Global landscape of protein complexes in the yeast Saccharomyces cerevisiae.

Identification of protein-protein interactions often provides insight into protein function, and many cellular processes are performed by stable protein complexes. We used tandem affinity purification to process 4,562 different tagged proteins of the yeast Saccharomyces cerevisiae. Each preparation was analysed by both matrix-assisted laser desorption/ionization-time of flight mass spectrometry and liquid chromatography tandem mass spectrometry to increase coverage and accuracy. Machine learning was used to integrate the mass spectrometry scores and assign probabilities to the protein-protein interactions. Among 4,087 different proteins identified with high confidence by mass spectrometry from 2,357 successful purifications, our core data set (median precision of 0.69) comprises 7,123 protein-protein interactions involving 2,708 proteins. A Markov clustering algorithm organized these interactions into 547 protein complexes averaging 4.9 subunits per complex, about half of them absent from the MIPS database, as well as 429 additional interactions between pairs of complexes. The data (all of which are available online) will help future studies on individual proteins as well as functional genomics and systems biology.

Modularity of the transcriptional response of protein complexes in yeast.

A comprehensive study is performed on the condition-dependent expression of genes coding for the components of hand curated multi-protein complexes of the yeast Saccharomyces cerevisiae, in order to identify coherent transcriptional modules within these complexes. Such modules are defined as groups of genes within complexes whose expression profiles under a common set of experimental conditions allow us to discriminate them from random sets of genes. Our analysis reveals that complexes such as the cytoplasmic ribosome, the proteasome and the respiration chain complexes previously characterized as "stable" or "permanent" represent transcriptional modules that are coherently up or down-regulated in many different conditions. Overall however, some level of coherent expression is detected only in 71 out of the total of 113 complexes with at least five different protein components that could be reliably analyzed. Of these, 26 behave as coherently expressed transcriptional modules encompassing all the components of the complex. In another 15, at least half of the components make up such modules and in ten, few or no modules are detected. In an additional 20 complexes coherent expression is detected, but in too few conditions to enable reliable module detection. Interestingly, the transcriptional modules, when detected, often correspond to one or more known sub-complexes with specific functions. Furthermore, detected modules are generally consistent with transcriptional modules identified on the basis of predicted cis-regulatory sequence motifs. Also, groups of genes shared between complexes that carry out related functions tend to be part of overlapping transcriptional modules identified in these complexes. Together these findings suggest that transcriptional modules may represent basic functional and evolutionary building blocs of protein complexes.

GenePro: a Cytoscape plug-in for advanced visualization and analysis of interaction networks.

MOTIVATION: Analyzing the networks of interactions between genes and proteins has become a central theme in systems biology. Versatile software tools for interactively displaying and analyzing these networks are therefore very much in demand. The public-domain open software environment Cytoscape has been developed with the goal of facilitating the design and development of such software tools by the scientific community. RESULTS: We present GenePro, a plugin to Cytoscape featuring a set of versatile tools that greatly facilitates the visualization and analysis of protein networks derived from high-throughput interactions data and the validation of various methods for parsing these networks into meaningful functional modules. AVAILABILITY: The GenePro plugin is available at the website http://genepro.ccb.sickkids.ca.

Integrative bioinformatics: making sense of the networks.

The focus of biology has shifted from the investigation of individual genes and proteins to the study of large complex networks featuring interactions between tens of thousands of molecular and cellular components. Information on these networks is obtained from genome-scale experimental and theoretical analyses, which yield valuable but noisy data, on biological processes that are still poorly understood. The new exciting developments in bioinformatics show great promise in meeting the challenge of extracting biological insight from these data.:

Improved immobilized metal affinity chromatography for large-scale phosphoproteomics applications.

Dysregulated protein phosphorylation is a primary culprit in multiple physiopathological states. Hence, although analysis of signaling cascades on a proteome-wide scale would provide significant insight into both normal and aberrant cellular function, such studies are simultaneously limited by sheer biological complexity and concentration dynamic range. In principle, immobilized metal affinity chromatography (IMAC) represents an ideal enrichment method for phosphoproteomics. However, anecdotal evidence suggests that this technique is not widely and successfully applied beyond analysis of simple standards, gel bands, and targeted protein immunoprecipitations. Here, we report significant improvements in IMAC-based methodology for enrichment of phosphopeptides from complex biological mixtures. Moreover, we provide detailed explanation for key variables that in our hands most influenced the outcome of these experiments. Our results indicate 5- to 10-fold improvement in recovery of singly- and multiply phosphorylated peptide standards in addition to significant improvement in the number of high-confidence phosphopeptide sequence assignments from global analysis of cellular lysate. In addition, we quantitatively track phosphopeptide recovery as a function of phosphorylation state, and provide guidance for impedance-matching IMAC column capacity with anticipated phosphopeptide content of complex mixtures. Finally, we demonstrate that our improved methodology provides for identification of phosphopeptide distributions that closely mimic physiological conditions.

The reproducible acquisition of comparative liquid chromatography/tandem mass spectrometry data from complex biological samples.

An in-depth study of the reproducibility of data acquired for comparative proteomics analysis using a prototype two-stage heated laminar flow chamber fitted to a commercial high-performance liquid chromatography/tandem mass spectrometry (HPLC/MS/MS) instrument was undertaken. The study is based on 24 replicate samples from four independent membrane preparations derived from two matched breast cancer cell lines. Variation and reproducibility in the data were evaluated at several levels highlighting the relative efficiency and variability of the acquisition routines used. Specifically, variation in the number and relative intensities of chromatographic peaks eluted from the LC column, precursor ion selection and sequence identification were evaluated. On average, approximately 6500 chromatographic peaks were generated for each acquisition with a corresponding coefficient of variance (CV) of less than 20%. Precursor ion selection and sequence identification averaged 1380 and 780 events per acquisition sample, respectively, with corresponding CVs of less than 10% for each. The reproducibility in the precursor ion selection was typically better than 60% between similar replicates. Using protein and peptide internal standards, it was found that the CV in retention time across the gradient between two acquisition pairs was typically less than 5%, whereas the average intensity ratio was 1.0 (expected) with a CV approaching 20%. An evaluation of the intensity ratios calculated from endogenous peptide sequences, identified across the acquisition set, indicated a CV of approximately 30%. Similarly, the CV associated with the top 1000 peptides indicated a mean and median of 28.4 and 26.95%. For a given acquisition pair it was also found that approximately 11% of the chromatographic peaks eluting from the column were linked to a sequence or identified. For these experiments, less than 10% of the peak pairs had absolute ratios greater than 2.0 and of those only approximately 10% had sequences linked to them. For each matched acquisition set on average 406 proteins were identified with a CV of less than 10%. Of the proteins that were identified approximately 30% had at least one predicted trans-membrane domain, indicating a four-fold increase over a crude homogenate sample with only minor enrichment. During these experiments it was found that the interface did not significantly alter the relative charge state distribution of ions, nor did it introduce significant interference from background ions. The interface was capable of 24-hour acquisition cycles.