Abstract: Proper cytokine gene expression is essential in development, homeostasis and immune responses. Studies on the transcriptional control of cytokine genes have mostly focused on highly researched transcription factors (TFs) and cytokines, resulting in an incomplete portrait of cytokine gene regulation. Here, we used enhanced yeast one-hybrid (eY1H) assays to derive a comprehensive network comprising 1380 interactions between 265 TFs and 108 cytokine gene promoters. Our eY1H-derived network greatly expands the known repertoire of TF-cytokine gene interactions and the set of TFs known to regulate cytokine genes. We found an enrichment of nuclear receptors and confirmed their role in cytokine regulation in primary macrophages. Additionally, we used the eY1H-derived network as a framework to identify pairs of TFs that can be targeted with commercially-available drugs to synergistically modulate cytokine production. Finally, we integrated the eY1H data with single cell RNA-seq and phenotypic datasets to identify novel TF-cytokine regulatory axes in immune diseases and immune cell lineage development. Overall, the eY1H data provides a rich resource to study cytokine regulation in a variety of physiological and disease contexts.

Abstract: The recent accumulation of cancer genomic data provides an opportunity to understand how a tumor's genomic characteristics can affect its responses to drugs. This field, called pharmacogenomics, is a key area in the development of precision oncology. Deep learning (DL) methodology has emerged as a powerful technique to characterize and learn from rapidly accumulating pharmacogenomics data. We introduce the fundamentals and typical model architectures of DL. We review the use of DL in classification of cancers and cancer subtypes (diagnosis and treatment stratification of patients), prediction of drug response and drug synergy for individual tumors (treatment prioritization for a patient), drug repositioning and discovery and the study of mechanism/mode of action of treatments. For each topic, we summarize current genomics and pharmacogenomics data resources such as pan-cancer genomics data for cancer cell lines (CCLs) and tumors, and systematic pharmacologic screens of CCLs. By revisiting the published literature, including our in-house analyses, we demonstrate the unprecedented capability of DL enabled by rapid accumulation of data resources to decipher complex drug response patterns, thus potentially improving cancer medicine. Overall, this review provides an in-depth summary of state-of-the-art DL methods and up-to-date pharmacogenomics resources and future opportunities and challenges to realize the goal of precision oncology.

Abstract: Coronavirus disease 2019 (COVID-19) is an infective disease generated by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the pandemic urgency and lack of an effective cure for this disease, drug repurposing could open the way for finding a solution. Lots of investigations are ongoing to test the compounds already identified as antivirals. On the other hand, induction of type I interferons are found to play an important role in the generation of immune responses against SARS-CoV-2. Therefore, it was opined that the antivirals capable of triggering the interferons and their signaling pathway, could rationally be beneficial for treating COVID-19. On this basis, using a database of antivirals, called drugvirus, some antiviral agents were derived, followed by searches on their relevance to interferon induction. The examined list included drugs from different categories such as antibiotics, immunosuppressants, anti-cancers, non-steroidal anti-inflammatory drugs (NSAID), calcium channel blocker compounds, and some others. The results as briefed here, could help in finding potential drug candidates for COVID-19 treatment. However, their advantages and risks should be taken into account through precise studies, considering a systemic approach. Even though the adverse effects of some of these drugs may overweight their benefits, considering their mechanisms and structures may give a clue for designing novel drugs in the future. Furthermore, the antiviral effect and IFN-modifying mechanisms possessed by some of these drugs might lead to a synergistic effect against SARS-CoV-2, which deserve to be evaluated in further investigations.

Abstract: The drug discovery process starts with identification of a disease-modifying target. This critical step traditionally begins with manual investigation of scientific literature and biomedical databases to gather evidence linking molecular target to disease, and to evaluate the efficacy, safety and commercial potential of the target. The high-throughput and affordability of current omics technologies, allowing quantitative measurements of many putative targets (e.g. DNA, RNA, protein, metabolite), has exponentially increased the volume of scientific data available for this arduous task. Therefore, computational platforms identifying and ranking disease-relevant targets from existing biomedical data sources, including omics databases, are needed. To date, more than 30 drug target discovery (DTD) platforms exist. They provide information-rich databases and graphical user interfaces to help scientists identify putative targets and pre-evaluate their therapeutic efficacy and potential side effects. Here we survey and compare a set of popular DTD platforms that utilize multiple data sources and omics-driven knowledge bases (either directly or indirectly) for identifying drug targets. We also provide a description of omics technologies and related data repositories which are important for DTD tasks.

Abstract: Skin permeation is an essential biological property of small organic compounds our body is exposed to, such as drugs in topic formulations, cosmetics, and environmental toxins. Despite the limited availability of experimental data, there is a lack of systematic analysis and structure. We present a novel resource on skin permeation data that collects all measurements available in the literature and systematically structures experimental conditions. Besides the skin permeation value kp, it includes experimental protocols such as skin source site, skin layer used, preparation technique, storage conditions, as well as test conditions such as temperature, pH as well as the type of donor and acceptor solution. It is important to include these parameters in the assessment of the skin permeation data. In addition, we provide an analysis of physicochemical properties and chemical space coverage, laying the basis for applicability domain determination of insights drawn from the collected data points. The database is freely accessible under https://huskindb.drug-design.de or https://doi.org/10.7303/syn21998881 .

Abstract: Malaria remains the leading cause of deaths globally, despite significant advancement towards understanding its epidemiology and availability of multiple therapeutic interventions. Poor efficacy of the approved vaccine, and the rapid emergence of antimalarial drug resistance, warrants an urgent need to expedite the process of development of new lead molecules targeting malaria. Aminoacyl-tRNA synthetases (aaRSs) are essential enzymes crucial for ribosomal protein synthesis and are valid antimalarial targets. This study explores the prospects of (re-)positioning the repertoire of approved drugs and natural products as potential malarial aaRS inhibitors. Molecular docking of these two sets of small-molecules to lysyl-, prolyl-, and tyrosyl- synthetases from Plasmodium followed by a comparison of the top-ranking docked compounds against human homologs facilitated identification of promising molecular scaffolds. Raltitrexed and Cefprozil, an anticancer drug and an antibiotic, respectively, showed stronger binding to Plasmodium aaRSs compared to human homologs with > 4 kcal/mol difference in the docking scores. Similarly, a difference of ~ 3 kcal/mol in Glide scores was observed for docked Calcipotriol, a drug used for psoriasis treatment, against the two lysyl-tRNA synthetases. Natural products such as Dihydroxanthohumol and Betmidin, having aromatic rings as a substructure, showed preferential docking to the purine binding pocket in Plasmodium tyrosyl-tRNA synthetase as evident from the calculated change in binding free energies. We present detailed analyses of the calculated intermolecular interaction for all top-scoring docked poses. Overall, this study provides a compelling foundation to design and develop specific antimalarials.

Abstract: Medicinal plants have widely been used in the traditional treatment of ailments and have been proven effective. Their contribution still holds an important place in modern drug discovery due to their chemical, and biological diversities. However, the poor documentation of traditional medicine, in developing African countries for instance, can lead to the loss of knowledge related to such practices. In this study, we present the Eastern Africa Natural Products Database (EANPDB) containing the structural and bioactivity information of 1870 unique molecules isolated from about 300 source species from the Eastern African region. This represents the largest collection of natural products (NPs) from this geographical region, covering literature data of the period from 1962 to 2019. The computed physicochemical properties and toxicity profiles of each compound have been included. A comparative analysis of some physico-chemical properties like molecular weight, H-bond donor/acceptor, logPo/w , etc. as well scaffold diversity analysis has been carried out with other published NP databases. EANPDB was combined with the previously published Northern African Natural Products Database (NANPDB), to form a merger African Natural Products Database (ANPDB), containing approximately 6500 unique molecules isolated from about 1000 source species (freely available at http://african-compounds.org). As a case study, latrunculins A and B isolated from the sponge Negombata magnifica (Podospongiidae) with previously reported antitumour activities, were identified via substructure searching as molecules to be explored as putative binders of histone deacetylases (HDACs).

Abstract: The repurposing of FDA approved drugs is presently receiving attention for COVID-19 drug discovery. Previous studies revealed the binding potential of several FDA-approved drugs towards specific targets of SARS-CoV-2; however, limited studies are focused on the structural and molecular basis of interaction of these drugs towards multiple targets of SARS-CoV-2. The present study aimed to predict the binding potential of six FDA drugs towards fifteen protein targets of SARS-CoV-2 and propose the structural and molecular basis of the interaction by molecular docking and dynamic simulation. Based on the literature survey, fifteen potential targets of SARS-CoV-2, and six FDA drugs (Chloroquine, Hydroxychloroquine, Favipiravir, Lopinavir, Remdesivir, and Ritonavir) were selected. The binding potential of individual drug towards the selected targets was predicted by molecular docking in comparison with the binding of the same drugs with their usual targets. The stabilities of the best-docked conformations were confirmed by molecular dynamic simulation and energy calculations. Among the selected drugs, Ritonavir and Lopinavir showed better binding towards the prioritized targets with minimum binding energy (kcal/mol), cluster-RMS, number of interacting residues, and stabilizing forces when compared with the binding of Chloroquine, Favipiravir, and Hydroxychloroquine, later drugs demonstrated better binding when compared to the binding with their usual targets. Remdesvir showed better binding to the prioritized targets in comparison with the binding of Chloroquine, Favipiravir, and Hydroxychloroquine, but showed lesser binding potential when compared to the interaction between Ritonavir and Lopinavir and the prioritized targets. The structural and molecular basis of interactions suggest that the FDA drugs can be repurposed towards multiple targets of SARS-CoV-2, and the present computational models provide insights on the scope of repurposed drugs against COVID-19.

Abstract: Coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 virus, has developed into a pandemic causing major disruptions and hundreds of thousands of deaths in wide parts of the world. As of July 3, 2020, neither vaccines nor approved drugs for effective treatment are available. In this article, we showcase how to individuate drug targets and potentially repurposable drugs in silico using CoVex a recently presented systems medicine platform for COVID-19 drug repurposing. Starting from initial hypotheses, CoVex leverages network algorithms to individuate host proteins involved in COVID-19 disease mechanisms, as well as existing drugs targeting these potential drug targets. Our analysis reveals GLA, PLAT, and GGCX as potential drug targets, and urokinase, argatroban, dabigatran etexilate, betrixaban, ximelagatran and anisindione as potentially repurposable drugs.

Abstract: The COVID-19 pandemic caused by the SARS-CoV-2 requires a fast development of antiviral drugs. SARS-CoV-2 viral main protease (Mpro, also called 3C-like protease, 3CLpro) is a potential target for drug design. Crystal and co-crystal structures of the SARS-CoV-2 Mpro have been solved, enabling the rational design of inhibitory compounds. In this study we analyzed the available SARS-CoV-2 and the highly similar SARS-CoV-1 crystal structures. We identified within the active site of the Mpro, in addition to the inhibitory ligands' interaction with the catalytic C145, two key H-bond interactions with the conserved H163 and E166 residues. Both H-bond interactions are present in almost all co-crystals and are likely to occur also during the viral polypeptide cleavage process as suggested from docking of the Mpro cleavage recognition sequence. We screened in silico a library of 6900 FDA-approved drugs (ChEMBL) and filtered using these key interactions and selected 29 non-covalent compounds predicted to bind to the protease. Additional screen, using DOCKovalent was carried out on DrugBank library (11,414 experimental and approved drugs) and resulted in 6 covalent compounds. The selected compounds from both screens were tested in vitro by a protease activity inhibition assay. Two compounds showed activity at the 50 microM concentration range. Our analysis and findings can facilitate and focus the development of highly potent inhibitors against SARS-CoV-2 infection.