Alexey I. Nesvizhskii

Interpretation of Shotgun Proteomic Data The Protein Inference Problem

The shotgun proteomic strategy based on digesting proteins into peptides and sequencing them using tandem mass spectrometry and automated database searching has become the method of choice for identifying proteins in most large scale studies. However, the peptide-centric nature of shotgun proteomics complicates the analysis and biological interpretation of the data especially in the case of higher eukaryote organisms. The same peptide sequence can be present in multiple different proteins or protein isoforms. Such shared peptides therefore can lead to ambiguities in determining the identities of sample proteins. In this article we illustrate the difficulties of interpreting shotgun proteomic data and discuss the need for common nomenclature and transparent informatic approaches. We also discuss related issues such as the state of protein sequence databases and their role in shotgun proteomic analysis, interpretation of relative peptide quantification data in the presence of multiple protein isoforms, the integration of proteomic and transcriptional data, and the development of a computational infrastructure for the integration of multiple diverse datasets.

Analysis and validation of proteomic data generated by tandem mass spectrometry

The analysis of the large amount of data generated in mass spectrometry–based proteomics experiments represents a significant challenge and is currently a bottleneck in many proteomics projects. In this review we discuss critical issues related to data processing and analysis in proteomics and describe available methods and tools. We place special emphasis on the elaboration of results that are supported by sound statistical arguments.

A Global Protein Kinase and Phosphatase Interaction Network in Yeast

Budding Yeast Kinome Revealed Covalent modification of proteins by phosphorylation is a primary means by which cells control the biochemical activities and functions of proteins. To better understand the full spectrum of cellular control mechanisms mediated by phosphorylation, Breitkreutz et al. (p. 1043; see the Perspective by Levy et al.) used mass spectrometry to identify proteins that interacted with the complete set of protein kinases from budding yeast and with other molecules, including phosphatases, which influence phosphorylation reactions. The results reveal a network of interacting protein kinases and phosphatases, and analysis of other interacting proteins suggests previously undiscovered roles for many of these enzymes. Phosphorylation reactions in budding yeast reveal the regulatory architecture of a fundamental cellular control system. The interactions of protein kinases and phosphatases with their regulatory subunits and substrates underpin cellular regulation. We identified a kinase and phosphatase interaction (KPI) network of 1844 interactions in budding yeast by mass spectrometric analysis of protein complexes. The KPI network contained many dense local regions of interactions that suggested new functions. Notably, the cell cycle phosphatase Cdc14 associated with multiple kinases that revealed roles for Cdc14 in mitogen-activated protein kinase signaling, the DNA damage response, and metabolism, whereas interactions of the target of rapamycin complex 1 (TORC1) uncovered new effector kinases in nitrogen and carbon metabolism. An extensive backbone of kinase-kinase interactions cross-connects the proteome and may serve to coordinate diverse cellular responses.

A guided tour of the Trans‐Proteomic Pipeline

The Trans‐Proteomic Pipeline (TPP) is a suite of software tools for the analysis of MS/MS data sets. The tools encompass most of the steps in a proteomic data analysis workflow in a single, integrated software system. Specifically, the TPP supports all steps from spectrometer output file conversion to protein‐level statistical validation, including quantification by stable isotope ratios. We describe here the full workflow of the TPP and the tools therein, along with an example on a sample data set, demonstrating that the setup and use of the tools are straightforward and well supported and do not require specialized informatic resources or knowledge.

The PeptideAtlas project

The completion of the sequencing of the human genome and the concurrent, rapid development of high-throughput proteomic methods have resulted in an increasing need for automated approaches to archive proteomic data in a repository that enables the exchange of data among researchers and also accurate integration with genomic data. PeptideAtlas () addresses these needs by identifying peptides by tandem mass spectrometry (MS/MS), statistically validating those identifications and then mapping identified sequences to the genomes of eukaryotic organisms. A meaningful comparison of data across different experiments generated by different groups using different types of instruments is enabled by the implementation of a uniform analytic process. This uniform statistical validation ensures a consistent and high-quality set of peptide and protein identifications. The raw data from many diverse proteomic experiments are made available in the associated PeptideAtlas repository in several formats. Here we present a summary of our process and details about the Human, Drosophila and Yeast PeptideAtlas builds.

The CRAPome: a Contaminant Repository for Affinity Purification Mass Spectrometry Data

Affinity purification coupled with mass spectrometry (AP-MS) is a widely used approach for the identification of protein-protein interactions. However, for any given protein of interest, determining which of the identified polypeptides represent bona fide interactors versus those that are background contaminants (for example, proteins that interact with the solid-phase support, affinity reagent or epitope tag) is a challenging task. The standard approach is to identify nonspecific interactions using one or more negative-control purifications, but many small-scale AP-MS studies do not capture a complete, accurate background protein set when available controls are limited. Fortunately, negative controls are largely bait independent. Hence, aggregating negative controls from multiple AP-MS studies can increase coverage and improve the characterization of background associated with a given experimental protocol. Here we present the contaminant repository for affinity purification (the CRAPome) and describe its use for scoring protein-protein interactions. The repository (currently available for Homo sapiens and Saccharomyces cerevisiae) and computational tools are freely accessible at http://www.crapome.org/.

The Need for Guidelines in Publication of Peptide and Protein Identification Data Working Group On Publication Guidelines For Peptide And Protein Identification Data

Over the past few years, the number and size of proteomic datasets composed of mass spectrometry-derived protein identifications reported in the literature have grown dramatically. This is a direct result of the widespread availability of instruments, methods, and easy-to-use software for collecting large amounts of data and for converting the observed peptide and fragment-ion masses to peptide and then protein identities. In particular, the analysis of samples containing large numbers of proteins by multidimensional liquid chromatography (LC/LC) coupled on-line with tandem mass spectrometry (MS/MS) is now a common component of many biological projects. Clearly it is in the interest of the scientific community to make such data readily available. However, the publication of large proteomic datasets poses new and significant challenges for authors, reviewers, and readers as universally accepted and widely available computational tools for validation of the published results are not yet available (1). In an effort to ensure that high-quality, significant data are entering the proteomics literature, Molecular & Cellular Proteomics (MCP) is introducing guidelines for authors planning to submit manuscripts containing large numbers of proteins identified primarily by LC-MS/MS. The need for these guidelines is driven in part by the fact that a significant but undefined number of the proteins being reported as “identified” in proteomics articles are likely to be false positives (2). These incorrect matches probably result most often from the use of low-quality peptide MS/MS data to search the database. However, even high-quality data can produce invalid identifications if, for example, the actual peptide sequence is not in the database being searched. Many different algorithms are being used for peptide and protein assignment (e.g. MSTag, Mascot, SEQUEST, SpectrumMill, Sonar, etc.), and each has unique rules for scoring to move the most probable peptide assignment to the top of the “hit” list. In addition, new filtering criteria are being developed that, when layered onto the results from the above algorithms, help to eliminate a certain additional percentage of false positives (3, 4). It is very important that the users of these tools, our authors, have at least a working understanding of how the algorithm they use works. However, even the judicious use of scoring, threshold parameters, and additional filtering criteria for search engines, while serving the very important purpose of reducing the number of misassigned peptides and proteins, does not eliminate the problem. It is almost always possible to match a MS/MS spectrum to a peptide in the database; the difficult part is validating that the match is correct. This is not to imply that the situation is bleak. In fact, most assignments of proteins made with high-quality data and using more than a single peptide to identify a protein are likely correct. Furthermore, improved methods are being developed at a rapid pace. Recently, application of statistical methods to validate peptide assignments to MS/MS spectra of peptides has been shown to be a promising approach, and a number of groups are working in this area (2, 5–11). However, these programs are only beginning to be widely used and they are not universally accepted. MCP fully supports continued development and testing of such programs and will publish new search and filtering approaches to make them widely available to the proteomics community. However, in the absence of accepted standards and widely available tools that operate on such standards, there are guidelines that the journal can formulate that would help ensure the publication of highquality information and to assist readers in being able to make their own assessment of the validity of the assignments in manuscripts. Thus, we introduce the initial set of such guidelines in this issue of the journal. The rationale and purpose of each of these is described below. The first (and obvious) guideline is to obtain sufficient information from authors to document what search engine was used and how peptide and protein assignments were made using that software. Guideline 2 defines how peptides should be counted toward the identification of a protein. We do not at this time attempt to deal with the related issue of what constitutes a “unique” peptide with respect to the proteins identified. For From the ‡Broad Institute, Cambridge, MA 02139; ¶Institute for Systems Biology, Seattle, WA 98103; University of California, San Francisco, CA 94143; and **Millennium Pharmaceuticals, Cambridge, MA 02139 Received, April 7, 2004 Published, MCP Papers in Press, April 8, 2004, DOI 10.1074/mcp.T400006-MCP200 1 The abbreviations used are: LC/LC, multidimensional liquid chromatography; MS/MS, tandem mass spectrometry; MCP, Molecular & Cellular Proteomics. Editorial

An embryonic stem cell chromatin remodeling complex, esBAF, is essential for embryonic stem cell self-renewal and pluripotency

Mammalian SWI/SNF [also called BAF (Brg/Brahma-associated factors)] ATP-dependent chromatin remodeling complexes are essential for formation of the totipotent and pluripotent cells of the early embryo. In addition, subunits of this complex have been recovered in screens for genes required for nuclear reprogramming in Xenopus and mouse embryonic stem cell (ES) morphology. However, the mechanism underlying the roles of these complexes is unclear. Here, we show that BAF complexes are required for the self-renewal and pluripotency of mouse ES cells but not for the proliferation of fibroblasts or other cells. Proteomic studies reveal that ES cells express distinctive complexes (esBAF) defined by the presence of Brg (Brahma-related gene), BAF155, and BAF60A, and the absence of Brm (Brahma), BAF170, and BAF60C. We show that this specialized subunit composition is required for ES cell maintenance and pluripotency. Our proteomic analysis also reveals that esBAF complexes interact directly with key regulators of pluripotency, suggesting that esBAF complexes are specialized to interact with ES cell-specific regulators, providing a potential explanation for the requirement of BAF complexes in pluripotency.

A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics

This manuscript provides a comprehensive review of the peptide and protein identification process using tandem mass spectrometry (MS/MS) data generated in shotgun proteomic experiments. The commonly used methods for assigning peptide sequences to MS/MS spectra are critically discussed and compared, from basic strategies to advanced multi-stage approaches. A particular attention is paid to the problem of false-positive identifications. Existing statistical approaches for assessing the significance of peptide to spectrum matches are surveyed, ranging from single-spectrum approaches such as expectation values to global error rate estimation procedures such as false discovery rates and posterior probabilities. The importance of using auxiliary discriminant information (mass accuracy, peptide separation coordinates, digestion properties, and etc.) is discussed, and advanced computational approaches for joint modeling of multiple sources of information are presented. This review also includes a detailed analysis of the issues affecting the interpretation of data at the protein level, including the amplification of error rates when going from peptide to protein level, and the ambiguities in inferring the identifies of sample proteins in the presence of shared peptides. Commonly used methods for computing protein-level confidence scores are discussed in detail. The review concludes with a discussion of several outstanding computational issues.

Significance Analysis of Spectral Count Data in Label-free Shotgun Proteomics

Spectral counting has become a commonly used approach for measuring protein abundance in label-free shotgun proteomics. At the same time, the development of data analysis methods has lagged behind. Currently most studies utilizing spectral counts rely on simple data transforms and posthoc corrections of conventional signal-to-noise ratio statistics. However, these adjustments can neither handle the bias toward high abundance proteins nor deal with the drawbacks due to the limited number of replicates. We present a novel statistical framework (QSpec) for the significance analysis of differential expression with extensions to a variety of experimental design factors and adjustments for protein properties. Using synthetic and real experimental data sets, we show that the proposed method outperforms conventional statistical methods that search for differential expression for individual proteins. We illustrate the flexibility of the model by analyzing a data set with a complicated experimental design involving cellular localization and time course.