Christian X. Weichenberger

Techniques, tools and best practices for ligand electron-density analysis and results from their application to deposited crystal structures.

As a result of substantial instrumental automation and the continuing improvement of software, crystallographic studies of biomolecules are conducted by non-experts in increasing numbers. While improved validation almost ensures that major mistakes in the protein part of structure models are exceedingly rare, in ligand-protein complex structures, which in general are most interesting to the scientist, ambiguous ligand electron density is often difficult to interpret and the modelled ligands are generally more difficult to properly validate. Here, (i) the primary technical reasons and potential human factors leading to problems in ligand structure models are presented; (ii) the most common categories of building errors or overinterpretation are classified; (iii) a few instructive and specific examples are discussed in detail, including an electron-density-based analysis of ligand structures that do not contain any ligands; (iv) means of avoiding such mistakes are suggested and the implications for database validity are discussed and (v) a user-friendly software tool that allows non-expert users to conveniently inspect ligand density is provided.

Visualizing ligand molecules in Twilight electron density.

Three-dimensional models of protein structures determined by X-ray crystallography are based on the interpretation of experimentally derived electron-density maps. The real-space correlation coefficient (RSCC) provides an easily comprehensible, objective measure of the residue-based fit of atom coordinates to electron density. Among protein structure models, protein-ligand complexes are of special interest, given their contribution to understanding the molecular underpinnings of biological activity and to drug design. For consumers of such models, it is not trivial to determine the degree to which ligand-structure modelling is biased by subjective electron-density interpretation. A standalone script, Twilight, is presented for the analysis, visualization and annotation of a pre-filtered set of 2815 protein-ligand complexes deposited with the PDB as of 15 January 2012 with ligand RSCC values that are below a threshold of 0.6. It also provides simplified access to the visualization of any protein-ligand complex available from the PDB and annotated by the Uppsala Electron Density Server. The script runs on various platforms and is available for download at http://www.ruppweb.org/twilight/.

Profiling of Parkin-Binding Partners Using Tandem Affinity Purification

Parkinsons disease (PD) is a progressive neurodegenerative disorder affecting approximately 1–2% of the general population over age 60. It is characterized by a rather selective loss of dopaminergic neurons in the substantia nigra and the presence of α-synuclein-enriched Lewy body inclusions. Mutations in the Parkin gene (PARK2) are the major cause of autosomal recessive early-onset parkinsonism. The Parkin protein is an E3 ubiquitin ligase with various cellular functions, including the induction of mitophagy upon mitochondrial depolarizaton, but the full repertoire of Parkin-binding proteins remains poorly defined. Here we employed tandem affinity purification interaction screens with subsequent mass spectrometry to profile binding partners of Parkin. Using this approach for two different cell types (HEK293T and SH-SY5Y neuronal cells), we identified a total of 203 candidate Parkin-binding proteins. For the candidate proteins and the proteins known to cause heritable forms of parkinsonism, protein-protein interaction data were derived from public databases, and the associated biological processes and pathways were analyzed and compared. Functional similarity between the candidates and the proteins involved in monogenic parkinsonism was investigated, and additional confirmatory evidence was obtained using published genetic interaction data from Drosophila melanogaster. Based on the results of the different analyses, a prioritization score was assigned to each candidate Parkin-binding protein. Two of the top ranking candidates were tested by co-immunoprecipitation, and interaction to Parkin was confirmed for one of them. New candidates for involvement in cell death processes, protein folding, the fission/fusion machinery, and the mitophagy pathway were identified, which provide a resource for further elucidating Parkin function.

Ten years of probabilistic estimates of biocrystal solvent content: new insights via nonparametric kernel density estimate.

The probabilistic estimate of the solvent content (Matthews probability) was first introduced in 2003. Given that the Matthews probability is based on prior information, revisiting the empirical foundation of this widely used solvent-content estimate is appropriate. The parameter set for the original Matthews probability distribution function employed in MATTPROB has been updated after ten years of rapid PDB growth. A new nonparametric kernel density estimator has been implemented to calculate the Matthews probabilities directly from empirical solvent-content data, thus avoiding the need to revise the multiple parameters of the original binned empirical fit function. The influence and dependency of other possible parameters determining the solvent content of protein crystals have been examined. Detailed analysis showed that resolution is the primary and dominating model parameter correlated with solvent content. Modifications of protein specific density for low molecular weight have no practical effect, and there is no correlation with oligomerization state. A weak, and in practice irrelevant, dependency on symmetry and molecular weight is present, but cannot be satisfactorily explained by simple linear or categorical models. The Bayesian argument that the observed resolution represents only a lower limit for the true diffraction potential of the crystal is maintained. The new kernel density estimator is implemented as the primary option in the MATTPROB web application at http://www.ruppweb.org/mattprob/.

The solvent component of macromolecular crystals

On average, the mother liquor or solvent and its constituents occupy about 50% of a macromolecular crystal. Ordered as well as disordered solvent components need to be accurately accounted for in modelling and refinement, often with considerable complexity.

Dintor: functional annotation of genomic and proteomic data

BackgroundDuring the last decade, a great number of extremely valuable large-scale genomics and proteomics datasets have become available to the research community. In addition, dropping costs for conducting high-throughput sequencing experiments and the option to outsource them considerably contribute to an increasing number of researchers becoming active in this field. Even though various computational approaches have been developed to analyze these data, it is still a laborious task involving prudent integration of many heterogeneous and frequently updated data sources, creating a barrier for interested scientists to accomplish their own analysis.ResultsWe have implemented Dintor, a data integration framework that provides a set of over 30 tools to assist researchers in the exploration of genomics and proteomics datasets. Each of the tools solves a particular task and several tools can be combined into data processing pipelines. Dintor covers a wide range of frequently required functionalities, from gene identifier conversions and orthology mappings to functional annotation of proteins and genetic variants up to candidate gene prioritization and Gene Ontology-based gene set enrichment analysis. Since the tools operate on constantly changing datasets, we provide a mechanism to unambiguously link tools with different versions of archived datasets, which guarantees reproducible results for future tool invocations. We demonstrate a selection of Dintor’s capabilities by analyzing datasets from four representative publications. The open source software can be downloaded and installed on a local Unix machine. For reasons of data privacy it can be configured to retrieve local data only. In addition, the Dintor tools are available on our public Galaxy web service at http://dintor.eurac.edu.ConclusionsDintor is a computational annotation framework for the analysis of genomic and proteomic datasets, providing a rich set of tools that cover the most frequently encountered tasks. A major advantage is its capability to consistently handle multiple versions of tool-associated datasets, supporting the researcher in delivering reproducible results.

FamAgg: an R package to evaluate familial aggregation of traits in large pedigrees

Summary: Familial aggregation analysis is the first fundamental step to perform when assessing the extent of genetic background of a disease. However, there is a lack of software to analyze the familial clustering of complex phenotypes in very large pedigrees. Such pedigrees can be utilized to calculate measures that express trait aggregation on both the family and individual level, providing valuable directions in choosing families for detailed follow-up studies. We developed FamAgg, an open source R package that contains both established and novel methods to investigate familial aggregation of traits in large pedigrees. We demonstrate its use and interpretation by analyzing a publicly available cancer dataset with more than 20 000 participants distributed across approximately 400 families. Availability and implementation: The FamAgg package is freely available at the Bioconductor repository, http://www.bioconductor.org/packages/FamAgg. Contact: Christian.Weichenberger@eurac.edu Supplementary information: Supplementary data are available at Bioinformatics online.

FlyOde - a platform for community curation and interactive visualization of dynamic gene regulatory networks in Drosophila eye development

Motivation: Understanding the regulatory mechanisms governing eye development of the model organism Drosophila melanogaster (D. m.) requires structured knowledge of the involved genes and proteins, their interactions, and dynamic expression patterns. Especially the latter information is however to a large extent scattered throughout the literature. Results: FlyOde is an online platform for the systematic assembly of data on D. m. eye development. It consists of data on eye development obtained from the literature, and a web interface for users to interactively display these data as a gene regulatory network. Our manual curation process provides high standard structured data, following a specifically designed ontology. Visualization of gene interactions provides an overview of network topology, and filtering according to user-defined expression patterns makes it a versatile tool for daily tasks, as demonstrated by usage examples. Users are encouraged to submit additional data via a simple online form.

Comparative assessment of different familial aggregation methods in the context of large and unstructured pedigrees

Motivation: Familial aggregation analysis is an important early step for characterizing the genetic determinants of phenotypes in epidemiological studies. To facilitate this analysis, a collection of methods to detect familial aggregation in large pedigrees has been made available recently. However, efficacy of these methods in real world scenarios remains largely unknown. Here, we assess the performance of five aggregation methods to identify individuals or groups of related individuals affected by a Mendelian trait within a large set of decoys. We investigate method performance under a representative set of combinations of causal variant penetrance, trait prevalence and number of affected generations in the pedigree. These methods are then applied to assess familial aggregation of familial hypercholesterolemia and stroke, in the context of the Cooperative Health Research in South Tyrol (CHRIS) study. Results: We find that in some situations statistical hypothesis testing with a binomial null distribution achieves performance similar to methods that are based on kinship information, while kinship based methods perform better when information is available on fewer generations. Potential case families from the CHRIS study are reported and the results are discussed taking into account insights from the performance assessment. Availability and implementation: The familial aggregation analysis package is freely available at the Bioconductor repository, http://www.bioconductor.org/packages/FamAgg. Supplementary information: Supplementary data are available at Bioinformatics online.

Inclusion of biological knowledge in a Bayesian shrinkage model for joint estimation of SNP effects: Pereira et al.

With the aim of improving detection of novel single‐nucleotide polymorphisms (SNPs) in genetic association studies, we propose a method of including prior biological information in a Bayesian shrinkage model that jointly estimates SNP effects. We assume that the SNP effects follow a normal distribution centered at zero with variance controlled by a shrinkage hyperparameter. We use biological information to define the amount of shrinkage applied on the SNP effects distribution, so that the effects of SNPs with more biological support are less shrunk toward zero, thus being more likely detected. The performance of the method was tested in a simulation study (1,000 datasets, 500 subjects with ∼200 SNPs in 10 linkage disequilibrium (LD) blocks) using a continuous and a binary outcome. It was further tested in an empirical example on body mass index (continuous) and overweight (binary) in a dataset of 1,829 subjects and 2,614 SNPs from 30 blocks. Biological knowledge was retrieved using the bioinformatics tool Dintor, which queried various databases. The joint Bayesian model with inclusion of prior information outperformed the standard analysis: in the simulation study, the mean ranking of the true LD block was 2.8 for the Bayesian model versus 3.6 for the standard analysis of individual SNPs; in the empirical example, the mean ranking of the six true blocks was 8.5 versus 9.3 in the standard analysis. These results suggest that our method is more powerful than the standard analysis. We expect its performance to improve further as more biological information about SNPs becomes available.