Project management system for structural and functional proteomics: Sesame

A computing infrastructure (Sesame) has been designed to manage and link individual steps in complex projects. Sesame is being developed to support a large-scale structural proteomics pilot project. When complete, the system is expected to manage all steps from target selection to data-bank deposition and report writing. We report here on the design criteria of the Sesame system and on results demonstrating successful achievement of the basic goals of its architecture. The Sesame software package, which follows the client/server paradigm, consists of a framework, which supports secure interactions among the three tiers of the system (the client, server, and database tiers), and application modules that carry out specific tasks. The framework utilizes industry standards. The client tier is written in Java2 and can be accessed anywhere through the Internet. All the development on the server tier is also carried out in Java2 so as to accommodate a wide variety of computer platforms. The database tier employs a commercial database management system. Each Sesame application module consists of a simple user interface in the client tier, corresponding objects in the server tier, and relevant data stored in the centralized database. For security, access to stored data is controlled by access privileges. The system facilitates both local and remote collaborations. Because users interact with the system using Java Web Start or through a web browser, access is limited only by the availability of an Internet connection. We describe several Sesame modules that have been developed to the point where they are being utilized routinely to support steps involved in structural and functional proteomics. This software is available to parties interested in using it and assisting to guide its further development.Deceased, 30 August 2000     

PRISM: a data management system for high-throughput proteomics

Advanced proteomic research efforts involving areas such as systems biology or biomarker discovery are enabled by the use of high level informatics tools that allow the effective analysis of large quantities of differing types of data originating from various studies. Performing such analyses on a large scale is not feasible without a computational platform that performs data processing and management tasks. Such a platform must be able to provide high-throughput operation while having sufficient flexibility to accommodate evolving data analysis tools and methodologies. The Proteomics Research Information Storage and Management system (PRISM) provides a platform that serves the needs of the accurate mass and time tag approach developed at Pacific Northwest National Laboratory. PRISM incorporates a diverse set of analysis tools and allows a wide range of operations to be incorporated by using a state machine that is accessible to independent, distributed computational nodes. The system has scaled well as data volume has increased over several years, while allowing adaptability for incorporating new and improved data analysis tools for more effective proteomics research.     

Utilization of protein intrinsic disorder knowledge in structural proteomics

Intrinsically disordered proteins (IDPs) and proteins with long disordered regions are highly abundant in various proteomes. Despite their lack of well-defined ordered structure, these proteins and regions are frequently involved in crucial biological processes. Although in recent years these proteins have attracted the attention of many researchers, IDPs represent a significant challenge for structural characterization since these proteins can impact many of the processes in the structure determination pipeline. Here we investigate the effects of IDPs on the structure determination process and the utility of disorder prediction in selecting and improving proteins for structural characterization. Examination of the extent of intrinsic disorder in existing crystal structures found that relatively few protein crystal structures contain extensive regions of intrinsic disorder. Although intrinsic disorder is not the only cause of crystallization failures and many structured proteins cannot be crystallized, filtering out highly disordered proteins from structure-determination target lists is still likely to be cost effective. Therefore it is desirable to avoid highly disordered proteins from structure-determination target lists and we show that disorder prediction can be applied effectively to enrich structure determination pipelines with proteins more likely to yield crystal structures. For structural investigation of specific proteins, disorder prediction can be used to improve targets for structure determination. Finally, a framework for considering intrinsic disorder in the structure determination pipeline is proposed.     

Recent developments in structural proteomics for protein structure determination

The major challenges in structural proteomics include identifying all the proteins on the genome-wide scale, determining their structure-function relationships, and outlining the precise three-dimensional structures of the proteins. Protein structures are typically determined by experimental approaches such as X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy. However, the knowledge of three-dimensional space by these techniques is still limited. Thus, computational methods such as comparative and de novo approaches and molecular dynamic simulations are intensively used as alternative tools to predict the three-dimensional structures and dynamic behavior of proteins. This review summarizes recent developments in structural proteomics for protein structure determination; including instrumental methods such as X-ray crystallography and NMR spectroscopy, and computational methods such as comparative and de novo structure prediction and molecular dynamics simulations.     

CAERUS: predicting CAncER oUtcomeS using relationship between protein structural information, protein networks, gene expression data, and mutation data

Carcinogenesis is a complex process with multiple genetic and environmental factors contributing to the development of one or more tumors. Understanding the underlying mechanism of this process and identifying related markers to assess the outcome of this process would lead to more directed treatment and thus significantly reduce the mortality rate of cancers. Recently, molecular diagnostics and prognostics based on the identification of patterns within gene expression profiles in the context of protein interaction networks were reported. However, the predictive performances of these approaches were limited. In this study we propose a novel integrated approach, named CAERUS, for the identification of gene signatures to predict cancer outcomes based on the domain interaction network in human proteome. We first developed a model to score each protein by quantifying the domain connections to its interacting partners and the somatic mutations present in the domain. We then defined proteins as gene signatures if their scores were above a preset threshold. Next, for each gene signature, we quantified the correlation of the expression levels between this gene signature and its neighboring proteins. The results of the quantification in each patient were then used to predict cancer outcome by a modified naïve Bayes classifier. In this study we achieved a favorable accuracy of 88.3%, sensitivity of 87.2%, and specificity of 88.9% on a set of well-documented gene expression profiles of 253 consecutive breast cancer patients with different outcomes. We also compiled a list of cancer-associated gene signatures and domains, which provided testable hypotheses for further experimental investigation. Our approach proved successful on different independent breast cancer data sets as well as an ovarian cancer data set. This study constitutes the first predictive method to classify cancer outcomes based on the relationship between the domain organization and protein network.     

Capturing protein communities by structural proteomics in a thermophilic eukaryote

The arrangement of proteins into complexes is a key organizational principle for many cellular functions. Although the topology of many complexes has been systematically analyzed in isolation, their molecular sociology in situ remains elusive. Here, we show that crude cellular extracts of a eukaryotic thermophile, Chaetomium thermophilum, retain basic principles of cellular organization. Using a structural proteomics approach, we simultaneously characterized the abundance, interactions, and structure of a third of the C. thermophilum proteome within these extracts. We identified 27 distinct protein communities that include 108 interconnected complexes, which dynamically associate with each other and functionally benefit from being in close proximity in the cell. Furthermore, we investigated the structure of fatty acid synthase within these extracts by cryoEM and this revealed multiple, flexible states of the enzyme in adaptation to its association with other complexes, thus exemplifying the need for in situ studies. As the components of the captured protein communities are known—at both the protein and complex levels—this study constitutes another step forward toward a molecular understanding of subcellular organization.     

Quantitative proteomics: assessing the spectrum of in-gel protein detection methods

Proteomics research relies heavily on visualization methods for detection of proteins separated by polyacrylamide gel electrophoresis. Commonly used staining approaches involve colorimetric dyes such as Coomassie Brilliant Blue, fluorescent dyes including Sypro Ruby, newly developed reactive fluorophores, as well as a plethora of others. The most desired characteristic in selecting one stain over another is sensitivity, but this is far from the only important parameter. This review evaluates protein detection methods in terms of their quantitative attributes, including limit of detection (i.e., sensitivity), linear dynamic range, inter-protein variability, capacity for spot detection after 2D gel electrophoresis, and compatibility with subsequent mass spectrometric analyses. Unfortunately, many of these quantitative criteria are not routinely or consistently addressed by most of the studies published to date. We would urge more rigorous routine characterization of stains and detection methodologies as a critical approach to systematically improving these critically important tools for quantitative proteomics. In addition, substantial improvements in detection technology, particularly over the last decade or so, emphasize the need to consider renewed characterization of existing stains; the quantitative stains we need, or at least the chemistries required for their future development, may well already exist.     

Structural proteomics: developments in structure-to-function predictions

The major challenge for post-genomic research is to functionally assign and validate a large number of novel target genes and their corresponding proteins. Functional genomics approaches have, therefore, gained considerable attention in the quest to convert this massive data set into useful information. One of the crucial components for the functional understanding of unassigned proteins is the analysis of their experimental or modeled 3D structures. Structural proteomics initiatives are generating protein structures at an unprecedented rate but our current knowledge of 3D-structural space is still limited. Estimates on the completeness of the 3D-structural coverage of proteins vary but it is generally accepted that only a minority of the structural proteome has a template structure from which reliable conclusions can be drawn. Thus, structural proteomics has set out to build a map of protein structures that will represent all protein folds included in the 'global proteome'.     

Plasma/serum proteomics: pre-analytical issues

High-throughput proteomics technologies tend to provide highly sensitive information about living tissues and biological fluids. Analytes are characterized by intrinsic and extrinsic properties, the latter depending on each phase of their preparation, sometimes adding artifacts with crucial repercussions in result reliability and interpretation. This review aims to address some issues that can be encountered when handling plasma and serum in experimental and clinical proteomic settings.     

Plasma/serum proteomics: pre-analytical issues

High-throughput proteomics technologies tend to provide highly sensitive information about living tissues and biological fluids. Analytes are characterized by intrinsic and extrinsic properties, the latter depending on each phase of their preparation, sometimes adding artifacts with crucial repercussions in result reliability and interpretation. This review aims to address some issues that can be encountered when handling plasma and serum in experimental and clinical proteomic settings.     

KYSS: Mass spectrometry data quality assessment for protein analysis and large-scale proteomics

We introduce the computer tool “Know Your Samples” (KYSS) for assessment and visualisation of large scale proteomics datasets, obtained by mass spectrometry (MS) experiments. KYSS facilitates the evaluation of sample preparation protocols, LC peptide separation, and MS and MS/MS performance by monitoring the number of missed cleavages, precursor ion charge states, number of protein identifications and peptide mass error in experiments. KYSS generates several different protein profiles based on protein abundances, and allows for comparative analysis of multiple experiments. KYSS was adapted for blood plasma proteomics and provides concentrations of identified plasma proteins. We demonstrate the utility of the KYSS tool for MS based proteome analysis of blood plasma and for assessment of hydrogel particles for depletion of abundant proteins in plasma. The KYSS software is open source and is freely available at http://kyssproject.github.io/.     

Target selection of soluble protein complexes for structural proteomics studies

Background  

Protein expression in E. coli is the most commonly used system to produce protein for structural studies, because it is fast and inexpensive and can produce large quantity of proteins. However, when proteins from other species such as mammalian are produced in this system, problems of protein expression and solubility arise [1]. Structural genomics project are currently investigating proteomics pipelines that would produce sufficient quantities of recombinant proteins for structural studies of protein complexes. To investigate how the E. coli protein expression system could be used for this purpose, we purified apoptotic binary protein complexes formed between members of the Caspase Associated Recruitment Domain (CARD) family.     

Structural proteomics of minimal organisms: Conservation of protein fold usage and evolutionary implications

Background  

Determining the complete repertoire of protein structures for all soluble, globular proteins in a single organism has been one of the major goals of several structural genomics projects in recent years.