A Galaxy of informatics resources for MS-based proteomics.

INTRODUCTION: Continuous advances in mass spectrometry (MS) technologies have enabled deeper and more reproducible proteome characterization and a better understanding of biological systems when integrated with other 'omics data. Bioinformatic resources meeting the analysis requirements of increasingly complex MS-based proteomic data and associated multi-omic data are critically needed. These requirements included availability of software that would span diverse types of analyses, scalability for large-scale, compute-intensive applications, and mechanisms to ease adoption of the software. AREAS COVERED: The Galaxy ecosystem meets these requirements by offering a multitude of open-source tools for MS-based proteomics analyses and applications, all in an adaptable, scalable, and accessible computing environment. A thriving global community maintains these software and associated training resources to empower researcher-driven analyses. EXPERT OPINION: The community-supported Galaxy ecosystem remains a crucial contributor to basic biological and clinical studies using MS-based proteomics. In addition to the current status of Galaxy-based resources, we describe ongoing developments for meeting emerging challenges in MS-based proteomic informatics. We hope this review will catalyze increased use of Galaxy by researchers employing MS-based proteomics and inspire software developers to join the community and implement new tools, workflows, and associated training content that will add further value to this already rich ecosystem.

Fast and accurate genome-wide predictions and structural modeling of protein-protein interactions using Galaxy.

BACKGROUND: Protein-protein interactions play a crucial role in almost all cellular processes. Identifying interacting proteins reveals insight into living organisms and yields novel drug targets for disease treatment. Here, we present a publicly available, automated pipeline to predict genome-wide protein-protein interactions and produce high-quality multimeric structural models. RESULTS: Application of our method to the Human and Yeast genomes yield protein-protein interaction networks similar in quality to common experimental methods. We identified and modeled Human proteins likely to interact with the papain-like protease of SARS-CoV2's non-structural protein 3. We also produced models of SARS-CoV2's spike protein (S) interacting with myelin-oligodendrocyte glycoprotein receptor and dipeptidyl peptidase-4. CONCLUSIONS: The presented method is capable of confidently identifying interactions while providing high-quality multimeric structural models for experimental validation. The interactome modeling pipeline is available at usegalaxy.org and usegalaxy.eu.

Metaproteomics Analysis of SARS-CoV-2-Infected Patient Samples Reveals Presence of Potential Coinfecting Microorganisms.

In this Letter, we reanalyze published mass spectrometry data sets of clinical samples with a focus on determining the coinfection status of individuals infected with SARS-CoV-2 coronavirus. We demonstrate the use of ComPIL 2.0 software along with a metaproteomics workflow within the Galaxy platform to detect cohabitating potential pathogens in COVID-19 patients using mass spectrometry-based analysis. From a sample collected from gargling solutions, we detected Streptococcus pneumoniae (opportunistic and multidrug-resistant pathogen) and Lactobacillus rhamnosus (a probiotic component) along with SARS-Cov-2. We could also detect Pseudomonas sps. Bc-h from COVID-19 positive samples and Acinetobacter ursingii and Pseudomonas monteilii from COVID-19 negative samples collected from oro- and nasopharyngeal samples. We believe that the early detection and characterization of coinfections by using metaproteomics from COVID-19 patients will potentially impact the diagnosis and treatment of patients affected by SARS-CoV-2 infection.

Survey of metaproteomics software tools for functional microbiome analysis.

To gain a thorough appreciation of microbiome dynamics, researchers characterize the functional relevance of expressed microbial genes or proteins. This can be accomplished through metaproteomics, which characterizes the protein expression of microbiomes. Several software tools exist for analyzing microbiomes at the functional level by measuring their combined proteome-level response to environmental perturbations. In this survey, we explore the performance of six available tools, to enable researchers to make informed decisions regarding software choice based on their research goals. Tandem mass spectrometry-based proteomic data obtained from dental caries plaque samples grown with and without sucrose in paired biofilm reactors were used as representative data for this evaluation. Microbial peptides from one sample pair were identified by the X! tandem search algorithm via SearchGUI and subjected to functional analysis using software tools including eggNOG-mapper, MEGAN5, MetaGOmics, MetaProteomeAnalyzer (MPA), ProPHAnE, and Unipept to generate functional annotation through Gene Ontology (GO) terms. Among these software tools, notable differences in functional annotation were detected after comparing differentially expressed protein functional groups. Based on the generated GO terms of these tools we performed a peptide-level comparison to evaluate the quality of their functional annotations. A BLAST analysis against the NCBI non-redundant database revealed that the sensitivity and specificity of functional annotation varied between tools. For example, eggNOG-mapper mapped to the most number of GO terms, while Unipept generated more accurate GO terms. Based on our evaluation, metaproteomics researchers can choose the software according to their analytical needs and developers can use the resulting feedback to further optimize their algorithms. To make more of these tools accessible via scalable metaproteomics workflows, eggNOG-mapper and Unipept 4.0 were incorporated into the Galaxy platform.

Impact of angiogenic activation and inhibition on miRNA profiles of human retinal endothelial cells.

BACKGROUND: Human retinal microvascular endothelial cells (HRMVECs) are involved in the pathogenesis of retinopathy of prematurity. In this study, the microRNA (miRNA) expression profiles of HRMVECs were investigated under resting conditions, angiogenic stimulation (VEGF treatment) and anti-VEGF treatment. MATERIALS AND METHODS: The miRNA profiles of HRMVECs under resting and angiogenic conditions (VEGF treatment), as well as after addition of aflibercept, bevacizumab or ranibizumab were evaluated by analyzing the transcriptome of small non-coding RNAs. Differentially expressed miRNAs were validated using qPCR and classified using Gene Ontology enrichment analysis. RESULTS: Ten miRNAs were found to be significantly changed more than 2-fold. Seven of these miRNAs were changed between resting conditions and angiogenic stimulation. Four of these miRNAs (miR-139-5p/-3p and miR-335-5p/-3p) were validated by qPCR in independent experiments and were found to be associated with angiogenesis and cell migration in Gene Ontology analysis. In addition, analysis of the most abundant miRNAs in the HRMVEC miRNome (representing at least 1% of the miRNome) was conducted and identified miR-21-5p, miR-29a-3p, miR-100-5p and miR-126-5p/-3p to be differently expressed by at least 15% between resting conditions and angiogenic conditions. These miRNAs were found to be associated with apoptotic signaling, regulation of kinase activity, intracellular signal transduction, cell surface receptor signaling and positive regulation of cell differentiation in Gene Ontology analysis. No differentially regulated miRNAs between angiogenic stimulation and angiogenic stimulation plus anti-VEGF treatment were identified. CONCLUSION: In this study we characterized the miRNA profile of HRMVECs under resting, angiogenic and anti-angiogenic conditions and identified several miRNAs of potential pathophysiologic importance for angioproliferative retinal diseases. Our results have implications for possible miRNA-targeted angiomodulatory approaches in diseases like diabetic retinopathy or retinopathy of prematurity.

Disseminating Metaproteomic Informatics Capabilities and Knowledge Using the Galaxy-P Framework.

The impact of microbial communities, also known as the microbiome, on human health and the environment is receiving increased attention. Studying translated gene products (proteins) and comparing metaproteomic profiles may elucidate how microbiomes respond to specific environmental stimuli, and interact with host organisms. Characterizing proteins expressed by a complex microbiome and interpreting their functional signature requires sophisticated informatics tools and workflows tailored to metaproteomics. Additionally, there is a need to disseminate these informatics resources to researchers undertaking metaproteomic studies, who could use them to make new and important discoveries in microbiome research. The Galaxy for proteomics platform (Galaxy-P) offers an open source, web-based bioinformatics platform for disseminating metaproteomics software and workflows. Within this platform, we have developed easily-accessible and documented metaproteomic software tools and workflows aimed at training researchers in their operation and disseminating the tools for more widespread use. The modular workflows encompass the core requirements of metaproteomic informatics: (a) database generation; (b) peptide spectral matching; (c) taxonomic analysis and (d) functional analysis. Much of the software available via the Galaxy-P platform was selected, packaged and deployed through an online metaproteomics "Contribution Fest" undertaken by a unique consortium of expert software developers and users from the metaproteomics research community, who have co-authored this manuscript. These resources are documented on GitHub and freely available through the Galaxy Toolshed, as well as a publicly accessible metaproteomics gateway Galaxy instance. These documented workflows are well suited for the training of novice metaproteomics researchers, through online resources such as the Galaxy Training Network, as well as hands-on training workshops. Here, we describe the metaproteomics tools available within these Galaxy-based resources, as well as the process by which they were selected and implemented in our community-based work. We hope this description will increase access to and utilization of metaproteomics tools, as well as offer a framework for continued community-based development and dissemination of cutting edge metaproteomics software.

SuperLooper--a prediction server for the modeling of loops in globular and membrane proteins.

SuperLooper provides the first online interface for the automatic, quick and interactive search and placement of loops in proteins (LIP). A database containing half a billion segments of water-soluble proteins with lengths up to 35 residues can be screened for candidate loops. A specified database containing 180,000 membrane loops in proteins (LIMP) can be searched, alternatively. Loop candidates are scored based on sequence criteria and the root mean square deviation (RMSD) of the stem atoms. Searching LIP, the average global RMSD of the respective top-ranked loops to the original loops is benchmarked to be <2 A, for loops up to six residues or <3 A for loops shorter than 10 residues. Other suitable conformations may be selected and directly visualized on the web server from a top-50 list. For user guidance, the sequence homology between the template and the original sequence, proline or glycine exchanges or close contacts between a loop candidate and the remainder of the protein are denoted. For membrane proteins, the expansions of the lipid bilayer are automatically modeled using the TMDET algorithm. This allows the user to select the optimal membrane protein loop concerning its relative orientation to the lipid bilayer. The server is online since October 2007 and can be freely accessed at URL: http://bioinformatics.charite.de/superlooper/.

SuperToxic: a comprehensive database of toxic compounds.

Within our everyday life, we are confronted with a variety of toxic substances of natural or artificial origin. Toxins are already used, e.g. in medicine, but there is still an increasing number of toxic compounds, representing a tremendous potential to extract new substances. Since predictive toxicology gains in importance, the careful and extensive investigation of known toxins is the basis to assess the properties of unknown substances. In order to achieve this aim, we have collected toxic compounds from literature and web sources in the database SuperToxic. The current version of this database compiles about 60,000 compounds and their structures. These molecules are classified according to their toxicity, based on more than 2 million measurements. The SuperToxic database provides a variety of search options like name, CASRN, molecular weight and measured values of toxicity. With the aid of implemented similarity searches, information about possible biological interactions can be gained. Furthermore, connections to the Protein Data Bank, UniProt and the KEGG database are available, to allow the identification of targets and those pathways, the searched compounds are involved in. This database is available online at: http://bioinformatics.charite.de/supertoxic.

Voronoia: analyzing packing in protein structures.

The packing of protein atoms is an indicator for their stability and functionality, and applied in determining thermostability, in protein design, ligand binding and to identify flexible regions in proteins. Here, we present Voronoia, a database of atomic-scale packing data for protein 3D structures. It is based on an improved Voronoi Cell algorithm using hyperboloid interfaces to construct atomic volumes, and to resolve solvent-accessible and -inaccessible regions of atoms. The database contains atomic volumes, local packing densities and interior cavities calculated for 61 318 biological units from the PDB. A report for each structure summarizes the packing by residue and atom types, and lists the environment of interior cavities. The packing data are compared to a nonredundant set of structures from SCOP superfamilies. Both packing densities and cavities can be visualized in the 3D structures by the Jmol plugin. Additionally, PDB files can be submitted to the Voronoia server for calculation. This service performs calculations for most full-atomic protein structures within a few minutes. For batch jobs, a standalone version of the program with an optional PyMOL plugin is available for download. The database can be freely accessed at: http://bioinformatics.charite.de/voronoia.

SuperScent--a database of flavors and scents.

Volatiles are efficient mediators of chemical communication acting universally as attractant, repellent or warning signal in all kingdoms of life. Beside this broad impact volatiles have in nature, scents are also widely used in pharmaceutical, food and cosmetic industries, so the identification of new scents is of great industrial interest. Despite this importance as well as the vast number and diversity of volatile compounds, there is currently no comprehensive public database providing information on structure and chemical classification of volatiles. Therefore, the database SuperScent was established to supply users with detailed information on the variety of odor components. The version of the database presented here comprises the 2D/3D structures of approximately 2100 volatiles and around 9200 synonyms as well as physicochemical properties, commercial availability and references. The volatiles are classified according to their origin, functionality and odorant groups. The information was extracted from the literature and web resources. SuperScent offers several search options, e.g. name, Pubchem ID number, species, functional groups, or molecular weight. SuperScent is available online at: http://bioinformatics.charite.de/superscent.

Toxicity versus potency: elucidation of toxicity properties discriminating between toxins, drugs, and natural compounds.

Within our everyday life we are confronted with a variety of toxic substances. A number of these compounds are already used as lead structures for the development of new drugs, but the amount of toxic substances is still a rich resource of new bioactive compounds. During the identification and development of new potential drugs, risk estimation of health hazards is an essential and topical subject in pharmaceutical industry. To face this challenge, an extensive investigation of known toxic compounds is going to be helpful to estimate the toxicity of potential drugs. "Toxicity properties" found during those investigations will also function as a guideline for the toxicological classification of other unknown substances. We have compiled a dataset of approximately 50,000 toxic compounds from literature and web sources. All compounds were classified according to their toxicity. During this study the collection of toxic compounds was investigated extensively regarding their chemical, functional, and structural properties and compared with a dataset of drugs and natural compounds. We were able to identify differences in properties within the toxic compounds as well as in comparison to drugs and natural compounds. These properties include molecular weight, hydrogen bond donors and acceptors, and functional groups which can be regarded as "toxicity properties", i.e. attributes defining toxicity.