Protein Identification Techniques Research Articles

POTAMOS mass spectrometry calculator: Computer aided mass spectrometry to the post-translational modifications of proteins. A focus on histones

Mass spectrometry is a widely used technique for protein identification and it has also become the method of choice in order to detect and characterize the post-translational modifications (PTMs) of proteins. Many software tools have been developed to deal with this complication. In this paper we introduce a new, free and user friendly online software tool, named POTAMOS Mass Spectrometry Calculator, which was developed in the open source application framework Ruby on Rails. It can provide calculated mass spectrometry data in a time saving manner, independently of instrumentation. In this web application we have focused on a well known protein family of histones whose PTMs are believed to play a crucial role in gene regulation, as suggested by the so called “histone code” hypothesis. The PTMs implemented in this software are: methylations of arginines and lysines, acetylations of lysines and phosphorylations of serines and threonines. The application is able to calculate the kind, the number and the combinations of the possible PTMs corresponding to a given peptide sequence and a given mass along with the full set of the unique primary structures produced by the possible distributions along the amino acid sequence. It can also calculate the masses and charges of a fragmented histone variant, which carries predefined modifications already implemented. Additional functionality is provided by the calculation of the masses of fragments produced upon protein cleavage by the proteolytic enzymes that are most widely used in proteomics studies.

Stable Isotope Labeling of Phosphoproteins for Large-scale Phosphorylation Rate Determination

Signals that control responses to stimuli and cellular function are transmitted through the dynamic phosphorylation of thousands of proteins by protein kinases. Many techniques have been developed to study phosphorylation dynamics, including several mass spectrometry (MS)-based methods. Over the past few decades, substantial developments have been made in MS techniques for the large-scale identification of proteins and their post-translational modifications. Nevertheless, all of the current MS-based techniques for quantifying protein phosphorylation dynamics rely on the measurement of changes in peptide abundance levels, and many methods suffer from low confidence in phosphopeptide identification due to poor fragmentation. Here we have optimized an approach for the stable isotope labeling of amino acids by phosphate using [γ-¹⁸O₄]ATP in nucleo to determine global site-specific phosphorylation rates. The advantages of this metabolic labeling technique are increased confidence in phosphorylated peptide identification, direct labeling of phosphorylation sites, measurement phosphorylation rates, and the identification of actively phosphorylated sites in a cell-like environment. In this study we calculated approximate rate constants for over 1,000 phosphorylation sites based on labeling progress curves. We measured a wide range of phosphorylation rate constants from 0.34 min⁻¹ to 0.001 min⁻¹. Finally, we applied stable isotope labeling of amino acids by phosphate to identify sites that have different phosphorylation kinetics during G1/S and M phase. We found that most sites had very similar phosphorylation rates under both conditions; however, a small subset of sites on proteins involved in the mitotic spindle were more actively phosphorylated during M phase, whereas proteins involved in DNA replication and transcription were more actively phosphorylated during G1/S phase. The data have been deposited to the ProteomeXchange with the identifier PXD000680.

Empirical Multidimensional Space for Scoring Peptide Spectrum Matches in Shotgun Proteomics

Data-dependent tandem mass spectrometry (MS/MS) is one of the main techniques for protein identification in shotgun proteomics. In a typical LC-MS/MS workflow, peptide product ion mass spectra (MS/MS spectra) are compared with those derived theoretically from a protein sequence database. Scoring of these matches results in peptide identifications. A set of peptide identifications is characterized by false discovery rate (FDR), which determines the fraction of false identifications in the set. The total number of peptides targeted for fragmentation is in the range of 10,000 to 20,000 for a several-hour LC-MS/MS run. Typically, <50% of these MS/MS spectra result in peptide-spectrum matches (PSMs). A small fraction of PSMs pass the preset FDR level (commonly 1%) giving a list of identified proteins, yet a large number of correct PSMs corresponding to the peptides originally present in the sample are left behind in the "grey area" below the identity threshold. Following the numerous efforts to recover these correct PSMs, here we investigate the utility of a scoring scheme based on the multiple PSM descriptors available from the experimental data. These descriptors include retention time, deviation between experimental and theoretical mass, number of missed cleavages upon in-solution protein digestion, precursor ion fraction (PIF), PSM count per sequence, potential modifications, median fragment mass error, (13)C isotope mass difference, charge states, and number of PSMs per protein. The proposed scheme utilizes a set of metrics obtained for the corresponding distributions of each of the descriptors. We found that the proposed PSM scoring algorithm differentiates equally or more efficiently between correct and incorrect identifications compared with existing postsearch validation approaches.

Acute Synthesis of CPEB Is Required for Plasticity of Visual Avoidance Behavior in Xenopus

Neural plasticity requires protein synthesis, but theidentity of newly synthesized proteins generated in response to plasticity-inducing stimuli remains unclear. We used invivo bio-orthogonal noncanonical amino acid tagging (BONCAT) with the methionine analog azidohomoalanine (AHA) combined with themultidimensional protein identification technique (MudPIT) to identify proteins that are synthesized inthe tadpole brain over 24hr. We induced conditioning-dependent plasticity of visual avoidance behavior, which required N-methyl-D-aspartate (NMDA) and Ca(2+)-permeable α-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors, αCaMKII, and rapid protein synthesis. Combining BONCAT with western blots revealed that proteins including αCaMKII, MEK1, CPEB, and GAD65 are synthesized during conditioning. Acute synthesis of CPEB during conditioning is required for behavioral plasticity as well asconditioning-induced synaptic and structural plasticity in the tectal circuit. We outline a signaling pathway that regulates protein-synthesis-dependent behavioral plasticity in intact animals, identify newly synthesized proteins induced by visual experience, and demonstrate a requirement for acute synthesis of CPEB in plasticity.

Ribosome Display and Photo-Cross-Linking Techniques for In Vitro Identification of Target Proteins of Bioactive Small Molecules

The identification of target proteins of bioactive small molecules as bioprobe candidates or drug seeds is indispensable for elucidating their actions and predicting their side effects. To meet the current need, we developed a scheme for detection and identification of target proteins by using ribosome display and photo-cross-linking techniques, and demonstrated the feasibility. The mRNAs encoding full-length human proteins (FHPs) were constructed and translated in vitro to prepare pools of FHP-ribosome-mRNA complexes used for ribosome display selection. Expression levels of the FHPs were confirmed by Western blot analysis, and photo-cross-linked small-molecule beads were assessed through cell-free synthesized FHP binding assay. After ribosome display selection against photo-cross-linked small-molecule beads, RT-PCR using mRNAs recovered from the selected ternary complexes and electrophoresis of the PCR products allowed specific detection of the target proteins binding to the beads. In addition, a repeat of ribosome display selection enabled us to identify the target proteins even if the molar quantity was one ten-thousandth of that of the other proteins in a cell-free synthesized FHP pool. Therefore, these results showed that ribosome display using photo-cross-linked small-molecule beads and further extended FHP pool could be one of the powerful techniques for identification of unknown target proteins of bioactive small molecules.

Investigating a novel protein using mass spectrometry: the example of tumor differentiation factor (TDF).

Better understanding of central nervous system (CNS) molecules can include the identification of new molecules and their receptor systems. Discovery of novel proteins and elucidation of receptor targets can be accomplished using mass spectrometry (MS). We describe a case study of such a molecule, which our lab has studied using MS in combination with other protein identification techniques, such as immunohistochemistry (IHC) and Western blotting. This molecule is known as tumor differentiation factor (TDF), a recently-found protein secreted by the pituitary into the blood. TDF mRNA has been detected in brain; not heart, placenta, lung, liver, skeletal muscle, or pancreas. Currently TDF has an unclear function, and prior to our studies, its localization was only minimally understood, with no understanding of receptor targets. We investigated the distribution of TDF in the rat brain using IHC and immunofluorescence (IF). TDF protein was detected in pituitary and most other brain regions, in specific neurons but not astrocytes. We found TDF immunoreactivity in cultured neuroblastoma, not astrocytoma. These data suggest that TDF is localized to neurons, not to astrocytes. Our group also conducted studies to identify the TDF receptor (TDF-R). Using LC-MS/MS and Western blotting, we identified the members of the Heat Shock 70-kDa family of proteins (HSP70) as potential TDF-R candidates in both MCF7 and BT-549 human breast cancer cells (HBCC) and PC3, DU145, and LNCaP human prostate cancer cells (HPCC), but not in HeLa cells, NG108 neuroblastoma, or HDF-a and BLK CL.4 cell fibroblasts or fibroblast-like cells. These studies have combined directed protein identification techniques with mass spectrometry to increase our understanding of a novel protein that may have distinct actions as a hormone in the body and as a growth factor in the brain.

Effective peak alignment for mass spectrometry data analysis using two-phase clustering approach.

In recent years, mass spectrometry data analysis has become an important protein identification technique. The mass spectrometry technologies emerge as useful tools for biomarker discovery through studying protein profiles in various biological specimens. In mining mass spectrometry datasets, peak alignment is a critical issue among the preprocessing steps that affect the quality of analysis results. However, the existing peak alignment methods are sensitive to noise peaks across various mass spectrometry samples. In this paper, we proposed a novel algorithm named Two-Phase Clustering for peak Alignment (TPC-Align) to align mass spectrometry peaks across samples in the pre-processing phase. The TPC-Align algorithm sequentially considers the distribution of intensity values and the locations of mass-to-charge ratio values of peaks between samples. Moreover, TPC-Align algorithm can also report a list of significantly differential peaks between samples, which serve as the candidate biomarkers for further biological study. The proposed peak alignment method was compared to the current peak alignment approach based on one-dimension hierarchical clustering through experimental evaluations and the results show that TPC-Align outperforms the traditional method on the real dataset.

Laser‐Capture Microdissection

Laser‐Capture Microdissection

Soybean Proteomics for Unraveling Abiotic Stress Response Mechanism

Plant response to abiotic stresses depends upon the fast activation of molecular cascades involving stress perception, signal transduction, changes in gene and protein expression and post-translational modification of stress-induced proteins. Legumes are extremely sensitive to flooding, drought, salinity and heavy metal stresses, and soybean is not an exception of that. Invention of immobilized pH gradient strips followed by advancement in mass spectrometry has made proteomics a fast, sensitive and reliable technique for separation, identification and characterization of stress-induced proteins. As the functional translated portion of the genome plays an essential role in plant stress response, proteomic studies provide us a finer picture of protein networks and metabolic pathways primarily involved in stress tolerance mechanism. Identifying master regulator proteins that play key roles in the abiotic stress response pathway is fundamental in providing opportunities for developing genetically engineered stress-tolerant crop plants. This review highlights recent contributions in the field of soybean biology to comprehend the complex mechanism of abiotic stress acclimation. Furthermore, strengths and weaknesses of different proteomic methodologies of extracting complete proteome and challenges and future prospects of soybean proteome study both at organ and whole plant levels are discussed in detail to get new insights into the plant abiotic stress response mechanism.

Proteomics identifies ubiquitin–proteasome targets and new roles for chromatin-remodeling in the Arabidopsis response to phosphate starvation

In order to identify new regulators of the phosphate (Pi) starvation signaling pathway in plants, we analyzed variation in the abundance of nuclear-enriched proteins isolated from Arabidopsis roots that depends on Pi supply. We used 2-D fluorescence difference gel electrophoresis and MALDI-TOF/TOF techniques for proteome separation, visualization and relative protein abundance quantification and identification. Pi-controlled proteins identified in our analysis included components of the chromatin remodeling, DNA replication, and mRNA splicing machineries. In addition, by combining Pi starvation conditions with proteasome inhibitor treatments, we characterized the role of the ubiquitin-proteasome system, a major mechanism for targeted protein degradation in eukaryotes, in the control of the stability of Pi-responsive proteins. Among Pi-responsive proteins, the histone chaperone NAP1;2 was selected for further characterization, and was shown to display differential nucleo-cytoplasmic accumulation in response to Pi deprivation. We also found that mutants affecting three members of the NAP1 family accumulate lower Pi levels and display reduced expression of Pi starvation-inducible genes, reflecting a potential regulatory role for these chromatin-remodeling proteins in Pi homeostasis. In this study, we explore the feasibility of nuclear proteomics to identify regulatory proteins and ubiquitin-proteasome targets within a specific stress signaling pathway in plants, in our case phosphate starvation signaling in Arabidopsis. It will be of interest for researchers involved in the dissection of any signaling pathway in plants, in particular those with an interest in the ubiquitin-proteasome functions, and for the plant nutrition community.

The effect of starvation stress on Lactobacillus brevis L62 protein profile determined by de novo sequencing in positive and negative mass spectrometry ion mode

We describe a novel negative chemically activated fragmentation/positive chemically activated fragmentation (CAF-/CAF+) technique for protein identification. The technique was used to investigate Lactobacillus brevis adaptation to nutrient deprivation. The CAF-/CAF+ method enables de novo sequencing of derivate peptides with negative and positive ion mode matrix-assisted laser desorption/ionization (MALDI) tandem mass spectrometry (MS/MS). Peptide sequences obtained from MS/MS spectra were matched against the National Center for Biotechnology Information (NCBI) non-redundant (nr) database and confirmed by the mass spectrometry data of elucidated peptide mass sequences derived from the annotated genome. This improved protein identification method highlighted 36 differentially expressed proteins in the proteome of L. brevis after 75 days of starvation. The results revealed the key differences in the metabolic pathways that are responsible for the survival of L. brevis in a hostile environment. Proteomics analysis demonstrated that numerous proteins engaged in glucose and amino-acid catabolizing pathways, glycerolipid metabolizing pathways, and stress-response mechanisms are differentially expressed after long-term starvation. Amino acid and proteomics analysis indicated that starved L. brevis metabolized arginine, glycine, and histidine from dead cells as alternative nutrient sources. The production of lactic acid also varied between the parent cells and the starved cells. Differentially expressed proteins identified exclusively by peptide sequence reading provided promising results for CAF-/CAF+ implementation in a standard proteomics workflow (e.g., biomarker and mutation discovery and biotyping). The practical performance of a reliable de novo sequencing technique in routine proteomics analysis is emphasized in this article.

Clinical proteomics and OMICS clues useful in translational medicine research

Since the advent of the new proteomics era more than a decade ago, large-scale studies of protein profiling have been used to identify distinctive molecular signatures in a wide array of biological systems, spanning areas of basic biological research, clinical diagnostics, and biomarker discovery directed toward therapeutic applications. Recent advances in protein separation and identification techniques have significantly improved proteomic approaches, leading to enhancement of the depth and breadth of proteome coverage.Proteomic signatures, specific for multiple diseases, including cancer and pre-invasive lesions, are emerging. This article combines, in a simple manner, relevant proteomic and OMICS clues used in the discovery and development of diagnostic and prognostic biomarkers that are applicable to all clinical fields, thus helping to improve applications of clinical proteomic strategies for translational medicine research.

SNAP-tag Based Proteomics Approach for the Study of the Retrograde Route

Proteomics is a powerful technique for protein identification at large scales. A number of proteomics approaches have been developed to study the steady state composition of intracellular compartments. Here, we report a novel vectorial proteomics strategy to identify plasma membrane proteins that undergo retrograde transport to the trans-Golgi network (TGN). This strategy is based on the covalent modification of the plasma membrane proteome with a membrane impermeable benzylguanine derivative. Benzylguanine-tagged plasma membrane proteins that are subsequently targeted to the retrograde route are covalently captured by a TGN-localized SNAP-tagged fusion protein, which allows for their identification. The approach was validated step-by-step using a well explored retrograde cargo protein, the B-subunit of Shiga toxin. It was then extended to the proteomics format. Among other hits we found one of the historically first identified cargo proteins that undergo retrograde transport, which further validated our approach. Most of the other hits were kinases, receptors or transporters. In conclusion, we have pioneered a vectorial proteomics approach that complements traditional methods for the study of retrograde protein trafficking. This approach is of generic nature and could in principle be extended to other endocytic pathways.

Targeting Proteases in Cardiovascular Diseases by Mass Spectrometry-Based Proteomics

Proteases hydrolyze peptide bonds, thereby controlling the function of proteins and peptides on the posttranslational level. In the cardiovascular system, proteases play pivotal roles in the regulation of blood pressure, coagulation and other essential physiological processes. Accordingly, proteases are prime targets for therapeutic interventions and diagnostics. Proteases are part of complex proteolytic networks comprised of enzymes, inhibitors, activators, substrates and cleavage products. Analyzing these networks on a system-wide level is essential to understanding cardiovascular function and how dysregulation can lead to pathological conditions. Mass spectrometry-based quantitative and dynamic proteomics approaches are leading the way to enhance our knowledge of proteolytic networks such as the renin-angiotensin-system. Here, we critically review proteomics tools utilized in protease biology and provide an overview on how these methods can be used to characterize and validate protease function.

Virtual Labs in proteomics: New E-learning tools

Web-based educational resources have gained enormous popularity recently and are increasingly becoming a part of modern educational systems. Virtual Labs are E-learning platforms where learners can gain the experience of practical experimentation without any direct physical involvement on real bench work. They use computerized simulations, models, videos, animations and other instructional technologies to create interactive content. Proteomics being one of the most rapidly growing fields of the biological sciences is now an important part of college and university curriculums. Consequently, many E-learning programs have started incorporating the theoretical and practical aspects of different proteomic techniques as an element of their course work in the form of Video Lectures and Virtual Labs. To this end, recently we have developed a Virtual Proteomics Lab at the Indian Institute of Technology Bombay, which demonstrates different proteomics techniques, including basic and advanced gel and MS-based protein separation and identification techniques, bioinformatics tools and molecular docking methods, and their applications in different biological samples. This Tutorial will discuss the prominent Virtual Labs featuring proteomics content, including the Virtual Proteomics Lab of IIT-Bombay, and E-resources available for proteomics study that are striving to make proteomic techniques and concepts available and accessible to the student and research community. This Tutorial is part of the International Proteomics Tutorial Programme (IPTP 14). Details can be found at: http://www.proteomicstutorials.org/.

Biomarker Discovery and Drug Development: A Proteomics Approach

The dynamic nature of the proteome of cells (diseased) provides ample information for studying a disease at protein level, but to gather all the information from a cell requires implementation of multiple strategies and technologies [1]. Two traditionally used techniques in proteomics are two dimensional polyacrylamide gel electrophoresis (2-DE) and mass spectrometry (MS). Many improvements have been made in these techniques to make them more effective and informative. Moreover, other advanced non-gel-based techniques like protein chip technology, phage display, activity based assays, two hybrid assays, isotope coded affinity tagging are being used in disease proteomics. Some recently developed strategies for classical proteome analysis are isotope coded affinity tagging (ICAT) and multidimensional protein identification technique (MudPIT). ICAT categorize and quantify all the proteins in the proteome. It is a very sensitive technique with high throughput. MudPIT is another method for classical proteome study. It is the liquid chromatography/mass spectrometry (LC/MS) method which can be directly applied on crude samples. Functional analysis of proteome involves protein arrays, phage display methods and twohybrid systems. These techniques quantify proteins and also help in determining protein-protein interactions. Some of them form bridge between classical and functional approach, like activity based probes which analyze the active protein component of proteomes [2]. Proteomics is being extensively used to study molecular basis of various diseases and development of novel drugs with better understanding of targets. Substantial interest has been generated in identifying disease biomarkers. It is a molecule that indicates changes in the physiology of a cell under diseased state and hence can be used as a diagnostic tool, therapy guidance and prognosis monitoring of diseases [3]. Cancer biomarkers are a good example which is not only help in diagnosis of disease but also helps in determining different stages of cancer, studying therapy response and verification of clinical end points [4]. Impressive data can be collected by comparative analysis of disease tissue and its normal counterpart to identify protein with aberrant expression. The sera of the patients can also be screened for auto-antibodies against tumor antigens. Immune response against tumor cells in patients with cancer is being increasingly reported resulting in production of auto-antibodies against various intracellular and surface antigens. Identification of these antigens might help in cancer screening, diagnosis and immunotherapy against the disease. Lung cancer has been extensively studied using proteomic approach. Cytokeratin isoforms were reported to correlate with patient survival and oncoprotein 18 over expression was associated with poor differentiation status in lung carcinoma [5,6]. Another type of cancer that has been studied is bladder tumor, including transitional cell carcinoma, squamous cell carcinoma, and adenocarcinoma. Researchers have also concentrated on identification of biomarkers of breast cancer, ovarian cancer and colon cancer. A new technology, differential in-gel electrophoresis (DIGE), combined with LC/MS, has been claimed to be a powerful proteomic procedure for the molecular characterization of tumor develpoment and for the detection of tumorspecific biomarkers in esophageal scans cell cancer [7]. Moreover, proteome analysis has also been reported to provide insights into cardiovascular diseases, inflammatory and immune diseases like rheumatoid arthritis and hepatitis. Proteomics play an important role in drug development. All major pharmaceutical companies are implementing proteomic programs. As majority of drugs act by targeting proteins or they are protein themselves, proteomics along with bioinformatics can meet the needs of pharmaceutical industry in identifying new targets to understand insight of drug action. Bioinformatics is a tool that interprets biological information using computer-aided data. It offers algorithms for gene and protein identification, structure and function relationship predictions and functional interactions among proteins [8-10]. Information technology enables mining of DNA and protein sequence databases for similarities, screening of active compounds in silico by virtual screening and docking of analyses. Informatics enables easy optimization of leads in drug design, and the selection of pre-clinical candidates [9]. Growth in computing power and systematic databases has made such automated analyses possible [11].

Plant Cell Organelle Proteomics in Response to Abiotic Stress

Proteomics is one of the finest molecular techniques extensively being used for the study of protein profiling of a given plant species experiencing stressed conditions. Plants respond to a stress by alteration in the pattern of protein expression, either by up-regulating of the existing protein pool or by the synthesizing novel proteins primarily associated with plants antioxidative defense mechanism. Improved protein extraction protocols and advance techniques for identification of novel proteins have been standardized in different plant species at both cellular and whole plant level for better understanding of abiotic stress sensing and intracellular stress signal transduction mechanisms. In contrast, an in-depth proteome study of subcellular organelles could generate much detail information about the intrinsic mechanism of stress response as it correlates the possible relationship between the protein abundance and plant stress tolerance. Although a wealth of reviews devoted to plant proteomics are available, review articles dedicated to plant cell organelle proteins response under abiotic stress are very scanty. In the present review, an attempt has been made to summarize all significant contributions related to abiotic stresses and their impacts on organelle proteomes for better understanding of plants abiotic stress tolerance mechanism at protein level. This review will not only provide new insights into the plants stress response mechanisms, which are necessary for future development of genetically engineered stress tolerant crop plants for the benefit of humankind, but will also highlight the importance of studying changes in protein abundance within the cell organelles in response to abiotic stress.

Advances in Proteomic Strategies toward the Early Detection of Lung Cancer

Since the advent of the new proteomics era more than a decade ago, large-scale studies of protein profiling have been exploited to identify the distinctive molecular signatures in a wide array of biological systems spanning areas of basic biological research, various disease states, and biomarker discovery directed toward therapeutic applications. Recent advances in protein separation and identification techniques have significantly improved proteomics approaches, leading to enhancement of the depth and breadth of proteome coverage. Proteomic signatures specific for invasive lung cancer and preinvasive lesions have begun to emerge. In this review we provide a critical assessment of the state of recent advances in proteomic approaches and the biological lessons they have yielded, with specific emphasis on the discovery of biomarker signatures for the early detection of lung cancer.

Proteomics Analyses of the Opportunistic Pathogen Burkholderia vietnamiensis Using Protein Fractionations and Mass Spectrometry

The main objectives of this work were to obtain a more extensive coverage of the Burkholderia vietnamiensis proteome than previously reported and to identify virulence factors using tandem mass spectrometry. The proteome of B. vietnamiensis was precipitated into four fractions to as extracellular, intracellular, cell surface and cell wall proteins. Two different approaches were used to analyze the proteins. The first was a gel-based method where 1D SDS-PAGE was used for separation of the proteins prior to reverse phase liquid chromatography tandem mass spectrometry (LC-MS/MS). The second method used MudPIT analysis (Multi dimensional Protein Identification Technique), where proteins are digested and separated using cation exchange and reversed phase separations before the MS/MS analysis (LC/LC-MS/MS). Overall, gel-based LC-MS/MS analysis resulted in more protein identifications than the MudPIT analysis. Combination of the results lead to identification of more than 1200 proteins, approximately 16% of the proteins coded from the annotated genome of Burkholderia species. Several virulence factors were detected including flagellin, porin, peroxiredoxin and zinc proteases.

Proteomic profiling of Myc-associated proteins

Mammalian c-Myc is a member of a small family of three closely related transcription factors. The Myc family of proto-oncogenes are among the most potent activators of tumorigenesis, and are frequently overexpressed in diverse cancers. c-Myc has an unusually broad array of regulatory functions, which include, in addition to roles in the cell cycle and apoptosis, effects on a variety of metabolic functions, cell differentiation, senescence, and stem cell maintenance. A significant number of c-Myc interacting proteins have already been defined, but it is widely believed that the c-Myc interactome is vastly larger than currently documented. In addition to interactions with components of the transcription machinery, transcription independent nuclear interactions with the DNA replication and RNA processing pathways have been reported. Cytoplasmic roles of c-Myc have also been recently substantiated. Recent advances in proteomics have opened new possibilities for the isolation of protein complexes under native conditions and confidently identifying the components using ultrasensitive, high mass accuracy and high resolution mass spectrometry techniques. In this communication we report a new tandem affinity purification (TAP) c-Myc interaction screen that employed new cell lines with near-physiological levels of c-Myc expression with multi-dimensional protein identification techniques (MudPIT) for the detection and quantification of proteins. Both label-free and the recently developed stable isotope labeling with amino acids in cell culture (SILAC) methodologies were used. Combined data from multiple biological replicates provided a dataset of 418 non-redundant proteins, 389 of which are putative novel interactors. This new information should significantly advance our understanding of this interesting and important master regulator.