Abstract: Drug-disease association is an important piece of information which participates in all stages of drug repositioning. Although the number of drug-disease associations identified by high-throughput technologies is increasing, the experimental methods are time consuming and expensive. As supplement to them, many computational methods have been developed for an accurate in silico prediction for new drug-disease associations. In this work, we present a novel computational model combining sparse auto-encoder and rotation forest (SAEROF) to predict drug-disease association. Gaussian interaction profile kernel similarity, drug structure similarity and disease semantic similarity were extracted for exploring the association among drugs and diseases. On this basis, a rotation forest classifier based on sparse auto-encoder is proposed to predict the association between drugs and diseases. In order to evaluate the performance of the proposed model, we used it to implement 10-fold cross validation on two golden standard datasets, Fdataset and Cdataset. As a result, the proposed model achieved AUCs (Area Under the ROC Curve) of Fdataset and Cdataset are 0.9092 and 0.9323, respectively. For performance evaluation, we compared SAEROF with the state-of-the-art support vector machine (SVM) classifier and some existing computational models. Three human diseases (Obesity, Stomach Neoplasms and Lung Neoplasms) were explored in case studies. As a result, more than half of the top 20 drugs predicted were successfully confirmed by the Comparative Toxicogenomics Database(CTD database). This model is a feasible and effective method to predict drug-disease correlation, and its performance is significantly improved compared with existing methods.

Abstract: BACKGROUND: The key to modern drug discovery is to find, identify and prepare drug molecular targets. However, due to the influence of throughput, precision and cost, traditional experimental methods are difficult to be widely used to infer these potential Drug-Target Interactions (DTIs). Therefore, it is urgent to develop effective computational methods to validate the interaction between drugs and target. METHODS: We developed a deep learning-based model for DTIs prediction. The proteins evolutionary features are extracted via Position Specific Scoring Matrix (PSSM) and Legendre Moment (LM) and associated with drugs molecular substructure fingerprints to form feature vectors of drug-target pairs. Then we utilized the Sparse Principal Component Analysis (SPCA) to compress the features of drugs and proteins into a uniform vector space. Lastly, the deep long short-term memory (DeepLSTM) was constructed for carrying out prediction. RESULTS: A significant improvement in DTIs prediction performance can be observed on experimental results, with AUC of 0.9951, 0.9705, 0.9951, 0.9206, respectively, on four classes important drug-target datasets. Further experiments preliminary proves that the proposed characterization scheme has great advantage on feature expression and recognition. We also have shown that the proposed method can work well with small dataset. CONCLUSION: The results demonstration that the proposed approach has a great advantage over state-of-the-art drug-target predictor. To the best of our knowledge, this study first tests the potential of deep learning method with memory and Turing completeness in DTIs prediction.

Abstract: A new cyclic peptide, kakeromamide B (1), and previously described cytotoxic cyanobacterial natural products ulongamide A (2), lyngbyabellin A (3), 18E-lyngbyaloside C (4), and lyngbyaloside (5) were identified from an antimalarial extract of the Fijian marine cyanobacterium Moorea producens. Compound 1 exhibited moderate activity against Plasmodium falciparum blood-stages with an EC50 value of 8.9 microM whereas 2 and 3 were more potent with EC50 values of 0.99 microM and 1.5 nM, respectively. Compounds 1, 4, and 5 displayed moderate liver-stage antimalarial activity against P. berghei liver schizonts with EC50 values of 11, 7.1, and 4.5 microM, respectively. The threading-based computational method FINDSITE(comb2.0) predicted the binding of 1 and 2 to potentially druggable proteins of Plasmodium falciparum, prompting formulation of hypotheses about possible mechanisms of action. Kakeromamide B (1) was predicted to bind to several Plasmodium actin-like proteins and a sortilin protein suggesting possible interference with parasite invasion of host cells. When 1 was tested in a mammalian actin polymerization assay, it stimulated actin polymerization in a dose-dependent manner, suggesting that 1 does, in fact, interact with actin.

Abstract: BACKGROUND TaohongSiwu decoction (THSWT), a traditional herbal formula, has been used to treat cardiovascular and cerebrovascular diseases such as essential hypertension (EH) in China. However, the pharmacological mechanism is not clear. To investigate the mechanisms of THSWT in the treatment of EH, we performed compounds, targets prediction and network analysis using a network pharmacology method. MATERIAL AND METHODS We selected chemical constituents and targets of THSWT according to TCMSP and UniProtKB databases and collected therapeutic targets on EH from Online Mendelian Inheritance in Man (OMIM), Drugbank and DisGeNET databases. The protein-protein interaction (PPI) was analyzed by using String database. Then network was constructed by using Cytoscape_v3.7.1, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment was performed by using Database for Annotation, Visualization and Integrated Discovery (DAVID) software. RESULTS The results of our network pharmacology research showed that the THSWT, composed of 6 Chinese herbs, contained 15 compounds, and 23 genes regulated the main signaling pathways related to EH. Moreover, the PPI network based on targets of THSWT on EH revealed the interaction relationship between targets. These core compounds were 6 of the 15 disease-related compounds in the network, kaempferol, quercetin, luteolin, Myricanone, beta-sitosterol, baicalein, and the core genes contained ADRB2, CALM1, HMOX1, JUN, PPARG, and VEGFA, which were regulated by more than 3 compounds and significantly associated with Calcium signaling pathway, cGMP-PKG signaling pathway, cAMP signaling pathway, PI3K-Akt signaling pathway, Rap1 signaling pathway, and Ras signaling pathway. CONCLUSIONS This network pharmacological study can reveal potential mechanisms of multi-target and multi-component THSWT in the treatment of EH, provide a scientific basis for studying the mechanism.

Abstract: Inactive ingredients and generally recognized as safe compounds are regarded by the US Food and Drug Administration (FDA) as benign for human consumption within specified dose ranges, but a growing body of research has revealed that many inactive ingredients might have unknown biological effects at these concentrations and might alter treatment outcomes. To speed up such discoveries, we apply state-of-the-art machine learning to delineate currently unknown biological effects of inactive ingredients-focusing on P-glycoprotein (P-gp) and uridine diphosphate-glucuronosyltransferase-2B7 (UGT2B7), two proteins that impact the pharmacokinetics of approximately 20% of FDA-approved drugs. Our platform identifies vitamin A palmitate and abietic acid as inhibitors of P-gp and UGT2B7, respectively; in silico, in vitro, ex vivo, and in vivo validations support these interactions. Our predictive framework can elucidate biological effects of commonly consumed chemical matter with implications on food- and excipient-drug interactions and functional drug formulation development.

Abstract: Human coronaviruses (HCoVs), including severe acute respiratory syndrome coronavirus (SARS-CoV) and 2019 novel coronavirus (2019-nCoV, also known as SARS-CoV-2), lead global epidemics with high morbidity and mortality. However, there are currently no effective drugs targeting 2019-nCoV/SARS-CoV-2. Drug repurposing, representing as an effective drug discovery strategy from existing drugs, could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we present an integrative, antiviral drug repurposing methodology implementing a systems pharmacology-based network medicine platform, quantifying the interplay between the HCoV-host interactome and drug targets in the human protein-protein interaction network. Phylogenetic analyses of 15 HCoV whole genomes reveal that 2019-nCoV/SARS-CoV-2 shares the highest nucleotide sequence identity with SARS-CoV (79.7%). Specifically, the envelope and nucleocapsid proteins of 2019-nCoV/SARS-CoV-2 are two evolutionarily conserved regions, having the sequence identities of 96% and 89.6%, respectively, compared to SARS-CoV. Using network proximity analyses of drug targets and HCoV-host interactions in the human interactome, we prioritize 16 potential anti-HCoV repurposable drugs (e.g., melatonin, mercaptopurine, and sirolimus) that are further validated by enrichment analyses of drug-gene signatures and HCoV-induced transcriptomics data in human cell lines. We further identify three potential drug combinations (e.g., sirolimus plus dactinomycin, mercaptopurine plus melatonin, and toremifene plus emodin) captured by the "Complementary Exposure" pattern: the targets of the drugs both hit the HCoV-host subnetwork, but target separate neighborhoods in the human interactome network. In summary, this study offers powerful network-based methodologies for rapid identification of candidate repurposable drugs and potential drug combinations targeting 2019-nCoV/SARS-CoV-2.

Abstract: BACKGROUND: The selection and prioritization of drug targets is a central problem in drug discovery. Computational approaches can leverage the growing number of large-scale human genomics and proteomics data to make in-silico target identification, reducing the cost and the time needed. RESULTS: We developed a machine learning approach to score proteins to generate a druggability score of novel targets. In our model we incorporated 70 protein features which included properties derived from the sequence, features characterizing protein functions as well as network properties derived from the protein-protein interaction network. The advantage of this approach is that it is unbiased and even less studied proteins with limited information about their function can score well as most of the features are independent of the accumulated literature. We build models on a training set which consist of targets with approved drugs and a negative set of non-drug targets. The machine learning techniques help to identify the most important combination of features differentiating validated targets from non-targets. We validated our predictions on an independent set of clinical trial drug targets, achieving a high accuracy characterized by an Area Under the Curve (AUC) of 0.89. Our most predictive features included biological function of proteins, network centrality measures, protein essentiality, tissue specificity, localization and solvent accessibility. Our predictions, based on a small set of 102 validated oncology targets, recovered the majority of known drug targets and identifies a novel set of proteins as drug target candidates. CONCLUSIONS: We developed a machine learning approach to prioritize proteins according to their similarity to approved drug targets. We have shown that the method proposed is highly predictive on a validation dataset consisting of 277 targets of clinical trial drug confirming that our computational approach is an efficient and cost-effective tool for drug target discovery and prioritization. Our predictions were based on oncology targets and cancer relevant biological functions, resulting in significantly higher scores for targets of oncology clinical trial drugs compared to the scores of targets of trial drugs for other indications. Our approach can be used to make indication specific drug-target prediction by combining generic druggability features with indication specific biological functions.

Abstract: Abundant life activities are maintained by various biomolecule relationships in human cells. However, many previous computational models only focus on isolated objects, without considering that cell is a complete entity with ample functions. Inspired by holism, we constructed a Molecular Associations Network (MAN) including 9 kinds of relationships among 5 types of biomolecules, and a prediction model called MAN-GF. More specifically, biomolecules can be represented as vectors by the algorithm called biomarker2vec which combines 2 kinds of information involved the attribute learned by k-mer, etc and the behavior learned by Graph Factorization (GF). Then, Random Forest classifier is applied for training, validation and test. MAN-GF obtained a substantial performance with AUC of 0.9647 and AUPR of 0.9521 under 5-fold Cross-validation. The results imply that MAN-GF with an overall perspective can act as ancillary for practice. Besides, it holds great hope to provide a new insight to elucidate the regulatory mechanisms.

Abstract: BACKGROUND: Alcohol use disorder (AUD) is one of the most common forms of substance use disorders with a strong contribution of genetic (50%-60%) and environmental factors. Genome-wide association studies (GWAS) have identified a number of AUD-associated variants, including those in alcohol metabolism genes. These genetic variants may modulate gene expression, making individuals more susceptible to AUD. A long-term alcohol consumption can also change the transcriptome patterns of subjects via epigenetic modulations. METHODS: To explore the interactive effect of genetic and epigenetic factors on AUD, we conducted a secondary analysis by integrating GWAS, CNV, brain transcriptome and DNA methylation data to unravel novel AUD-associated genes/variants. We applied the mega-analysis of OR (MegaOR) method to prioritise AUD candidate genes (AUDgenes). RESULTS: We identified a consensus set of 206 AUDgenes based on the multi-omics data. We demonstrated that these AUDgenes tend to interact with each other more frequent than chance expectation. Functional annotation analysis indicated that these AUDgenes were involved in substance dependence, synaptic transmission, glial cell proliferation and enriched in neuronal and liver cells. We obtained a multidimensional evidence that AUD is a polygenic disorder influenced by both genetic and epigenetic factors as well as the interaction of them. CONCLUSION: We characterised multidimensional evidence of genetic, epigenetic and transcriptomic data in AUD. We found that 206 AUD associated genes were highly expressed in liver, brain cerebellum, frontal cortex, hippocampus and pituitary. Our studies provides important insights into the molecular mechanism of AUD and potential target genes for AUD treatment.

Abstract: Tuberculosis (TB) is a leading cause of death worldwide and its impact has intensified due to the emergence of multi drug-resistant (MDR) and extensively drug-resistant (XDR) TB strains. Protein phosphorylation plays a vital role in the virulence of Mycobacterium tuberculosis (M.tb) mediated by protein kinases. Protein tyrosine phosphatase A (MptpA) undergoes phosphorylation by a unique tyrosine-specific kinase, protein tyrosine kinase A (PtkA), identified in the M.tb genome. PtkA phosphorylates PtpA on the tyrosine residues at positions 128 and 129, thereby increasing PtpA activity and promoting pathogenicity of MptpA. In the present study, we performed an extensive investigation of the conformational behavior of the intrinsically disordered domain (IDD) of PtkA using replica exchange molecular dynamics simulations. Long-term molecular dynamics (MD) simulations were performed to elucidate the role of IDD on the catalytic activity of kinase core domain (KCD) of PtkA. This was followed by identification of the probable inhibitors of PtkA using drug repurposing to block the PtpA-PtkA interaction. The inhibitory role of IDD on KCD has already been established; however, various analyses conducted in the present study showed that IDDPtkA had a greater inhibitory effect on the catalytic activity of KCDPtkA in the presence of the drugs esculin and inosine pranobex. The binding of drugs to PtkA resulted in formation of stable complexes, indicating that these two drugs are potentially useful as inhibitors of M.tb.