Abstract: Existing drug discovery processes follow a reductionist model of "one-drug-one-gene-one-disease," which is inadequate to tackle complex diseases involving multiple malfunctioned genes. The availability of big omics data offers opportunities to transform drug discovery process into a new paradigm of systems pharmacology that focuses on designing drugs to target molecular interaction networks instead of a single gene. Here, we develop a reliable multi-rank, multi-layered recommender system, ANTENNA, to mine large-scale chemical genomics and disease association data for prediction of novel drug-gene-disease associations. ANTENNA integrates a novel tri-factorization based dual-regularized weighted and imputed One Class Collaborative Filtering (OCCF) algorithm, tREMAP, with a statistical framework based on Random Walk with Restart and assess the reliability of specific predictions. In the benchmark, tREMAP clearly outperforms the single-rank OCCF. We apply ANTENNA to a real-world problem: repurposing old drugs for new clinical indications without effective treatments. We discover that FDA-approved drug diazoxide can inhibit multiple kinase genes responsible for many diseases including cancer and kill triple negative breast cancer (TNBC) cells efficiently [Formula: see text]. TNBC is a deadly disease without effective targeted therapies. Our finding demonstrates the power of big data analytics in drug discovery and developing a targeted therapy for TNBC.

Abstract: BACKGROUND: Cancer genes tend to be highly mutated under positive selection. Better understanding the recurrently mutated genes (RMGs) in cancer is critical for explicating the mechanisms of tumorigenesis and providing vital clues for therapy. Although some studies have investigated functional impacts of RMGs in specific cancer types, a comprehensive analysis of RMGs and their mutational impacts across cancers is still needed. METHODS: We obtained data from The Cancer Genome Atlas (TCGA) and calculated mutation rate of each gene in 31 cancer types. Functional analysis was performed to identify the important signaling pathways and enriched protein types of RMGs. In order to evaluate functional impacts of RMGs, differential expression, survival, and pairwise mutation patterns analyses were performed. RESULTS: Totally, we identified 897 RMGs and 624 of them were specifically mutant in only a single cancer type. Functional analysis demonstrated that these RMGs were enriched in hydrolases, cytoskeletal protein, and pathways like MAPK, cell cycle, PI3K-Akt, ECM receptor interaction, and energy metabolism. The differentially expressed genes potentially affected by the same common RMG showed a relatively low overlap across different cancer types. For the 19 Mucin (MUC) family genes, nine of them were RMGs and four of them (MUC17, MUC5B, MUC4, and MUC16) were common RMGs shared in 8 to 17 cancer types. The results showed that recurrent mutations in MUC genes were significantly associated with better survival prognosis. Only a small part of RMGs was differentially expressed due to their own mutations and most of them were downregulated. In addition, pairwise mutation pattern analysis revealed the high frequency of co-occurred mutations among RMGs in STAD. CONCLUSION: Through the functional analysis of RMGs, we found that six signaling pathways were disrupted in most cancer types and that energy metabolism was abnormal in tumors. The results also revealed a strong correlation between recurrently mutated genes from MUC family and human survival. In addition, gene expression and survival prognosis were associated with different mutation types of RMGs.

Abstract: Here, we propose an in silico fragment-mapping method as a potential tool for fragment-based/structure-based drug discovery (FBDD/SBDD). For this method, we created a database named Canonical Subsite-Fragment DataBase (CSFDB) and developed a knowledge-based fragment-mapping program, Fsubsite. CSFDB consists of various pairs of subsite-fragments derived from X-ray crystal structures of known protein-ligand complexes. Using three-dimensional similarity-matching between subsites on one protein and another, Fsubsite compares the surface of a target protein with all subsites in CSFDB. When a local topography similar to the subsite is found on the surface, Fsubsite places a fragment combined with the subsite in CSFDB on the target protein. For validation purposes, we applied the method to the apo-structure of cyclin-dependent kinase 2 (CDK2) and identified four compounds containing three mapped fragments that existed in the list of known inhibitors of CDK2. Next, the utility of our fragment-mapping method for fragment-growing was examined on the complex structure of tRNA-guanine transglycosylase with a small ligand. Fsubsite mapped appropriate fragments on the same position as the binding ligand or in the vicinity of the ligand. Finally, a 3D-pharmacophore model was constructed from the fragments mapped on the apo-structure of heat shock protein 90-alpha (HSP90alpha). Then, 3D pharmacophore-based virtual screening was carried out using a commercially available compound database. The resultant hit compounds were very similar to a known ligand of HSP90alpha. As a result of these findings, this in silico fragment-mapping method seems to be a useful tool for computational FBDD and SBDD.

Abstract: Drug metabolites (DMs) are critical in pharmacology research areas, such as drug metabolism pathways and drug-drug interactions. However, there is no terminology dictionary containing comprehensive drug metabolite names, and there is no named entity recognition (NER) algorithm focusing on drug metabolite identification. In this article, we developed a novel NER system, DrugMetab, to identify DMs from the PubMed abstracts. DrugMetab utilizes the features characterized from the Part-of-Speech, drug index, and pre/suffix, and determines DMs within context. To evaluate the performance, a gold-standard corpus was manually constructed. In this task, DrugMetab with sequential minimal optimization (SMO) classifier achieves 0.89 precision, 0.77 recall, and 0.83 F-measure in the internal testing set; and 0.86 precision, 0.85 recall, and 0.86 F-measure in the external validation set. We further compared the performance between DrugMetab and whatizitChemical, which was designed for identifying small molecules or chemical entities. DrugMetab outperformed whatizitChemical, which had a lower recall rate of 0.65.

Abstract: The bipartite network representation of the drug-target interactions (DTIs) in a biosystem enhances understanding of the drugs' multifaceted action modes, suggests therapeutic switching for approved drugs and unveils possible side effects. As experimental testing of DTIs is costly and time-consuming, computational predictors are of great aid. Here, for the first time, state-of-the-art DTI supervised predictors custom-made in network biology were compared-using standard and innovative validation frameworks-with unsupervised pure topological-based models designed for general-purpose link prediction in bipartite networks. Surprisingly, our results show that the bipartite topology alone, if adequately exploited by means of the recently proposed local-community-paradigm (LCP) theory-initially detected in brain-network topological self-organization and afterwards generalized to any complex network-is able to suggest highly reliable predictions, with comparable performance with the state-of-the-art-supervised methods that exploit additional (non-topological, for instance biochemical) DTI knowledge. Furthermore, a detailed analysis of the novel predictions revealed that each class of methods prioritizes distinct true interactions; hence, combining methodologies based on diverse principles represents a promising strategy to improve drug-target discovery. To conclude, this study promotes the power of bio-inspired computing, demonstrating that simple unsupervised rules inspired by principles of topological self-organization and adaptiveness arising during learning in living intelligent systems (like the brain) can efficiently equal perform complicated algorithms based on advanced, supervised and knowledge-based engineering.

Abstract: BACKGROUND: Human schistosomiasis is a neglected tropical disease caused by parasitic worms of the genus Schistosoma that still affects some 200 million people. The mainstay of schistosomiasis control is a single drug, praziquantel. The reliance on this drug carries a risk of resistance emerging to this anthelmintic, such that research towards alternative anti-schistosomal drugs is warranted. In this context, a number of studies have employed computational approaches to prioritise proteins for investigation as drug targets, based on extensive genomic, transcriptomic and small-molecule data now available. METHODS: Here, we established a customisable, online application for the prioritisation of drug targets and applied it, for the first time, to the entire inferred proteome of S. haematobium. This application enables selection of weighted and ranked proteins representing potential drug targets, and integrates transcriptional data, orthology and gene essentiality information as well as drug-drug target associations and chemical properties of predicted ligands. RESULTS: Using this application, we defined 25 potential drug targets in S. haematobium that associated with approved drugs, and 3402 targets that (although they could not be linked to any compounds) are conserved among a range of socioeconomically important flatworm species and might represent targets for new trematocides. CONCLUSIONS: The online application developed here represents an interactive, customisable, expandable and reproducible drug target ranking and prioritisation approach that should be useful for the prediction of drug targets in schistosomes and other species of parasitic worms in the future. We have demonstrated the utility of this online application by predicting potential drug targets in S. haematobium that can now be evaluated using functional genomics tools and/or small molecules, to establish whether they are indeed essential for parasite survival, and to assist in the discovery of novel anti-schistosomal compounds.

Abstract: Computational approaches for predicting protein-ligand interactions can facilitate drug lead discovery and drug target determination. We have previously developed a threading/structural-based approach, FINDSITE(comb), for the virtual ligand screening of proteins that has been extensively experimentally validated. Even when low resolution predicted protein structures are employed, FINDSITE(comb) has the advantage of being faster and more accurate than traditional high-resolution structure-based docking methods. It also overcomes the limitations of traditional QSAR methods that require a known set of seed ligands that bind to the given protein target. Here, we further improve FINDSITE(comb) by enhancing its template ligand selection from the PDB/DrugBank/ChEMBL libraries of known protein-ligand interactions by (1) parsing the template proteins and their corresponding binding ligands in the DrugBank and ChEMBL libraries into domains so that the ligands with falsely matched domains to the targets will not be selected as template ligands; (2) applying various thresholds to filter out falsely matched template structures in the structure comparison process and thus their corresponding ligands for template ligand selection. With a sequence identity cutoff of 30% of target to templates and modeled target structures, FINDSITE(comb2.0) is shown to significantly improve upon FINDSITE(comb) on the DUD-E benchmark set by increasing the 1% enrichment factor from 16.7 to 22.1, with a p-value of 4.3 x 10(-3) by the Student t-test. With an 80% sequence identity cutoff of target to templates for the DUD-E set and modeled target structures, FINDSITE(comb2.0), having a 1% ROC enrichment factor of 52.39, also outperforms state-of-the-art methods that employ machine learning such as a deep convolutional neural network, CNN, with an enrichment of 29.65. Thus, FINDSITE(comb2.0) represents a significant improvement in the state-of-the-art. The FINDSITE(comb2.0) web service is freely available for academic users at http://pwp.gatech.edu/cssb/FINDSITE-COMB-2 .

Abstract: We present a novel approach for the molecular transformation and analysis of patient clinical phenotypes. Building on the fact that drugs perturb the function of targets/genes, we integrated data from 8.2 million clinical reports detailing drug-induced side effects with the molecular world of drug-target information. Using this dataset, we extracted 1.8 million associations of clinical phenotypes to 770 human drug-targets. This collection is perhaps the largest phenotypic profiling reference of human targets to-date, and unique in that it enables rapid development of testable molecular hypotheses directly from human-specific information. We also present validation results demonstrating analytical utilities of the approach, including drug safety prediction, and the design of novel combination therapies. Challenging the long-standing notion that molecular perturbation studies cannot be performed in humans, our data allows researchers to capitalize on the vast tomes of clinical information available throughout the healthcare system.

Abstract: Health-strengthening (Fu-Zheng) herbs is a representative type of traditional Chinese medicine (TCM) widely used for cancer treatment in China, which is in contrast to pathogen eliminating (Qu-Xie) herbs. However, the commonness in the biological basis of health-strengthening herbs remains to be holistically elucidated. In this study, an innovative high-throughput research strategy integrating computational and experimental methods of network pharmacology was proposed, and 22 health-strengthening herbs were selected for the investigation. Additionally, 25 pathogen-eliminating herbs were included for comparison. First, based on network-based, large-scale target prediction, we analyzed the target profiles of 1446 TCM compounds. Next, the actions of 166 compounds on 420 antitumor or immune-related genes were measured using a unique high-throughput screening strategy by high-throughput sequencing, referred to as HTS(2). Furthermore, the structural information and the antitumor activity of the compounds in health-strengthening and pathogen-eliminating herbs were compared. Using network pharmacology analysis, we discovered that: (1) Functionally, the predicted targets of compounds from health strengthening herbs were enriched in both immune-related and antitumor pathways, similar to those of pathogen eliminating herbs. As a case study, galloylpaeoniflorin, a compound in a health strengthening herb Radix Paeoniae Alba (Bai Shao), was found to exert antitumor effects both in vivo and in vitro. Yet the inhibitory effects of the compounds from pathogen eliminating herbs on tumor cells proliferation as a whole were significantly stronger than those in health-strengthening herbs (p < 0.001). Moreover, the percentage of assay compounds in health-strengthening herbs with the predicted targets enriched in the immune-related pathways (e.g., natural killer cell mediated cytotoxicity and antigen processing and presentation) were significantly higher than that in pathogen-eliminating herbs (p < 0.05). This finding was supported by the immune-enhancing effects of a group of compounds from health-strengthening herbs indicated by differentially expressed genes in the HTS(2) results. (2) Compounds in the same herb may exhibit the same or distinguished mechanisms in cancer treatment, which was demonstrated as the compounds influence pathway gene expressions in the same or opposite directions. For example, acetyl ursolic acid and specnuezhenide in a health-strengthening herb Fructus Ligustri lucidi (Nv Zhen Zi) both upregulated gene expressions in T cell receptor signaling pathway. Together, this study suggested greater potentials in tumor immune microenvironment regulation and tumor prevention than in direct killing tumor cells of health-strengthening herbs generally, and provided a systematic strategy for unveiling the commonness in the biological basis of health-strengthening herbs in cancer treatment.