Abstract: Crystal structures of protein-ligand complexes are often used to infer biology and inform structure-based drug discovery. Hence, it is important to build accurate, reliable models of ligands that give confidence in the interpretation of the respective protein-ligand complex. This paper discusses key stages in the ligand-fitting process, including ligand binding-site identification, ligand description and conformer generation, ligand fitting, refinement and subsequent validation. The CCP4 suite contains a number of software tools that facilitate this task: AceDRG for the creation of ligand descriptions and conformers, Lidia and JLigand for two-dimensional and three-dimensional ligand editing and visual analysis, Coot for density interpretation, ligand fitting, analysis and validation, and REFMAC5 for macromolecular refinement. In addition to recent advancements in automatic carbohydrate building in Coot (LO/Carb) and ligand-validation tools (FLEV), the release of the CCP4i2 GUI provides an integrated solution that streamlines the ligand-fitting workflow, seamlessly passing results from one program to the next. The ligand-fitting process is illustrated using instructive practical examples, including problematic cases such as post-translational modifications, highlighting the need for careful analysis and rigorous validation.

Abstract: Viruses are obligatory intracellular pathogens and completely depend on their hosts for survival and reproduction. The strategies adopted by viruses to exploit host cell processes and to evade host immune systems during infections may differ largely with the type of the viral genetic material. An improved understanding of these viral infection mechanisms is only possible through a better understanding of the pathogen-host interactions (PHIs) that enable viruses to enter into the host cells and manipulate the cellular mechanisms to their own advantage. Experimentally-verified protein-protein interaction (PPI) data of pathogen-host systems only became available at large scale within the last decade. In this study, we comparatively analyzed the current PHI networks belonging to DNA and RNA viruses and their human host, to get insights into the infection strategies used by these viral groups. We investigated the functional properties of human proteins in the PHI networks, to observe and compare the attack strategies of DNA and RNA viruses. We observed that DNA viruses are able to attack both human cellular and metabolic processes simultaneously during infections. On the other hand, RNA viruses preferentially interact with human proteins functioning in specific cellular processes as well as in intracellular transport and localization within the cell. Observing virus-targeted human proteins, we propose heterogeneous nuclear ribonucleoproteins and transporter proteins as potential antiviral therapeutic targets. The observed common and specific infection mechanisms in terms of viral strategies to attack human proteins may provide crucial information for further design of broad and specific next-generation antiviral therapeutics.

Abstract: The success of mechanism-based drug discovery depends on the definition of the drug target. This definition becomes even more important as we try to link drug response to genetic variation, understand stratified clinical efficacy and safety, rationalize the differences between drugs in the same therapeutic class and predict drug utility in patient subgroups. However, drug targets are often poorly defined in the literature, both for launched drugs and for potential therapeutic agents in discovery and development. Here, we present an updated comprehensive map of molecular targets of approved drugs. We curate a total of 893 human and pathogen-derived biomolecules through which 1,578 US FDA-approved drugs act. These biomolecules include 667 human-genome-derived proteins targeted by drugs for human disease. Analysis of these drug targets indicates the continued dominance of privileged target families across disease areas, but also the growth of novel first-in-class mechanisms, particularly in oncology. We explore the relationships between bioactivity class and clinical success, as well as the presence of orthologues between human and animal models and between pathogen and human genomes. Through the collaboration of three independent teams, we highlight some of the ongoing challenges in accurately defining the targets of molecular therapeutics and present conventions for deconvoluting the complexities of molecular pharmacology and drug efficacy.

Abstract: Protein phosphorylation underlies cellular response pathways across eukaryotes and is governed by the opposing actions of phosphorylating kinases and de-phosphorylating phosphatases. While kinases and phosphatases have been extensively studied, their organization and the mechanisms by which they balance each other are not well understood. To address these questions we performed quantitative analyses of large-scale 'omics' datasets from yeast, fly, plant, mouse and human. We uncovered an asymmetric balance of a previously-hidden scale: Each organism contained many different kinase genes, and these were balanced by a small set of highly abundant phosphatase proteins. Kinases were much more responsive to perturbations at the gene and protein levels. In addition, kinases had diverse scales of phenotypic impact when manipulated. Phosphatases, in contrast, were stable, highly robust and flatly organized, with rather uniform impact downstream. We validated aspects of this organization experimentally in nematode, and supported additional aspects by theoretic analysis of the dynamics of protein phosphorylation. Our analyses explain the empirical bias in the protein phosphorylation field toward characterization and therapeutic targeting of kinases at the expense of phosphatases. We show quantitatively and broadly that this is not only a historical bias, but stems from wide-ranging differences in their organization and impact. The asymmetric balance between these opposing regulators of protein phosphorylation is also common to opposing regulators of two other post-translational modification systems, suggesting its fundamental value.

Abstract: PURPOSE: To develop an alternative method for estimating vitreal half-lifes in the rabbit eye based on simple equations for the physical processes of dissipation and the physiochemical properties of therapeutic substances applied by intravitreal drug administration. METHODS: Equations were derived to describe diffusion in the vitreous humor and permeation through the back-of-the-eye tissue, and the volume of distribution. The model was validated using reported half-life values from 83 compounds collected from literature. RESULTS: The rate limiting step for dissipation from the vitreous depends mainly on the molecular weight. Dissipation of very low molecular weight (MW) substances (<350 Da) is limited by diffusional transport to the back of the eye, for substances with a MW >350 Da uptake into the back of the eye tissue becomes limiting, and large molecules >500 Da predominantly take an alternative path being cleared through the front of the eye for which diffusion towards the posterior chamber turns out to be limiting. Taking the three rate determining processes into account, the derived model can estimate dissipation rates and respectively vitreal half-life values of small compounds and macromolecules from their molecular weight with very few exceptions. CONCLUSIONS: The equations derived in this analysis provide a simple method to predict vitreal half-lifes for a diverse group of molecules and can be easily implemented in early drug development.

Abstract: Research on extracting biomedical relations has received growing attention recently, with numerous biological and clinical applications including those in pharmacogenomics, clinical trial screening and adverse drug reaction detection. The ability to accurately capture both semantic and syntactic structures in text expressing these relations becomes increasingly critical to enable deep understanding of scientific papers and clinical narratives. Shared task challenges have been organized by both bioinformatics and clinical informatics communities to assess and advance the state-of-the-art research. Significant progress has been made in algorithm development and resource construction. In particular, graph-based approaches bridge semantics and syntax, often achieving the best performance in shared tasks. However, a number of problems at the frontiers of biomedical relation extraction continue to pose interesting challenges and present opportunities for great improvement and fruitful research. In this article, we place biomedical relation extraction against the backdrop of its versatile applications, present a gentle introduction to its general pipeline and shared resources, review the current state-of-the-art in methodology advancement, discuss limitations and point out several promising future directions.

Abstract: Traditionally, biologists have devoted their careers to studying individual biological entities of their own interest, partly due to lack of available data regarding that entity. Large, highthroughput data, too complex for conventional processing methods (i.e., "big data"), has accumulated in cancer biology, which is freely available in public data repositories. Such challenges urge biologists to inspect their biological entities of interest using novel approaches, firstly including repository data retrieval. Essentially, these revolutionary changes demand new interpretations of huge datasets at a systems-level, by so called "systems biology". One of the representative applications of systems biology is to generate a biological network from high-throughput big data, providing a global map of molecular events associated with specific phenotype changes. In this review, we introduce the repositories of cancer big data and cutting-edge systems biology tools for network generation, and improved identification of therapeutic targets. [BMB Reports 2017; 50(1): 12-19].

Abstract: Viral infections constantly jeopardize the global public health due to lack of effective antiviral therapeutics. Therefore, there is an imperative need to speed up the drug discovery process to identify novel and efficient drug candidates. In this study, we have developed quantitative structure-activity relationship (QSAR)-based models for predicting antiviral compounds (AVCs) against deadly viruses like human immunodeficiency virus (HIV), hepatitis C virus (HCV), hepatitis B virus (HBV), human herpesvirus (HHV) and 26 others using publicly available experimental data from the ChEMBL bioactivity database. Support vector machine (SVM) models achieved a maximum Pearson correlation coefficient of 0.72, 0.74, 0.66, 0.68, and 0.71 in regression mode and a maximum Matthew's correlation coefficient 0.91, 0.93, 0.70, 0.89, and 0.71, respectively, in classification mode during 10-fold cross-validation. Furthermore, similar performance was observed on the independent validation sets. We have integrated these models in the AVCpred web server, freely available at http://crdd.osdd.net/servers/avcpred. In addition, the datasets are provided in a searchable format. We hope this web server will assist researchers in the identification of potential antiviral agents. It would also save time and cost by prioritizing new drugs against viruses before their synthesis and experimental testing.

Abstract: BACKGROUND: Drug product data is available on the Web in a distributed fashion. The reasons lie within the regulatory domains, which exist on a national level. As a consequence, the drug data available on the Web are independently curated by national institutions from each country, leaving the data in varying languages, with a varying structure, granularity level and format, on different locations on the Web. Therefore, one of the main challenges in the realm of drug data is the consolidation and integration of large amounts of heterogeneous data into a comprehensive dataspace, for the purpose of developing data-driven applications. In recent years, the adoption of the Linked Data principles has enabled data publishers to provide structured data on the Web and contextually interlink them with other public datasets, effectively de-siloing them. Defining methodological guidelines and specialized tools for generating Linked Data in the drug domain, applicable on a global scale, is a crucial step to achieving the necessary levels of data consolidation and alignment needed for the development of a global dataset of drug product data. This dataset would then enable a myriad of new usage scenarios, which can, for instance, provide insight into the global availability of different drug categories in different parts of the world. RESULTS: We developed a methodology and a set of tools which support the process of generating Linked Data in the drug domain. Using them, we generated the LinkedDrugs dataset by seamlessly transforming, consolidating and publishing high-quality, 5-star Linked Drug Data from twenty-three countries, containing over 248,000 drug products, over 99,000,000 RDF triples and over 278,000 links to generic drugs from the LOD Cloud. Using the linked nature of the dataset, we demonstrate its ability to support advanced usage scenarios in the drug domain. CONCLUSIONS: The process of generating the LinkedDrugs dataset demonstrates the applicability of the methodological guidelines and the supporting tools in transforming drug product data from various, independent and distributed sources, into a comprehensive Linked Drug Data dataset. The presented user-centric and analytical usage scenarios over the dataset show the advantages of having a de-siloed, consolidated and comprehensive dataspace of drug data available via the existing infrastructure of the Web.

Abstract: Invasive fungal infections are difficult to treat. The few available antifungal drugs have problems with toxicity or efficacy, and resistance is increasing. To overcome these challenges, existing therapies may be enhanced by synergistic combination with another agent. Previously, we found amphotericin B (AMB) and the iron chelator, lactoferrin (LF), were synergistic against a range of different fungal pathogens. This study investigates the mechanism of AMB-LF synergy, using RNA-seq and network analyses. AMB treatment resulted in increased expression of genes involved in iron homeostasis and ATP synthesis. Unexpectedly, AMB-LF treatment did not lead to increased expression of iron and zinc homeostasis genes. However, genes involved in adaptive response to zinc deficiency and oxidative stress had decreased expression. The clustering of co-expressed genes and network analysis revealed that many iron and zinc homeostasis genes are targets of transcription factors Aft1p and Zap1p. The aft1Delta and zap1Delta mutants were hypersensitive to AMB and H2O2, suggesting they are key regulators of the drug response. Mechanistically, AMB-LF synergy could involve AMB affecting the integrity of the cell wall and membrane, permitting LF to disrupt intracellular processes. We suggest that Zap1p- and Aft1p-binding molecules could be combined with existing antifungals to serve as synergistic treatments.