Abstract: Micro RNA or miRNA is a highly conserved class of non-coding RNA that plays an important role in many diseases. Identifying miRNA-disease associations can pave the way for better clinical diagnosis and finding potential drug targets. We propose a biologically-motivated data-driven approach for the miRNA-disease association prediction, which overcomes the data scarcity problem by exploiting information from multiple data sources. The key idea is to enrich the existing miRNA/disease-protein-coding gene (PCG) associations via a message passing framework, followed by the use of disease ontology information for further feature filtering. The enriched and filtered PCG associations are then used to construct the inter-connected miRNA-PCG-disease network to train a structural deep network embedding (SDNE) model. Finally, the pre-trained embeddings and the biologically relevant features from the miRNA family and disease semantic similarity are concatenated to form the pair input representations to a Random Forest classifier whose task is to predict the miRNA-disease association probabilities. We present large-scale comparative experiments, ablation, and case studies to showcase our approach's superiority. Besides, we make the model prediction results for 1618 miRNAs and 3679 diseases, along with all related information, publicly available at http://software.mpm.leibniz-ai-lab.de/ to foster assessments and future adoption.

Abstract: Available drugs have been used as an urgent attempt through clinical trials to minimize severe cases of hospitalizations with Coronavirus disease (COVID-19), however, there are limited data on common pharmacogenomics affecting concomitant medications response in patients with comorbidities. To identify the genomic determinants that influence COVID-19 susceptibility, we use a computational, statistical, and network biology approach to analyze relationships of ineffective concomitant medication with an adverse effect on patients. We statistically construct a pharmacogenetic/biomarker network with significant drug-gene interactions originating from gene-disease associations. Investigation of the predicted pharmacogenes encompassing the gene-disease-gene pharmacogenomics (PGx) network suggests that these genes could play a significant role in COVID-19 clinical manifestation due to their association with autoimmune, metabolic, neurological, cardiovascular, and degenerative disorders, some of which have been reported to be crucial comorbidities in a COVID-19 patient.

Abstract: Acute myeloid leukaemia (AML) is one of the most common types of haematopoietic malignancy. Ribonucleotide reductase (RNR) is a key enzyme required for DNA synthesis and cell proliferation, and its small subunit RRM2 plays a key role for the enzymatic activity. We predicted monobenzone (MB) as a potential RRM2 target compound based on the crystal structure of RRM2. In vitro, MB inhibited recombinant RNR activity (IC50 = 0.25 muM). Microscale thermophoresis indicated that MB inhibited RNR activity by binding to RRM2. MB inhibited cell proliferation (MTT IC50 = 6-18 muM) and caused dose-dependent DNA synthesis inhibition, cell cycle arrest, and apoptosis in AML cells. The cell cycle arrest was reversed by the addition of deoxyribonucleoside triphosphates precursors, suggesting that RNR was the intracellular target of the compound. Moreover, MB overcame drug resistance to the common AML drugs cytarabine and doxorubicin, and treatment with the combination of MB and the Bcl-2 inhibitor ABT-737 exerted a synergistic inhibitory effect. Finally, the nude mice xenografts study indicated that MB administration produced a significant inhibitory effect on AML growth with relatively weak toxicity. Thus, we propose that MB has the potential as a novel anti-AML therapeutic agent in the future.

Abstract: IFITM3 is a transmembrane protein that confers innate immunity. It has been established to restrict entry of multiple viruses. Overexpression of IFITM3 has been shown to be associated with multiple cancers, implying IFITM3 to be good therapeutic target. The regulation of IFITM3 activity is mediated by multiple post-translational modifications (PTM). In this study, we have modelled the structure of IFITM3, consistent with experimental predictions on its membrane topology. MD simulation in membrane-aqueous environment revealed the stability of the model. Ligand binding sites on the IFITM3 surface were predicted and it was observed that the best site includes important residues involved in PTM and has good druggable score. Molecular docking was performed using FDA approved ligands and natural ligands from Super Natural II database. The ligands were re-ranked by calculating binding free energy. Select docking complexes were simulated again to substantiate the binding between ligand and IFITM3. We observed that known drugs like Eluxadoline and natural products like SN00224572 and Parishin A have good binding affinity against IFITM3. These ligands form persistent interactions with key lysine residues (Lys83, Lys104) and hence can potentially alter the activity of IFITM3. The results of this computational study can provide a starting point for experimental investigations on IFITM3 modulators.

Abstract: In the current study, we introduce an integrative machine learning strategy for the autonomous molecular design of protein kinase inhibitors using variational autoencoders and a novel cluster-based perturbation approach for exploration of the chemical latent space. The proposed strategy combines autoencoder-based embedding of small molecules with a cluster-based perturbation approach for efficient navigation of the latent space and a feature-based kinase inhibition likelihood classifier that guides optimization of the molecular properties and targeted molecular design. In the proposed generative approach, molecules sharing similar structures tend to cluster in the latent space, and interpolating between two molecules in the latent space enables smooth changes in the molecular structures and properties. The results demonstrated that the proposed strategy can efficiently explore the latent space of small molecules and kinase inhibitors along interpretable directions to guide the generation of novel family-specific kinase molecules that display a significant scaffold diversity and optimal biochemical properties. Through assessment of the latent-based and chemical feature-based binary and multiclass classifiers, we developed a robust probabilistic evaluator of kinase inhibition likelihood that is specifically tailored to guide the molecular design of novel SRC kinase molecules. The generated molecules originating from LCK and ABL1 kinase inhibitors yielded ~40% of novel and valid SRC kinase compounds with high kinase inhibition likelihood probability values (p > 0.75) and high similarity (Tanimoto coefficient > 0.6) to the known SRC inhibitors. By combining the molecular perturbation design with the kinase inhibition likelihood analysis and similarity assessments, we showed that the proposed molecular design strategy can produce novel valid molecules and transform known inhibitors of different kinase families into potential chemical probes of the SRC kinase with excellent physicochemical profiles and high similarity to the known SRC kinase drugs. The results of our study suggest that task-specific manipulation of a biased latent space may be an important direction for more effective task-oriented and target-specific autonomous chemical design models.

Abstract: Due to the concerning rise in the number of multiple- and prolonged-drug-resistant (MDR and XDR) Mycobacterium tuberculosis (Mtb) strains, unprecedented demand has been created to design and develop novel therapeutic drugs with higher efficacy and safety. In this study, with a focused view on implementing an in silico drug design pipeline, a diverse set of glycosylated flavonoids were screened against the Mtb cytochrome-P450 enzyme 121 (CYP121), which is established as an approved drug target for the treatment of Mtb infection. A total of 148 glycosylated flavonoids were screened using structure-based virtual screening against the crystallized ligand, i.e., the L44 inhibitor, binding pocket in the Mtb CYP121 protein. Following this, only the top six compounds with the highest binding scores (kcal/mol) were considered for further intermolecular interaction and dynamic stability using 100 ns classical molecular dynamics simulation. These results suggested a considerable number of hydrogen and hydrophobic interactions and thermodynamic stability in comparison to the reference complex, i.e., the CYP121-L44 inhibitor. Furthermore, binding free energy via the MMGBSA method conducted on the last 10 ns interval of MD simulation trajectories revealed the substantial affinity of glycosylated compounds with Mtb CYP121 protein against reference complex. Notably, both the docked poses and residual energy decomposition via the MMGBSA method demonstrated the essential role of active residues in the interactions with glycosylated compounds by comparison with the reference complex. Collectively, this study demonstrates the viability of these screened glycosylated flavonoids as potential inhibitors of Mtb CYP121 for further experimental validation to develop a therapy for the treatment of drug-resistant Mtb strains.

Abstract: The majority of primary and secondary metabolites in nature have yet to be identified, representing a major challenge for metabolomics studies that currently require reference libraries from analyses of authentic compounds. Using currently available analytical methods, complete chemical characterization of metabolomes is infeasible for both technical and economic reasons. For example, unambiguous identification of metabolites is limited by the availability of authentic chemical standards, which, for the majority of molecules, do not exist. Computationally predicted or calculated data are a viable solution to expand the currently limited metabolite reference libraries, if such methods are shown to be sufficiently accurate. For example, determining nuclear magnetic resonance (NMR) spectroscopy spectra in silico has shown promise in the identification and delineation of metabolite structures. Many researchers have been taking advantage of density functional theory (DFT), a computationally inexpensive yet reputable method for the prediction of carbon and proton NMR spectra of metabolites. However, such methods are expected to have some error in predicted (13)C and (1)H NMR spectra with respect to experimentally measured values. This leads us to the question-what accuracy is required in predicted (13)C and (1)H NMR chemical shifts for confident metabolite identification? Using the set of 11,716 small molecules found in the Human Metabolome Database (HMDB), we simulated both experimental and theoretical NMR chemical shift databases. We investigated the level of accuracy required for identification of metabolites in simulated pure and impure samples by matching predicted chemical shifts to experimental data. We found 90% or more of molecules in simulated pure samples can be successfully identified when errors of (1)H and (13)C chemical shifts in water are below 0.6 and 7.1 ppm, respectively, and below 0.5 and 4.6 ppm in chloroform solvation, respectively. In simulated complex mixtures, as the complexity of the mixture increased, greater accuracy of the calculated chemical shifts was required, as expected. However, if the number of molecules in the mixture is known, e.g., when NMR is combined with MS and sample complexity is low, the likelihood of confident molecular identification increased by 90%.

Abstract: Quercetin and kaempferol are flavonoids widely present in fruits, vegetables, and medicinal plants. They have attracted much attention due to their antioxidant, anti-inflammatory, anticancer, antibacterial, and neuroprotective properties. As the guarantee cells in direct contact with germ cells, Sertoli cells exert the role of support, nutrition, and protection in spermatogenesis. In the current study, network pharmacology was used to explore the targets and signaling pathways of quercetin and kaempferol in treating spermatogenic disorders. In vitro experiments were integrated to verify the results of quercetin and kaempferol against heat stress-induced Sertoli cell injury. The online platform was used to analyze the GO biological pathway and KEGG pathway. The results of the network pharmacology showed that quercetin and kaempferol intervention in spermatogenesis disorders were mostly targeting the oxidative response to oxidative stress, the ROS metabolic process and the NFkappaB pathway. The results of the cell experiment showed that Quercetin and kaempferol can prevent the decline of cell viability induced by heat stress, reduce the expression levels of HSP70 and ROS in Sertoli cells, reduce p-NF-kappaB-p65 and p-IkappaB levels, up-regulate the expression of occludin, vimentin and F-actin in Sertoli cells, and protect cell structure. Our research is the first to demonstrate that quercetin and kaempferol may exert effects in resisting the injury of cell viability and structure under heat stress.

Abstract: Clear cell Renal Cell Carcinoma (ccRCC) is among the 10 most common cancers in both men and women and causes more than 140,000 deaths worldwide every year. In order to elucidate the underlying molecular mechanisms orchestrated by phosphorylation modifications, we performed a comprehensive quantitative phosphoproteomics characterization of ccRCC tumor and normal adjacent tissues. Here, we identified 16,253 phosphopeptides, of which more than 9000 were singly quantified. Our in-depth analysis revealed 600 phosphopeptides to be significantly differentially regulated between tumor and normal tissues. Moreover, our data revealed that significantly up-regulated phosphoproteins are associated with protein synthesis and cytoskeletal re-organization which suggests proliferative and migratory behavior of renal tumors. This is supported by a mesenchymal profile of ccRCC phosphorylation events. Our rigorous characterization of the renal phosphoproteome also suggests that both epidermal growth factor receptor and vascular endothelial growth factor receptor are important mediators of phospho signaling in RCC pathogenesis. Furthermore, we determined the kinases p21-activated kinase 2, cyclin-dependent kinase 1 and c-Jun N-terminal kinase 1 to be master kinases that are responsible for phosphorylation of many substrates associated with cell proliferation, inflammation and migration. Moreover, high expression of p21-activated kinase 2 is associated with worse survival outcome of ccRCC patients. These master kinases are targetable by inhibitory drugs such as fostamatinib, minocycline, tamoxifen and bosutinib which can serve as novel therapeutic agents for ccRCC treatment.

Abstract: Prothionamide, a second-line drug for multidrug-resistant tuberculosis (MDR-TB), has been in use for a few decades. However, its pharmacokinetic (PK) profile remains unclear. This study aimed to develop a population PK model for prothionamide and then apply the model to determine the optimal dosing regimen for MDR-TB patients. Multiple plasma samples were collected from 27 MDR-TB patients who had been treated with prothionamide at 2 different study hospitals. Prothionamide was administered according to the weight-band dose regimen (500 mg/day for weight <50 kg and 750 mg/day for weight >50 kg) recommended by the World Health Organization. The population PK model was developed using nonlinear mixed-effects modeling. The probability of target attainment, based on systemic exposure and MIC, was used as a response target. Fixed-dose regimens (500 or 750 mg/day) were simulated to compare the efficacies of various dosing regimens. PK profiles adequately described the two-compartment model with first-order elimination and the transit absorption compartment model with allometric scaling on clearance. All dosing regimens had effectiveness >90% for MIC values <0.4 mug/mL in 1.0-log kill target. However, a fixed dose of 750 mg/day was the only regimen that achieved the target resistance suppression of >/=90% for MIC values of <0.2 mug/mL. In conclusion, fixed-dose prothionamide (750 mg/day), regardless of weight-band, was appropriate for adult MDR-TB patients with weights of 40 to 67 kg.