Computational and informatics strategies for identification of specific protein interaction partners in affinity purification mass spectrometry experiments

False discovery rate: the Achilles' heel of proteogenomics

Proteogenomics refers to the integrated analysis of the genome and proteome that leverages mass-spectrometry (MS)-based proteomics data to improve genome annotations, understand gene expression control through proteoforms and find sequence variants to develop novel insights for disease classification and therapeutic strategies. However, proteogenomic studies often suffer from reduced sensitivity and specificity due to inflated database size. To control the error rates, proteogenomics depends on the target-decoy search strategy, the de-facto method for false discovery rate (FDR) estimation in proteomics. The proteogenomic databases constructed from three- or six-frame nucleotide database translation not only increase the search space and compute-time but also violate the equivalence of target and decoy databases. These searches result in poorer separation between target and decoy scores, leading to stringent FDR thresholds. Understanding these factors and applying modified strategies such as two-pass database search or peptide-class-specific FDR can result in a better interpretation of MS data without introducing additional statistical biases. Based on these considerations, a user can interpret the proteogenomics results appropriately and control false positives and negatives in a more informed manner. In this review, first, we briefly discuss the proteogenomic workflows and limitations in database construction, followed by various considerations that can influence potential novel discoveries in a proteogenomic study. We conclude with suggestions to counter these challenges for better proteogenomic data interpretation.

CANVS: an easy-to-use application for the analysis and visualization of mass spectrometry-based protein-protein interaction/association data

The elucidation of a protein’s interaction/association network is important for defining its biological function. Mass spectrometry–based proteomic approaches have emerged as powerful tools for identifying protein–protein interactions (PPIs) and protein–protein associations (PPAs). However, interactome/association experiments are difficult to interpret, considering the complexity and abundance of data that are generated. Although tools have been developed to identify protein interactions/associations quantitatively, there is still a pressing need for easy-to-use tools that allow users to contextualize their results. To address this, we developed CANVS, a computational pipeline that cleans, analyzes, and visualizes mass spectrometry–based interactome/association data. CANVS is wrapped as an interactive Shiny dashboard with simple requirements, allowing users to interface easily with the pipeline, analyze complex experimental data, and create PPI/A networks. The application integrates systems biology databases such as BioGRID and CORUM to contextualize the results. Furthermore, CANVS features a Gene Ontology tool that allows users to identify relevant GO terms in their results and create visual networks with proteins associated with relevant GO terms. Overall, CANVS is an easy-to-use application that benefits all researchers, especially those who lack an established bioinformatic pipeline and are interested in studying interactome/association data.

Global mapping of Salmonella enterica-host protein-protein interactions during infection

Intracellular bacterial pathogens inject effector proteins to hijack host cellular processes and promote their survival and proliferation. To systematically map effector-host protein-protein interactions (PPIs) during infection, we generated a library of 32 Salmonella enterica serovar Typhimurium (STm) strains expressing chromosomally encoded affinity-tagged effectors and quantified PPIs in macrophages and epithelial cells. We identified 446 effector-host PPIs, 25 of which were previously described, and validated 13 by reciprocal co-immunoprecipitation. While effectors converged on the same host cellular processes, most had multiple targets, which often differed between cell types. We demonstrate that SseJ, SseL, and SifA modulate cholesterol accumulation at the Salmonella-containing vacuole (SCV) partially via the cholesterol transporter Niemann-Pick C1 protein. PipB recruits the organelle contact site protein PDZD8 to the SCV, and SteC promotes actin bundling by phosphorylating formin-like proteins. This study provides a method for probing host-pathogen PPIs during infection and a resource for interrogating STm effector mechanisms.

From Affinity to Proximity Techniques to Investigate Protein Complexes in Plants

The study of protein–protein interactions (PPIs) is fundamental in understanding the unique role of proteins within cells and their contribution to complex biological systems. While the toolkit to study PPIs has grown immensely in mammalian and unicellular eukaryote systems over recent years, application of these techniques in plants remains under-utilized. Affinity purification coupled to mass spectrometry (AP-MS) and proximity labeling coupled to mass spectrometry (PL-MS) are two powerful techniques that have significantly enhanced our understanding of PPIs. Relying on the specific binding properties of a protein to an immobilized ligand, AP is a fast, sensitive and targeted approach used to detect interactions between bait (protein of interest) and prey (interacting partners) under near-physiological conditions. Similarly, PL, which utilizes the close proximity of proteins to identify potential interacting partners, has the ability to detect transient or hydrophobic interactions under native conditions. Combined, these techniques have the potential to reveal an unprecedented spatial and temporal protein interaction network that better understands biological processes relevant to many fields of interest. In this review, we summarize the advantages and disadvantages of two increasingly common PPI determination techniques: AP-MS and PL-MS and discuss their important application to plant systems.

Open Science Resources for the Mass Spectrometry-Based Analysis of SARS-CoV-2

The SARS-CoV-2 virus is the causative agent of the 2020 pandemic leading to the COVID-19 respiratory disease. With many scientific and humanitarian efforts ongoing to develop diagnostic tests, vaccines, and treatments for COVID-19, and to prevent the spread of SARS-CoV-2, mass spectrometry research, including proteomics, is playing a role in determining the biology of this viral infection. Proteomics studies are starting to lead to an understanding of the roles of viral and host proteins during SARS-CoV-2 infection, their protein-protein interactions, and post-translational modifications. This is beginning to provide insights into potential therapeutic targets or diagnostic strategies that can be used to reduce the long-term burden of the pandemic. However, the extraordinary situation caused by the global pandemic is also highlighting the need to improve mass spectrometry data and workflow sharing. We therefore describe freely available data and computational resources that can facilitate and assist the mass spectrometry-based analysis of SARS-CoV-2. We exemplify this by reanalyzing a virus-host interactome data set to detect protein-protein interactions and identify host proteins that could potentially be used as targets for drug repurposing.

A new strategy of studying protein-protein interactions: Integrated strong anion exchange/reversed-phase chromatography/immunoprecipitation coupled with mass spectrometry for large-scale identification of proteins interact with immunoglobulin G in HeLa cells

Recently, significant research efforts have been devoted to the development of technology for large-scale analysis of protein-protein interactions. Herein, a comprehensive method by coupling the first-dimension strong anion exchange chromatography with the second-dimension reversed-phase liquid chromatography via immunoprecipitation technique and high-resolution mass spectrometry analysis was developed for analyzing protein-protein interactions. After two-dimensional liquid chromatography separation, 108 fractions were obtained in one experiment. Immunoglobulin G from human serum was used as a model of an interacting protein. As a result, 919 proteins in these fractions were identified to interact with immunoglobulin G. By searching STRING database and data analysis, 27 of 919 proteins were inferred to directly interact with immunoglobulin G. Moreover, important target proteins that interacted with immunoglobulin G were mapped in the two-dimensional liquid chromatography system, which facilitated selection of these proteins from specific fractions. These results demonstrated that the proposed method can be useful for large-scale investigation of protein-protein interactions and as an advanced tool for the isolation of target proteins.

Methods of Computational Interactomics for Investigating Interactions of Human Proteoforms

Human genome contains ca. 20,000 protein-coding genes that could be translated into millions of unique protein species (proteoforms). Proteoforms coded by a single gene often have different functions, which implies different protein partners. By interacting with each other, proteoforms create a network reflecting the dynamics of cellular processes in an organism. Perturbations of protein-protein interactions change the network topology, which often triggers pathological processes. Studying proteoforms is a relatively new research area in proteomics, and this is why there are comparatively few experimental studies on the interaction of proteoforms. Bioinformatics tools can facilitate such studies by providing valuable complementary information to the experimental data and, in particular, expanding the possibilities of the studies of proteoform interactions.

Bait Correlation Improves Interactor Identification by Tandem Mass Tag-Affinity Purification-Mass Spectrometry

The quantitative multiplexing capacity of isobaric tandem mass tags (TMT) has increased the throughput of affinity purification mass spectrometry (AP-MS) to characterize protein interaction networks of immunoprecipitated bait proteins. However, variable bait levels between replicates can convolute interactor identification. We compared the Student's t-test and Pearson's R correlation as methods to generate t-statistics and assessed the significance of interactors following TMT-AP-MS. Using a simple linear model of protein recovery in immunoprecipitates to simulate reporter ion ratio distributions, we found that correlation-derived t-statistics protect against bait variance while robustly controlling type I errors (false positives). We experimentally determined the performance of these two approaches for determining t-statistics under two experimental conditions: irreversible prey association to the Hsp40 mutant DNAJB8^H31Q followed by stringent washing, and reversible association to 14-3-3ζ with gentle washing. Correlation-derived t-statistics performed at least as well as Student's t-statistics for each sample and with substantial improvement in performance for experiments with high bait-level variance. Deliberately varying bait levels over a large range fails to improve selectivity but does increase the robustness between runs. The use of correlation-derived t-statistics should improve identification of interactors using TMT-AP-MS. Data are available via ProteomeXchange with identifier PXD016613.

Targeted Identification of Protein Interactions in Eukaryotic mRNA Translation

To identify protein-protein interactions and phosphorylated amino acid sites in eukaryotic mRNA translation, replicate TAP-MudPIT and control experiments are performed targeting Saccharomyces cerevisiae genes previously implicated in eukaryotic mRNA translation by their genetic and/or functional roles in translation initiation, elongation, termination, or interactions with ribosomal complexes. Replicate tandem affinity purifications of each targeted yeast TAP-tagged mRNA translation protein coupled with multidimensional liquid chromatography and tandem mass spectrometry analysis are used to identify and quantify copurifying proteins. To improve sensitivity and minimize spurious, nonspecific interactions, a novel cross-validation approach is employed to identify the most statistically significant protein-protein interactions. Using experimental and computational strategies discussed herein, the previously described protein composition of the canonical eukaryotic mRNA translation initiation, elongation, and termination complexes is calculated. In addition, statistically significant unpublished protein interactions and phosphorylation sites for S. cerevisiae's mRNA translation proteins and complexes are identified.

A seed-extended algorithm for detecting protein complexes based on density and modularity with topological structure and GO annotations

Background

The detection of protein complexes is of great significance for researching mechanisms underlying complex diseases and developing new drugs. Thus, various computational algorithms have been proposed for protein complex detection. However, most of these methods are based on only topological information and are sensitive to the reliability of interactions. As a result, their performance is affected by false-positive interactions in PPINs. Moreover, these methods consider only density and modularity and ignore protein complexes with various densities and modularities.

Results

To address these challenges, we propose an algorithm to exploit protein complexes in PPINs by a Seed-Extended algorithm based on Density and Modularity with Topological structure and GO annotations, named SE-DMTG to improve the accuracy of protein complex detection. First, we use common neighbors and GO annotations to construct a weighted PPIN. Second, we define a new seed selection strategy to select seed nodes. Third, we design a new fitness function to detect protein complexes with various densities and modularities. We compare the performance of SE-DMTG with that of thirteen state-of-the-art algorithms on several real datasets.

Conclusion

The experimental results show that SE-DMTG not only outperforms some classical algorithms in yeast PPINs in terms of the F-measure and Jaccard but also achieves an ideal performance in terms of functional enrichment. Furthermore, we apply SE-DMTG to PPINs of several other species and demonstrate the outstanding accuracy and matching ratio in detecting protein complexes compared with other algorithms.

Mass Spectrometric Analysis Identifies AIMP1 and LTA4H as FSCN1-Binding Proteins in Laryngeal Squamous Cell Carcinoma

Dysregulation of fascin actin-bundling protein 1 (FSCN1) enhances cell proliferation, invasion, and motility in laryngeal squamous cell carcinoma (LSCC), while the mechanism remains unclear. Here, co-immunoprecipitation and mass spectrometry is utilized to identify potential FSCN1-binding proteins. Functional annotation of FSCN1-binding proteins are performed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis. Furthermore, the protein-protein interaction network of FSNC1-binding proteins is constructed and the interactions between FSCN1 and novel identified interacting proteins AIMP1 and LTA4H are validated. Moreover, the expression and functional role of AIMP1 and LTA4H in LSCC are investigated. A total of 123 proteins are identified as potential FSCN1-binding proteins, and functional annotation shows that FSCN1-binding proteins are significantly enriched in carcinogenic processes, such as filopodium assembly-regulation and GTPase activity. Co-IP/western blotting and immunofluorescence confirm that AIMP1 and LTA4H bind and colocalize with FSCN1. Furthermore, both AIMP1 and LTA4H are upregulated in LSCC tissues, and knockdown of AIMP1 or LTA4H inhibits LSCC cell proliferation, migration, and invasion. Collectively, the identification of FSCN1-binding partners enhances understanding of the mechanism of FSCN1-mediated malignant phenotypes, and these findings indicate that FSCN1 binds to AIMP1 and LTA4H might promote the progression of LSCC.

Experimental Analysis of Viral-Host Interactions

Viral and pathogen protein complexity is often limited by their relatively small genomes, thus critical functions are often accomplished by complexes of host and pathogen proteins. This requirement makes the study of host-pathogen interactions critical for the understanding of pathogenicity and virology. This review article discusses proteomic methods that offer an opportunity to experimentally identify and analyze the binding partners of a target protein and presents the representative studies performed with these methods. These methods divide into two classes: ex situ and in situ. Ex situ assays depend on bindings that occur outside of the normal cellular environment and include yeast two hybrids, pull-downs, and nucleic acid-programmable protein arrays (NAPPA). In situ assays depend on bindings that occur inside of host cells and include affinity purification (AP) and proximity dependent labeling (PDL). Either ex or in situ methods can be reliably used for generating protein-protein interactions networks but it is important to understand and recognize the limitations of the chosen methods when developing an interactomic network. In summary, proteomic methods can be extremely useful for interactomics but it is important to recognize the nature of the method when designing and analyzing an experiment.

Exploring the protein-protein interaction landscape in plants

Protein-protein interactions (PPIs) represent an essential aspect of plant systems biology. Identification of key protein players and their interaction networks provide crucial insights into the regulation of plant developmental processes and into interactions of plants with their environment. Despite the great advance in the methods for the discovery and validation of PPIs, still several challenges remain. First, the PPI networks are usually highly dynamic, and the in vivo interactions are often transient and difficult to detect. Therefore, the properties of the PPIs under study need to be considered to select the most suitable technique, because each has its own advantages and limitations. Second, besides knowledge on the interacting partners of a protein of interest, characteristics of the interaction, such as the spatial or temporal dynamics, are highly important. Hence, multiple approaches have to be combined to obtain a comprehensive view on the PPI network present in a cell. Here, we present the progress in commonly used methods to detect and validate PPIs in plants with a special emphasis on the PPI features assessed in each approach and how they were or can be used for the study of plant interactions with their environment.

Functional equivalence of germ plasm organizers

The proteins Oskar (Osk) in Drosophila and Bucky ball (Buc) in zebrafish act as germ plasm organizers. Both proteins recapitulate germ plasm activities but seem to be unique to their animal groups. Here, we discover that Osk and Buc show similar activities during germ cell specification. Drosophila Osk induces additional PGCs in zebrafish. Surprisingly, Osk and Buc do not show homologous protein motifs that would explain their related function. Nonetheless, we detect that both proteins contain stretches of intrinsically disordered regions (IDRs), which seem to be involved in protein aggregation. IDRs are known to rapidly change their sequence during evolution, which might obscure biochemical interaction motifs. Indeed, we show that Buc binds to the known Oskar interactors Vasa protein and nanos mRNA indicating conserved biochemical activities. These data provide a molecular framework for two proteins with unrelated sequence but with equivalent function to assemble a conserved core-complex nucleating germ plasm.

Multicellular organisms use gametes for their propagation. Gametes are formed from germ cells, which are specified during embryogenesis in some animals by the inheritance of RNP granules known as germ plasm. Transplantation of germ plasm induces extra germ cells, whereas germ plasm ablation leads to the loss of gametes and sterility. Therefore, germ plasm is key for germ cell formation and reproduction. However, the molecular mechanisms of germ cell specification by germ plasm in the vertebrate embryo remain an unsolved question. Proteins, which assemble the germ plasm, are known as germ plasm organizers. Here, we show that the two germ plasm organizers Oskar from the fly and Bucky ball from the fish show similar functions by using a cross species approach. Both are intrinsically disordered proteins, which rapidly changed their sequence during evolution. Moreover, both proteins still interact with conserved components of the germ cell specification pathway. These data might provide a first example of two proteins with the same biological role, but distinct sequence.

PAF1 complex interactions with SETDB1 mediate promoter H3K9 methylation and transcriptional repression of Hoxa9 and Meis1 in acute myeloid leukemia

The Polymerase Associated Factor 1 complex (PAF1c) is an epigenetic co-modifying complex that directly contacts RNA polymerase II (RNAPII) and several epigenetic regulating proteins. Mutations, overexpression and loss of expression of subunits of the PAF1c are observed in various forms of cancer suggesting proper regulation is needed for cellular development. However, the biochemical interactions with the PAF1c that allow dynamic gene regulation are unclear. We and others have shown that the PAF1c makes a direct interaction with MLL fusion proteins, which are potent oncogenic drivers of acute myeloid leukemia (AML). This interaction is critical for the maintenance of MLL translocation driven AML by targeting MLL fusion proteins to the target genes Meis1 and Hoxa9. Here, we use a proteomics approach to identify protein-protein interactions with the PAF1c subunit CDC73 that regulate the function of the PAF1c. We identified a novel interaction with a histone H3 lysine 9 (H3K9) methyltransferase protein, SETDB1. This interaction is stabilized with a mutant CDC73 that is incapable of supporting AML cell growth. Importantly, transcription of Meis1 and Hoxa9 is reduced and promoter H3K9 trimethylation (H3K9me3) increased by overexpression of SETDB1 or stabilization of the PAF1c-SETDB1 interaction in AML cells. These findings were corroborated in human AML patients where increased SETDB1 expression was associated with reduced HOXA9 and MEIS1. To our knowledge, this is the first proteomics approach to search for CDC73 protein-protein interactions in AML, and demonstrates that the PAF1c may play a role in H3K9me3-mediated transcriptional repression in AML.

Mapping of Plasma Membrane Proteins Interacting With Arabidopsis thaliana Flotillin 2

Arabidopsis flotillin 2 (At5g25260) belongs to the group of plant flotillins, which are not well characterized. In contrast, metazoan flotillins are well known as plasma membrane proteins associated with membrane microdomains that act as a signaling hub. The similarity of plant and metazoan flotillins, whose functions most likely consist of affecting other proteins via protein-protein interactions, determines the necessity of detecting their interacting partners in plants. Nevertheless, identifying the proteins that form complexes on the plasma membrane is a challenging task due to their low abundance and hydrophobic character. Here we present an approach for mapping Arabidopsis thaliana flotillin 2 plasma membrane interactors, based on the immunoaffinity purification of crosslinked and enriched plasma membrane proteins with mass spectrometry detection. Using this approach, 61 proteins were enriched in the AtFlot-GFP plasma membrane fraction, and 19 of them were proposed to be flotillin 2 interaction partners. Among our proposed partners of Flot2, proteins playing a role in the plant response to various biotic and abiotic stresses were detected. Additionally, the use of the split-ubiquitin yeast system helped us to confirm that plasma-membrane ATPase 1, early-responsive to dehydration stress protein 4, syntaxin-71, harpin-induced protein-like 3, hypersensitive-induced response protein 2 and two aquaporin isoforms interact with flotillin 2 directly. Based on the results of our study and the reported properties of Flot2 interactors, we propose that Flot2 complexes may be involved in plant-pathogen interactions, water transport and intracellular trafficking.

Functional diversity of topological modules in human protein-protein interaction networks

A large-scale molecular interaction network of protein-protein interactions (PPIs) enables the automatic detection of molecular functional modules through a computational approach. However, the functional modules that are typically detected by topological community detection algorithms may be diverse in functional homogeneity and are empirically considered to be default functional modules. Thus, a significant challenge that has been described but not elucidated is investigating the relationship between topological modules and functional modules. We systematically investigated this issue by initially using seven widely used community detection algorithms to partition the PPI network into communities. Four homogeneity measures were subsequently implemented to evaluate the functional homogeneity of protein community. We determined that a significant portion of topological modules with heterogeneous functionality exists and should be further investigated; moreover, these findings indicated that topologically based functional module detection approaches must be reconsidered. Furthermore, we found that the functional homogeneity of topological modules is positively correlated with their edge densities, degree of association with diseases and general Gene Ontology (GO) terms. Thus, topologically based module detection approaches should be used with caution in the identification of functional modules with high homogeneity

A two-step framework for inferring direct protein-protein interaction network from AP-MS data

Background

Affinity purification-mass spectrometry (AP-MS) has been widely used for generating bait-prey data sets so as to identify underlying protein-protein interactions and protein complexes. However, the AP-MS data sets in terms of bait-prey pairs are highly noisy, where candidate pairs contain many false positives. Recently, numerous computational methods have been developed to identify genuine interactions from AP-MS data sets. However, most of these methods aim at removing false positives that contain contaminants, ignoring the distinction between direct interactions and indirect interactions.

Results

In this paper, we present an initialization-and-refinement framework for inferring direct PPI networks from AP-MS data, in which an initial network is first generated with existing scoring methods and then a refined network is constructed by the application of indirect association removal methods. Experimental results on several real AP-MS data sets show that our method is capable of identifying more direct interactions than traditional scoring methods.

Conclusions

The proposed framework is sufficiently general to incorporate any feasible methods in each step so as to have potential for handling different types of AP-MS data in the future applications.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-017-0452-y) contains supplementary material, which is available to authorized users.

Unravelling the biology of chromatin in health and cancer using proteomic approaches

INTRODUCTION

Chromatin remodeling complexes play important roles in the control of genome regulation in both normal and diseased states, and are therefore critical components for the regulation of epigenetic states in cells. Given the role epigenetics plays in cancer, for example, chromatin remodeling complexes are routinely targeted for therapeutic intervention. Areas covered: Protein mass spectrometry and proteomics are powerful technologies used to study and understand chromatin remodeling. While impressive progress has been made in this area, there remain significant challenges in the application of proteomic technologies to the study of chromatin remodeling. As parts of large multi-subunit complexes that can be heavily modified with dynamic post-translational modifications, challenges in the study of chromatin remodeling complexes include defining the content, determining the regulation, and studying the dynamics of the complexes under different cellular states. Expert commentary: Impwortant considerations in the study of chromatin remodeling complexes include the complexity of sample preparation, the choice of proteomic methods for the analysis of samples, and data analysis challenges. Continued research in these three areas promise to yield even greater insights into the biology of chromatin remodeling and epigenetics and the dynamics of these systems in human health and cancer.

Protein complex analysis: From raw protein lists to protein interaction networks

The elucidation of molecular interaction networks is one of the pivotal challenges in the study of biology. Affinity purification-mass spectrometry and other co-complex methods have become widely employed experimental techniques to identify protein complexes. These techniques typically suffer from a high number of false negatives and false positive contaminants due to technical shortcomings and purification biases. To support a diverse range of experimental designs and approaches, a large number of computational methods have been proposed to filter, infer and validate protein interaction networks from experimental pull-down MS data. Nevertheless, this expansion of available methods complicates the selection of the most optimal ones to support systems biology-driven knowledge extraction. In this review, we give an overview of the most commonly used computational methods to process and interpret co-complex results, and we discuss the issues and unsolved problems that still exist within the field. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 36:600-614, 2017.

The role of mass spectrometry analysis in bacterial effector characterization

Many secreted bacterial effector proteins play a critical role in host–pathogen interactions by mediating a variety of post-translational modifications, some of which do not occur natively within the eukaryotic proteome. The characterization of bacterial effector protein activity remains an important step to understanding the subversion of host cell biology during pathogen infection and although molecular biology and immunochemistry remain critical tools for gaining insights into bacterial effector functions, increasingly mass spectrometry (MS) and proteomic approaches are also playing an indispensable role. The focus of this editorial is to highlight the strengths of specific MS approaches and their utility for the characterization of bacterial effector activity. With the capability of new generation MS instrumentation, MS-based technologies can provide information that is inaccessible using traditional molecular or immunochemical approaches.

Automated multigroup outlier identification in molecular high-throughput data using bagplots and gemplots

Background

Analyses of molecular high-throughput data often lack in robustness, i.e. results are very sensitive to the addition or removal of a single observation. Therefore, the identification of extreme observations is an important step of quality control before doing further data analysis. Standard outlier detection methods for univariate data are however not applicable, since the considered data are high-dimensional, i.e. multiple hundreds or thousands of features are observed in small samples. Usually, outliers in high-dimensional data are solely detected by visual inspection of a graphical representation of the data by the analyst. Typical graphical representation for high-dimensional data are hierarchical cluster tree or principal component plots. Pure visual approaches depend, however, on the individual judgement of the analyst and are hard to automate. Existing methods for automated outlier detection are only dedicated to data of a single experimental groups.

Results

In this work we propose to use bagplots, the 2-dimensional extension of the boxplot, to automatically identify outliers in the subspace of the first two principal components of the data. Furthermore, we present for the first time the gemplot, the 3-dimensional extension of boxplot and bagplot, which can be used in the subspace of the first three principal components. Bagplot and gemplot surround the regular observations with convex hulls and observations outside these hulls are regarded as outliers. The convex hulls are determined separately for the observations of each experimental group while the observations of all groups can be displayed in the same subspace of principal components. We demonstrate the usefulness of this approach on multiple sets of artificial data as well as one set of gene expression data from a next-generation sequencing experiment, and compare the new method to other common approaches. Furthermore, we provide an implementation of the gemplot in the package ‘gemPlot’ for the R programming environment.

Conclusions

Bagplots and gemplots in subspaces of principal components are useful for automated and objective outlier identification in high-dimensional data from molecular high-throughput experiments. A clear advantage over other methods is that multiple experimental groups can be displayed in the same figure although outlier detection is performed for each individual group.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1645-5) contains supplementary material, which is available to authorized users.

Proteome-Scale Human Interactomics

Cellular functions are mediated by complex interactome networks of physical, biochemical, and functional interactions between DNA sequences, RNA molecules, proteins, lipids, and small metabolites. A thorough understanding of cellular organization requires accurate and relatively complete models of interactome networks at proteome scale. The recent publication of four human protein-protein interaction (PPI) maps represents a technological breakthrough and an unprecedented resource for the scientific community, heralding a new era of proteome-scale human interactomics. Our knowledge gained from these and complementary studies provides fresh insights into the opportunities and challenges when analyzing systematically generated interactome data, defines a clear roadmap towards the generation of a first reference interactome, and reveals new perspectives on the organization of cellular life.

Mapping Protein-Protein Interactions Using Affinity Purification and Mass Spectrometry

The mapping of protein-protein interaction (PPI) networks and their dynamics are crucial steps to deciphering the function of a protein and its role in cellular pathways, making it critical to have comprehensive knowledge of a protein's interactome. Advances in affinity purification and mass spectrometry technology (AP-MS) have provided a powerful and unbiased method to capture higher-order protein complexes and decipher dynamic PPIs. However, the unbiased calling of nonspecific interactions and the ability to detect transient interactions remains challenging when using AP-MS, thereby hampering the detection of biologically meaningful complexes. Additionally, there are plant-specific challenges with AP-MS, such as a lack of protein-specific antibodies, which must be overcome to successfully identify PPIs. Here we discuss and describe a protocol designed to bypass the traditional challenges of AP-MS and provide a roadmap to identify bona fide PPIs in plants.

Native Piezo2 Interactomics Identifies Pericentrin as a Novel Regulator of Piezo2 in Somatosensory Neurons

The ability of somatosensory neurons to perceive mechanical stimuli relies on specialized mechanotransducing proteins and their molecular environment. Only recently has the identity of a major transducer of mechanical forces in vertebrates been revealed by the discovery of Piezo2. Further work has established its pivotal role for innocuous touch in mice. Therefore, Piezo2 offers a unique platform for the molecular investigation of somatosensory mechanosensation. We performed a mass spectrometry-based interactomics screen on native Piezo2 in somatosensory neurons of mouse dorsal root ganglia (DRG). Stringent and quantitative data analysis yielded the identity of 36 novel binding partners of Piezo2. The biological significance of this data set is reflected by functional experiments demonstrating a role for Pericentrin in modulating Piezo2 activity and membrane expression in somatosensory neurons. Collectively, our findings provide a framework for understanding Piezo2 physiology and serve as a rich resource for the molecular dissection of mouse somatosensation.

Revealing Higher Order Protein Structure Using Mass Spectrometry

The development of rapid, sensitive, and accurate mass spectrometric methods for measuring peptides, proteins, and even intact protein assemblies has made mass spectrometry (MS) an extraordinarily enabling tool for structural biology. Here, we provide a personal perspective of the increasingly useful role that mass spectrometric techniques are exerting during the elucidation of higher order protein structures. Areas covered in this brief perspective include MS as an enabling tool for the high resolution structural biologist, for compositional analysis of endogenous protein complexes, for stoichiometry determination, as well as for integrated approaches for the structural elucidation of protein complexes. We conclude with a vision for the future role of MS-based techniques in the development of a multi-scale molecular microscope. Graphical Abstract ᅟ.

SAFER, an Analysis Method of Quantitative Proteomic Data, Reveals New Interactors of the C. elegans Autophagic Protein LGG-1

Affinity purifications followed by mass spectrometric analysis are used to identify protein-protein interactions. Because quantitative proteomic data are noisy, it is necessary to develop statistical methods to eliminate false-positives and identify true partners. We present here a novel approach for filtering false interactors, named "SAFER" for mass Spectrometry data Analysis by Filtering of Experimental Replicates, which is based on the reproducibility of the replicates and the fold-change of the protein intensities between bait and control. To identify regulators or targets of autophagy, we characterized the interactors of LGG1, a ubiquitin-like protein involved in autophagosome formation in C. elegans. LGG-1 partners were purified by affinity, analyzed by nanoLC-MS/MS mass spectrometry, and quantified by a label-free proteomic approach based on the mass spectrometric signal intensity of peptide precursor ions. Because the selection of confident interactions depends on the method used for statistical analysis, we compared SAFER with several statistical tests and different scoring algorithms on this set of data. We show that SAFER recovers high-confidence interactors that have been ignored by the other methods and identified new candidates involved in the autophagy process. We further validated our method on a public data set and conclude that SAFER notably improves the identification of protein interactors.

PIPINO: A Software Package to Facilitate the Identification of Protein-Protein Interactions from Affinity Purification Mass Spectrometry Data

The functionality of most proteins is regulated by protein-protein interactions. Hence, the comprehensive characterization of the interactome is the next milestone on the path to understand the biochemistry of the cell. A powerful method to detect protein-protein interactions is a combination of coimmunoprecipitation or affinity purification with quantitative mass spectrometry. Nevertheless, both methods tend to precipitate a high number of background proteins due to nonspecific interactions. To address this challenge the software Protein-Protein-Interaction-Optimizer (PIPINO) was developed to perform an automated data analysis, to facilitate the selection of bona fide binding partners, and to compare the dynamic of interaction networks. In this study we investigated the STAT1 interaction network and its activation dependent dynamics. Stable isotope labeling by amino acids in cell culture (SILAC) was applied to analyze the STAT1 interactome after streptavidin pull-down of biotagged STAT1 from human embryonic kidney 293T cells with and without activation. Starting from more than 2,000 captured proteins 30 potential STAT1 interaction partners were extracted. Interestingly, more than 50% of these were already reported or predicted to bind STAT1. Furthermore, 16 proteins were found to affect the binding behavior depending on STAT1 phosphorylation such as STAT3 or the importin subunits alpha 1 and alpha 6.

Proteomics of Skeletal Muscle: Focus on Insulin Resistance and Exercise Biology

Skeletal muscle is the largest tissue in the human body and plays an important role in locomotion and whole body metabolism. It accounts for ~80% of insulin stimulated glucose disposal. Skeletal muscle insulin resistance, a primary feature of Type 2 diabetes, is caused by a decreased ability of muscle to respond to circulating insulin. Physical exercise improves insulin sensitivity and whole body metabolism and remains one of the most promising interventions for the prevention of Type 2 diabetes. Insulin resistance and exercise adaptations in skeletal muscle might be a cause, or consequence, of altered protein expressions profiles and/or their posttranslational modifications (PTMs). Mass spectrometry (MS)-based proteomics offer enormous promise for investigating the molecular mechanisms underlying skeletal muscle insulin resistance and exercise-induced adaptation; however, skeletal muscle proteomics are challenging. This review describes the technical limitations of skeletal muscle proteomics as well as emerging developments in proteomics workflow with respect to samples preparation, liquid chromatography (LC), MS and computational analysis. These technologies have not yet been fully exploited in the field of skeletal muscle proteomics. Future studies that involve state-of-the-art proteomics technology will broaden our understanding of exercise-induced adaptations as well as molecular pathogenesis of insulin resistance. This could lead to the identification of new therapeutic targets.

Determining the Composition and Stability of Protein Complexes Using an Integrated Label-Free and Stable Isotope Labeling Strategy

In biological systems, proteins catalyze the fundamental reactions that underlie all cellular functions, including metabolic processes and cell survival and death pathways. These biochemical reactions are rarely accomplished alone. Rather, they involve a concerted effect from many proteins that may operate in a directed signaling pathway and/or may physically associate in a complex to achieve a specific enzymatic activity. Therefore, defining the composition and regulation of protein complexes is critical for understanding cellular functions. In this chapter, we describe an approach that uses quantitative mass spectrometry (MS) to assess the specificity and the relative stability of protein interactions. Isolation of protein complexes from mammalian cells is performed by rapid immunoaffinity purification, and followed by in-solution digestion and high-resolution mass spectrometry analysis. We employ complementary quantitative MS workflows to assess the specificity of protein interactions using label-free MS and statistical analysis, and the relative stability of the interactions using a metabolic labeling technique. For each candidate protein interaction, scores from the two workflows can be correlated to minimize nonspecific background and profile protein complex composition and relative stability.

Sequential Elution Interactome Analysis of the Mind Bomb 1 Ubiquitin Ligase Reveals a Novel Role in Dendritic Spine Outgrowth

The mind bomb 1 (Mib1) ubiquitin ligase is essential for controlling metazoan development by Notch signaling and possibly the Wnt pathway. It is also expressed in postmitotic neurons and regulates neuronal morphogenesis and synaptic activity by mechanisms that are largely unknown. We sought to comprehensively characterize the Mib1 interactome and study its potential function in neuron development utilizing a novel sequential elution strategy for affinity purification, in which Mib1 binding proteins were eluted under different stringency and then quantified by the isobaric labeling method. The strategy identified the Mib1 interactome with both deep coverage and the ability to distinguish high-affinity partners from low-affinity partners. A total of 817 proteins were identified during the Mib1 affinity purification, including 56 high-affinity partners and 335 low-affinity partners, whereas the remaining 426 proteins are likely copurified contaminants or extremely weak binding proteins. The analysis detected all previously known Mib1-interacting proteins and revealed a large number of novel components involved in Notch and Wnt pathways, endocytosis and vesicle transport, the ubiquitin-proteasome system, cellular morphogenesis, and synaptic activities. Immunofluorescence studies further showed colocalization of Mib1 with five selected proteins: the Usp9x (FAM) deubiquitinating enzyme, alpha-, beta-, and delta-catenins, and CDKL5. Mutations of CDKL5 are associated with early infantile epileptic encephalopathy-2 (EIEE2), a severe form of mental retardation. We found that the expression of Mib1 down-regulated the protein level of CDKL5 by ubiquitination, and antagonized CDKL5 function during the formation of dendritic spines. Thus, the sequential elution strategy enables biochemical characterization of protein interactomes; and Mib1 analysis provides a comprehensive interactome for investigating its role in signaling networks and neuronal development.

DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics

As a result of recent improvements in mass spectrometry (MS), there is increased interest in data-independent acquisition (DIA) strategies in which all peptides are systematically fragmented using wide mass-isolation windows ('multiplex fragmentation'). DIA-Umpire (http://diaumpire.sourceforge.net/), a comprehensive computational workflow and open-source software for DIA data, detects precursor and fragment chromatographic features and assembles them into pseudo-tandem MS spectra. These spectra can be identified with conventional database-searching and protein-inference tools, allowing sensitive, untargeted analysis of DIA data without the need for a spectral library. Quantification is done with both precursor- and fragment-ion intensities. Furthermore, DIA-Umpire enables targeted extraction of quantitative information based on peptides initially identified in only a subset of the samples, resulting in more consistent quantification across multiple samples. We demonstrated the performance of the method with control samples of varying complexity and publicly available glycoproteomics and affinity purification-MS data.

Quantitative proteomics reveals protein-protein interactions with fibroblast growth factor 12 as a component of the voltage-gated sodium channel 1.2 (nav1.2) macromolecular complex in Mammalian brain

Voltage-gated sodium channels (Nav1.1–Nav1.9) are responsible for the initiation and propagation of action potentials in neurons, controlling firing patterns, synaptic transmission and plasticity of the brain circuit. Yet, it is the protein–protein interactions of the macromolecular complex that exert diverse modulatory actions on the channel, dictating its ultimate functional outcome. Despite the fundamental role of Nav channels in the brain, information on its proteome is still lacking. Here we used affinity purification from crude membrane extracts of whole brain followed by quantitative high-resolution mass spectrometry to resolve the identity of Nav1.2 protein interactors. Of the identified putative protein interactors, fibroblast growth factor 12 (FGF12), a member of the nonsecreted intracellular FGF family, exhibited 30-fold enrichment in Nav1.2 purifications compared with other identified proteins. Using confocal microscopy, we visualized native FGF12 in the brain tissue and confirmed that FGF12 forms a complex with Nav1.2 channels at the axonal initial segment, the subcellular specialized domain of neurons required for action potential initiation. Co-immunoprecipitation studies in a heterologous expression system validate Nav1.2 and FGF12 as interactors, whereas patch-clamp electrophysiology reveals that FGF12 acts synergistically with CaMKII, a known kinase regulator of Nav channels, to modulate Nav1.2-encoded currents. In the presence of CaMKII inhibitors we found that FGF12 produces a bidirectional shift in the voltage-dependence of activation (more depolarized) and the steady-state inactivation (more hyperpolarized) of Nav1.2, increasing the channel availability. Although providing the first characterization of the Nav1.2 CNS proteome, we identify FGF12 as a new functionally relevant interactor. Our studies will provide invaluable information to parse out the molecular determinant underlying neuronal excitability and plasticity, and extending the relevance of iFGFs signaling in the normal and diseased brain.

Investigating Bacterial Protein Synthesis Using Systems Biology Approaches

Protein synthesis is essential for bacterial growth and survival. Its study in Escherichia coli helped uncover features conserved among bacteria as well as universally. The pattern of discovery and the identification of some of the longest-known components of the protein synthesis machinery, including the ribosome itself, tRNAs, and translation factors proceeded through many stages of successively more refined biochemical purifications, finally culminating in the isolation to homogeneity, identification, and mapping of the smallest unit required for performing the given function. These early studies produced a wealth of information. However, many unknowns remained. Systems biology approaches provide an opportunity to investigate protein synthesis from a global perspective, overcoming the limitations of earlier ad hoc methods to gain unprecedented insights. This chapter reviews innovative systems biology approaches, with an emphasis on those designed specifically for investigating the protein synthesis machinery in E. coli.

Rapid analyses of proteomes and interactomes using an integrated solid-phase extraction-liquid chromatography-MS/MS system

Here, we explore applications of a LC system using disposable solid-phase extraction (SPE) cartridges and very short LC-MS/MS gradients that allows for rapid analyses in less than 10 min analysis time. The setup consists of an autosampler harboring two sets of 96 STAGE tips that function as precolumns and a short RP analytical column running 6.5 min gradients. This system combines efficiently with several proteomics workflows such as offline prefractionation methods, including 1D gel electrophoresis and strong-cation exchange chromatography. It also enables the analysis of interactomes obtained by affinity purification with an analysis time of approximately 1 h. In a typical shotgun proteomics experiment involving 36 SCX fractions of an AspN digested cell lysate, we detected over 3600 protein groups with an analysis time of less than 5.5 h. This innovative fast LC system reduces proteome analysis time while maintaining sufficient proteomic detail. This has particular relevance for the use of proteomics within a clinical environment, where large sample numbers and fast turnover times are essential.

Mass spectrometry-based proteomic approaches for discovery of HIV-host interactions

A molecular understanding of viral infection requires a multi-disciplinary approach. Mass spectrometry has emerged as an indispensable tool to investigate the complex and dynamic interactions between HIV-1 and its host. It has been employed to study protein associations, changes in protein abundance and post-translational modifications occurring after viral infection. Here, we review and provide examples of mass spectrometry-based proteomic approaches currently used to explore virus-host interaction. Efforts in this area are certain to accelerate the discovery of the unique molecular strategies utilized by the virus to commandeer the cell as well as mechanisms of host defense.

A comprehensive strategy to identify stoichiometric membrane protein interactomes

There are numerous experimental approaches to identify the interaction networks of soluble proteins, but strategies for the identification of membrane protein interactomes remain limited. We discuss in detail the logic of an experimental design that led us to identify the interactome of a membrane protein of complex membrane topology, the calcium activated chloride channel Anoctamin 1/Tmem16a (Ano1). We used covalent chemical stabilizers of protein-protein interactions combined with magnetic bead immuno-affinity chromatography, quantitative SILAC mass-spectrometry and in silico network construction. This strategy led us to define a putative Ano1 interactome from which we selected key components for functional testing. We propose a combination of procedures to narrow down candidate proteins interacting with a membrane protein of interest for further functional studies.

The life cycle and pathogenesis of human cytomegalovirus infection: lessons from proteomics

Viruses have coevolved with their hosts, acquiring strategies to subvert host cellular pathways for effective viral replication and spread. Human cytomegalovirus (HCMV), a widely-spread β-herpesvirus, is a major cause of birth defects and opportunistic infections in HIV-1/AIDS patients. HCMV displays an intricate system-wide modulation of the human cell proteome. An impressive array of virus-host protein interactions occurs throughout the infection. To investigate the virus life cycle, proteomics has recently become a significant component of virology studies. Here, we review the mass spectrometry-based proteomics approaches used in HCMV studies, as well as their contribution to understanding the HCMV life cycle and the virus-induced changes to host cells. The importance of the biological insights gained from these studies clearly demonstrate the impact that proteomics has had and can continue to have on understanding HCMV biology and identifying new therapeutic targets.

WITHDRAWN: Systems Biology and Immune Aging

The Publisher regrets that this article is an accidental duplication of anarticle that has already been published, http://dx.doi.org/10.1016/j.imlet.2014.09.009. The duplicate article has therefore been withdrawn.

From proteomes to complexomes in the era of systems biology

Protein complexes carry out almost the entire signaling and functional processes in the cell. The protein complex complement of a cell, and its network of complex-complex interactions, is referred to here as the complexome. Computational methods to predict protein complexes from proteomics data, resulting in network representations of complexomes, have recently being developed. In addition, key advances have been made toward understanding the network and structural organization of complexomes. We review these bioinformatics advances, and their discovery-potential, as well as the merits of integrating proteomics data with emerging methods in systems biology to study protein complex signaling. It is envisioned that improved integration of proteomics and systems biology, incorporating the dynamics of protein complexes in space and time, may lead to more predictive models of cell signaling networks for effective modulation.

A novel immuno-competitive capture mass spectrometry strategy for protein-protein interaction profiling reveals that LATS kinases regulate HCV replication through NS5A phosphorylation

Mapping protein-protein interactions is essential to fully characterize the biological function of a protein and improve our understanding of diseases. Affinity purification coupled to mass spectrometry (AP-MS) using selective antibodies against a target protein has been commonly applied to study protein complexes. However, one major limitation is a lack of specificity as a substantial part of the proposed binders is due to nonspecific interactions. Here, we describe an innovative immuno-competitive capture mass spectrometry (ICC-MS) method to allow systematic investigation of protein-protein interactions. ICC-MS markedly increases the specificity of classical immunoprecipitation (IP) by introducing a competition step between free and capturing antibody prior to IP. Instead of comparing only one experimental sample with a control, the methodology generates a 12-concentration antibody competition profile. Label-free quantitation followed by a robust statistical analysis of the data is then used to extract the cellular interactome of a protein of interest and to filter out background proteins. We applied this new approach to specifically map the interactome of hepatitis C virus (HCV) nonstructural protein 5A (NS5A) in a cellular HCV replication system and uncovered eight new NS5A-interacting protein candidates along with two previously validated binding partners. Follow-up biological validation experiments revealed that large tumor suppressor homolog 1 and 2 (LATS1 and LATS2, respectively), two closely related human protein kinases, are novel host kinases responsible for NS5A phosphorylation at a highly conserved position required for optimal HCV genome replication. These results are the first illustration of the value of ICC-MS for the analysis of endogenous protein complexes to identify biologically relevant protein-protein interactions with high specificity.

The Impact of Mass Spectrometry-Based Proteomics on Fundamental Discoveries in Virology

In recent years, mass spectrometry has emerged as a core component of fundamental discoveries in virology. As a consequence of their coevolution, viruses and host cells have established complex, dynamic interactions that function either in promoting virus replication and dissemination or in host defense against invading pathogens. Thus, viral infection triggers an impressive range of proteome changes. Alterations in protein abundances, interactions, posttranslational modifications, subcellular localizations, and secretion are temporally regulated during the progression of an infection. Consequently, understanding viral infection at the molecular level requires versatile approaches that afford both breadth and depth of analysis. Mass spectrometry is uniquely positioned to bridge this experimental dichotomy. Its application to both unbiased systems analyses and targeted, hypothesis-driven studies has accelerated discoveries in viral pathogenesis and host defense. Here, we review the contributions of mass spectrometry-based proteomic approaches to understanding viral morphogenesis, replication, and assembly and to characterizing host responses to infection.

Pre- and post-processing workflow for affinity purification mass spectrometry data

The reliable detection of protein-protein interactions by affinity purification mass spectrometry (AP-MS) is crucial for the understanding of biological processes. Quantitative information can be used to separate truly interacting proteins from false-positives by contrasting counts of proteins binding to specific baits with counts of negative controls. Several approaches have been proposed for computing scores for potential interaction proteins, for example, the commonly used SAINT software. However, it remains a subjective decision where to set the cutoff score for candidate selection; furthermore, no precise control for the expected number of false-positives is provided. In related fields, successful data analysis strongly relies on statistical pre- and post-processing steps, which, so far, have played only a minor role in AP-MS data analysis. We introduce a complete workflow, embedding either the scoring method SAINT or alternatively a two-stage Poisson model into a pre- and post-processing framework. To this end, we investigate different normalization methods and apply a statistical filter adjusted to AP-MS data. Furthermore, we propose permutation and adjustment procedures, which allow the replacement of scores by statistical p values. The performance of the workflow is assessed on simulations as well as on a study focusing on interactions with the T3SS in Salmonella Typhimurium. Preprocessing methods significantly increase the number of detected truly interacting proteins, while a constant false-discovery rate is maintained. The software solution is freely available.

Identification of protein partners in mycobacteria using a single-step affinity purification method

Tuberculosis is a leading cause of death in developing countries. Efforts are being made to both prevent its spread and improve curability rates. Understanding the biology of the bacteria causing the disease, Mycobacterium tuberculosis (M. tuberculosis), is thus vital. We have implemented improved screening methods for protein–protein interactions based on affinity purification followed by high-resolution mass spectrometry. This method can be efficiently applied to both medium- and high-throughput studies aiming to characterize protein–protein interaction networks of tubercle bacilli. Of the 4 tested epitopes FLAG, enhanced green fluorescent protein (eGFP), protein A and haemagglutinin, the eGFP tag was found to be most useful on account of its easily monitored expression and its ability to function as a simultaneous tool for subcellular localization studies. It presents a relatively low background with cost-effective purification. RNA polymerase subunit A (RpoA) was used as a model for investigation of a large protein complex. When used as bait, it co-purified with all remaining RNA polymerase core subunits as well as many accessory proteins. The amount of RpoA strongly correlated with the amount of quantification peptide used as part of the tagging system in this study (SH), making it applicable for semi-quantification studies. Interactions between the components of the RpoA-eGFP protein complex were further confirmed using protein cross-linking. Dynamic changes in the composition of protein complexes under induction of UV damage were observed when UvrA-eGFP expressing cells treated with UV light were used to co-purify UvrA interaction partners.

CAMS-RS: Clustering Algorithm for Large-Scale Mass Spectrometry Data Using Restricted Search Space and Intelligent Random Sampling

High-throughput mass spectrometers can produce massive amounts of redundant data at an astonishing rate with many of them having poor signal-to-noise (S/N) ratio. These low S/N ratio spectra may not get interpreted using conventional spectra-to-database matching techniques. In this paper, we present an efficient algorithm, CAMS-RS (Clustering Algorithm for Mass Spectra using Restricted Space and Sampling) for clustering of raw mass spectrometry data. CAMS-RS utilizes a novel metric (called F-set) that exploits the temporal and spatial patterns to accurately assess similarity between two given spectra. The F-set similarity metric is independent of the retention time and allows clustering of mass spectrometry data from independent LC-MS/MS runs. A novel restricted search space strategy is devised to limit the comparisons of the number of spectra. An intelligent sampling method is executed on individual bins that allow merging of the results to make the final clusters. Our experiments, using experimentally generated data sets, show that the proposed algorithm is able to cluster spectra with high accuracy and is helpful in interpreting low S/N ratio spectra. The CAMS-RS algorithm is highly scalable with increasing number of spectra and our implementation allows clustering of up to a million spectra within minutes.

Seed proteomics

Rather than providing a single specific protocol, the inclusive area of seed proteomics is reviewed; methods are described and compared and primary literature citations are provided. The limitations and challenges of proteomics as an approach to study seed biology are emphasized. The proteomic analysis of seeds encounters some specific problems that do not impinge on analyses of other plant cells, tissues, or organs. There are anatomic considerations. Seeds comprise the seed coat, the storage organ(s), and the embryonic axis. Are these to be studied individually or as a composite? The physiological status of the seeds must be considered; developing, mature, or germinating? If mature, are they quiescent or dormant? If mature and quiescent, then orthodox or recalcitrant? The genetic uniformity of the population of seeds being compared must be considered. Finally, seeds are protein-rich and the extreme abundance of the storage proteins results in a study-subject with a dynamic range that spans several orders of magnitude. This represents a problem that must be dealt with if the study involves analysis of proteins that are of "normal" to low abundance. Several different methods of prefractionation are described and the results compared.

SAINTexpress: improvements and additional features in Significance Analysis of INTeractome software

Significance Analysis of INTeractome (SAINT) is a statistical method for probabilistically scoring protein-protein interaction data from affinity purification-mass spectrometry (AP-MS) experiments. The utility of the software has been demonstrated in many protein-protein interaction mapping studies, yet the extensive testing also revealed some practical drawbacks. In this paper, we present a new implementation, SAINTexpress, with simpler statistical model and quicker scoring algorithm, leading to significant improvements in computational speed and sensitivity of scoring. SAINTexpress also incorporates external interaction data to compute supplemental topology-based scores to improve the likelihood of identifying co-purifying protein complexes in a probabilistically objective manner. Overall, these changes are expected to improve the performance and user experience of SAINT across various types of high quality datasets.

BIOLOGICAL SIGNIFICANCE

We present SAINTexpress, an upgraded implementation of Significance Analysis of INTeractome (SAINT) for filtering high confidence interaction data from affinity purification-mass spectrometry (AP-MS) experiments. SAINTexpress features faster computation and incorporation of external data sources into the scoring, improving the performance and user experience of SAINT across various types of datasets. This article is part of a Special Issue entitled: Can Proteomics Fill the Gap Between Genomics and Phenotypes?