atomium-a Python structure parser.

SUMMARY: Structural biology relies on specific file formats to convey information about macromolecular structures. Traditionally this has been the PDB format, but increasingly newer formats, such as PDBML, mmCIF and MMTF are being used. Here we present atomium, a modern, lightweight, Python library for parsing, manipulating and saving PDB, mmCIF and MMTF file formats. In addition, we provide a web service, pdb2json, which uses atomium to give a consistent JSON representation to the entire Protein Data Bank. AVAILABILITY AND IMPLEMENTATION: atomium is implemented in Python and its performance is equivalent to the existing library BioPython. However, it has significant advantages in features and API design. atomium is available from atomium.bioinf.org.uk and pdb2json can be accessed at pdb2json.bioinf.org.uk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Zincbindpredict-Prediction of Zinc Binding Sites in Proteins.

Background: Zinc binding proteins make up a significant proportion of the proteomes of most organisms and, within those proteins, zinc performs rôles in catalysis and structure stabilisation. Identifying the ability to bind zinc in a novel protein can offer insights into its functions and the mechanism by which it carries out those functions. Computational means of doing so are faster than spectroscopic means, allowing for searching at much greater speeds and scales, and thereby guiding complimentary experimental approaches. Typically, computational models of zinc binding predict zinc binding for individual residues rather than as a single binding site, and typically do not distinguish between different classes of binding site-missing crucial properties indicative of zinc binding. Methods: Previously, we created ZincBindDB, a continuously updated database of known zinc binding sites, categorised by family (the set of liganding residues). Here, we use this dataset to create ZincBindPredict, a set of machine learning methods to predict the most common zinc binding site families for both structure and sequence. Results: The models all achieve an MCC ≥ 0.88, recall ≥ 0.93 and precision ≥ 0.91 for the structural models (mean MCC = 0.97), while the sequence models have MCC ≥ 0.64, recall ≥ 0.80 and precision ≥ 0.83 (mean MCC = 0.87), with the models for binding sites containing four liganding residues performing much better than this. Conclusions: The predictors outperform competing zinc binding site predictors and are available online via a web interface and a GraphQL API.

The IUPHAR/BPS Guide to PHARMACOLOGY in 2018: updates and expansion to encompass the new guide to IMMUNOPHARMACOLOGY.

The IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb, www.guidetopharmacology.org) and its precursor IUPHAR-DB, have captured expert-curated interactions between targets and ligands from selected papers in pharmacology and drug discovery since 2003. This resource continues to be developed in conjunction with the International Union of Basic and Clinical Pharmacology (IUPHAR) and the British Pharmacological Society (BPS). As previously described, our unique model of content selection and quality control is based on 96 target-class subcommittees comprising 512 scientists collaborating with in-house curators. This update describes content expansion, new features and interoperability improvements introduced in the 10 releases since August 2015. Our relationship matrix now describes ∼9000 ligands, ∼15 000 binding constants, ∼6000 papers and ∼1700 human proteins. As an important addition, we also introduce our newly funded project for the Guide to IMMUNOPHARMACOLOGY (GtoImmuPdb, www.guidetoimmunopharmacology.org). This has been 'forked' from the well-established GtoPdb data model and expanded into new types of data related to the immune system and inflammatory processes. This includes new ligands, targets, pathways, cell types and diseases for which we are recruiting new IUPHAR expert committees. Designed as an immunopharmacological gateway, it also has an emphasis on potential therapeutic interventions.

Thermal melt circular dichroism spectroscopic studies for identifying stabilising amphipathic molecules for the voltage-gated sodium channel NavMs.

Purified integral membrane proteins require amphipathic molecules to maintain their solubility in aqueous solutions. These complexes, in turn, are used in studies to characterise the protein structures by a variety of biophysical and structural techniques, including spectroscopy, crystallography, and cryo-electron microscopy. Typically the amphilphiles used have been detergent molecules, but more recently they have included amphipols, which are polymers of different sizes and compositions designed to create smaller, more well-defined solubilised forms of the membrane proteins. In this study we used circular dichroism spectroscopy to compare the secondary structures and thermal stabilities of the NavMs voltage-gated sodium channel in different amphipols and detergents as a means of identifying amphipathic environments that maximally maintain the protein structure whilst providing a stabilising environment. These types of characterisations also have potential as means of screening for sample types that may be more suitable for crystallisation and/or cryo-electron microscopy structure determinations.

GraphQL for the delivery of bioinformatics web APIs and application to ZincBind.

Motivation: Many bioinformatics resources are provided as 'web services', with large databases and analysis software stored on a central server, and clients interacting with them using the hypertext transport protocol (HTTP). While some provide only a visual HTML interface, requiring a web browser to use them, many provide programmatic access using a web application programming interface (API) which returns XML, JSON or plain text that computer programs can interpret more easily. This allows access to be automated. Initially, many bioinformatics APIs used the 'simple object access protocol' (SOAP) and, more recently, representational state transfer (REST). Results: GraphQL is a novel, increasingly prevalent alternative to REST and SOAP that represents the available data in the form of a graph to which any conceivable query can be submitted, and which is seeing increasing adoption in industry. Here, we review the principles of GraphQL, outline its particular suitability to the delivery of bioinformatics resources and describe its implementation in our ZincBind resource. Availability and implementation: https://api.zincbind.net. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

ZincBind-the database of zinc binding sites.

Zinc is one of the most important biologically active metals. Ten per cent of the human genome is thought to encode a zinc binding protein and its uses encompass catalysis, structural stability, gene expression and immunity. At present, there is no specific resource devoted to identifying and presenting all currently known zinc binding sites. Here we present ZincBind, a database of zinc binding sites and its web front-end. Using the structural data in the Protein Data Bank, ZincBind identifies every instance of zinc binding to a protein, identifies its binding site and clusters sites based on 90% sequence identity. There are currently 24 992 binding sites, clustered into 7489 unique sites. The data are available over the web where they can be browsed and downloaded, and via a REST API. ZincBind is regularly updated and will continue to be updated with new data and features.

Inverse pharmacology: Approaches and tools for introducing druggability into engineered proteins.

A major feature of twenty-first century medical research is the development of therapeutic strategies that use 'biologics' (large molecules, usually engineered proteins) and living cells instead of, or as well as, the small molecules that were the basis of pharmacology in earlier eras. The high power of these techniques can bring correspondingly high risk, and therefore the need for the potential for external control. One way of exerting control on therapeutic proteins is to make them responsive to small molecules; in a clinical context, these small molecules themselves have to be safe. Conventional pharmacology has resulted in thousands of small molecules licensed for use in humans, and detailed structural data on their binding to their protein targets. In principle, these data can be used to facilitate the engineering of drug-responsive modules, taken from natural proteins, into synthetic proteins. This has been done for some years (for example, Cre-ERT2) but usually in a painstaking manner. Recently, we have developed the bioinformatic tool SynPharm to facilitate the design of drug-responsive proteins. In this review, we outline the history of the field, the design and use of the Synpharm tool, and describe our own experiences in engineering druggability into the Cpf1 effector of CRISPR gene editing.

SynPharm: A Guide to PHARMACOLOGY Database Tool for Designing Drug Control into Engineered Proteins.

A major challenge in synthetic biology, particularly for mammalian systems, is the inclusion of adequate external control for the synthetic system activities. Control at the transcriptional level can be achieved by adaptation of bacterial repressor-operator systems (e.g., TetR), but altering the activity of a protein by controlling transcription is indirect and for longer half-life mRNAs, decreasing activity this way can be inconveniently slow. Where possible, direct modulation of protein activity by soluble ligands has many advantages, including rapid action. Decades of drug discovery and pharmacological research have uncovered detailed information on the interactions between large numbers of small molecules and their primary protein targets (as well as off-target secondary interactions), many of which have been well studied in mammals, including humans. In principle, this accumulated knowledge would be a powerful resource for synthetic biology. Here, we present SynPharm, a tool that draws together information from the pharmacological database GtoPdb and the structural database, PDB, to help synthetic biologists identify ligand-binding domains of natural proteins. Consequently, as sequence cassettes, these may be suitable for building into engineered proteins to confer small-molecule modulation on them. The tool has ancillary utilities which include assessing contact changes among different ligands in the same protein, predicting possible effects of genetic variants on binding residues, and insights into ligand cross-reactivity among species.