains (BRDs) are conserved protein interaction modules which recognize (read) acetyl-lysine modifications, however their role(s) in regulating cellular states and their potential as targets for the development of targeted treatment strategies is poorly understood. Here we present a set of 25 chemical probes, selective small molecule inhibitors, covering 29 human bromodomain targets. We comprehensively evaluate the selectivity of this probe-set using BROMOscan and demonstrate the utility of the set identifying roles of BRDs in cellular processes and potential translational applications. For instance, we discovered crosstalk between histone acetylation and the glycolytic pathway resulting in a vulnerability of breast cancer cell lines under conditions of glucose deprivation or GLUT1 inhibition to inhibition of BRPF2/3 BRDs. This chemical probe-set will serve as a resource for future applications in the discovery of new physiological roles of bromodomain proteins in normal and disease states, and as a toolset for bromodomain target validation.
An expanded chemical space is essential for improved identification of small molecules for emerging therapeutic targets. However, the identification of targets for novel compounds is biased towards the synthesis of known scaffolds that bind familiar protein families, limiting the exploration of chemical space. To change this paradigm, we validated a new pipeline that identifies small molecule-protein interactions and works even for compounds lacking similarity to known drugs. Based on differential mRNA profiles in multiple cell types exposed to drugs and in which gene knockdowns (KD) were conducted, we showed that drugs induce gene regulatory networks that correlate with those produced after silencing protein-coding genes. Next, we applied supervised machine learning to exploit drug-KD signature correlations and enriched our predictions using an orthogonal structure-based screen. As a proof-of-principle for this regimen, top-10/top-100 target prediction accuracies of 26% and 41%, respectively, were achieved on a validation set 152 FDA-approved drugs and 3104 potential targets. We then predicted targets for 1680 compounds and validated chemical interactors with four targets that have proven difficult to chemically modulate, including non-covalent inhibitors of HRAS and KRAS. Importantly, drug-target interactions manifest as gene expression correlations between drug treatment and both target gene KD and KD of genes that act up- or down-stream of the target, even for relatively weak binders. These correlations provide new insights on the cellular response of disrupting protein interactions and highlight the complex genetic phenotypes of drug treatment. With further refinement, our pipeline may accelerate the identification and development of novel chemical classes by screening compound-target interactions.