Abstract: UNLABELLED: Biological matter is organized in functional networks of different natures among which kinase-substrate and protein-protein interactions play an important role. Large public data collections allowed us to compile an important corpus of interaction data around human protein kinases. One of the most interesting observations analyzing this network is that coherence in kinase functional activity relies on kinase substrate interactions primarily and not on which protein complexes are formed around them. Further dissecting the two types of interactions at the level of kinase groups (CMGCs, Tyrosine kinases, etc.) we show a prevalence of intra-group interconnectivity, which we can naturally relate to current scenarios of evolution of biological networks. Tracking publication dates we observe high correlation of kinase interaction research focus with general kinase research. We find a similar bias in the targets of kinase inhibitors that feature high redundancy. Finally, intersecting kinase inhibitor specificity with sets of kinases located at specific positions in the kinase network, we propose alternative options for future therapeutic strategies using these compounds. BIOLOGICAL SIGNIFICANCE: Despite its importance for cellular regulation and the fact that protein kinases feature prominent targets of modern therapeutic approaches, the structure and logic of the global, integrated protein phosphorylation network have not been investigated intensively. To focus on the regulatory skeleton of the phosphorylation network, we contemplated a network consisting of kinases, their substrates, and publicly available physical protein interactions. Analysis of this network at multiple levels allowed establishing a series of interesting properties such as prevalence of kinase substrate interactions as opposed to general protein-protein interactions for establishing a holistic control over kinases activities. Kinases controlling many or a few only other kinases, in addition to non-kinases, were distributed in cellular compartments differently. They were also targeted by kinase inhibitors with distinct success rates. Non-kinases tightly regulated by a large number of kinases were involved in biological processes both specific and shared with their regulators while being preferably localized in the nucleus. Collectively, these observations may provide for a new perspective in the elaboration of pharmacological intervention strategies. We complemented our study of kinase interactions with a perspective of how this type of data is generated in comparison with general research about those enzymes. Namely, what was the temporal evolution of the research community attention for interaction versus non-interaction-based kinase experiments. This article is part of a Special Issue entitled: "20years of Proteomics" in memory of Viatliano Pallini" Guest Editors: Luca Bini, Juan J. Calvete, Natacha Turck, Denis Hochstrasser and Jean-Charles Sanchez.

Abstract: Drug-drug interactions (DDIs) may cause serious side-effects that draw great attention from both academia and industry. Since some DDIs are mediated by unexpected drug-human protein interactions, it is reasonable to analyze the chemical-protein interactome (CPI) profiles of the drugs to predict their DDIs. Here we introduce the DDI-CPI server, which can make real-time DDI predictions based only on molecular structure. When the user submits a molecule, the server will dock user's molecule across 611 human proteins, generating a CPI profile that can be used as a feature vector for the pre-constructed prediction model. It can suggest potential DDIs between the user's molecule and our library of 2515 drug molecules. In cross-validation and independent validation, the server achieved an AUC greater than 0.85. Additionally, by investigating the CPI profiles of predicted DDI, users can explore the PK/PD proteins that might be involved in a particular DDI. A 3D visualization of the drug-protein interaction will be provided as well. The DDI-CPI is freely accessible at http://cpi.bio-x.cn/ddi/.

Abstract: We sought a new approach to treating infections by intracellular bacteria, namely, by altering host cell functions that support their growth. We screened a library of 640 Food and Drug Administration (FDA)-approved compounds for agents that render THP-1 cells resistant to infection by four intracellular pathogens. We identified numerous drugs that are not antibiotics but were highly effective in inhibiting intracellular bacterial growth with limited toxicity to host cells. These compounds are likely to target three kinds of host functions: (i) G protein-coupled receptors, (ii) intracellular calcium signals, and (iii) membrane cholesterol distribution. The compounds that targeted G protein receptor signaling and calcium fluxes broadly inhibited Coxiella burnetii, Legionella pneumophila, Brucella abortus, and Rickettsia conorii, while those directed against cholesterol traffic strongly attenuated the intracellular growth of C. burnetii and L. pneumophila. These pathways probably support intracellular pathogen growth so that drugs that perturb them may be therapeutic candidates. Combining host- and pathogen-directed treatments is a strategy to decrease the emergence of drug-resistant intracellular bacterial pathogens. Importance: Although antibiotic treatment is often successful, it is becoming clear that alternatives to conventional pathogen-directed therapy must be developed in the face of increasing antibiotic resistance. Moreover, the costs and timing associated with the development of novel antimicrobials make repurposed FDA-approved drugs attractive host-targeted therapeutics. This paper describes a novel approach of identifying such host-targeted therapeutics against intracellular bacterial pathogens. We identified several FDA-approved drugs that inhibit the growth of intracellular bacteria, thereby implicating host intracellular pathways presumably utilized by bacteria during infection.

Abstract: UNLABELLED: The chemical structures of biomolecules, whether naturally occurring or synthetic, are composed of functionally important building blocks. Given a set of small molecules-for example, those known to bind a particular protein-computationally decomposing them into chemically meaningful fragments can help elucidate their functional properties, and may be useful for designing novel compounds with similar properties. Here we introduce molBLOCKS, a suite of programs for breaking down sets of small molecules into fragments according to a predefined set of chemical rules, clustering the resulting fragments, and uncovering statistically enriched fragments. Among other applications, our software should be a great aid in large-scale chemical analysis of ligands binding specific targets of interest. AVAILABILITY AND IMPLEMENTATION: molBLOCKS is available as GPL C++ source code at http://compbio.cs.princeton.edu/molblocks.

Abstract: MOTIVATION: Somatic homozygous deletions of chromosomal regions in cancer, while not necessarily oncogenic, may lead to therapeutic vulnerabilities specific to cancer cells compared with normal cells. A recently reported example is the loss of one of the two isoenzymes in glioblastoma cancer cells such that the use of a specific inhibitor selectively inhibited growth of the cancer cells, which had become fully dependent on the second isoenzyme. We have now made use of the unprecedented conjunction of large-scale cancer genomics profiling of tumor samples in The Cancer Genome Atlas (TCGA) and of tumor-derived cell lines in the Cancer Cell Line Encyclopedia, as well as the availability of integrated pathway information systems, such as Pathway Commons, to systematically search for a comprehensive set of such epistatic vulnerabilities. RESULTS: Based on homozygous deletions affecting metabolic enzymes in 16 TCGA cancer studies and 972 cancer cell lines, we identified 4104 candidate metabolic vulnerabilities present in 1019 tumor samples and 482 cell lines. Up to 44% of these vulnerabilities can be targeted with at least one Food and Drug Administration-approved drug. We suggest focused experiments to test these vulnerabilities and clinical trials based on personalized genomic profiles of those that pass preclinical filters. We conclude that genomic profiling will in the future provide a promising basis for network pharmacology of epistatic vulnerabilities as a promising therapeutic strategy. AVAILABILITY AND IMPLEMENTATION: A web-based tool for exploring all vulnerabilities and their details is available at http://cbio.mskcc.org/cancergenomics/statius/ along with supplemental data files.

Abstract: There are significant gaps in our understanding of the pathways by which drugs act. This incomplete knowledge limits our ability to use mechanistic molecular information rationally to repurpose drugs, understand their side effects, and predict their interactions with other drugs. Here, we present DrugRouter, a novel method for generating drug-specific pathways of action by linking target genes, disease genes, and pharmacogenes using gene interaction networks. We construct pathways for more than a hundred drugs and show that the genes included in our pathways (i) co-occur with the query drug in the literature, (ii) significantly overlap or are adjacent to known drug-response pathways, and (iii) are adjacent to genes that are hits in genome-wide association studies assessing drug response. Finally, these computed pathways suggest novel drug-repositioning opportunities (e.g., statins for follicular thyroid cancer), gene-side effect associations, and gene-drug interactions. Thus, DrugRouter generates hypotheses about drug actions using systems biology data.

Abstract: In this study we report on potential drug-drug interactions between drugs occurring in patient clinical data. Results are based on relationships in SemMedDB, a database of structured knowledge extracted from all MEDLINE citations (titles and abstracts) using SemRep. The core of our methodology is to construct two potential drug-drug interaction schemas, based on relationships extracted from SemMedDB. In the first schema, Drug1 and Drug2 interact through Drug1's effect on some gene, which in turn affects Drug2. In the second, Drug1 affects Gene1, while Drug2 affects Gene2. Gene1 and Gene2, together, then have an effect on some biological function. After checking each drug pair from the medication lists of each of 22 patients, we found 19 known and 62 unknown drug-drug interactions using both schemas. For example, our results suggest that the interaction of Lisinopril, an ACE inhibitor commonly prescribed for hypertension, and the antidepressant sertraline can potentially increase the likelihood and possibly the severity of psoriasis. We also assessed the relationships extracted by SemRep from a linguistic perspective and found that the precision of SemRep was 0.58 for 300 randomly selected sentences from MEDLINE. Our study demonstrates that the use of structured knowledge in the form of relationships from the biomedical literature can support the discovery of potential drug-drug interactions occurring in patient clinical data. Moreover, SemMedDB provides a good knowledge resource for expanding the range of drugs, genes, and biological functions considered as elements in various drug-drug interaction pathways.

Abstract: Recent industry-academic partnerships involve collaboration among disciplines, locations, and organizations using publicly funded "open-access" and proprietary commercial data sources. These require the effective integration of chemical and biological information from diverse data sources, which presents key informatics, personnel, and organizational challenges. The BioAssay Research Database (BARD) was conceived to address these challenges and serve as a community-wide resource and intuitive web portal for public-sector chemical-biology data. Its initial focus is to enable scientists to more effectively use the National Institutes of Health Roadmap Molecular Libraries Program (MLP) data generated from the 3-year pilot and 6-year production phases of the Molecular Libraries Probe Production Centers Network (MLPCN), which is currently in its final year. BARD evolves the current data standards through structured assay and result annotations that leverage BioAssay Ontology and other industry-standard ontologies, and a core hierarchy of assay definition terms and data standards defined specifically for small-molecule assay data. We initially focused on migrating the highest-value MLP data into BARD and bringing it up to this new standard. We review the technical and organizational challenges overcome by the interdisciplinary BARD team, veterans of public- and private-sector data-integration projects, who are collaborating to describe (functional specifications), design (technical specifications), and implement this next-generation software solution.

Abstract: The explosive global spreading of multidrug resistant Mycobacterium tuberculosis (Mtb) has provoked an urgent need to discover novel anti-TB agents. Enoyl-acyl carrier protein reductase from Mtb is a well-known and thoroughly studied target for anti-tuberculosis therapy. In the present analysis, virtual screening techniques performed from Drug bank database by utilizing INH-NAD adduct as query for the discovery of potent anti-TB agents. About 100 molecules sharing similar scaffold with INH-NAD adduct were analyzed for their binding effectiveness. The initial screening based on number of rotatable bonds gave 42 hit molecules. Subsequently, physiochemical properties such as toxicity, solubility, drug-likeness and drug score were analyzed for the filtered set of compounds. Final data reduction was performed by means of molecular docking and normal mode docking analysis. The result indicates that DB04362, adenosine diphosphate 5-(beta-ethyl)-4-methyl-thiazole-2-carboxylic acid could be a promising lead compound and be effective in treating sensitive as well as drug-resistant strains of Mtb. We believe that this novel scaffolds might be the good starting point for lead compounds and certainly aid the experimental designing of anti-tuberculosis drug in a short time.

Abstract: Due to the diverse medicinal effects, polyphenols are among the most intensively studied natural products. However, it is a great challenge to elucidate the polypharmacological mechanisms of polyphenols. To address this challenge, we establish a method for identifying multiple targets of chemical agents through analyzing the module profiles of gene expression upon chemical treatments. By using FABIA algorithm, we have performed a biclustering analysis of gene expression profiles derived from Connectivity Map (cMap), and clustered the profiles into 49 gene modules. This allowed us to define a 49 dimensional binary vector to characterize the gene module profiles, by which we can compare the expression profiles for each pair of chemical agents with Tanimoto coefficient. For the agent pairs with similar gene expression profiles, we can predict the target of one agent from the other. Drug target enrichment analysis indicated that this method is efficient to predict the multiple targets of chemical agents. By using this method, we identify 148 targets for 20 polyphenols derived from cMap. A large part of the targets are validated by experimental observations. The results show that the medicinal effects of polyphenols are far beyond their well-known antioxidant activities. This method is also applicable to dissect the polypharmacology of other natural products.