The Protein Information and Property Explorer 2: gaggle-like exploration of biological proteomic data within one webpage

The Protein Information and Property Explorer 2 (PIPE2) is an enhanced software program and updated web application that aims at providing the proteomic researcher a simple, intuitive user interface through which to begin inquiry into the biological significance of a list of proteins typically produced by MS/MS proteomic processing software. PIPE2 includes an improved interface, new data visualization options, and new data analysis methods for combining disparate, but related, data sets. In particular, PIPE2 has been enhanced to handle multi-dimensional data such as protein abundance, gene expression, and/or interaction data. The current architecture of PIPE2, modeled after that of Gaggle (a programming infrastructure for interoperability between separately developed software tools), contains independent functional units that can be instantiated and pieced together at the user's discretion to form a pipelined analysis workflow. Among these functional units is the Network Viewer component, which adds rich network analysis capabilities to the suite of existing proteomic web resources. Additionally, PIPE2 implements a framework within which new analysis procedures can be easily deployed and distributed over the World Wide Web. PIPE2 is available as a web service at http://pipe2.systemsbiology.net/.     

Microarray image analysis: background estimation using quantile and morphological filters

Background

Identification of protein interaction networks has received considerable attention in the post-genomic era. The currently available biochemical approaches used to detect protein-protein interactions are all time and labour intensive. Consequently there is a growing need for the development of computational tools that are capable of effectively identifying such interactions.

Results

Here we explain the development and implementation of a novel Protein-Protein Interaction Prediction Engine termed PIPE. This tool is capable of predicting protein-protein interactions for any target pair of the yeast Saccharomyces cerevisiae proteins from their primary structure and without the need for any additional information or predictions about the proteins. PIPE showed a sensitivity of 61% for detecting any yeast protein interaction with 89% specificity and an overall accuracy of 75%. This rate of success is comparable to those associated with the most commonly used biochemical techniques. Using PIPE, we identified a novel interaction between YGL227W (vid30) and YMR135C (gid8) yeast proteins. This lead us to the identification of a novel yeast complex that here we term vid30 complex (vid30c). The observed interaction was confirmed by tandem affinity purification (TAP tag), verifying the ability of PIPE to predict novel protein-protein interactions. We then used PIPE analysis to investigate the internal architecture of vid30c. It appeared from PIPE analysis that vid30c may consist of a core and a secondary component. Generation of yeast gene deletion strains combined with TAP tagging analysis indicated that the deletion of a member of the core component interfered with the formation of vid30c, however, deletion of a member of the secondary component had little effect (if any) on the formation of vid30c. Also, PIPE can be used to analyse yeast proteins for which TAP tagging fails, thereby allowing us to predict protein interactions that are not included in genome-wide yeast TAP tagging projects.

Conclusion

PIPE analysis can predict yeast protein-protein interactions. Also, PIPE analysis can be used to study the internal architecture of yeast protein complexes. The data also suggests that a finite set of short polypeptide signals seem to be responsible for the majority of the yeast protein-protein interactions.     

Proteome profiling reveals potential toxicity and detoxification pathways following exposure of BEAS-2B cells to engineered nanoparticle titanium dioxide

Oxidative stress is known to play important roles in engineered nanomaterial‐induced cellular toxicity. However, the proteins and signaling pathways associated with the engineered nanomaterial‐mediated oxidative stress and toxicity are largely unknown. To identify these toxicity pathways and networks that are associated with exposure to engineered nanomaterials, an integrated proteomic study was conducted using human bronchial epithelial cells, BEAS‐2B and nanoscale titanium dioxide. Utilizing 2‐DE and MS, we identified 46 proteins that were altered at protein expression levels. The protein changes detected by 2‐DE/MS were verified by functional protein assays. These identified proteins include some key proteins involved in cellular stress response, metabolism, adhesion, cytoskeletal dynamics, cell growth, cell death, and cell signaling. The differentially expressed proteins were mapped using Ingenuity Pathway Analyses^? canonical pathways and Ingenuity Pathway Analyses tox lists to create protein‐interacting networks and proteomic pathways. Twenty protein canonical pathways and tox lists were generated, and these pathways were compared to signaling pathways generated from genomic analyses of BEAS‐2B cells treated with titanium dioxide. There was a significant overlap in the specific pathways and lists generated from the proteomic and the genomic data. In addition, we also analyzed the phosphorylation profiles of protein kinases in titanium dioxide‐treated BEAS‐2B cells for a better understanding of upstream signaling pathways in response to the titanium dioxide treatment and the induced oxidative stress. In summary, the present study provides the first protein‐interacting network maps and novel insights into the biological responses and potential toxicity and detoxification pathways of titanium dioxide.     

Sperm membrane physiology and relevance for fertilization

This paper aims to overview recent insights in sperm surface remodelling pertinent to fertilization. A basic understanding of this remodelling is required to interpret the high amount of data appearing from high-throughput identification techniques for proteins presently applied in reproductive biology. From the extensive lists of protein candidates identified by proteomics, only a few are recognized to be directly involved in fertilization. Others are indirectly involved, but many are not yet considered to be involved in fertilization. Some of these newly identified and unexpected proteins may shed new light in the current molecular models for fertilization. However, the gathered lists of sperm proteins possibly involved in fertilization do only tell a part of the story regarding how fertilization is accomplished. When considering the identification of proteins involved in fertilization, one also needs to take into account the fundamental mechanisms involved in the redistribution of sperm surface proteins in membrane protein complexes and the involvement of cell signalling events that regulate their post-translational modification status. Both processes are likely requisite for protein configuration and grouping into functional membrane protein complexes necessary to elicit their delicate roles in fertilization. This paper emphasizes biochemical models for membrane surface modelling and their potential involvement for remodelling the sperm surface in the above described processes.     

LS-SNP: large-scale annotation of coding non-synonymous SNPs based on multiple information sources

MOTIVATION: The NCBI dbSNP database lists over 9 million single nucleotide polymorphisms (SNPs) in the human genome, but currently contains limited annotation information. SNPs that result in amino acid residue changes (nsSNPs) are of critical importance in variation between individuals, including disease and drug sensitivity. RESULTS: We have developed LS-SNP, a genomic scale software pipeline to annotate nsSNPs. LS-SNP comprehensively maps nsSNPs onto protein sequences, functional pathways and comparative protein structure models, and predicts positions where nsSNPs destabilize proteins, interfere with the formation of domain-domain interfaces, have an effect on protein-ligand binding or severely impact human health. It currently annotates 28,043 validated SNPs that produce amino acid residue substitutions in human proteins from the SwissProt/TrEMBL database. Annotations can be viewed via a web interface either in the context of a genomic region or by selecting sets of SNPs, genes, proteins or pathways. These results are useful for identifying candidate functional SNPs within a gene, haplotype or pathway and in probing molecular mechanisms responsible for functional impacts of nsSNPs. AVAILABILITY: http://www.salilab.org/LS-SNP CONTACT: rachelk@salilab.org SUPPLEMENTARY INFORMATION: http://salilab.org/LS-SNP/supp-info.pdf.     

Analyzing protein lists with large networks: edge-count probabilities in random graphs with given expected degrees.

We present an analytical framework to analyze lists of proteins with large undirected graphs representing their known functional relationships. We consider edge-count variables such as the number of interactions between a protein and a list, the size of a subgraph induced by a list, and the number of interactions bridging two lists. We derive approximate analytical expressions for the probability distributions of these variables in a model of a random graph with given expected degrees. Probabilities obtained with the analytical expressions are used to mine a protein interaction network for functional modules, characterize the connectedness of protein functional categories, and measure the strength of relations between modules.     

Towards complete sets of farnesylated and geranylgeranylated proteins

Three different prenyltransferases attach isoprenyl anchors to C-terminal motifs in substrate proteins. These lipid anchors serve for membrane attachment or protein-protein interactions in many pathways. Although well-tolerated selective prenyltransferase inhibitors are clinically available, their mode of action remains unclear since the known substrate sets of the various prenyltransferases are incomplete. The Prenylation Prediction Suite (PrePS) has been applied for large-scale predictions of prenylated proteins. To prioritize targets for experimental verification, we rank the predictions by their functional importance estimated by evolutionary conservation of the prenylation motifs within protein families. The ranked lists of predictions are accessible as PRENbase (http://mendel.imp.univie.ac.at/sat/PrePS/PRENbase) and can be queried for verification status, type of modifying enzymes (anchor type), and taxonomic distribution. Our results highlight a large group of plant metal-binding chaperones as well as several newly predicted proteins involved in ubiquitin-mediated protein degradation, enriching the known functional repertoire of prenylated proteins. Furthermore, we identify two possibly prenylated proteins in Mimivirus. The section HumanPRENbase provides complete lists of predicted prenylated human proteins-for example, the list of farnesyltransferase targets that cannot become substrates of geranylgeranyltransferase 1 and, therefore, are especially affected by farnesyltransferase inhibitors (FTIs) used in cancer and anti-parasite therapy. We report direct experimental evidence verifying the prediction of the human proteins Prickle1, Prickle2, the BRO1 domain-containing FLJ32421 (termed BROFTI), and Rab28 (short isoform) as exclusive farnesyltransferase targets. We introduce PRENbase, a database of large-scale predictions of protein prenylation substrates ranked by evolutionary conservation of the motif. Experimental evidence is presented for the selective farnesylation of targets with an evolutionary conserved modification site.     

Back to the future—The value of single protein species investigations

In proteomics, in the past years, there was a focus on high throughput and reaching of large numbers of identified proteins with the basic discourse of protein expression. To avoid the impression of producing pure lists attempts are usually made to correlate proteins changed in amount between two biological situations to different pathways or protein interactions. This discourse is based on two simplifications, which limit the applicability of proteomics drastically: (i) it is sufficient to quantify a protein from several enzymatic digestion products; (ii) a biological situation is sufficiently described, if a peptide with its PTM is identified, resulting in long lists of modified peptides with data amounts, which are not anymore made accessible for the reader of a publication. The elucidation of N‐terminal methylation of proteasome subunit Rpt1 in yeast by Kimura et al. (Proteomics 2013, 13, 3167–3174) , which represents the focus on one protein, shows the value of solid chemical analysis with a complete data documentation and paves the way to proteomics based on the protein speciation discourse.     

SNVDis: A Proteome-wide Analysis Service for Evaluating nsSNVs in Protein Functional Sites and Pathways

Amino acid changes due to non-synonymous variation are included as annotations for individual proteins in UniProtKB/Swiss-Prot and RefSeq which present biological data in a protein-or gene-centric fashion. Unfortunately, proteome-wide analysis of non-synonymous singlenucleotide variations (nsSNVs) is not easy to perform because information on nsSNVs and functionally important sites are not well integrated both within and between databases and their search engines. We have developed SNVDis that allows evaluation of proteome-wide nsSNV distribution in functional sites, domains and pathways. More specifically, we have integrated human-specific data from major variation databases (UniProtKB, dbSNP and COSMIC), comprehensive sequence feature annotation from UniProtKB, Pfam, RefSeq, Conserved Domain Database (CDD) and pathway information from Protein ANalysis THrough Evolutionary Relationships (PANTHER) and mapped all of them in a uniform and comprehensive way to the human reference proteome provided by UniProtKB/Swiss-Prot. Integrated information of active sites, pathways, binding sites, domains, which are extracted from a number of different sources, provides a detailed overview of how nsSNVs are distributed over the human proteome and pathways and how they intersect with functional sites of proteins. Additionally, it is possible to find out whether there is an over-or under-representation of nsSNVs in specific domains, pathways or user-defined protein lists. The underlying datasets are updated once every 3 months. SNVDis is freely available at http://hive.biochemistry.gwu.edu/tool/snvdis.     

Proteomic parsimony through bipartite graph analysis improves accuracy and transparency

Assembling peptides identified from LC-MS/MS spectra into a list of proteins is a critical step in analyzing shotgun proteomics data. As one peptide sequence can be mapped to multiple proteins in a database, na?ve protein assembly can substantially overstate the number of proteins found in samples. We model the peptide-protein relationships in a bipartite graph and use efficient graph algorithms to identify protein clusters with shared peptides and to derive the minimal list of proteins. We test the effects of this parsimony analysis approach using MS/MS data sets generated from a defined human protein mixture, a yeast whole cell extract, and a human serum proteome after MARS column depletion. The results demonstrate that the bipartite parsimony technique not only simplifies protein lists but also improves the accuracy of protein identification. We use bipartite graphs for the visualization of the protein assembly results to render the parsimony analysis process transparent to users. Our approach also groups functionally related proteins together and improves the comprehensibility of the results. We have implemented the tool in the IDPicker package. The source code and binaries for this protein assembly pipeline are available under Mozilla Public License at the following URL: http://www.mc.vanderbilt.edu/msrc/bioinformatics/.     

Robust and highly accurate automatic NOESY assignment and structure determination with Rosetta

We have developed a novel and robust approach for automatic and unsupervised simultaneous nuclear Overhauser effect (NOE) assignment and structure determination within the CS-Rosetta framework. Starting from unassigned peak lists and chemical shift assignments, autoNOE-Rosetta determines NOE cross-peak assignments and generates structural models. The approach tolerates incomplete and raw NOE peak lists as well as incomplete or partially incorrect chemical shift assignments, and its performance has been tested on 50 protein targets ranging from 50 to 200 residues in size. We find a significantly improved performance compared to established programs, particularly for larger proteins and for NOE data obtained on perdeuterated protein samples. X-ray crystallographic structures allowed comparison of Rosetta and conventional, PDB-deposited, NMR models in 20 of 50 test cases. The unsupervised autoNOE-Rosetta models were often of significantly higher accuracy than the corresponding expert-supervised NMR models deposited in the PDB. We also tested the method with unrefined peak lists and found that performance was nearly as good as for refined peak lists. Finally, demonstrating our method’s remarkable robustness against problematic input data, we provided correct models for an incorrect PDB-deposited NMR solution structure.     

Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions

MOTIVATION: Most approaches in predicting protein function from protein-protein interaction data utilize the observation that a protein often share functions with proteins that interacts with it (its level-1 neighbours). However, proteins that interact with the same proteins (i.e. level-2 neighbours) may also have a greater likelihood of sharing similar physical or biochemical characteristics. We speculate that functional similarity between a protein and its neighbours from the two different levels arise from two distinct forms of functional association, and a protein is likely to share functions with its level-1 and/or level-2 neighbours. We are interested in finding out how significant is functional association between level-2 neighbours and how they can be exploited for protein function prediction. RESULTS: We made a statistical study on recent interaction data and observed that functional association between level-2 neighbours is clearly observable. A substantial number of proteins are observed to share functions with level-2 neighbours but not with level-1 neighbours. We develop an algorithm that predicts the functions of a protein in two steps: (1) assign a weight to each of its level-1 and level-2 neighbours by estimating its functional similarity with the protein using the local topology of the interaction network as well as the reliability of experimental sources and (2) scoring each function based on its weighted frequency in these neighbours. Using leave-one-out cross validation, we compare the performance of our method against that of several other existing approaches and show that our method performs relatively well.     

FIMM, a database of functional molecular immunology: update 2002

FIMM database (http://sdmc.krdl.org.sg:8080/fimm) contains data relevant to functional molecular immunology, focusing on cellular immunology. It contains fully referenced data on protein antigens, major histocompatibility complex (MHC) molecules, MHC-associated peptides and relevant disease associations. FIMM has a set of search tools for extraction of information and results are presented as lists or as reports.     

FIMM, a database of functional molecular immunology

FIMM database (http://sdmc.krdl.org.sg:8080/fimm ) contains data relevant to functional molecular immunology, focusing on cellular immunology. It contains fully referenced data on protein antigens, major histocompatibility complex (MHC) molecules, MHC-associated peptides and relevant disease associations. FIMM has a set of search tools for extraction of information and results are presented as lists or as reports.     

PDQ Wizard: automated prioritization and characterization of gene and protein lists using biomedical literature

SUMMARY: PDQ Wizard automates the process of interrogating biomedical references using large lists of genes, proteins or free text. Using the principle of linkage through co-citation biologists can mine PubMed with these proteins or genes to identify relationships within a biological field of interest. In addition, PDQ Wizard provides novel features to define more specific relationships, highlight key publications describing those activities and relationships, and enhance protein queries. PDQ Wizard also outputs a metric that can be used for prioritization of genes and proteins for further research. AVAILABILITY: PDQ Wizard is freely available from http://www.gti.ed.ac.uk/pdqwizard/.     

Helicobacter pylori proteomics by 2‐DE/MS, 1‐DE‐LC/MS and functional data mining

With its predicted proteome of 1550 proteins (data set Etalon) Helicobacter pylori 26695 represents a perfect model system of medium complexity for investigating basic questions in proteomics. We analyzed urea‐solubilized proteins by 2‐DE/MS (data set 2‐DE) and by 1‐DE‐LC/MS (Supprot); proteins insoluble in 9 M urea but solubilized by SDS (Pellet); proteins precipitating in the Sephadex layer at the application side of IEF (Sephadex) by 1‐DE‐LC/MS; and proteins precipitating close to the application side within the IEF gel by LC/MS (Startline). The experimental proteomics data of H. pylori comprising 567 proteins (protein coverage: 36.6%) were stored in the Proteome Database System for Microbial Research ( http://www.mpiib‐berlin.mpg.de/2D‐PAGE/ ), which gives access to raw mass spectra (MALDI‐TOF/TOF) in T2D format, as well as to text files of peak lists. For data mining the protein mapping and comparison tool PROMPT ( http://webclu.bio.wzw.tum.de/prompt/ ) was used. The percentage of proteins with transmembrane regions, relative to all proteins detected, was 0, 0.2, 0, 0.5, 3.8 and 6.3% for 2‐DE, Supprot, Startline, Sephadex, Pellet, and Etalon, respectively. 2‐DE does not separate membrane proteins because they are insoluble in 9 M urea/70 mM DTT and 2% CHAPS. SDS solubilizes a considerable portion of the urea‐insoluble proteins and makes them accessible for separation by SDS‐PAGE and LC. The 2‐DE/MS analysis with urea‐solubilized proteins and the 1‐DE‐LC/MS analysis with the urea‐insoluble protein fraction (Pellet) are complementary procedures in the pursuit of a complete proteome analysis. Access to the PROMPT‐generated diagrams in the Proteome Database allows the mining of experimental data with respect to other functional aspects.