Relative predicted protein levels of functionally associated proteins are conserved across organisms

We show that the predicted protein levels of functionally related proteins change in a coordinated fashion over many unicellular organisms. For each protein, we created a profile containing a protein abundance measure in each of a set of organisms. We show that for functionally related proteins these profiles tend to be correlated. Using the Codon Adaptation Index as a predictor of protein abundance in 48 unicellular organisms, we demonstrated this phenomenon for two types of functional relations: for proteins that physically interact and for proteins involved in consecutive steps within a metabolic pathway. Our results suggest that the protein abundance levels of functionally related proteins co-evolve.     

Improvements in proteomic metrics of low abundance proteins through proteome equalization using ProteoMiner prior to MudPIT

Ideally, shotgun proteomics would facilitate the identification of an entire proteome with 100% protein sequence coverage. In reality, the large dynamic range and complexity of cellular proteomes results in oversampling of abundant proteins, while peptides from low abundance proteins are undersampled or remain undetected. We tested the proteome equalization technology, ProteoMiner, in conjunction with Multidimensional Protein Identification Technology (MudPIT) to determine how the equalization of protein dynamic range could improve shotgun proteomics methods for the analysis of cellular proteomes. Our results suggest low abundance protein identifications were improved by two mechanisms: (1) depletion of high abundance proteins freed ion trap sampling space usually occupied by high abundance peptides and (2) enrichment of low abundance proteins increased the probability of sampling their corresponding more abundant peptides. Both mechanisms also contributed to dramatic increases in the quantity of peptides identified and the quality of MS/MS spectra acquired due to increases in precursor intensity of peptides from low abundance proteins. From our large data set of identified proteins, we categorized the dominant physicochemical factors that facilitate proteome equalization with a hexapeptide library. These results illustrate that equalization of the dynamic range of the cellular proteome is a promising methodology to improve low abundance protein identification confidence, reproducibility, and sequence coverage in shotgun proteomics experiments, opening a new avenue of research for improving proteome coverage.     

Protein identification in complex mixtures

This paper investigates the prospects of successful mass spectrometric protein identification based on mass data from proteolytic digests of complex protein mixtures. Sets of proteolytic peptide masses representing various numbers of digested proteins in a mixture were generated in silico. In each set, different proteins were selected from a protein sequence collection and for each protein the sequence coverage was randomly selected within a particular regime (15-30% or 30-60%). We demonstrate that the Probity algorithm, which is characterized by an optimal tolerance for random interference, employed in an iterative procedure can correctly identify >95% of proteins at a desired significance level in mixtures composed of hundreds of yeast proteins under realistic mass spectrometric experimental constraints. By using a model of the distribution of protein abundance, we demonstrate that the very high efficiency of identification of protein mixtures that can be achieved by appropriate choices of informatics procedures is hampered by limitations of the mass spectrometric dynamic range. The results stress the desire to choose carefully experimental protocols for comprehensive proteome analysis, focusing on truly critical issues such as the dynamic range, which potentially limits the possibilities of identifying low abundance proteins.     

A Sampling of the Yeast Proteome

In this study, we examined yeast proteins by two-dimensional (2D) gel electrophoresis and gathered quantitative information from about 1,400 spots. We found that there is an enormous range of protein abundance and, for identified spots, a good correlation between protein abundance, mRNA abundance, and codon bias. For each molecule of well-translated mRNA, there were about 4,000 molecules of protein. The relative abundance of proteins was measured in glucose and ethanol media. Protein turnover was examined and found to be insignificant for abundant proteins. Some phosphoproteins were identified. The behavior of proteins in differential centrifugation experiments was examined. Such experiments with 2D gels can give a global view of the yeast proteome.     

Coverage of protein sequence space by current structural genomics targets

By its purest definition the ultimate goal of structural genomics (SG) is the determination of the structures of all proteins encoded by genomes. Most of these will be obtained by homology modeling using the structures of a set of target proteins for experimental determination. Thanks to the open exchange of SG target information, we are able to analyze the sequences of the current target list to evaluate the extent of its coverage of protein sequence space. The presence of homologous sequences currently either in the Protein Data Bank (PDB) or among SG targets has been determined for each of the protein sequences in several organisms. In this way we are able to evaluate the coverage by existing or targeted structural data for the non-membranous parts of entire proteomes. For small bacterial proteomes such as that of H. influenzae almost all proteins have homologous sequences among SG targets or in the PDB. There is significantly lower coverage for more complex organisms, such as C. elegans. We have mapped the SG target list onto the ProtoMap clustering of protein sequences. Clusters occupied by SG targets represent over 150,000 protein sequences, which is approximately 44% of the total protein sequences classified by ProtoMap. The mapping of SG targets also enables an evaluation of the degree of overlap within the target list. An SG target typically occupies a ProtoMap cluster with more than six other homologous targets.     

Regular patterns for proteome-wide distribution of protein abundance across species

A proteome of the bio-entity, including cell, tissue, organ, and organism, consists of proteins of diverse abundance. The principle that determines the abundance of different proteins in a proteome is of fundamental significance for an understanding of the building blocks of the bio-entity. Here, we report three regular patterns in the proteome-wide distribution of protein abundance across species such as human, mouse, fly, worm, yeast, and bacteria: in most cases, protein abundance is positively correlated with the protein's origination time or sequence conservation during evolution; it is negatively correlated with the protein's domain number and positively correlated with domain coverage in protein structure, and the correlations became stronger during the course of evolution; protein abundance can be further stratified by the function of the protein, whereby proteins that act on material conversion and transportation (mass category) are more abundant than those that act on information modulation (information category). Thus, protein abundance is intrinsically related to the protein's inherent characters of evolution, structure, and function.     

Proteomic and phosphoproteomic comparison of human ES and iPS cells

Combining high-mass-accuracy mass spectrometry, isobaric tagging and software for multiplexed, large-scale protein quantification, we report deep proteomic coverage of four human embryonic stem cell and four induced pluripotent stem cell lines in biological triplicate. This 24-sample comparison resulted in a very large set of identified proteins and phosphorylation sites in pluripotent cells. The statistical analysis afforded by our approach revealed subtle but reproducible differences in protein expression and protein phosphorylation between embryonic stem cells and induced pluripotent cells. Merging these results with RNA-seq analysis data, we found functionally related differences across each tier of regulation. We also introduce the Stem Cell-Omics Repository (SCOR), a resource to collate and display quantitative information across multiple planes of measurement, including mRNA, protein and post-translational modifications.     

Estimation of absolute protein quantities of unlabeled samples by selected reaction monitoring mass spectrometry

For many research questions in modern molecular and systems biology, information about absolute protein quantities is imperative. This information includes, for example, kinetic modeling of processes, protein turnover determinations, stoichiometric investigations of protein complexes, or quantitative comparisons of different proteins within one sample or across samples. To date, the vast majority of proteomic studies are limited to providing relative quantitative comparisons of protein levels between limited numbers of samples. Here we describe and demonstrate the utility of a targeting MS technique for the estimation of absolute protein abundance in unlabeled and nonfractionated cell lysates. The method is based on selected reaction monitoring (SRM) mass spectrometry and the "best flyer" hypothesis, which assumes that the specific MS signal intensity of the most intense tryptic peptides per protein is approximately constant throughout a whole proteome. SRM-targeted best flyer peptides were selected for each protein from the peptide precursor ion signal intensities from directed MS data. The most intense transitions per peptide were selected from full MS/MS scans of crude synthetic analogs. We used Monte Carlo cross-validation to systematically investigate the accuracy of the technique as a function of the number of measured best flyer peptides and the number of SRM transitions per peptide. We found that a linear model based on the two most intense transitions of the three best flying peptides per proteins (TopPep3/TopTra2) generated optimal results with a cross-correlated mean fold error of 1.8 and a squared Pearson coefficient R(2) of 0.88. Applying the optimized model to lysates of the microbe Leptospira interrogans, we detected significant protein abundance changes of 39 target proteins upon antibiotic treatment, which correlate well with literature values. The described method is generally applicable and exploits the inherent performance advantages of SRM, such as high sensitivity, selectivity, reproducibility, and dynamic range, and estimates absolute protein concentrations of selected proteins at minimized costs.     

Accuracy improvement in protein complex prediction from protein interaction networks by refining cluster overlaps

Background

Recent computational techniques have facilitated analyzing genome-wide protein-protein interaction data for several model organisms. Various graph-clustering algorithms have been applied to protein interaction networks on the genomic scale for predicting the entire set of potential protein complexes. In particular, the density-based clustering algorithms which are able to generate overlapping clusters, i.e. the clusters sharing a set of nodes, are well-suited to protein complex detection because each protein could be a member of multiple complexes. However, their accuracy is still limited because of complex overlap patterns of their output clusters.

Results

We present a systematic approach of refining the overlapping clusters identified from protein interaction networks. We have designed novel metrics to assess cluster overlaps: overlap coverage and overlapping consistency. We then propose an overlap refinement algorithm. It takes as input the clusters produced by existing density-based graph-clustering methods and generates a set of refined clusters by parameterizing the metrics. To evaluate protein complex prediction accuracy, we used the f-measure by comparing each refined cluster to known protein complexes. The experimental results with the yeast protein-protein interaction data sets from BioGRID and DIP demonstrate that accuracy on protein complex prediction has increased significantly after refining cluster overlaps.

Conclusions

The effectiveness of the proposed cluster overlap refinement approach for protein complex detection has been validated in this study. Analyzing overlaps of the clusters from protein interaction networks is a crucial task for understanding of functional roles of proteins and topological characteristics of the functional systems.

     

Biodiversity informatics: organizing and linking information across the spectrum of life

Biological knowledge can be inferred from three major levels of information: molecules, organisms and ecologies. Bioinformatics is an established field that has made significant advances in the development of systems and techniques to organize contemporary molecular data; biodiversity informatics is an emerging discipline that strives to develop methods to organize knowledge at the organismal level extending back to the earliest dates of recorded natural history. Furthermore, while bioinformatics studies generally focus on detailed examinations of key 'model' organisms, biodiversity informatics aims to develop over-arching hypotheses that span the entire tree of life. Biodiversity informatics is presented here as a discipline that unifies biological information from a range of contemporary and historical sources across the spectrum of life using organisms as the linking thread. The present review primarily focuses on the use of organism names as a universal metadata element to link and integrate biodiversity data across a range of data sources.     

Proteome sampling by the HLA class I antigen processing pathway

The peptide repertoire that is presented by the set of HLA class I molecules of an individual is formed by the different players of the antigen processing pathway and the stringent binding environment of the HLA class I molecules. Peptide elution studies have shown that only a subset of the human proteome is sampled by the antigen processing machinery and represented on the cell surface. In our study, we quantified the role of each factor relevant in shaping the HLA class I peptide repertoire by combining peptide elution data, in silico predictions of antigen processing and presentation, and data on gene expression and protein abundance. Our results indicate that gene expression level, protein abundance, and rate of potential binding peptides per protein have a clear impact on sampling probability. Furthermore, once a protein is available for the antigen processing machinery in sufficient amounts, C-terminal processing efficiency and binding affinity to the HLA class I molecule determine the identity of the presented peptides. Having studied the impact of each of these factors separately, we subsequently combined all factors in a logistic regression model in order to quantify their relative impact. This model demonstrated the superiority of protein abundance over gene expression level in predicting sampling probability. Being able to discriminate between sampled and non-sampled proteins to a significant degree, our approach can potentially be used to predict the sampling probability of self proteins and of pathogen-derived proteins, which is of importance for the identification of autoimmune antigens and vaccination targets.