Regulation of phosphoribosyl ubiquitination by a calmodulin-dependent glutamylase

The bacterial pathogen Legionella pneumophila creates an intracellular niche permissive for its replication by extensively modulating host-cell functions using hundreds of effector proteins delivered by its Dot/Icm secretion system¹. Among these, members of the SidE family (SidEs) regulate several cellular processes through a unique phosphoribosyl ubiquitination mechanism that bypasses the canonical ubiquitination machinery^2-4. The activity of SidEs is regulated by another Dot/Icm effector known as SidJ⁵; however, the mechanism of this regulation is not completely understood^6,7. Here we demonstrate that SidJ inhibits the activity of SidEs by inducing the covalent attachment of glutamate moieties to SdeA-a member of the SidE family-at E860, one of the catalytic residues that is required for the mono-ADP-ribosyltransferase activity involved in ubiquitin activation². This inhibition by SidJ is spatially restricted in host cells because its activity requires the eukaryote-specific protein calmodulin (CaM). We solved a structure of SidJ-CaM in complex with AMP and found that the ATP used in this reaction is cleaved at the α-phosphate position by SidJ, which-in the absence of glutamate or modifiable SdeA-undergoes self-AMPylation. Our results reveal a mechanism of regulation in bacterial pathogenicity in which a glutamylation reaction that inhibits the activity of virulence factors is activated by host-factor-dependent acyl-adenylation.

Author Correction: Informed-Proteomics: open-source software package for top-down proteomics

In the version of this article initially published, the authors erroneously reported the search mode that was used for ProSightPC 3.0 in the Online Methods and in Supplementary Table 3.

Blazing Signature Filter: a library for fast pairwise similarity comparisons

Background

Identifying similarities between datasets is a fundamental task in data mining and has become an integral part of modern scientific investigation. Whether the task is to identify co-expressed genes in large-scale expression surveys or to predict combinations of gene knockouts which would elicit a similar phenotype, the underlying computational task is often a multi-dimensional similarity test. As datasets continue to grow, improvements to the efficiency, sensitivity or specificity of such computation will have broad impacts as it allows scientists to more completely explore the wealth of scientific data.

Results

The Blazing Signature Filter (BSF) is a highly efficient pairwise similarity algorithm which enables extensive data mining within a reasonable amount of time. The algorithm transforms datasets into binary metrics, allowing it to utilize the computationally efficient bit operators and provide a coarse measure of similarity. We demonstrate the utility of our algorithm using two common bioinformatics tasks: identifying data sets with similar gene expression profiles, and comparing annotated genomes.

Conclusions

The BSF is a highly efficient pairwise similarity algorithm that can scale to billions of comparisons without the need for specialized hardware.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2210-6) contains supplementary material, which is available to authorized users.

LIQUID: an-open source software for identifying lipids in LC-MS/MS-based lipidomics data

Summary

We introduce an open-source software, LIQUID, for semi-automated processing and visualization of LC-MS/MS-based lipidomics data. LIQUID provides users with the capability to process high throughput data and contains a customizable target library and scoring model per project needs. The graphical user interface provides visualization of multiple lines of spectral evidence for each lipid identification, allowing rapid examination of data for making confident identifications of lipid molecular species. LIQUID was compared to other freely available software commonly used to identify lipids and other small molecules (e.g. CFM-ID, MetFrag, GNPS, LipidBlast and MS-DIAL), and was found to have a faster processing time to arrive at a higher number of validated lipid identifications.

Availability and Implementation

LIQUID is available at http://github.com/PNNL-Comp-Mass-Spec/LIQUID .

Contact

jennifer.kyle@pnnl.gov or thomas.metz@pnnl.gov.

Supplementary information

Supplementary data are available at Bioinformatics online.

Informed-Proteomics: open-source software package for top-down proteomics

Top-down proteomics, the analysis of intact proteins in their endogenous form, preserves valuable information about post-translation modifications, isoforms and proteolytic processing. The quality of top-down liquid chromatography-tandem MS (LC-MS/MS) data sets is rapidly increasing on account of advances in instrumentation and sample-processing protocols. However, top-down mass spectra are substantially more complex than conventional bottom-up data. New algorithms and software tools for confident proteoform identification and quantification are needed. Here we present Informed-Proteomics, an open-source software suite for top-down proteomics analysis that consists of an LC-MS feature-finding algorithm, a database search algorithm, and an interactive results viewer. We compare our tool with several other popular tools using human-in-mouse xenograft luminal and basal breast tumor samples that are known to have significant differences in protein abundance based on bottom-up analysis.

A Skyline Plugin for Pathway-Centric Data Browsing

For targeted proteomics to be broadly adopted in biological laboratories as a routine experimental protocol, wet-bench biologists must be able to approach selected reaction monitoring (SRM) and parallel reaction monitoring (PRM) assay design in the same way they approach biological experimental design. Most often, biological hypotheses are envisioned in a set of protein interactions, networks, and pathways. We present a plugin for the popular Skyline tool that presents public mass spectrometry data in a pathway-centric view to assist users in browsing available data and determining how to design quantitative experiments. Selected proteins and their underlying mass spectra are imported to Skyline for further assay design (transition selection). The same plugin can be used for hypothesis-driven data-independent acquisition (DIA) data analysis, again utilizing the pathway view to help narrow down the set of proteins that will be investigated. The plugin is backed by the Pacific Northwest National Laboratory (PNNL) Biodiversity Library, a corpus of 3 million peptides from >100 organisms, and the draft human proteome. Users can upload personal data to the plugin to use the pathway navigation prior to importing their own data into Skyline. Graphical Abstract ᅟ.

Comprehensive quantitative analysis of ovarian and breast cancer tumor peptidomes

Aberrant degradation of proteins is associated with many pathological states, including cancers. Mass spectrometric analysis of tumor peptidomes, the intracellular and intercellular products of protein degradation, has the potential to provide biological insights on proteolytic processing in cancer. However, attempts to use the information on these smaller protein degradation products from tumors for biomarker discovery and cancer biology studies have been fairly limited to date, largely due to the lack of effective approaches for robust peptidomics identification and quantification and the prevalence of confounding factors and biases associated with sample handling and processing. Herein, we have developed an effective and robust analytical platform for comprehensive analyses of tissue peptidomes, which is suitable for high-throughput quantitative studies. The reproducibility and coverage of the platform, as well as the suitability of clinical ovarian tumor and patient-derived breast tumor xenograft samples with postexcision delay of up to 60 min before freezing for peptidomics analysis, have been demonstrated. Moreover, our data also show that the peptidomics profiles can effectively separate breast cancer subtypes, reflecting tumor-associated protease activities. Peptidomics complements results obtainable from conventional bottom-up proteomics and provides insights not readily obtainable from such approaches.

Accounting for population variation in targeted proteomics

Individual proteomes typically differ from the reference human proteome at ∼10,000 single amino acid variants. When viewed on the population scale, this individual variation results in a wide variety of protein sequences. In targeted proteomics experiments, such variability can confound accurate protein quantification. To assist researchers in identifying target peptides with high variability within the human population, we have created the Population Variation plug-in for Skyline, which provides easy access to the polymorphisms stored in dbSNP. Given a set of peptides, the tool reports minor allele frequency for common polymorphisms. We highlight the importance of considering genetic variation by applying the tool to public data sets.