Natural Compounds as Non-Nucleoside Inhibitors of Zika Virus Polymerase through Integration of In Silico and In Vitro Approaches.

Although the past epidemic of Zika virus (ZIKV) resulted in severe neurological consequences for infected infants and adults, there are still no approved drugs to treat ZIKV infection. In this study, we applied computational approaches to screen an in-house database of 77 natural and semi-synthetic compounds against ZIKV NS5 RNA-dependent RNA-polymerase (NS5 RdRp), an essential protein for viral RNA elongation during the replication process. For this purpose, we integrated computational approaches such as binding-site conservation, chemical space analysis and molecular docking. As a result, we prioritized nine virtual hits for experimental evaluation. Enzymatic assays confirmed that pedalitin and quercetin inhibited ZIKV NS5 RdRp with IC50 values of 4.1 and 0.5 µM, respectively. Moreover, pedalitin also displayed antiviral activity on ZIKV infection with an EC50 of 19.28 µM cell-based assays, with low toxicity in Vero cells (CC50 = 83.66 µM) and selectivity index of 4.34. These results demonstrate the potential of the natural compounds pedalitin and quercetin as candidates for structural optimization studies towards the discovery of new anti-ZIKV drug candidates.

Update and elucidation of Plasmodium kinomes: Prioritization of kinases as potential drug targets for malaria.

Malaria is a tropical disease caused by Plasmodium spp. and transmitted by the bite of infected Anopheles mosquitoes. Protein kinases (PKs) play key roles in the life cycle of the etiological agent of malaria, turning these proteins attractive targets for antimalarial drug discovery campaigns. As part of an effort to understand parasite signaling functions, we report the results of a bioinformatics pipeline analysis of PKs of eight Plasmodium species. To date, no P. malariae and P. ovale kinome assemble has been conducted. We classified, curated and annotated predicted kinases to update P. falciparum, P. vivax, P. yoelii, P. berghei, P. chabaudi, and P. knowlesi kinomes published to date, as well as report for the first time the kinomes of P. malariae and P. ovale. Overall, from 76 to 97 PKs were identified among all Plasmodium spp. kinomes. Most of the kinases were assigned to seven of nine major kinase groups: AGC, CAMK, CMGC, CK1, STE, TKL, OTHER; and the Plasmodium-specific group FIKK. About 30% of kinases have been deeply classified into group, family and subfamily levels and only about 10% remained unclassified. Furthermore, updating and comparing the kinomes of P. vivax and P. falciparum allowed for the prioritization and selection of kinases as potential drug targets that could be explored for discovering new drugs against malaria. This integrated approach resulted in the selection of 37 protein kinases as potential targets and the identification of investigational compounds with moderate in vitro activity against asexual P. falciparum (3D7 and Dd2 strains) stages that could serve as starting points for the search of potent antimalarial leads in the future.

Chemogenomics and bioinformatics approaches for prioritizing kinases as drug targets for neglected tropical diseases.

Neglected tropical diseases (NTDs) are a group of twenty-one diseases classified by the World Health Organization that prevail in regions with tropical and subtropical climate and affect more than one billion people. There is an urgent need to develop new and safer drugs for these diseases. Protein kinases are a potential class of targets for developing new drugs against NTDs, since they play crucial role in many biological processes, such as signaling pathways, regulating cellular communication, division, metabolism and death. Bioinformatics is a field that aims to organize large amounts of biological data as well as develop and use tools for understanding and analyze them in order to produce meaningful information in a biological manner. In combination with chemogenomics, which analyzes chemical-biological interactions to screen ligands against selected targets families, these approaches can be used to stablish a rational strategy for prioritizing new drug targets for NTDs. Here, we describe how bioinformatics and chemogenomics tools can help to identify protein kinases and their potential inhibitors for the development of new drugs for NTDs. We present a review of bioinformatics tools and techniques that can be used to define an organisms kinome for drug prioritization, drug and target repurposing, multi-quinase inhibition approachs and selectivity profiling. We also present some successful examples of the application of such approaches in recent case studies.

Chemoproteomic identification of molecular targets of antifungal prototypes, thiosemicarbazide and a camphene derivative of thiosemicarbazide, in Paracoccidioides brasiliensis.

Paracoccidioidomycosis (PCM) is a neglected human systemic disease caused by species of the genus Paracoccidioides. The disease attacks the host's lungs and may disseminate to many other organs. Treatment involves amphotericin B, sulfadiazine, trimethoprim-sulfamethoxazole, itraconazole, ketoconazole, or fluconazole. The treatment duration is usually long, from 6 months to 2 years, and many adverse effects may occur in relation to the treatment; co-morbidities and poor treatment adherence have been noted. Therefore, the discovery of more effective and less toxic drugs is needed. Thiosemicarbazide (TSC) and a camphene derivative of thiosemicarbazide (TSC-C) were able to inhibit P. brasiliensis growth at a low dosage and were not toxic to fibroblast cells. In order to investigate the mode of action of those compounds, we used a chemoproteomic approach to determine which fungal proteins were bound to each of these compounds. The compounds were able to inhibit the activities of the enzyme formamidase and interfered in P. brasiliensis dimorphism. In comparison with the transcriptomic and proteomic data previously obtained by our group, we determined that TSC and TSC-C were multitarget compounds that exerted effects on the electron-transport chain and cell cycle regulation, increased ROS formation, inhibited proteasomes and peptidases, modulated glycolysis, lipid, protein and carbohydrate metabolisms, and caused suppressed the mycelium to yeast transition.

Computational drug discovery for the Zika virus

Few Zika virus (ZIKV) outbreaks had been reported since its first detection in 1947, until the recent epidemics occurred in South America (2014/2015) and expeditiously became a global public health emergency. This arbovirus reached 0.5-1.3 million cases of ZIKV infection in Brazil in 2015 and rapidly spread in new geographic areas such as the Americas. Despite the mild symptoms of the Zika fever, the major concern is related to the related severe neurological disorders, especially microcephaly in newborns. Advances in ZIKV drug discovery have been made recently and constitute promising approaches to ZIKV treatment. In this review, we summarize current computational drug discovery efforts and their applicability to discovery of anti-ZIKV drugs. Lastly, we present successful examples of the use of computational approaches to ZIKV drug discovery.