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Key Points Chemogenomics is the study of the genomic and/or proteomic response of an intact biological system to chemical compounds, or the ability of isolated molecular targets to interact with such compounds. In chemogenomics, large collections of chemical compounds are screened for the parallel identification of biological targets and biologically active compounds. In reverse chemogenomics, gene sequences of interest are expressed as target proteins and screened in a high-throughput, 'target-based' manner by compound libraries. On the basis of the structure–activity relationship homology concept, particular emphasis is placed on the parallel exploration of gene and protein families. In forward chemogenomics, active compounds are identified on the basis of their conditional phenotypic effect on a whole biological system rather than on their inhibition of a specific protein target, followed by the subsequent study of the mechanistic basis of the phenotype. Predictive chemogenomics strategies primarily attempt to holistically characterize gene–compound response associations by concurrently considering the response profiles of thousands of drug responses, coupled with the secondary aim of identifying novel therapeutic molecules. Computational or in silico methods complement experimental chemogenomics strategies in the search for targets and drugs. Chemogenomics has garnered support from virtually all areas of medical research. Cancer research was particularly poised to take advantage of the high-throughput nature of chemogenomics, as the approach can help to identify treatment strategies that selectively target the multiple and complex molecular alterations that are observed in human tumours. Chemogenomics faces unique and mainly technical challenges, including the need for a more refined integration of bioinformatics and chemoinformatics data, a more rational approach to selecting designed compounds from an almost infinite number of synthetic possibilities and the ability to build more focused libraries for screening. A recent trend in chemogenomics focuses on data quality rather than on the number of data points that can be generated. Chemogenomics is an emerging discipline that combines the latest tools of genomics and chemistry and applies them to target and drug discovery. Its strength lies in eliminating the bottleneck that currently occurs in target identification by measuring the broad, conditional effects of chemical libraries on whole biological systems or by screening large chemical libraries quickly and efficiently against selected targets. The hope is that chemogenomics will concurrently identify and validate therapeutic targets and detect drug candidates to rapidly and effectively generate new treatments for many human diseases.

The respiratory syncytial virus polymerase complex, consisting of the polymerase (L) and phosphoprotein (P), catalyzes nucleotide polymerization, cap addition, and cap methylation via the RNA dependent RNA polymerase, capping, and Methyltransferase domains on L. Several nucleoside and non-nucleoside inhibitors have been reported to inhibit this polymerase complex, but the structural details of the exact inhibitor-polymerase interactions have been lacking. Here, we report a non-nucleoside inhibitor JNJ-8003 with sub-nanomolar inhibition potency in both antiviral and polymerase assays. Our 2.9 Å resolution cryo-EM structure revealed that JNJ-8003 binds to an induced-fit pocket on the capping domain, with multiple interactions consistent with its tight binding and resistance mutation profile. The minigenome and gel-based de novo RNA synthesis and primer extension assays demonstrated that JNJ-8003 inhibited nucleotide polymerization at the early stages of RNA transcription and replication. Our results support that JNJ-8003 binding modulates a functional interplay between the capping and RdRp domains, and this molecular insight could accelerate the design of broad-spectrum antiviral drugs. Cryo-EM structure and inhibition assays reveal a non-nucleoside inhibitor of the respiratory syncytial virus polymerase complex that acts by binding to an induced-fit pocket in the capping domain.

Integration of open access, curated, high-quality information from multiple disciplines in the Life and Biomedical Sciences provides a holistic understanding of the domain. Additionally, the effective linking of diverse data sources can unearth hidden relationships and guide potential research strategies. However, given the lack of consistency between descriptors and identifiers used in different resources and the absence of a simple mechanism to link them, gathering and combining relevant, comprehensive information from diverse databases remains a challenge. The Open Pharmacological Concepts Triple Store (Open PHACTS) is an Innovative Medicines Initiative project that uses semantic web technology approaches to enable scientists to easily access and process data from multiple sources to solve real-world drug discovery problems. The project draws together sources of publicly-available pharmacological, physicochemical and biomolecular data, represents it in a stable infrastructure and provides well-defined information exploration and retrieval methods. Here, we highlight the utility of this platform in conjunction with workflow tools to solve pharmacological research questions that require interoperability between target, compound, and pathway data. Use cases presented herein cover 1) the comprehensive identification of chemical matter for a dopamine receptor drug discovery program 2) the identification of compounds active against all targets in the Epidermal growth factor receptor (ErbB) signaling pathway that have a relevance to disease and 3) the evaluation of established targets in the Vitamin D metabolism pathway to aid novel Vitamin D analogue design. The example workflows presented illustrate how the Open PHACTS Discovery Platform can be used to exploit existing knowledge and generate new hypotheses in the process of drug discovery.

The four members of the epidermal growth factor receptor (EGFR/ERBB) family form homo- and heterodimers which mediate ligand-specific regulation of many key cellular processes in normal and cancer tissues. While signaling through the EGFR has been extensively studied on the molecular level, signal transduction through ERBB3/ERBB4 heterodimers is less well understood. Here, we generated isogenic mouse Ba/F3 cells that express full-length and functional membrane-integrated ERBB3 and ERBB4 or ERBB4 alone, to serve as a defined cellular model for biological and phosphoproteomics analysis of ERBB3/ERBB4 signaling. ERBB3 co-expression significantly enhanced Ba/F3 cell proliferation upon neuregulin-1 (NRG1) treatment. For comprehensive signaling studies we performed quantitative mass spectrometry (MS) experiments to compare the basal ERBB3/ERBB4 cell phosphoproteome to NRG1 treatment of ERBB3/ERBB4 and ERBB4 cells. We employed a workflow comprising differential isotope labeling with mTRAQ reagents followed by chromatographic peptide separation and final phosphopeptide enrichment prior to MS analysis. Overall, we identified 9686 phosphorylation sites which could be confidently localized to specific residues. Statistical analysis of three replicate experiments revealed 492 phosphorylation sites which were significantly changed in NRG1-treated ERBB3/ERBB4 cells. Bioinformatics data analysis recapitulated regulation of mitogen-activated protein kinase and Akt pathways, but also indicated signaling links to cytoskeletal functions and nuclear biology. Comparative assessment of NRG1-stimulated ERBB4 Ba/F3 cells revealed that ERBB3 did not trigger defined signaling pathways but more broadly enhanced phosphoproteome regulation in cells expressing both receptors. In conclusion, our data provide the first global picture of ERBB3/ERBB4 signaling and provide numerous potential starting points for further mechanistic studies.

Hydrophobic interactions play a key role in the folding and maintenance of the 3-dimensional structure of proteins, as well as in the binding of ligands (e.g. drugs) to protein targets. Therefore, quantitative assessment of spatial hydrophobic (lipophilic) properties of these molecules is indispensable for the development of efficient computational methods in drug design. One possible solution to the problem lies in application of a concept of the 3-dimensional molecular hydrophobicity potential (MHP). The formalism of MHP utilizes a set of atomic physicochemical parameters evaluated from octanol-water partition coefficients (log P) of numerous chemical compounds. It permits detailed assessment of the hydrophobic and/or hydrophilic properties of various parts of molecules and may be useful in analysis of protein-protein and protein-ligand interactions. This review surveys recent applications of MHP-based techniques to a number of biologically relevant tasks. Among them are: (i) Detailed assessment of hydrophobic/hydrophilic organization of proteins; (ii) Application of this data to the modeling of structure, dynamics, and function of globular and membrane proteins, membrane-active peptides, etc. (iii) Employment of the MHP-based criteria in docking simulations for ligands binding to receptors. It is demonstrated that the application of the MHP-based techniques in combination with other molecular modeling tools (e.g. Monte Carlo and molecular dynamics simulations, docking, etc.) permits significant improvement to the standard computational approaches, provides additional important insights into the intimate molecular mechanisms driving protein assembling in water and in biological membranes, and helps in the computer-aided drug discovery process.

High throughput docking (HTD) is routinely used for in silico screening of compound libraries with the aim to find novel leads in a drug discovery program. In the absence of an experimentally determined structure, a homology model can be used instead. Here we present an assessment of the utility of homology models in HTD by docking 300,000 anticipated inactive compounds along with 642 known actives into the binding site of the insulin-like growth factor 1 receptor (IGF-1R) kinase constructed by homology modeling. Twenty-one different templates were selected and the enrichment curves obtained by the homology models were compared to those obtained by three IGF-1R crystal structures. The results show a wide range of enrichments from random to as good as two of the three IGF-1R crystal structures. Nevertheless, if we consider the enrichment obtained at 2% of the database screened as a performance criterion, the best crystal structure outperforms the best homology model. Surprisingly, the sequence identity of the template to the target is not a good descriptor to predict the enrichment obtained by a homology model. The three homology models that yield the worst enrichment have the smallest binding-site volume. Based on our results, we propose ensemble docking to perform HTD with homology models.