Complex Portal 2018: extended content and enhanced visualization tools for macromolecular complexes.

The Complex Portal (www.ebi.ac.uk/complexportal) is a manually curated, encyclopaedic database that collates and summarizes information on stable, macromolecular complexes of known function. It captures complex composition, topology and function and links out to a large range of domain-specific resources that hold more detailed data, such as PDB or Reactome. We have made several significant improvements since our last update, including improving compliance to the FAIR data principles by providing complex-specific, stable identifiers that include versioning. Protein complexes are now available from 20 species for download in standards-compliant formats such as PSI-XML, MI-JSON and ComplexTAB or can be accessed via an improved REST API. A component-based JS front-end framework has been implemented to drive a new website and this has allowed the use of APIs from linked services to import and visualize information such as the 3D structure of protein complexes, its role in reactions and pathways and the co-expression of complex components in the tissues of multi-cellular organisms. A first draft of the complete complexome of Saccharomyces cerevisiae is now available to browse and download.

Integrative Data Mining, Scaffold Analysis, and Sequential Binary Classification Models for Exploring Ligand Profiles of Hepatic Organic Anion Transporting Polypeptides.

Hepatocellular organic anion transporting polypeptides (OATP1B1, OATP1B3, and OATP2B1) are important for proper liver function and the regulation of the drug elimination process. Understanding their roles in different conditions of liver toxicity and cancer requires an in-depth investigation of hepatic OATP-ligand interactions and selectivity. However, such studies are impeded by the lack of crystal structures, the promiscuous nature of these transporters, and the limited availability of reliable bioactivity data, which are spread over different data sources in the open domain. To this end, we integrated ligand bioactivity data for hepatic OATPs from five open data sources (ChEMBL, the UCSF-FDA TransPortal database, DrugBank, Metrabase, and IUPHAR) in a semiautomatic KNIME workflow. Highly curated data sets were analyzed with respect to enriched scaffolds, and their activity profiles and interesting scaffold series providing indication for selective, dual-, or pan-inhibitory activity toward hepatic OATPs could be extracted. In addition, a sequential binary modeling approach revealed common and distinctive ligand features for inhibitory activity toward the individual transporters. The workflows designed for integrating data from open sources, data curation, and subsequent substructure analyses are freely available and fully adaptable. The new data sets for inhibitors and substrates of hepatic OATPs as well as the insights provided by the feature and substructure analyses will guide future structure-based studies on hepatic OATP-ligand interactions and selectivity.

Current Advances in Studying Clinically Relevant Transporters of the Solute Carrier (SLC) Family by Connecting Computational Modeling and Data Science.

Organic anion and cation transporting proteins (OATs, OATPs, and OCTs), as well as the Multidrug and Toxin Extrusion (MATE) transporters of the Solute Carrier (SLC) family are playing a pivotal role in the discovery and development of new drugs due to their involvement in drug disposition, drug-drug interactions, adverse drug effects and related toxicity. Computational methods to understand and predict clinically relevant transporter interactions can provide useful guidance at early stages in drug discovery and design, especially if they include contemporary data science approaches. In this review, we summarize the current state-of-the-art of computational approaches for exploring ligand interactions and selectivity for these drug (uptake) transporters. The computational methods discussed here by highlighting interesting examples from the current literature are ranging from semiautomatic data mining and integration, to ligand-based methods (such as quantitative structure-activity relationships, and combinatorial pharmacophore modeling), and finally structure-based methods (such as comparative modeling, molecular docking, and molecular dynamics simulations). We are focusing on promising computational techniques such as fold-recognition methods, proteochemometric modeling or techniques for enhanced sampling of protein conformations used in the context of these ADMET-relevant SLC transporters with a special focus on methods useful for studying ligand selectivity.