Abstract: BACKGROUND: Despite increased investment in pharmaceutical research and development, fewer and fewer new drugs are entering the marketplace. This has prompted studies in repurposing existing drugs for use against diseases with unmet medical needs. A popular approach is to develop a classification model based on drugs with and without a desired therapeutic effect. For this approach to be statistically sound, it requires a large number of drugs in both classes. However, given few or no approved drugs for the diseases of highest medical urgency and interest, different strategies need to be investigated. RESULTS: We developed a computational method termed "drug-protein interaction-based repurposing" (DPIR) that is potentially applicable to diseases with very few approved drugs. The method, based on genome-wide drug-protein interaction information and Bayesian statistics, first identifies drug-protein interactions associated with a desired therapeutic effect. Then, it uses key drug-protein interactions to score other drugs for their potential to have the same therapeutic effect. CONCLUSIONS: Detailed cross-validation studies using United States Food and Drug Administration-approved drugs for hypertension, human immunodeficiency virus, and malaria indicated that DPIR provides robust predictions. It achieves high levels of enrichment of drugs approved for a disease even with models developed based on a single drug known to treat the disease. Analysis of our model predictions also indicated that the method is potentially useful for understanding molecular mechanisms of drug action and for identifying protein targets that may potentiate the desired therapeutic effects of other drugs (combination therapies).

Abstract: The human kinome is gaining importance through its promising cancer therapeutic targets, yet no general model to address the kinase inhibitor resistance has emerged. Here, we constructed a systems biology-based framework to catalogue the human kinome, including 538 kinase genes, in the broader context of the human interactome. Specifically, we constructed three networks: a kinase-substrate interaction network containing 7,346 pairs connecting 379 kinases to 36,576 phosphorylation sites in 1,961 substrates, a protein-protein interaction network (PPIN) containing 92,699 pairs, and an atomic resolution PPIN containing 4,278 pairs. We identified the conserved regulatory phosphorylation motifs (e.g., Ser/Thr-Pro) using a sequence logo analysis. We found the typical anticancer target selection strategy that uses network hubs as drug targets, might lead to a high adverse drug reaction risk. Furthermore, we found the distinct network centrality of kinases creates a high anticancer drug resistance risk by feedback or crosstalk mechanisms within cellular networks. This notion is supported by the systematic network and pathway analyses that anticancer drug resistance genes are significantly enriched as hubs and heavily participate in multiple signaling pathways. Collectively, this comprehensive human kinome interactome map sheds light on anticancer drug resistance mechanisms and provides an innovative resource for rational kinase inhibitor design.

Abstract: Recent work demonstrates that the MmpL (mycobacterial membrane protein large) transporters are dedicated to the export of mycobacterial lipids for cell wall biosynthesis. An MmpL transporter frequently works with an accessory protein, belonging to the MmpS (mycobacterial membrane protein small) family, to transport these key virulence factors. One such efflux system in Mycobacterium tuberculosis is the MmpS5-MmpL5 transporter. The expression of MmpS5-MmpL5 is controlled by the MarR-like transcriptional regulator Rv0678, whose open reading frame is located downstream of the mmpS5-mmpL5 operon. To elucidate the structural basis of Rv0678 regulation, we have determined the crystal structure of this regulator, to 1.64 A resolution, revealing a dimeric two-domain molecule with an architecture similar to members of the MarR family of transcriptional regulators. Rv0678 is distinct from other MarR regulators in that its DNA-binding and dimerization domains are clustered together. These two domains seemingly cooperate to bind an inducing ligand that we identified as 2-stearoylglycerol, which is a fatty acid glycerol ester. The structure also suggests that the conformational change leading to substrate-mediated derepression is primarily caused by a rigid body rotational motion of the entire DNA-binding domain of the regulator toward the dimerization domain. This movement results in a conformational state that is incompatible with DNA binding. We demonstrate using electrophoretic mobility shift assays that Rv0678 binds to the mmpS5-mmpL5, mmpS4-mmpL4, and the mmpS2-mmpL2 promoters. Binding by Rv0678 was reversed upon the addition of the ligand. These findings provide new insight into the mechanisms of gene regulation in the MarR family of regulators.

Abstract: Recycling old drugs, rescuing shelved drugs and extending patents' lives make drug repositioning an attractive form of drug discovery. Drug repositioning accounts for approximately 30% of the newly US Food and Drug Administration (FDA)-approved drugs and vaccines in recent years. The prevalence of drug-repositioning studies has resulted in a variety of innovative computational methods for the identification of new opportunities for the use of old drugs. Questions often arise from customizing or optimizing these methods into efficient drug-repositioning pipelines for alternative applications. It requires a comprehensive understanding of the available methods gained by evaluating both biological and pharmaceutical knowledge and the elucidated mechanism-of-action of drugs. Here, we provide guidance for prioritizing and integrating drug-repositioning methods for specific drug-repositioning pipelines.

Abstract: BACKGROUND: Epidemiological studies in the recent years have investigated the relationship between dietary habits and disease risk demonstrating that diet has a direct effect on public health. Especially plant-based diets -fruits, vegetables and herbs- are known as a source of molecules with pharmacological properties for treatment of several malignancies. Unquestionably, for developing specific intervention strategies to reduce cancer risk there is a need for a more extensive and holistic examination of the dietary components for exploring the mechanisms of action and understanding the nutrient-nutrient interactions. Here, we used colon cancer as a proof-of-concept for understanding key regulatory sites of diet on the disease pathway. RESULTS: We started from a unique vantage point by having a database of 158 plants positively associated to colon cancer reduction and their molecular composition (~3,500 unique compounds). We generated a comprehensive picture of the interaction profile of these edible and non-edible plants with a predefined candidate colon cancer target space consisting of ~1,900 proteins. This knowledge allowed us to study systematically the key components in colon cancer that are targeted synergistically by phytochemicals and identify statistically significant and highly correlated protein networks that could be perturbed by dietary habits. CONCLUSION: We propose here a framework for interrogating the critical targets in colon cancer processes and identifying plant-based dietary interventions as important modifiers using a systems chemical biology approach. Our methodology for better delineating prevention of colon cancer by nutritional interventions relies heavily on the availability of information about the small molecule constituents of our diet and it can be expanded to any other disease class that previous evidence has linked to lifestyle.

Abstract: Synthetic biology has been heralded as a new bioengineering platform for the production of bulk and specialty chemicals, drugs, and fuels. Here, we report for the first time a series of 74 novel compounds produced using a combinatorial genetics approach in baker's yeast. Based on the concept of "coevolution" with target proteins in an intracellular primary survival assay, the identified, mostly scaffold-sized (200-350 MW) compounds, which displayed excellent biological activity, can be considered as prevalidated hits. Of the molecules found, >75% have not been described previously; 20% of the compounds exhibit novel scaffolds. Their structural and physicochemical properties comply with established rules of drug- and fragment-likeness and exhibit increased structural complexities compared to synthetically produced fragments. In summary, the synthetic biology approach described here represents a completely new, complementary strategy for hit and early lead identification that can be easily integrated into the existing drug discovery process.

Abstract: MOTIVATION: Combinatorial therapies play increasingly important roles in combating complex diseases. Owing to the huge cost associated with experimental methods in identifying optimal drug combinations, computational approaches can provide a guide to limit the search space and reduce cost. However, few computational approaches have been developed for this purpose, and thus there is a great need of new algorithms for drug combination prediction. RESULTS: Here we proposed to formulate the optimal combinatorial therapy problem into two complementary mathematical algorithms, Balanced Target Set Cover (BTSC) and Minimum Off-Target Set Cover (MOTSC). Given a disease gene set, BTSC seeks a balanced solution that maximizes the coverage on the disease genes and minimizes the off-target hits at the same time. MOTSC seeks a full coverage on the disease gene set while minimizing the off-target set. Through simulation, both BTSC and MOTSC demonstrated a much faster running time over exhaustive search with the same accuracy. When applied to real disease gene sets, our algorithms not only identified known drug combinations, but also predicted novel drug combinations that are worth further testing. In addition, we developed a web-based tool to allow users to iteratively search for optimal drug combinations given a user-defined gene set. AVAILABILITY: Our tool is freely available for noncommercial use at http://www.drug.liuzlab.org/. CONTACT: zhandong.liu@bcm.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract: A systems pharmacology approach was undertaken to define and identify the proteins/genes significantly associated with clinical incidence and severity of drug-induced peripheral neuropathy (DIPN). Pharmacological networks of 234 DIPN drugs, their known targets (both intended and unintended), and the intermediator proteins/genes interacting with these drugs via their known targets were examined. A permutation test identified 230 DIPN-associated intermediators that were enriched with apoptosis and stress response genes. Neuropathy incidence and severity were curated from drug labels and literature and were used to build a predictive model of DIPN using a regression tree algorithm, based on the drug targets and their intermediators. DIPN drugs whose targets interacted with both v-myc avian myelocytomatosis viral oncogene homolog (MYC) and proliferating cell nuclear antigen-associated factor (PAF15) were associated with a neuropathy incidence of 38.1%, whereas drugs interacting only with MYC had an incidence of 2.9%. These results warrant further investigation in order to develop a predictive tool for the DIPN potential of a new drug.

Abstract: The minimum information about a biofilm experiment (MIABiE) initiative has arisen from the need to find an adequate and scientifically sound way to control the quality of the documentation accompanying the public deposition of biofilm-related data, particularly those obtained using high-throughput devices and techniques. Thereby, the MIABiE consortium has initiated the identification and organization of a set of modules containing the minimum information that needs to be reported to guarantee the interpretability and independent verification of experimental results and their integration with knowledge coming from other fields. MIABiE does not intend to propose specific standards on how biofilms experiments should be performed, because it is acknowledged that specific research questions require specific conditions which may deviate from any standardization. Instead, MIABiE presents guidelines about the data to be recorded and published in order for the procedure and results to be easily and unequivocally interpreted and reproduced. Overall, MIABiE opens up the discussion about a number of particular areas of interest and attempts to achieve a broad consensus about which biofilm data and metadata should be reported in scientific journals in a systematic, rigorous and understandable manner.

Abstract: Using small, flat aromatic rings as components of fragments or molecules is a common practice in fragment-based drug discovery and lead optimization. With an increasing focus on the exploration of novel biological and chemical space, and their improved synthetic accessibility, 3D fragments are attracting increasing interest. This study presents a detailed analysis of 3D and 2D ring fragments in marketed drugs. Several measures of properties were used, such as the type of ring assemblies and molecular shapes. The study also took into account the relationship between protein classes targeted by each ring fragment, providing target-specific information. The analysis shows the high structural and shape diversity of 3D ring systems and their importance in bioactive compounds. Major differences in 2D and 3D fragments are apparent in ligands that bind to the major drug targets such as GPCRs, ion channels, and enzymes.