SGC--structural biology and human health: a new approach to publishing structural biology results

MichelaNglo: sculpting protein views on web pages without coding

MOTIVATION

The sharing of macromolecular structural information online by scientists is predominantly performed via 2D static images, since the embedding of interactive 3D structures in webpages is non-trivial. Whilst the technologies to do so exist, they are often only implementable with significant web coding experience.

RESULTS

Michelaɴɢʟo is an accessible and open-source web-based application that supports the generation, customization and sharing of interactive 3D macromolecular visualizations for digital media without requiring programming skills. A PyMOL file, PDB file, PDB identifier code or protein/gene name can be provided to form the basis of visualizations using the NGL JavaScript library. Hyperlinks that control the view can be added to text within the page. Protein-coding variants can be highlighted to support interpretation of their potential functional consequences. The resulting visualizations and text can be customized and shared, as well as embedded within existing websites by following instructions and using a self-contained download. Michelaɴɢʟo allows researchers to move away from static images and instead engage, describe and explain their protein to a wider audience in a more interactive fashion.

AVAILABILITY AND IMPLEMENTATION

Michelaɴɢʟo is hosted at michelanglo.sgc.ox.ac.uk. The Python code is freely available at https://github.com/thesgc/MichelaNGLo, along with documentations about its implementation.

WONKA: objective novel complex analysis for ensembles of protein-ligand structures

WONKA is a tool for the systematic analysis of an ensemble of protein-ligand structures. It makes the identification of conserved and unusual features within such an ensemble straightforward. WONKA uses an intuitive workflow to process structural co-ordinates. Ligand and protein features are summarised and then presented within an interactive web application. WONKA's power in consolidating and summarising large amounts of data is described through the analysis of three bromodomain datasets. Furthermore, and in contrast to many current methods, WONKA relates analysis to individual ligands, from which we find unusual and erroneous binding modes. Finally the use of WONKA as an annotation tool to share observations about structures is demonstrated. WONKA is freely available to download and install locally or can be used online at http://wonka.sgc.ox.ac.uk.

Linking genotypes database with locus-specific database and genotype-phenotype correlation in phenylketonuria

The wide range of metabolic phenotypes in phenylketonuria is due to a large number of variants causing variable impairment in phenylalanine hydroxylase function. A total of 834 phenylalanine hydroxylase gene variants from the locus-specific database PAHvdb and genotypes of 4181 phenylketonuria patients from the BIOPKU database were characterized using FoldX, SIFT Blink, Polyphen-2 and SNPs3D algorithms. Obtained data was correlated with residual enzyme activity, patients' phenotype and tetrahydrobiopterin responsiveness. A descriptive analysis of both databases was compiled and an interactive viewer in PAHvdb database was implemented for structure visualization of missense variants. We found a quantitative relationship between phenylalanine hydroxylase protein stability and enzyme activity (r(s) = 0.479), between protein stability and allelic phenotype (r(s) = -0.458), as well as between enzyme activity and allelic phenotype (r(s) = 0.799). Enzyme stability algorithms (FoldX and SNPs3D), allelic phenotype and enzyme activity were most powerful to predict patients' phenotype and tetrahydrobiopterin response. Phenotype prediction was most accurate in deleterious genotypes (≈ 100%), followed by homozygous (92.9%), hemizygous (94.8%), and compound heterozygous genotypes (77.9%), while tetrahydrobiopterin response was correctly predicted in 71.0% of all cases. To our knowledge this is the largest study using algorithms for the prediction of patients' phenotype and tetrahydrobiopterin responsiveness in phenylketonuria patients, using data from the locus-specific and genotypes database.

Data management in the modern structural biology and biomedical research environment

Modern high-throughput structural biology laboratories produce vast amounts of raw experimental data. The traditional method of data reduction is very simple-results are summarized in peer-reviewed publications, which are hopefully published in high-impact journals. By their nature, publications include only the most important results derived from experiments that may have been performed over the course of many years. The main content of the published paper is a concise compilation of these data, an interpretation of the experimental results, and a comparison of these results with those obtained by other scientists.Due to an avalanche of structural biology manuscripts submitted to scientific journals, in many recent cases descriptions of experimental methodology (and sometimes even experimental results) are pushed to supplementary materials that are only published online and sometimes may not be reviewed as thoroughly as the main body of a manuscript. Trouble may arise when experimental results are contradicting the results obtained by other scientists, which requires (in the best case) the reexamination of the original raw data or independent repetition of the experiment according to the published description of the experiment. There are reports that a significant fraction of experiments obtained in academic laboratories cannot be repeated in an industrial environment (Begley CG & Ellis LM, Nature 483(7391):531-3, 2012). This is not an indication of scientific fraud but rather reflects the inadequate description of experiments performed on different equipment and on biological samples that were produced with disparate methods. For that reason the goal of a modern data management system is not only the simple replacement of the laboratory notebook by an electronic one but also the creation of a sophisticated, internally consistent, scalable data management system that will combine data obtained by a variety of experiments performed by various individuals on diverse equipment. All data should be stored in a core database that can be used by custom applications to prepare internal reports, statistics, and perform other functions that are specific to the research that is pursued in a particular laboratory.This chapter presents a general overview of the methods of data management and analysis used by structural genomics (SG) programs. In addition to a review of the existing literature on the subject, also presented is experience in the development of two SG data management systems, UniTrack and LabDB. The description is targeted to a general audience, as some technical details have been (or will be) published elsewhere. The focus is on "data management," meaning the process of gathering, organizing, and storing data, but also briefly discussed is "data mining," the process of analysis ideally leading to an understanding of the data. In other words, data mining is the conversion of data into information. Clearly, effective data management is a precondition for any useful data mining. If done properly, gathering details on millions of experiments on thousands of proteins and making them publicly available for analysis-even after the projects themselves have ended-may turn out to be one of the most important benefits of SG programs.

Embedding and publishing interactive, 3-dimensional, scientific figures in Portable Document Format (PDF) files

With the latest release of the S2PLOT graphics library, embedding interactive, 3-dimensional (3-d) scientific figures in Adobe Portable Document Format (PDF) files is simple, and can be accomplished without commercial software. In this paper, we motivate the need for embedding 3-d figures in scholarly articles. We explain how 3-d figures can be created using the S2PLOT graphics library, exported to Product Representation Compact (PRC) format, and included as fully interactive, 3-d figures in PDF files using the movie15 LaTeX package. We present new examples of 3-d PDF figures, explain how they have been made, validate them, and comment on their advantages over traditional, static 2-dimensional (2-d) figures. With the judicious use of 3-d rather than 2-d figures, scientists can now publish, share and archive more useful, flexible and faithful representations of their study outcomes.

The article you are reading does not have embedded 3-d figures. The full paper, with embedded 3-d figures, is recommended and is available as a supplementary download from PLoS ONE (File S2).

Toward in silico structure-based ADMET prediction in drug discovery

Quantitative structure-activity relationship (QSAR) methods and related approaches have been used to investigate the molecular features that influence the absorption, distribution, metabolism, excretion and toxicity (ADMET) of drugs. As the three-dimensional structures of several major ADMET proteins become available, structure-based (docking-scoring) computations can be carried out to complement or to go beyond QSAR studies. Applying docking-scoring methods to ADMET proteins is a challenging process because they usually have a large and flexible binding cavity; however, promising results relating to metabolizing enzymes have been reported. After reviewing current trends in the field we applied structure-based methods in the context of receptor flexibility in a case study involving the phase II metabolizing sulfotransferases. Overall, the explored concepts and results suggested that structure-based ADMET profiling will probably join the mainstream during the coming years.

High-throughput structural biology of metabolic enzymes and its impact on human diseases

The Structural Genomics Consortium (SGC) is a public-private partnership that aims to determine the three-dimensional structures of human proteins of medical relevance and place them into the public domain without restriction. To date, the Oxford Metabolic Enzyme Group at SGC has deposited the structures of more than 140 human metabolic enzymes from diverse protein families such as oxidoreductases, hydrolases, oxygenases and fatty acid transferases. A subset of our target proteins are involved in the inherited disorders of carbohydrate, fatty acid, amino acid and vitamin metabolism. This article will provide an overview of the structural data gathered from our high-throughput efforts and the lessons learnt in the structure-function relationship of these enzymes, small molecule development and the molecular basis of disease mutations.

Leaving the structural ivory tower, assisted by interactive 3D PDF

The ability to embed interactive three-dimensional (3D) models into electronic publications in portable document format (PDF) greatly enhances the accessibility of molecular structures. Here, we report advances in this procedure and discuss what is needed to develop this format into a truly useful tool for the structural biology community as well as for readers who are less well trained in molecular visualization.

The Scottish Structural Proteomics Facility: targets, methods and outputs

The Scottish Structural Proteomics Facility was funded to develop a laboratory scale approach to high throughput structure determination. The effort was successful in that over 40 structures were determined. These structures and the methods harnessed to obtain them are reported here. This report reflects on the value of automation but also on the continued requirement for a high degree of scientific and technical expertise. The efficiency of the process poses challenges to the current paradigm of structural analysis and publication. In the 5 year period we published ten peer-reviewed papers reporting structural data arising from the pipeline. Nevertheless, the number of structures solved exceeded our ability to analyse and publish each new finding. By reporting the experimental details and depositing the structures we hope to maximize the impact of the project by allowing others to follow up the relevant biology.