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ArticleApril 1, 2019

A Network Module for the Perseus Software for Computational Proteomics Facilitates Proteome Interaction Graph Analysis
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Jan Daniel Rudolph
Jan Daniel Rudolph
Computational Systems Biochemistry, Max-Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany
More by Jan Daniel Rudolph
http://orcid.org/0000-0003-4196-057X
Jürgen Cox*
Jürgen Cox
Computational Systems Biochemistry, Max-Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany
Department of Biological and Medical Psychology, University of Bergen, Jonas Liesvei 91, 5009 Bergen, Norway
*E-mail: [email protected]
More by Jürgen Cox
http://orcid.org/0000-0001-8597-205X

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Journal of Proteome Research

Cite this: J. Proteome Res. 2019, 18, 5, 2052–2064

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https://doi.org/10.1021/acs.jproteome.8b00927

Published April 1, 2019

Abstract

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Proteomics data analysis strongly benefits from not studying single proteins in isolation but taking their multivariate interdependence into account. We introduce PerseusNet, the new Perseus network module for the biological analysis of proteomics data. Proteomics is commonly used to generate networks, e.g., with affinity purification experiments, but networks are also used to explore proteomics data. PerseusNet supports the biomedical researcher for both modes of data analysis with a multitude of activities. For affinity purification, a volcano-plot-based statistical analysis method for network generation is featured which is scalable to large numbers of baits. For posttranslational modifications of proteins, such as phosphorylation, a collection of dedicated network analysis tools helps in elucidating cellular signaling events. Co-expression network analysis of proteomics data adopts established tools from transcriptome co-expression analysis. PerseusNet is extensible through a plugin architecture in a multi-lingual way, integrating analyses in C#, Python, and R, and is freely available at http://www.perseus-framework.org.

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Introduction

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The study of complex systems (1) is concerned with the question of how the relationships between the parts of a system give rise to its collective behavior. Complex systems often generate emergent properties (2) which are not present in an obvious way in its parts. The interactions between the components of a complex system define a network of connections consisting of nodes and edges. Examples of such networks range over all disciplines of science, including the study of social media networks, (3) scientific collaboration networks, (4) and the human brain and its interconnected neurons as a particularly interesting one. Much of the relevant content is concealed in the network constructed from these interactions and is not visible in the components themselves. For instance, the brain connectome, (5) and not the cellular content of the brain, is believed to make us who we are. (6,7) Similarly, the observation of cellular concentrations of biomolecules without considering their interaction would provide a limited picture that ignores potential emergent properties of the biomolecular complex system. Hence, it is mandatory to study biological systems, such as cellular concentrations of biomolecules, in the framework of network biology. (8)

At a fundamental level, all network connections between the cellular biomolecules are biochemical reactions, and their specification in biochemical pathways together with their subcellular spatial distribution would provide complete knowledge about the biological network state of the cell. This collective network of all biochemical reactions contains all metabolic reactions, the signaling cascades, gene regulatory networks, and all complex-forming non-covalent interactions between molecules, as for instance protein–protein interactions (PPIs). Due to the limitations of experimental and computational methods to map out this interaction network, we often obtain only partial knowledge about the complete biochemical reaction network from experiments. Networks are, however, not limited to describing fundamental physicochemical interactions between biomolecules. For instance, in a gene co-expression network analysis, (9) one looks for similarity of expression patterns of gene products over many samples. Strongly correlated expression implies that these genes have some kind of non-physical interaction; e.g., they are part of the same transcriptional regulatory program, or they share membership in the same pathway or protein complex. However, the exact relationship in terms of biochemical reactions remains unknown with these and other techniques. Hence, in these cases, networks describe a more coarsely grained level of detail, in which relationships between molecules are not necessarily biochemical reactions, but of a more general kind.

Computational proteomics is a mature data science that copes well with the large amounts of data produced in mass spectrometry (MS) experiments. (10) Perseus is an established framework for the downstream bioinformatics analysis of quantitative proteomics data. (11,12) The initial version of Perseus provided a comprehensive framework and set of activities to analyze data matrices originating from quantitative proteomics in a workflow environment. The main idea behind Perseus is to enable researchers in biomedical sciences to perform data analysis themselves. Here we describe how we extend this program to the analysis of biological networks in the context of proteomics. While cytoscape (13) exists as the de facto standard for network analysis and visualization, many proteomics-specific tasks for the generation and analysis of networks are lacking from this framework, as well as workflow navigation. PerseusNet fills this gap and enables non-computational experts to perform complete network-based analysis of their data. We explicitly do not want to re-invent existing methods and algorithms. Instead, we designed an extensible framework that integrates with existing tools, like cytoscape, and interoperates with existing code and scripts from the network analysis community that were written in diverse languages, like Python and R. The data structures within Perseus that hold the networks were set up in a way that facilitates studying dynamic changes in networks and finding differential network properties over complex experimental designs. Side-by-side analysis of networks with data matrices in a common workflow environment allows for a seamless transition between matrix-centric and network-centric approaches.

In the following we start with a general description of the new network framework in Perseus, including how it enables multilingual programming and usage of code resources from R and Python. We then introduce the new volcano-plot based analysis workflow scalable to large affinity purification-mass spectrometry (AP-MS) datasets. We describe how general and, more specifically, large-scale PPI networks are handled and curated in Perseus. A section on the analysis of posttranslational modification (PTM)-induced networks, like kinase–substrate relationships for phosphoproteomics, is next. Finally, we cover co-expression analysis in Perseus and its applications to clinical proteomics.

Experimental Section

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Creating Interaction Networks from Pulldown Experiments

We created an interaction network from a pull-down screen. (14) First, .RAW files were obtained from PRIDE (PXD003758) and processed with MaxQuant version 1.6.2.10. Mouse protein sequences were downloaded from UniProt (release 2017_07). Parameters “matching between runs” and “LFQ” were selected in addition to the default parameters. Downstream analysis of the ‘proteinGroups.txt’ output table was performed in Perseus using the tools described in this Article. Columns for baits Eed, Ring1b, and Bap1 and their controls in the ESC and NPC cell lines were selected and log transformed. Quantitative profiles were filtered for missing values, and were filtered independently for each of the bait control pairs, retaining only proteins that were quantified in all three replicates of either the bait or control pull-down. Missing values were imputed (width 0.3, down shift 1.8) before combining the tables and performing the multi-volcano analysis (Table S1). The s₀ and FDR parameters of the multi-volcano analysis for Class A (higher confidence, s₀ = 1, FDR = 0.01%) and Class B (lower confidence, s₀ = 1, FDR = 0.2%) were chosen by visual inspection, aiming for a low number of significantly depleted proteins in any of the experiments. Class C interactions, which are based on profile correlation between bait and prey, were not considered in this network due to the limited number of pull-downs in the dataset, which would result in inaccurate correlation estimation. Edges representing known protein complex interactions were annotated in the network. Due to missing mouse CORUM annotations for any of the baits, mouse CORUM annotations were obtained by mapping between mouse and human homologues as listed in the MGI database. (15)

Approximately Scale-Free Topology of the STRING Interaction Network

We downloaded the human STRING interaction network (v10.5) from the STRING website. After filtering for high confidence interactions (combined score > 0.9), the scale-free fit index was calculated according to ref (16). Node degrees were calculated and plotted against their frequency distribution on a log–log scale. The R² of a linear fit to the log–log space represents the scale-free fit index.

Network Analysis of a Phosphoproteomic Dataset of EGF Stimulation

Two separate analysis tool, PHOTON and KSEA, were applied to the same experimental dataset of 9184 phosphorylation sites with high localization probability (>0.75) (17) (Table S2). Log2 fold-changes for EGF from two replicates were averaged. For PHOTON analysis, we first generated a high-confidence PPI network. We downloaded all interactions from HIPPIE and filtered them for high-confidence interactions (confidence > 0.72), additionally removing high-degree nodes (degree < 700). Nodes in the HIPPIE network are identified by their Entrez GeneID. Therefore, the experimental data were mapped from UniProt to Entrez GeneIDs before the nodes of the network were annotated . Phosphorylation sites with multiple GeneIDs were mapped to all matching nodes in the network. We then performed PHOTON analysis with adjusted default parameters. Network reconstruction with ANAT was enabled with the 100 highest scoring proteins and EGF anchor (GeneID 1950). Additionally, we increased the number of permutations to 100 000. The KSEA analysis was performed on the human site-specific kinase–substrate network from PhosphositePlus. (18) Data and network were matched on the basis of UniProt identifiers.

Co-expression Analysis of a Clinical Proteomics Dataset

Protein quantification data and clinical annotation were obtained from Yanovic et al. (19) SILAC ratios were first transformed to log(light/heavy). The dataset was filtered for the 43 patients unique to ref (19). Using global hierarchical clustering of the patients, four outlier samples were identified and removed from the dataset. Additionally, proteins with less than 70% valid values were removed from the dataset, and the resulting patient profiles were Z-scored (Table S3). Following the WGCNA workflow, (16) the power parameter for the co-expression analysis was selected using the ‘Soft-threshold’ activity provided by PluginCoExpression. Co-expression analysis was performed in a signed network with biweight midcorrelation and the power parameters set to 10. The eigengene of each co-expression module was correlated with the provided clinical data using Pearson correlation and clustered using hierarchical clustering.

PluginInterop Provides a Central Entry Point for All External Plugins

The PluginInterop project is written in the C# programming language and implements several Perseus plugin APIs. For users it provides a number of activities in Perseus for executing script files written in the Python and R languages. Upon selection of any of these activities, users will be prompted with a parameter window, allowing them to pass additional arguments to the script and requiring them to specify the executable that should be used for processing. Since Perseus does not include an installation of Python or R, users will have to install those and any other dependencies separately. PluginInterop aids the user by trying to automatically detect an existing installation and provide meaningful error messages in case of missing dependencies. Developers can additionally leverage the functionality implemented in PluginInterop as a basis for parametrized scripts. In general, developers are free to choose which external scripting language or program they would like to utilize. We found the R and Python scripting languages to be most useful, which is why we provide two companion libraries, ‘perseuspy’ and ‘PerseusR’, to be used alongside PluginInterop. These libraries aid the communication between Perseus and the script.

The communication between Perseus and external scripts is straightforward and is easily implemented for any tools of choice. In short, Perseus will persist all necessary data to the hard-drive and call the specified tool with specific command-line arguments. The first arguments contain all the parameters specified by the user, per choice of the developer, either in an XML format or simply separated by spaces. Second, the input data from the workflow is saved to a temporary location which is passed to the script. The final arguments specify the expected location of the output data. The external process can provide status and progress updates to the user, as well as detailed error reporting by printing to stdout/stderr and indicating success or failure through the exit code. Once the process exits, Perseus will parse the output data for its expected location and insert it to the workflow. Any step in the pipeline is customizable for advanced scenarios, such as custom data formats.

The PluginInterop binary is automatically included in the latest Perseus version. The source code was published under the permissive, open-source MIT license on Github (https://github.com/cox-labs/PluginInterop). The website also provides more information on how to develop plugins, including a video demonstration. The plugins presented in this Article are all developed on top of PluginInterop and the perseuspy and PerseusR companion libraries.

Library Support for Scripting Languages

We implemented libraries in R and Python which facilitate the interoperability of Perseus with external scripting languages. The main aim of these libraries is to map the data structures of Perseus to a counterpart native to the external language. Developers proficient in these languages will be more comfortable and productive with these native data structures. The largest benefit comes from the resulting integration with the existing data science ecosystem, all now available to Perseus plugin developers.

The ‘perseuspy’ module provides data mappings for the Python language. The Perseus expression matrix is mapped to the ‘DataFrame’ object of the popular ‘pandas’ module, which is tightly integrated with ‘numpy’, the de facto standard for numerical computations in Python. The Perseus network collection data type maps to a list of networks from the ‘networkx’ package. It features a variety of graph algorithms and interfaces well with other modules, due to its usage of standard Python dictionaries. ‘perseuspy’ is distributed via The Python Package Index (PyPI), allowing for easy installation of the module for developers and users alike. The code of ‘perseuspy’ is published under the permissive, open-source MIT license, and is available alongside usage examples and more information on https://github.com/cox-labs/perseuspy.

For the R language, we implemented the ‘PerseusR’ package. It provides a mapping of the Perseus expression matrix to a custom wrapper class around the R ‘data.frame’ object. The wrapping was necessary to represent Perseus-specific information such as annotation rows. Alternatively, developers can load data as a Bioconductor ‘expressionSet’ object which enables the interface with the entire Bioconductors bioinformatics suite. Currently there is no support for network collections in ‘PerseusR’, but we plan to implement it in the near future. ‘PerseusR’ is also published under the MIT license and its code is available on https://github.com/cox-labs/PerseusR. ‘PerseusR’ is easily installed directly from CRAN.

Implementation of PluginPHOTON

We implemented a Perseus plugin for the PHOTON tool on top of the functionality provided by PluginInterop and perseuspy. PHOTON was previously capable to run only a single experiment at a time with a fixed human PPI network. We expanded its implementation to allow for parallel processing of any number of experiments on any network. These changes make large datasets from any species directly amenable to PHOTON analysis. PluginPHOTON is published under the MIT license, its code is available on https://github.com/jdrudolph/photon, and it is included in the latest Perseus release.

Implementation of PluginCoExpression

We implemented parts of the WGCNA pipeline as a Perseus plugin. PluginCoExpression provides access to the WGCNA functions implemented in the R language via PluginInterop and PerseusR.

Implementation of KSEA in Perseus

KSEA analysis was implemented in Perseus and tested for correctness against the reference implementation.

Results and Discussion

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Workflow-Based Biological Network Analysis

PerseusNet was devised to fulfill the computational needs of proteomics researchers wishing to accomplish network analysis of their data. While it is extensible through a new plugin application programming interface (API), and hence any network analysis functionality can be implemented, most tools needed for proteomics research and connecting it to generic network analysis platforms are included in the software (Figure 1). Dedicated activities for analyzing AP-MS datasets and phosphoproteomics experiments in the context of kinase–substrate networks belong to the basic infrastructure of PerseusNet. The most common standard data formats (tab, txt, csv, gml, sif, json) are supported as input. An extended multi-language plugin API allows leveraging many existing tools in the analysis workflow. As an important example, co-expression clustering tools are integrated in this way.

Figure 1. Schematic overview of the new network functionality in Perseus. PerseusNet implements a number of processing and analysis steps facilitated by the network collection data type. While including proteomics centric analyses, such as for the analysis of interaction screens, the network module also provides a number of general purpose tools, as, for instance, for network annotation, filtering, and topology determination. With the extension of the Perseus plugin API to networks and furthermore to other programing languages, it becomes possible to integrate existing network analysis tools in Perseus. Networks are easily imported to and exported from Perseus, due to its support for standard formats.

To accommodate PerseusNet, we extended the Perseus framework with a new data type termed network collection (Figure 2) that represents a set of one or more networks which are analyzed jointly in the workflow. Different networks within the same network collection can, for instance, represent networks derived from different individuals (patients), experimental conditions, or biological replicates. All information in the network collection is organized in data tables, leveraging the existing augmented data matrix (11) in Perseus. General information on the networks in the collection is stored in the networks table, where each row represents an individual network. Here, sample-related annotations, such as calculated global network properties, can be stored to enable their usage in analysis activities operating on a network collection. For instance, if the samples correspond to different patients, the networks table can hold patient-specific information as derived from patient records or questionnaires. These variables can then be used as independent or confounding factors in statistical analysis of the networks.

Figure 2. Schematic representation of the network collection data type. User-facing information is displayed in tabular form with tables listing the networks in the collection, as well as providing detailed information on the nodes and edges of each network. Internally an auxiliary graph data structure aids in the implementation of graph algorithms. Node- and edge-mapping provide the required cross-references between the tabular and graph representation.

The nodes and edges of each individual network are stored in a pair of separate tables. The nodes table further describes the entities in the network, while the edges table provides details on the connections between the entities. The entities in the nodes table can be annotated with local network properties, such as the node degree. In case the entities correspond to proteins, biologically meaningful annotations could include membership in gene ontology terms, pathways, or protein complexes. Similarly, edges can be annotated in the edges table with properties of pairwise relationships between proteins, as, for instance, interaction confidence measures. All of these properties are then accessible to the network analysis tools. Furthermore, all mentioned tables can be sorted and searched, allowing all information to be browsed and inspected intuitively. Internally, a graph data structure for each network enables the efficient execution of graph algorithms. We did not aim to include generic graphical representation of networks as node-link diagrams, since this can be achieved in other tools such as Cytoscape, for which we provide simple adaptors for the transfer of networks. However, several activities include specialized visualizations tailored to specific analyses.

In Perseus, all data analysis steps are performed within a graphical workflow (see Figure S1.) Enabled by the newly implemented network collection, the Perseus workflow is now capable of all import, processing, and analysis steps in the side-by-side analysis of expression matrices and networks. All data imported into Perseus is represented as a separate entity in the workflow. Any matrix or network undergoing a processing step is not modified in place but rather becomes a new entity that gets connected to the original data in the workflow. By inspecting both input and output data, every step in the analysis is traceable and easily understood. Certain processing steps allow for the transformation of matrices into networks and vice versa, or the mapping of data between the two. As a result, any analysis performed in Perseus, potentially including several side-by-side processing steps of networks and matrices, always remains transparent to the user.

Multilingual Plugin Activities

The network collection data structure (Figure 2) and the extended Perseus workflow provide the foundation for enabling various network analyses, many of which are available in Perseus. In general, networks either originate from external sources or are created in a data-driven manner from within the workflow. To facilitate the import of external networks into the workflow, we implemented parsers for standard network formats, such as edge table (.tab|.txt|.csv), GraphML (.gml) (http://graphml.graphdrawing.org/), Cytoscape’s simple interaction format (.sif) (http://manual.cytoscape.org/en/stable/Supported_Network_File_Formats.html), and D3js’s JSONgraph (.json) (http://jsongraphformat.info/), which enable loading interactions from most popular network databases, including STRING, (20) BioGRID, (21) IntAct, (22) CORUM, (23) and PhosphoSitePlus. (18) Furthermore, specific quantitative expression data, such as AP-MS, drives the creation of novel PPI networks, and phosphoproteomics datasets allow for a more detailed view or construction of kinase–substrate relationship networks. Specialized visualizations of such networks are provided (see later sections), which allow for an intuitive visual inspection of the results of the analysis. Perseus is not limited to physical interaction networks: co-expression clustering provides a powerful alternative to regular hierarchical clustering for expression proteomics studies. Finally, any network collection can be exported from the workflow in a plain text file format (Supplementary Data 1) for sharing or use in any other external tools, such as Cytoscape. In order to accommodate these new capabilities in the Perseus plugin system, we extended the Perseus plugin API with new programming interfaces for the network collection and other associated data types, as well as the respective import, processing, and analysis interfaces (see Figure S2.) This fully featured API is available to all developers wishing to extend Perseus’s functionality with plugins. All analyses presented in this Article adhere to the new API.

In order to better leverage the existing network analysis ecosystem, we additionally implemented a new mode of interoperability between Perseus and external tools (Figure 3). The PluginInterop project enables this functionality and allows the user to run external tools from within the Perseus workflow, most prominently scripts written in the popular R and Python languages. Open-source companion libraries for R (PerseusR, https://github.com/cox-labs/PerseusR) and Python (perseuspy, https://github.com/cox-labs/perseuspy) provide utilities for interfacing with Perseus. As a result, network analysis tools originally implemented in external tools can run from within the Perseus workflow with only minor adjustments. The implementations of the PHOTON and WGCNA plugins presented in this Article are based upon PluginInterop and its companion libraries. Instructions for interested developers on how to write scripts for Perseus or how to adapt existing tools can be found on the PluginInterop website (https://github.com/cox-labs/PluginInterop). In the following sections, we will present a number of network analyses which are now implemented in Perseus, with focus on their application to different types of proteomics data.

Figure 3. Schematic of the Perseus plugin system. Plugins written in C# are native to Perseus and implement their functionality directly on top of the application programming interfaces and data structures provided by the application framework. PluginInterop enables the execution of scripts in the Python and R languages, as well as other external programs. By communicating via the file system, data are transferred between Perseus and the external program. The companion libraries ‘perseuspy’ and ‘PerseusR’ enable developers to access the data science ecosystem in their language of choice. For custom graphical user interface elements and an improved user experience of external tools, developers can implement a thin C# wrapper class that extends the generic functionality of PluginInterop.

Affinity Enrichment MS Interactomics

Affinity purification or enrichment coupled to MS analysis has become a powerful tool for interrogating PPIs. (24,25) Not only is it able to provide a detailed view on proteins of interest, but it can also determine the basic building blocks for the assembly of large-scale PPI networks. (26,27) Historically, protein complex members were detected by subjecting the sample to a series of purification steps followed by MS identification. With the advent of quantitative MS, detecting even transient interactions has become possible by relying not on the identification itself, but instead on quantitative information. The sample is not purified but only enriched for the protein of interest and its interaction partners and then subjected to MS quantification. (28)

Confidently identifying bona fide interactions and distinguishing them from background binders, arising from off-target binding or contamination, require data analysis of replicate case and control measurements. Compared to purely fold-change-based methods, statistical tests provide a powerful way to compare case and control samples by calculating a test statistic and an associated p-value and limit the number of false-positives. For visual inspection of the results, the (negative logarithm of the) p-value can be plotted against the size of the effect, i.e., the difference between the means of logarithmic abundances, in a so-called volcano plot. Since one statistical test is performed for each protein, which amounts to a large number of tests performed simultaneously, the significance level needs to be adjusted to avoid increased numbers of false positives due to the multiple hypothesis testing problem. (29) A popular strategy to adjust for multiple testing is to control the false discovery rate (FDR), which can be achieved by permutation-based methods. Furthermore, in the volcano plot method it is necessary to define the functional form of the curves that separate significant from non-significant hits, either by straight lines or, in a more sophisticated way, introduced in the significance analysis of microarrays (SAM) method, (30) by modifying the t test statistic with the background variance parameter s₀. This standard workflow is available in Perseus but becomes increasingly cumbersome for interaction screens with more than a handful of baits. Parameter values for s₀ and the FDR thresholds are often applied separately for each pulldown, inviting overfitting and cherry-picking, and also requiring results be subsequently combined manually.

We implemented the interactive multi-volcano plot (Figure 4a) to analyze interaction screens with arbitrarily many baits and conditions simultaneously. Given the experimental design of the dataset, defined by baits and conditions, the analysis is applied to each experiment. FDR threshold and s₀ parameters for two different Class A (high) and Class B (low) confidence classes can be selected globally. For sufficiently large datasets, instead of dedicated control samples, an internal control can be assembled from the dataset for each pulldown consisting of pulldowns of other, unrelated baits. The results can be inspected through an interactive user interface. All volcano plots are displayed in the overview panel. A multi-functional detail panel shows more information on selected plots and provides zoom, protein selection, and labeling options. If a single plot is selected, the volcano plot is shown in the detail panel. When two plots are selected, the t test differences between the selected experiments are plotted against each other, highlighting changes in the enrichment of proteins between experiments (Figure 4b). Additionally, all data can be browsed in tabular form, making it easily searchable and allowing for rich styling options. Known interactors or gene ontology annotations matching the experiment can be used to highlight proteins in the plot and can serve as a positive control for the adjustment of test parameters. Since all test parameters are controlled on a global level, overfitting and cherry-picking parameter values is prevented effectively. We integrated the multi-volcano analysis into the new network module. Results from PPI screens can be exported as network objects into the Perseus workflow. A specialized node-link visualization based on the open-source cytoscape.js library (31,32) with multiple layers of information, allows for easy interpretation of the results (Figure 4c). A PPI network that was newly created in this way can be integrated with existing networks or exported in various formats using the functions available through the network module.

Figure 4. AP-MS. (a) The Hawaii plot provides an overview over an entire dataset, in this case consisting of three baits in two conditions (Table S1 and Experimental Section). (14) Each volcano plot displays the results of a pull-down of a specific bait (Bap1, Eed, Ring1b) in one of the ESC or NPC cell lines. Significant interactors are determined using a permutation-based FDR and the resulting high-confidence Class A (solid line) and low-confidence Class B (dashed lines) thresholds are displayed in the plot. In this case only in the Bap1 ESC pull-down, Class B interactions could be found. Class A interactors are displayed in dark gray, other proteins are shown in light gray. (b) Enrichment plot comparing the Eed pull-downs in ESC and NPC cell lines. Significant interactors in any of the two conditions are displayed in black, nonsignificant proteins are displayed in light gray. Proteins differentially enriched in one of the two conditions will be located far from the diagonal and can be identified visually. (c) Visualization of the resulting protein interaction network for both cell lines. Bait proteins are colored in green, and their interactors are colored in blue. Thick lines represent Class A interactions, thinner lines Class B. Interactions which were already annotated in the human CORUM database are highlighted in red.

As an example application, we obtained pull-down experiments of Polycomb group proteins from ref (14), covering the three baits Bap1, Eed, and Ring1 in mouse embryonic stem cells (ESCs) and neural progenitor cells (NPCs). The filtered dataset contained 2995 proteins (Table S1). Using the new multi-volcano analysis (Figure 4a), we obtained an interaction network connecting the bait proteins with their significantly enriched prey proteins. Bait proteins were identified by their gene name, as specified in the annotation rows of the dataset. In order to have a consistent representation, the protein groups of the preys are also identified by their gene names. The resulting network contained 134 nodes and 140 edges. The results were comparable to the original publication with overlaps between 55% (Ring1b ESC) and 91% (Bap1 ESC) between the previously reported interactions and detected Class A interactions. Differences can be explained by the slightly different methodology used in this Article. We used the s₀-modified t test with s₀ set to 1.0, and FDRs of 0.01% and 0.2% for Class A and B, respectively, while the authors of ref (14) used individually chosen fold-change and p-value cutoffs for each experiment. No Class C interactions were included. Using the built-in visualization features, such as the enrichment between experiments, we identified several interactions that were conditional on the cell type (Figure 4b). By annotating the newly created protein-interaction network with known complex interaction from CORUM and inspecting the resulting node-link network visualization (Figure 4c), previously known and possibly novel interactions could be distinguished.

Further confidence in the existence of an interaction between a protein identified in a pulldown and the bait can be obtained by correlation analysis. The correlation of the intensity profiles over many pulldowns with the bait intensity profile is reported in the output tables, together with the volcano plot-derived significance of the interaction. When assembling the interaction network, a threshold is applied to this correlation in order to define an additional class of interactions (Class C), which might not have been found by volcano plot analysis (Class A and Class B). This workflow is especially appealing for interaction screens with a large number of bait proteins.

Importing, Curating, and Probing Large-Scale PPI Networks

While protein interaction screens can uncover novel or condition-specific interactions, a wealth of detected and predicted interactions are already stored in PPI databases. (33) Analyzing large-scale PPI networks jointly with other omics data has great promise. However, a major obstacle to performing systems-level analysis on these large-scale networks is the lack of easy-to-use software solutions to transparently handle the processing and analysis of these networks. Many studies under-utilize the existing resources and mostly report the interactions of a single protein as an afterthought. In the following, we introduce the new network capabilities of Perseus to assemble, filter, and understand large-scale PPI networks, which lay the foundation for any network analysis.

The first task is assembling a high-confidence interaction network. Many databases, such as STRING, (20) BioGRID, (21) or HIPPIE, (34) allow researchers to download all interactions in a tabular format, which can be easily loaded into Perseus, even with sizes of up to few millions of interactions. Supporting information on the interactions such as, but not limited to, the interaction type or a measure of confidence remains available at each step in the subsequent data analysis. Networks are not restricted to originate from any single data source. Perseus provides all necessary tools to integrate information from any source, providing full control over the choice of identifier and handling of duplication and ambiguity in the mapping. Conversely, generalized interaction networks such as STRING can be filtered by interaction type to generate a physical interaction network. Confidence measures often integrate diverse knowledge into a single score, derived from how often, and by which experimental technique, an interaction was detected, combined with more abstract measures, such as co-expression and literature co-occurrence of the interaction partners. (20) There are two approaches for interaction confidence aware network analysis (Figure 5a). Applying a cutoff to the confidence score removes low-confidence interactions from the network, which is especially useful when applying methods that treat all interactions equally. The cutoff can be chosen according to the confidence score distribution and the targeted network size (Figure 5b). Other methods operate on weighted networks and distinguish between interactions with high or low confidence. In this case the confidence scores can be used as an edge weight. In addition to static confidence scores, one can devise dynamic confidence scores from experimental data which reflect, e.g., changes in abundance or localization of any of the interactors.

Figure 5. Handling large-scale protein interaction databases in Perseus. (a) Interactions in PPI databases are often annotated with confidence scores derived from various sources. Perseus provides tools to load and combine confidence scores derived a variety of data sources, including dynamic confidence adjustments based on condition-specific data. High-confidence networks can be obtained by removing edges below a given hard threshold or alternatively, confidence scores can be utilized directly as so-called edge weights, thereby allowing for the inclusion of lower-confidence interactions. (b) Histogram of the combined confidence score from the human STRING PPI network. Superimposed in orange is the number of interactions in the filtered network if the edges with scores lower than the current value were removed. Filtering out low confidence edges leads to a significant reduction in the number of edges in the final network. (c) Log–log plot of the node degree against the degree frequency generated from the human STRING PPI network. The R² value of the linear fit (orange) to these data represents the scale-free fit index.

A deeper understanding of the network requires a different perspective in addition to the interaction-centric view. Any list of interactions can be converted into a network collection with a single click. A dedicated set of network-specific processing activities are now available. While processing the list of interactions, the focus remains on the edges of the network. In the network view, the focus is shifted to the nodes. With the powerful identifier and data mapping mechanisms in Perseus, nodes are easily annotated with various annotations, such as gene ontology (GO), (35) or quantitative proteomics data. Any annotation can be subsequently used to filter the nodes of the network. One could, for example, extract a sub-network of proteins associated with a specific GO category and their interactions from the large-scale network. Using the data mapping from, e.g., deep proteomes of specific cell lines or tissues, condition-specific sub-networks can be created.

Further understanding is gained by studying the intrinsic properties of networks. By calculating node degrees, corresponding to the number of neighbors of each node in the network, hub nodes can be distinguished from peripheral nodes. By analyzing the distribution of the node degrees in the network, global network properties, such as approximate scale-freeness, (36,37) of the topology can be identified (Figure 5c). Furthermore, intrinsic local network properties, like the node degree, can be correlated with biological properties derived from protein annotations or experimental data. The proper construction of large-scale interaction networks and understanding of their basic properties are central to the successful application of more specialized analyses such as the integration of such networks with PTM data.

Network Analysis of PTM Data

The MS-based study of PTMs is now possible on a global scale for several types of modifications. The best known example is MS-based phosphoproteomics, (38) which is a powerful tool for interrogating signaling events on a large scale. However, drawing conclusions directly from phosphorylation changes is challenging, due to the mostly missing functional information on the inhibitory or excitatory action of a specific protein phosphorylation at a specific site. Network-based approaches for the analysis of phosphorylation data derive functional information on the protein level by interrogating the phosphorylation changes observed in the network neighborhood. (17,39,40)

We implemented the popular kinase–substrate enrichment analysis (39) (KSEA) tool for predicting kinase activities in Perseus. Site-specific kinase–substrate networks (Figure 6a) assign kinases to the experimentally observed phosphorylation sites. The core of the analysis is the calculation of a series of scores (mean, enrichment, Z-score, p-value, q-value) for each kinase, based on the quantitative phosphorylation changes of its substrates. These predicted kinase activities can be analyzed further to find differentially activated kinases. KSEA most often utilizes the curated kinase–substrate network from the PhosphoSitePlus database. (18,41,42) In order to extend the coverage of the network and thereby allow for the utilization of a larger fraction of the experimental data, the network can be supplemented with predicted kinase–substrate interactions from tools such as NetworKIN (43,44) or with low-specificity interactions derived from kinase target sequence motifs.

Figure 6. Network analysis of an EGF stimulation phosphoproteomics study. (a) Comparison of network topologies used for the analysis of phosphoproteomics data. Nodes in the network are represented as gray circles or pie charts where each slice represents the observed phosphorylation changes at a specific site on the protein. Physical protein–protein interactions (left side) are present between all classes of proteins and are by definition undirected. In order to capture the enzymatic action of kinases more accurately, directed interactions (right side) from kinase to substrate are defined in a site-specific manner. (b) KSEA Z-score and PHOTON signaling functionality scores derived from phosphoproteomics data measured after EGF stimulation (Table S2) only weakly correlate to each other (Pearson correlation 0.52). Kinases annotated in GO with the term ‘Epidermal growth factor receptor signaling pathway’ are highlighted in red. Both methods assign high scores to central members of the expected pathway. (c) Signaling network reconstructed by PHOTON from the 100 highest scoring proteins anchored at EGF. The interactive visualization has an automatic layout and phosphorylation data overlay.

PHOTON, (17) now available in Perseus, is an alternative approach to KSEA that calculates more broadly defined signaling functionality scores for any protein, rather than activities for kinases only. A data-annotated large-scale PPI network now serves as the input (Figure 6a). The resulting signaling functionality scores for each experimental condition are based on the observed phosphorylation in the neighborhood of each protein and are assigned a significance by a permutation scheme. The scores can either be analyzed directly, to find proteins with differentially changing signaling functionality, or utilized in a second step of the PHOTON pipeline, in which signaling pathways are automatically reconstructed from the large-scale network that connect the proteins with significant signaling functionality. (17)

The Perseus network module allows for performing both KSEA and PHOTON analysis on the same experimental data (17) and a choice of networks. (18,34) When applied to a phosphoproteomics dataset of EGF stimulation, (17) the trade-offs of both methods in terms of coverage can be compared at every step of the analysis by inspecting the matrices and network collections in the workflow. For KSEA, 583 (5.66%) phosphorylation sites could be mapped to 975 (9.43%) site-specific kinase substrate interactions. As expected, PHOTON provided more coverage, with 9148 (99.82%) sites mapped to 2070 (16.87%) nodes in the PPI network. Due to the differences in the utilized methodologies and the chosen networks, resulting scores will differ but can be compared with the analyses and visualizations provided by Perseus (Figure 6b). While KSEA is tailored to the analysis of phosphoproteomics data due to its focus on kinase–substrate interactions, PHOTON is not limited to phosphorylation. Any quantitative, large-scale PTM dataset can be mapped to the PPI network, signaling functionality scores can be calculated, and sub-networks can be reconstructed.

Both tools support the analysis of datasets with multiple conditions, effectively transforming the peptide-level phosphorylation data into protein-level scores. The entire well-established toolset for the analysis of protein quantification data can be applied to these scores, including hierarchical clustering, enrichment analysis, (17) and time-series analysis. To visualize PTM data in the context of any network, we implemented an interactive visualization of directly in Perseus (Figure 6c) using the cytoscape.js library. (31) The visualization allows for the joint visual inspection of the networks, e.g., sub-networks reconstructed by PHOTON, and the measured data. Browsing the quantitative PTM data in a reduced and highly structured network view while also considering the signaling functionality scores allows for the generation of hypotheses that explain the signal transduction mechanistically.

Co-expression Clustering and Clinical Data

When performing co-expression analysis, the correlation matrix between the proteins in the dataset describes a fully connected, weighted network, in which the weight on each edge denotes the correlation between the quantitative profiles of the two proteins (Figure 7a). Hence, the actual network usually remains implicit. A hierarchical clustering of the co-expression network can utilize the network neighborhood of each protein and integrate it into the similarity calculation. (45) The cluster dendrogram and the detected co-expression modules are then transferred back to the original data, where their interpretation is equivalent to ordinary hierarchical clustering. In addition to the clustering, a representative expression profile for each of the clusters is generated, which is termed eigengene. This highly reduced view on the data can be correlated with clinical or phenotype data and clustered to gain a better understanding of the behavior of the detected cluster (Figure 7b). The described co-expression analysis is available in Perseus through the R language interface provided by PluginInterop, which interfaces directly with the established WGCNA library. (16)

Figure 7. Co-expression network analysis on clinical data. (a) The correlation matrix is an equivalent representation of a fully connected network with edge weights corresponding to the correlation between the proteins. (b) Co-expression clustering and identified co-expression modules annotate the original expression matrix. Phenotype data can be correlated with representative co-expression module profiles and provide a high-level interpretation of the modules. (c) Parameter selection of the power parameter for the Yanovich et al. (19) dataset (Table S3 and Experimental Section). The lowest power reaching close to a high scale-free fit index of 0.9 (red line) was selected. (d) Co-expression cluster dendrogram. Each color corresponds to one co-expression module. (e) Correlation heat map between module eigengenes and clinical parameters.

We applied the WGCNA co-expression analysis to parts of a cancer proteomics dataset (19) following the recommended workflow (http://www.peterlangfelder.com/wgcna-resources-on-the-web/) from within Perseus. Bi-weight midcorrelation, a robust alternative to Pearson correlation, was chosen to calculate correlations between all pairs of proteins. In order to obtain a scale-free co-expression network, a power parameter of 10 was selected (Figure 7c), leading to an approximately scale-free network with a scale-free fit index of 0.9. Hierarchical clustering of the co-expression network identified 30 modules (Figure 7d). The representative expression profiles of each of the modules, as provided by the corresponding module eigengene, were correlated with the available clinical annotations. This high-level overview over the data was then visualized in a heatmap (Figure 7e). Several modules showed high correlations with specific clinical annotations. The magenta module showed high correlation with the triple-negative subtype (TN) and was significantly enriched for the ‘interferon-gamma-mediated signaling pathway’ GO category (q = 1.12 × 10^–05). The top module hub genes with kME > 0.8 were GBP1, TAP1, TAPBP, HLA-A, TAP2, STAT1, and EML4. The purple module showed high correlation with Stage III, but when inspecting the profile of its eigengene, we found it to have a single peak at one patient while being flat for all others. Hence, a set of proteins that are highly expressed in only a single patient, dominate the purple module, thereby limiting the validity of the module.

Software Implementation, Download, and Maintenance

The Perseus network module PerseusNet is implemented in the C# programming language using Visual Studio 2017, like the whole Perseus software. PerseusNet is distributed with Perseus by default and can be downloaded from http://www.perseus-framework.org. The current version, which is described in this Article, is 1.6.2.3. The PluginInterop and PHOTON plugins are also included in the standard download. In the current release, it is recommended to use Windows as operating system, although Linux support is underway, realized in the same way as for the MaxQuant software, (46,47) by ensuring Mono compatibility. A plugin API enables external programmers to extend the functionality of PerseusNet and Perseus in general, by programming their own workflow activities. Plugin extensions by the user community will be linked from the plugin store at http://www.coxdocs.org/doku.php?id=perseus:user:plugins:store upon request. Context-specific documentation is linked from each activity (Figure S3). Step-by-step guides for the integration of external tools, such as Python or R, that have to be installed and configured separately from the main Perseus software, are available online (https://github.com/jdrudolph/PluginInterop). A help forum for Perseus and PerseusNet is available at https://groups.google.com/group/perseus-list. Bugs that are reproducible in the latest available software version should be reported at https://maxquant.myjetbrains.com/youtrack. All presented analyses and necessary installations take less than an hour altogether.

Conclusions

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We introduced PerseusNet, the network analysis extension for the Perseus software. It enables proteomics researchers to perform most network analysis by themselves. PerseusNet is highly extensible through a plugin API and its extension to R and Python, which allows for the incorporation of a plethora of existing scripts and programs from the network community. We envision that large part of the future programming will be done not by local developers but by the global community through the plugin API. Programmers can release their plugins under licenses of their choice.

We have implemented powerful proteomics-specific activities for AP-MS network generation and PTM-related network analysis, presumably the two main applications for networking in proteomics. We plan to extend PerseusNet in the near future by activities from other proteomics sub-domains, as interaction determination by protein correlation profiling (48) and large-scale network generation from cross-linking experiments on whole-cell lysates. (49)

Supporting Information

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The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jproteome.8b00927.

Figure S1, Graphical workflow combining matrix and network activities; Figure S2, Organization of the new Perseus plugin API for networks; Figure S3, Context-specific documentation; and explanations of Tables S1–S3 (PDF)
Table S1, AP-MS pull screen (TXT)
Table S2, phosphoproteomics of EGF stimulation (TXT)
Table S3, clinical proteomics dataset (TXT)
Supplementary Data 1, Perseus network collection data format: example of a network collection describing three small, randomly generated networks (ZIP)

Terms & Conditions

Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

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Corresponding Author
- Jürgen Cox - Computational Systems Biochemistry, Max-Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany; Department of Biological and Medical Psychology, University of Bergen, Jonas Liesvei 91, 5009 Bergen, Norway; http://orcid.org/0000-0001-8597-205X; Email: [email protected]
Author
- Jan Daniel Rudolph - Computational Systems Biochemistry, Max-Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany; http://orcid.org/0000-0003-4196-057X
Author Contributions
J.D.R. and J.C. planned and performed the research, developed the software, and wrote the manuscript.
Notes
The authors declare no competing financial interest.

Acknowledgments

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We thank J. Sebastian Paez and Sung-Huan Yu for contributing to PerseusR, and Caroline Friedel and Tamar Geiger for helpful discussions. This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 686547 and from the FP7 grant agreement GA ERC-2012-SyG_318987–ToPAG.

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PhosphoSitePlus, 2014: mutations, PTMs and recalibrations
Hornbeck, Peter V.; Zhang, Bin; Murray, Beth; Kornhauser, Jon M.; Latham, Vaughan; Skrzypek, Elzbieta
Nucleic Acids Research (2015), 43 (D1), D512-D520CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)
PhosphoSitePlus (PSP), a knowledge-base dedicated to mammalian post-translational modifications (PTMs), contains over 330000 non-redundant PTMs, including phospho, acetyl, ubiquityl and Me groups. Over 95% of the sites are from mass spectrometry (MS) expts. In order to improve data reliability, early MS data have been reanalyzed, applying a common std. of anal. across over 1000000 spectra. Site assignments with P > 0.05 were filtered out. Two new downloads are available from PSP. The 'Regulatory sites' dataset includes curated information about modification sites that regulate downstream cellular processes, mol. functions and protein-protein interactions. The 'PTMVar' dataset, an intersect of missense mutations and PTMs from PSP, identifies over 25000 PTMVars (PTMs Impacted by Variants) that can rewire signaling pathways. The PTMVar data include missense mutations from UniPROTKB, TCGA and other sources that cause over 2000 diseases or syndromes (MIM) and polymorphisms, or are assocd. with hundreds of cancers. PTMVars include 18548 phosphorlyation sites, 3412 ubiquitylation sites, 2316 acetylation sites, 685 methylation sites and 245 succinylation sites.
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Yanovich, G. Clinical Proteomics of Breast Cancer Reveals a Novel Layer of Breast Cancer Classification. Cancer Res. 2018, 78, 6001– 6010, DOI: 10.1158/0008-5472.CAN-18-1079
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Clinical proteomics of breast cancer reveals a novel layer of breast cancer classification
Yanovich, Gali; Agmon, Hadar; Harel, Michal; Sonnenblick, Amir; Peretz, Tamar; Geiger, Tamar
Cancer Research (2018), 78 (20), 6001-6010CODEN: CNREA8; ISSN:0008-5472. (American Association for Cancer Research)
Breast cancer classification has been the focus of numerous worldwide efforts, analyzing the mol. basis of breast cancer subtypes and aiming to assoc. them with clin. outcome and to improve the current diagnostic routine. Genomic and transcriptomic profiles of breast cancer have been well established, however the proteomic contribution to these profiles has yet to be elucidated. In this work, we utilized mass spectrometry-based proteomic anal. on more than 130 clin. breast samples to demonstrate intertumor heterogeneity across three breast cancer subtypes and healthy tissue. Unsupervised anal. identified four proteomic clusters, among them, one that represents a novel luminal subtype characterized by increased PI3K signaling. This subtype was further validated using an independent protein-based dataset, but not in two independent transcriptome cohorts. These results demonstrate the importance of deep proteomic anal., which may affect cancer treatment decision making.
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Szklarczyk, D. The STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 2017, 45, D362– D368, DOI: 10.1093/nar/gkw937
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The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible
Szklarczyk, Damian; Morris, John H.; Cook, Helen; Kuhn, Michael; Wyder, Stefan; Simonovic, Milan; Santos, Alberto; Doncheva, Nadezhda T.; Roth, Alexander; Bork, Peer; Jensen, Lars J.; von Mering, Christian
Nucleic Acids Research (2017), 45 (D1), D362-D368CODEN: NARHAD; ISSN:1362-4962. (Oxford University Press)
A system-wide understanding of cellular function requires knowledge of all functional interactions between the expressed proteins. The STRING database aims to collect and integrate this information, by consolidating known and predicted protein-protein assocn. data for a large no. of organisms. The assocns. in STRING include direct (phys.) interactions, as well as indirect (functional) interactions, as long as both are specific and biol. meaningful. Apart from collecting and reassessing available exptl. data on protein-protein interactions, and importing known pathways and protein complexes from curated databases, interaction predictions are derived from the following sources: (i) systematic coexpression anal., (ii) detection of shared selective signals across genomes, (iii) automated text-mining of the scientific literature and (iv) computational transfer of interaction knowledge between organisms based on gene orthol. In the latest version 10.5 of STRING, the biggest changes are concerned with data dissemination: the web front-end has been completely redesigned to reduce dependency on outdated browser technologies, and the database can now also be queried from inside the popular Cytoscape software framework. Further improvements include automated background anal. of user inputs for functional enrichments, and streamlined download options. The STRING resource is available online, at http://string-db.org/.
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Chatr-Aryamontri, A. The BioGRID interaction database: 2017 update. Nucleic Acids Res. 2017, 45, D369– D379, DOI: 10.1093/nar/gkw1102
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The BioGRID interaction database: 2017 update
Chatr-aryamontri, Andrew; Oughtred, Rose; Boucher, Lorrie; Rust, Jennifer; Chang, Christie; Kolas, Nadine K.; O'Donnell, Lara; Oster, Sara; Theesfeld, Chandra; Sellam, Adnane; Stark, Chris; Breitkreutz, Bobby-Joe; Dolinski, Kara; Tyers, Mike
Nucleic Acids Research (2017), 45 (D1), D369-D379CODEN: NARHAD; ISSN:1362-4962. (Oxford University Press)
The Biol. General Repository for Interaction Datasets (BioGRID: https://thebiogrid.org) is an open access database dedicated to the annotation and archival of protein, genetic and chem. interactions for all major model organism species and humans. As of Sept. 2016 (build 3.4.140), the BioGRID contains 1 072 173 genetic and protein interactions, and 38 559 post-translational modifications, as manually annotated from 48 114 publications. This dataset represents interaction records for 66 model organisms and represents a 30% increase compared to the previous 2015 BioGRID update. BioGRID curates the biomedical literature for major model organism species, including humans, with a recent emphasis on central biol. processes and specific human diseases. To facilitate network-based approaches to drug discovery, BioGRID now incorporates 27 501 chem.-protein interactions for human drug targets, as drawn from the DrugBank database. A new dynamic interaction network viewer allows the easy navigation and filtering of all genetic and protein interaction data, as well as for bioactive compds. and their established targets. BioGRID data are directly downloadable without restriction in a variety of standardized formats and are freely distributed through partner model organism databases and meta-databases.
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Orchard, S. The MIntAct project - IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res. 2014, 42, D358– 63, DOI: 10.1093/nar/gkt1115
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The MIntAct project-IntAct as a common curation platform for 11 molecular interaction databases
Orchard, Sandra; Ammari, Mais; Aranda, Bruno; Breuza, Lionel; Briganti, Leonardo; Broackes-Carter, Fiona; Campbell, Nancy H.; Chavali, Gayatri; Chen, Carol; del-Toro, Noemi; Duesbury, Margaret; Dumousseau, Marine; Galeota, Eugenia; Hinz, Ursula; Iannuccelli, Marta; Jagannathan, Sruthi; Jimenez, Rafael; Khadake, Jyoti; Lagreid, Astrid; Licata, Luana; Lovering, Ruth C.; Meldal, Birgit; Melidoni, Anna N.; Milagros, Mila; Peluso, Daniele; Perfetto, Livia; Porras, Pablo; Raghunath, Arathi; Ricard-Blum, Sylvie; Roechert, Bernd; Stutz, Andre; Tognolli, Michael; van Roey, Kim; Cesareni, Gianni; Hermjakob, Henning
Nucleic Acids Research (2014), 42 (D1), D358-D363CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)
IntAct (freely available at http://www.ebi.ac.uk/intact) is an open-source, open data mol. interaction database populated by data either curated from the literature or from direct data depositions. IntAct has developed a sophisticated web-based curation tool, capable of supporting both IMEx- and MIMIx-level curation. This tool is now utilized by multiple addnl. curation teams, all of whom annotate data directly into the IntAct database. Members of the IntAct team supply appropriate levels of training, perform quality control on entries and take responsibility for long-term data maintenance. Recently, the MINT and IntAct databases decided to merge their sep. efforts to make optimal use of limited developer resources and maximize the curation output. All data manually curated by the MINT curators have been moved into the IntAct database at EMBL-EBI and are merged with the existing IntAct dataset. Both IntAct and MINT are active contributors to the IMEx consortium (http://www.imexconsortium.org).
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Ruepp, A. CORUM: The comprehensive resource of mammalian protein complexes-2009. Nucleic Acids Res. 2010, 38, D497– 501, DOI: 10.1093/nar/gkp914
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CORUM: the comprehensive resource of mammalian protein complexes-2009
Ruepp, Andreas; Waegele, Brigitte; Lechner, Martin; Brauner, Barbara; Dunger-Kaltenbach, Irmtraud; Fobo, Gisela; Frishman, Goar; Montrone, Corinna; Mewes, H.-Werner
Nucleic Acids Research (2010), 38 (Database Iss), D497-D501CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)
CORUM is a database that provides a manually curated repository of exptl. characterized protein complexes from mammalian organisms, mainly human (64%), mouse (16%) and rat (12%). Protein complexes are key mol. entities that integrate multiple gene products to perform cellular functions. The new CORUM 2.0 release encompasses 2837 protein complexes offering the largest and most comprehensive publicly available dataset of mammalian protein complexes. The CORUM dataset is built from 3198 different genes, representing ∼16% of the protein coding genes in humans. Each protein complex is described by a protein complex name, subunit compn., function as well as the literature ref. that characterizes the resp. protein complex. Recent developments include mapping of functional annotation to Gene Ontol. terms as well as cross-refs. to Entrez Gene identifiers. In addn., a Phylogenetic Conservation' anal. tool was implemented that analyses the potential occurrence of orthologous protein complex subunits in mammals and other selected groups of organisms. This allows one to predict the occurrence of protein complexes in different phylogenetic groups. CORUM is freely accessible at (http://mips.helmholtz-muenchen.de/genre/proj/corum/index.html).
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Gingras, A. C.; Gstaiger, M.; Raught, B.; Aebersold, R. Analysis of protein complexes using mass spectrometry. Nat. Rev. Mol. Cell Biol. 2007, 8, 645– 654, DOI: 10.1038/nrm2208
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Analysis of protein complexes using mass spectrometry
Gingras, Anne-Claude; Gstaiger, Matthias; Raught, Brian; Aebersold, Ruedi
Nature Reviews Molecular Cell Biology (2007), 8 (8), 645-654CODEN: NRMCBP; ISSN:1471-0072. (Nature Publishing Group)
A review. The versatile combination of affinity purifn. and mass spectrometry (AP-MS) has recently been applied to the detailed characterization of many protein complexes and large protein-interaction networks. The combination of AP-MS with other techniques, such as biochem. fractionation, intact mass measurement and chem. crosslinking, can help to decipher the supramol. organization of protein complexes. AP-MS can also be combined with quant. proteomics approaches to better understand the dynamics of protein-complex assembly.
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Dunham, W. H.; Mullin, M.; Gingras, A. C. Affinity-purification coupled to mass spectrometry: Basic principles and strategies. Proteomics 2012, 12, 1576, DOI: 10.1002/pmic.201100523
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Affinity-purification coupled to mass spectrometry: Basic principles and strategies
Dunham, Wade H.; Mullin, Michael; Gingras, Anne-Claude
Proteomics (2012), 12 (10), 1576-1590CODEN: PROTC7; ISSN:1615-9853. (Wiley-VCH Verlag GmbH & Co. KGaA)
A review. Identifying the interactions established by a protein of interest can be a crit. step in understanding its function. This is esp. true when an unknown protein of interest is demonstrated to phys. interact with proteins of known function. While many techniques have been developed to characterize protein-protein interactions, one strategy that has gained considerable momentum over the past decade for identification and quantification of protein-protein interactions, is affinity-purifn. followed by mass spectrometry (AP-MS). Here, we briefly review the basic principles used in affinity-purifn. coupled to mass spectrometry, with an emphasis on tools (both biochem. and computational), which enable the discovery and reporting of high quality protein-protein interactions.
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Hein, M. Y. A Human Interactome in Three Quantitative Dimensions Organized by Stoichiometries and Abundances. Cell 2015, 163, 712– 723, DOI: 10.1016/j.cell.2015.09.053
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A Human Interactome in Three Quantitative Dimensions Organized by Stoichiometries and Abundances
Hein, Marco Y.; Hubner, Nina C.; Poser, Ina; Cox, Juergen; Nagaraj, Nagarjuna; Toyoda, Yusuke; Gak, Igor A.; Weisswange, Ina; Mansfeld, Joerg; Buchholz, Frank; Hyman, Anthony A.; Mann, Matthias
Cell (Cambridge, MA, United States) (2015), 163 (3), 712-723CODEN: CELLB5; ISSN:0092-8674. (Cell Press)
The organization of a cell emerges from the interactions in protein networks. The interactome is critically dependent on the strengths of interactions and the cellular abundances of the connected proteins, both of which span orders of magnitude. However, these aspects have not yet been analyzed globally. Here, the authors have generated a library of HeLa cell lines expressing 1125 GFP-tagged proteins under near-endogenous control, which the authors used as input for a next-generation interaction survey. Using quant. proteomics, the authors detect specific interactions, est. interaction stoichiometries, and measure cellular abundances of interacting proteins. These three quant. dimensions reveal that the protein network is dominated by weak, substoichiometric interactions that play a pivotal role in defining network topol. The minority of stable complexes can be identified by their unique stoichiometry signature. This study provides a rich interaction dataset connecting thousands of proteins and introduces a framework for quant. network anal.
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Huttlin, E. L. The BioPlex Network: A Systematic Exploration of the Human Interactome. Cell 2015, 162, 425– 440, DOI: 10.1016/j.cell.2015.06.043
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The BioPlex Network: A Systematic Exploration of the Human Interactome
Huttlin, Edward L.; Ting, Lily; Bruckner, Raphael J.; Gebreab, Fana; Gygi, Melanie P.; Szpyt, John; Tam, Stanley; Zarraga, Gabriela; Colby, Greg; Baltier, Kurt; Dong, Rui; Guarani, Virginia; Vaites, Laura Pontano; Ordureau, Alban; Rad, Ramin; Erickson, Brian K.; Wuhr, Martin; Chick, Joel; Zhai, Bo; Kolippakkam, Deepak; Mintseris, Julian; Obar, Robert A.; Harris, Tim; Artavanis-Tsakonas, Spyros; Sowa, Mathew E.; De Camilli, Pietro; Paulo, Joao A.; Harper, J. Wade; Gygi, Steven P.
Cell (Cambridge, MA, United States) (2015), 162 (2), 425-440CODEN: CELLB5; ISSN:0092-8674. (Cell Press)
Protein interactions form a network whose structure drives cellular function and whose organization informs biol. inquiry. Using high-throughput affinity-purifn. mass spectrometry, the authors identify interacting partners for 2594 human proteins in HEK293T cells. The resulting network (BioPlex) contains 23,744 interactions among 7668 proteins with 86% previously undocumented. BioPlex accurately depicts known complexes, attaining 80%-100% coverage for most CORUM complexes. The network readily subdivides into communities that correspond to complexes or clusters of functionally related proteins. More generally, network architecture reflects cellular localization, biol. process, and mol. function, enabling functional characterization of thousands of proteins. Network structure also reveals assocns. among thousands of protein domains, suggesting a basis for examg. structurally related proteins. Finally, BioPlex, in combination with other approaches, can be used to reveal interactions of biol. or clin. significance. For example, mutations in the membrane protein VAPB implicated in familial amyotrophic lateral sclerosis perturb a defined community of interactors.
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Hubner, N. C. Quantitative proteomics combined with BAC TransgeneOmics reveals in vivo protein interactions. J. Cell Biol. 2010, 189, 739– 754, DOI: 10.1083/jcb.200911091
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Quantitative proteomics combined with BAC TransgeneOmics reveals in vivo protein interactions
Hubner, Nina C.; Bird, Alexander W.; Cox, Juergen; Splettstoesser, Bianca; Bandilla, Peter; Poser, Ina; Hyman, Anthony; Mann, Matthias
Journal of Cell Biology (2010), 189 (4), 739-754CODEN: JCLBA3; ISSN:0021-9525. (Rockefeller University Press)
Protein interactions are involved in all cellular processes. Their efficient and reliable characterization is therefore essential for understanding biol. mechanisms. In this study, we show that combining bacterial artificial chromosome (BAC) TransgeneOmics with quant. interaction proteomics, which we call quant. BAC-green fluorescent protein interactomics (QUBIC), allows specific and highly sensitive detection of interactions using rapid, generic, and quant. procedures with minimal material. We applied this approach to identify known and novel components of well-studied complexes such as the anaphase-promoting complex. Furthermore, we demonstrate second generation interaction proteomics by incorporating directed mutational transgene modification and drug perturbation into QUBIC. These methods identified domain/isoform-specific interactors of pericentrin- and phosphorylation-specific interactors of TACC3, which are necessary for its recruitment to mitotic spindles. The scalability, simplicity, cost effectiveness, and sensitivity of this method provide a basis for its general use in small-scale expts. and in mapping the human protein interactome.
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Noble, W. S. How does multiple testing correction work?. Nat. Biotechnol. 2009, 27, 1135– 1137, DOI: 10.1038/nbt1209-1135
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How does multiple testing correction work?
Noble, William S.
Nature Biotechnology (2009), 27 (12), 1135-1137CODEN: NABIF9; ISSN:1087-0156. (Nature Publishing Group)
A review. When prioritizing hits from a high-throughput expt., it is important to correct for random events that falsely appear significant. How is this done and what methods should be used. Techniques for doing this are discussed here.
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Tusher, V. G.; Tibshirani, R.; Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. U. S. A. 2001, 98, 5116– 5121, DOI: 10.1073/pnas.091062498
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Significance analysis of microarrays applied to the ionizing radiation response
Tusher, Virginia Goss; Tibshirani, Robert; Chu, Gilbert
Proceedings of the National Academy of Sciences of the United States of America (2001), 98 (9), 5116-5121CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
Microarrays can measure the expression of thousands of genes to identify changes in expression between different biol. states. Methods are needed to det. the significance of these changes while accounting for the enormous no. of genes. We describe a method, Significance Anal. of Microarrays (SAM), that assigns a score to each gene on the basis of change in gene expression relative to the std. deviation of repeated measurements. For genes with scores greater than an adjustable threshold, SAM uses permutations of the repeated measurements to est. the percentage of genes identified by chance, the false discovery rate (FDR). When the transcriptional response of human cells to ionizing radiation was measured by microarrays, SAM identified 34 genes that changed at least 1.5-fold with an estd. FDR of 12%, compared with FDRs of 60 and 84% by using conventional methods of anal. Of the 34 genes, 19 were involved in cell cycle regulation and 3 in apoptosis. Surprisingly, four nucleotide excision repair genes were induced, suggesting that this repair pathway for UV-damaged DNA might play a previously unrecognized role in repairing DNA damaged by ionizing radiation.
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Franz, M. Cytoscape.js: A graph theory library for visualisation and analysis. Bioinformatics 2015, 32, 309– 311, DOI: 10.1093/bioinformatics/btv557
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Dogrusoz, U.; Giral, E.; Cetintas, A.; Civril, A.; Demir, E. A layout algorithm for undirected compound graphs. Inf. Sci. (N. Y.) 2009, 179, 980– 994, DOI: 10.1016/j.ins.2008.11.017
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Pedamallu, C. S.; Ozdamar, L. A Review on protein-protein interaction network databases. In Springer Proceedings in Mathematics and Statistics; Springer, 2014; Vol. 73, pp 511– 519.
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Alanis-Lobato, G.; Andrade-Navarro, M. A.; Schaefer, M. H. HIPPIE v2.0: Enhancing meaningfulness and reliability of protein-protein interaction networks. Nucleic Acids Res. 2017, 45, D408– 14, DOI: 10.1093/nar/gkw985
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HIPPIE v2.0: enhancing meaningfulness and reliability of protein-protein interaction networks
Alanis-Lobato, Gregorio; Andrade-Navarro, Miguel A.; Schaefer, Martin H.
Nucleic Acids Research (2017), 45 (D1), D408-D414CODEN: NARHAD; ISSN:1362-4962. (Oxford University Press)
The increasing no. of exptl. detected interactions between proteins makes it difficult for researchers to ext. the interactions relevant for specific biol. processes or diseases. This makes it necessary to accompany the large-scale detection of protein-protein interactions (PPIs) with strategies and tools to generate meaningful PPI subnetworks. To this end, we generated the Human Integrated Protein-Protein Interaction ref. or HIPPIE (http://cbdm.uni-mainz.de/hippie/). HIPPIE is a one-stop resource for the generation and interpretation of PPI networks relevant to a specific research question. We provide means to generate highly reliable, context-specific PPI networks and to make sense out of them. We just released the second major update of HIPPIE, implementing various new features. HIPPIE grew substantially over the last years and now contains more than 270 000 confidence scored and annotated PPIs. We integrated different types of exptl. information for the confidence scoring and the construction of context-specific networks. We implemented basic graph algorithms that highlight important proteins and interactions. HIPPIE's graphical interface implements several ways for wet lab and computational scientists alike to access the PPI data.
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Gene Ontology Consortium, C. Gene Ontology Consortium: going forward. Nucleic Acids Res. 2015, 43, D1049– 56, DOI: 10.1093/nar/gku1179
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There is no corresponding record for this reference.
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Clauset, A.; Shalizi, C. R.; Newman, M. E. J. Power-law distributions in empirical data. SIAM Rev. 2009, 51, 661, DOI: 10.1137/070710111
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Albert, R. Scale-free networks in cell biology. J. Cell Sci. 2005, 118, 4947, DOI: 10.1242/jcs.02714
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Scale-free networks in cell biology
Albert, Reka
Journal of Cell Science (2005), 118 (21), 4947-4957CODEN: JNCSAI; ISSN:0021-9533. (Company of Biologists Ltd.)
A review. A cell's behavior is a consequence of the complex interactions between its numerous constituents, such as DNA, RNA, proteins and small mols. Cells use signaling pathways and regulatory mechanisms to coordinate multiple processes, allowing them to respond to and adapt to an ever-changing environment. The large no. of components, the degree of interconnectivity and the complex control of cellular networks are becoming evident in the integrated genomic and proteomic analyses that are emerging. It is increasingly recognized that the understanding of properties that arise from whole-cell function require integrated, theor. descriptions of the relationships between different cellular components. Recent theor. advances allow us to describe cellular network structure with graph concepts and have revealed organizational features shared with numerous non-biol. networks. We now have the opportunity to describe quant. a network of hundreds or thousands of interacting components. Moreover, the obsd. topologies of cellular networks give us clues about their evolution and how their organization influences their function and dynamic responses.
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Riley, N. M.; Coon, J. J. Phosphoproteomics in the Age of Rapid and Deep Proteome Profiling. Anal. Chem. 2016, 88, 74– 94, DOI: 10.1021/acs.analchem.5b04123
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Phosphoproteomics in the Age of Rapid and Deep Proteome Profiling
Riley, Nicholas M.; Coon, Joshua J.
Analytical Chemistry (Washington, DC, United States) (2016), 88 (1), 74-94CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)
A review. Protein phosphorylation is a post-translational modification (PTM) that orchestrates a diverse array of cellular processes. Because this modification serves as a rapid and reversible means to modulate protein activity and transduce signals, the regulation of phosphorylation is a central mechanism in cell health and disease. The addn. and removal of phosphoryl modifications via kinases and phosphates, resp., makes the landscape of phosphorylation particularly dynamic. Understanding the complex networks and functions coordinated by phosphorylation requires knowledge of specific amino acid modifications with both spatial and temporal resoln. - a task that remains a challenging anal. endeavor. Mass spectrometry (MS) has emerged as the premier tool for global PTM anal., boasting high sensitivity, considerable throughput, and the capacity to localize modifications to a single residue. Indeed, MS-centric phosphoproteomics has become a std. approach for investigating protein phosphorylation in labs. worldwide. Anal. Chem. last reviewed the contribution of MS and related technologies to phosphoproteomics in 2011. Since that time, MS methodol. has developed at an impressive pace. While routine proteomic expts. can now analyze thousands of proteins in just a few hours, rather than days or weeks, characterizing the global phosphoproteome is significantly more challenging than measuring non-modified proteins. The relative low abundance of phosphorylated peptides and the need for residue-specific information require special considerations in sample handling, data acquisition, and post-acquisition processing that constrain reproducibility, quant. efficacy, throughput, and depth in phosphoproteomic workflows. Advances in MS-based approaches have remarkably improved our abilities to investigate the many roles of protein phosphorylation across a diverse set of biol. contexts, but many tech. obstacles still exist. Poor run-to-run overlap, challenges in confident phosphosite assignment, and complications inherent to various quant. strategies limit biol. insight, despite ever-increasing nos. of detected phosphopeptides. Focusing on work from the past two years (2013-2015), this review examines major developments in MS technol. that have enabled the characterization of tens of thousands of phosphopeptides in a given expt., and considers the contribution of this anal. power to translational research. We also discuss how future innovation can address tech. challenges of today's methods, and we offer our perspective on how phosphoproteomics will continue to mature.
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Casado, P. Kinase-substrate enrichment analysis provides insights into the heterogeneity of signaling pathway activation in leukemia cells. Sci. Signaling 2013, 6, rs6– rs6, DOI: 10.1126/scisignal.2003573
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Hernandez-Armenta, C.; Ochoa, D.; Gonçalves, E.; Saez-Rodriguez, J.; Beltrao, P. Benchmarking substrate-based kinase activity inference using phosphoproteomic data. Bioinformatics 2017, 33, 1845– 1851, DOI: 10.1093/bioinformatics/btx082
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Benchmarking substrate-based kinase activity inference using phosphoproteomic data
Hernandez-Armenta, Claudia; Ochoa, David; Goncalves, Emanuel; Saez-Rodriguez, Julio; Beltrao, Pedro
Bioinformatics (2017), 33 (12), 1845-1851CODEN: BOINFP; ISSN:1460-2059. (Oxford University Press)
Motivation: Phosphoproteomic expts. are increasingly used to study the changes in signaling occurring across different conditions. It has been proposed that changes in phosphorylation of kinase target sites can be used to infer when a kinase activity is under regulation. However, these approaches have not yet been benchmarked due to a lack of appropriate benchmarking strategies. Results: We used curated phosphoproteomic expts. and a gold std. dataset contg. a total of 184 kinase-condition pairs where regulation is expected to occur to benchmark and compare different kinase activity inference strategies: Z-test, Kolmogorov Smirnov test, Wilcoxon rank sum test, gene set enrichment anal. (GSEA), and a multiple linear regression model. We also tested weighted variants of the Z-test and GSEA that include information on kinase sequence specificity as proxy for affinity. Finally, we tested how the no. of known substrates and the type of evidence (in vivo, in vitro or in silico) supporting these influence the predictions. Conclusions: Most models performed well with the Z-test and the GSEA performing best as detd. by the area under the ROC curve (Mean AUC 1/4 0.722). Weighting kinase targets by the kinase target sequence preference improves the results marginally. However, the no. of known substrates and the evidence supporting the interactions has a strong effect on the predictions.
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Herranz, N. mTOR regulates MAPKAPK2 translation to control the senescence-associated secretory phenotype. Nat. Cell Biol. 2015, 17, 1205, DOI: 10.1038/ncb3225
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mTOR regulates MAPKAPK2 translation to control the senescence-associated secretory phenotype
Herranz, Nicolas; Gallage, Suchira; Mellone, Massimiliano; Wuestefeld, Torsten; Klotz, Sabrina; Hanley, Christopher J.; Raguz, Selina; Acosta, Juan Carlos; Innes, Andrew J.; Banito, Ana; Georgilis, Athena; Montoya, Alex; Wolter, Katharina; Dharmalingam, Gopuraja; Faull, Peter; Carroll, Thomas; Martinez-Barbera, Juan Pedro; Cutillas, Pedro; Reisinger, Florian; Heikenwalder, Mathias; Miller, Richard A.; Withers, Dominic; Zender, Lars; Thomas, Gareth J.; Gil, Jesus
Nature Cell Biology (2015), 17 (9), 1205-1217CODEN: NCBIFN; ISSN:1465-7392. (Nature Publishing Group)
Senescent cells secrete a combination of factors collectively known as the senescence-assocd. secretory phenotype (SASP). The SASP reinforces senescence and activates an immune surveillance response, but it can also show pro-tumorigenic properties and contribute to age-related pathologies. In a drug screen to find new SASP regulators, we uncovered the mTOR inhibitor rapamycin as a potent SASP suppressor. Here we report a mechanism by which mTOR controls the SASP by differentially regulating the translation of the MK2 (also known as MAPKAPK2) kinase through 4EBP1. In turn, MAPKAPK2 phosphorylates the RNA-binding protein ZFP36L1 during senescence, inhibiting its ability to degrade the transcripts of numerous SASP components. Consequently, mTOR inhibition or constitutive activation of ZFP36L1 impairs the non-cell-autonomous effects of senescent cells in both tumor-suppressive and tumor-promoting contexts. Altogether, our results place regulation of the SASP as a key mechanism by which mTOR could influence cancer, age-related diseases and immune responses.
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Wilkes, E. H.; Terfve, C.; Gribben, J. G.; Saez-Rodriguez, J.; Cutillas, P. R. Empirical inference of circuitry and plasticity in a kinase signaling network. Proc. Natl. Acad. Sci. U. S. A. 2015, 112, 7719, DOI: 10.1073/pnas.1423344112
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Empirical inference of circuitry and plasticity in a kinase signaling network
Wilkes, Edmund H.; Terfve, Camille; Gribben, John G.; Saez-Rodriguez, Julio; Cutillas, Pedro Rodriguez
Proceedings of the National Academy of Sciences of the United States of America (2015), 112 (25), 7719-7724CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
Our understanding of physiol. and disease is hampered by the difficulty of measuring the circuitry and plasticity of signaling networks that regulate cell biol., and how these relate to phenotypes. Here, using mass spectrometry-based phosphoproteomics, we systematically characterized the topol. of a network comprising the PI3K/Akt/mTOR and MEK/ERK signaling axes and confirmed its biol. relevance by assessing its dynamics upon EGF and IGF1 stimulation. Measuring the activity of this network in models of acquired drug resistance revealed that cells chronically treated with PI3K or mTORC1/2 inhibitors differed in the way their networks were remodeled. Unexpectedly, we also obsd. a degree of heterogeneity in the network state between cells resistant to the same inhibitor, indicating that even identical and carefully controlled exptl. conditions can give rise to the evolution of distinct kinase network statuses. These data suggest that the initial conditions of the system do not necessarily det. the mechanism by which cancer cells become resistant to PI3K/mTOR targeted therapies. The patterns of signaling network activity obsd. in the resistant cells mirrored the patterns of response to several drug combination treatments, suggesting that the activity of the defined signaling network truly reflected the evolved phenotypic diversity.
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Linding, R. NetworKIN: A resource for exploring cellular phosphorylation networks. Nucleic Acids Res. 2007, 36, D695– 99, DOI: 10.1093/nar/gkm902
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Wiredja, D. D.; Koyutürk, M.; Chance, M. R. The KSEA App: a web-based tool for kinase activity inference from quantitative phosphoproteomics. Bioinformatics 2017, 33, 3489, DOI: 10.1093/bioinformatics/btx415
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The KSEA app: A web-based tool for kinase activity inference from quantitative phosphoproteomics
Wiredja, Danica D.; Koyuturk, Mehmet; Chance, Mark R.
Bioinformatics (2017), 33 (21), 3489-3491CODEN: BOINFP; ISSN:1367-4811. (Oxford University Press)
Summary: Computational characterization of differential kinase activity from phosphoproteomics datasets is crit. for correctly inferring cellular circuitry and how signaling cascades are altered in drug treatment and/or disease. Kinase-Substrate Enrichment Anal. (KSEA) offers a powerful approach to estg. changes in a kinase's activity based on the collective phosphorylation changes of its identified substrates. However, KSEA has been limited to programmers who are able to implement the algorithms. Thus, to make it accessible to the larger scientific community, we present a web-based application of this method: the KSEA App. Overall, we expect that this tool will offer a quick and user-friendly way of generating kinase activity ests. from high-throughput phosphoproteomics datasets.
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Zhang, B.; Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 2005, 4, 17, DOI: 10.2202/1544-6115.1128
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Cox, J.; Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 2008, 26, 1367– 1372, DOI: 10.1038/nbt.1511
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MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification
Cox, Juergen; Mann, Matthias
Nature Biotechnology (2008), 26 (12), 1367-1372CODEN: NABIF9; ISSN:1087-0156. (Nature Publishing Group)
Efficient anal. of very large amts. of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resoln., quant. MS data. Using correlation anal. and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over std. techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome expt. and allows statistically robust identification and quantification of >4000 proteins in mammalian cell lysates.
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Sinitcyn, P. MaxQuant goes Linux. Nat. Methods 2018, 15, 401, DOI: 10.1038/s41592-018-0018-y
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MaxQuant goes Linux
Sinitcyn, Pavel; Tiwary, Shivani; Rudolph, Jan; Gutenbrunner, Petra; Wichmann, Christoph; Yilmaz, Sule; Hamzeiy, Hamid; Salinas, Favio; Cox, Juergen
Nature Methods (2018), 15 (6), 401CODEN: NMAEA3; ISSN:1548-7091. (Nature Research)
There is no expanded citation for this reference.
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Kristensen, A. R.; Foster, L. J. Protein correlation profiling-SILAC to study protein-protein interactions. Methods Mol. Biol. 2014, 1188, 263, DOI: 10.1007/978-1-4939-1142-4_18
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Protein correlation profiling-SILAC to study protein-protein interactions
Kristensen Anders R; Foster Leonard J
Methods in molecular biology (Clifton, N.J.) (2014), 1188 (), 263-70 ISSN:.
An interactome describes the global organization of protein interactions within a cell and is typically generated using affinity purification-mass spectrometry (AP-MS), yeast two-hybrid screening, or protein-fragment complementation assays (Gavin et al. Nature 440: 631-636, 2006; Krogan et al. Nature 440: 637-643, 2006; Uetz et al. Nature 403: 623-627, 2000; Tarassov et al. Science 320: 1465-1470, 2008). These techniques have been widely used to depict the interactome as we know it today but current models of interactomes do not contain stoichiometric or temporal information. In this chapter we describe size-exclusion chromatography (SEC) combined with protein correlation profiling-stable isotope labeling by amino acids in cell culture (PCP-SILAC) to generate dynamic chromatographs for thousands of proteins (Kristensen et al. Nat Methods 9: 907-909, 2012). Using the precise co-elution of two proteins as evidence that they interact, it is possible to identify similar numbers of protein interactions without overexpression or creating fusion proteins as other high-throughput techniques require. In addition, triplex SILAC allows us to quantify protein stoichiometry and temporal changes to the interactome following perturbation. Finally, SEC-PCP-SILAC is very time efficient since it generates two orders of magnitude fewer samples for LC-MS analysis and avoids the tedious tagging and purification steps, making it possible for everyone with a single mass spectrometer to study the interactome.
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Liu, F.; Lössl, P.; Scheltema, R.; Viner, R.; Heck, A. J. R. Optimized fragmentation schemes and data analysis strategies for proteome-wide cross-link identification. Nat. Commun. 2017, 8, 15473, DOI: 10.1038/ncomms15473
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Optimized fragmentation schemes and data analysis strategies for proteome-wide cross-link identification
Liu, Fan; Loessl, Philip; Scheltema, Richard; Viner, Rosa; Heck, Albert J. R.
Nature Communications (2017), 8 (), 15473CODEN: NCAOBW; ISSN:2041-1723. (Nature Publishing Group)
We describe optimized fragmentation schemes and data anal. strategies substantially enhancing the depth and accuracy in identifying protein cross-links using non-restricted whole proteome databases. These include a novel hybrid data acquisition strategy to sequence cross-links at both MS2 and MS3 level and a new algorithmic design XlinkX v2.0 for data anal. As proof-of-concept we investigated proteome-wide protein interactions in E. coli and HeLa cell lysates, resp., identifying 1,158 and 3,301 unique cross-links at ~ 1% false discovery rate. These protein interaction repositories provide meaningful structural information on many endogenous macromol. assemblies, as we showcase on several protein complexes involved in translation, protein folding and carbohydrate metab.
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Journal of Proteome Research

Cite this: J. Proteome Res. 2019, 18, 5, 2052–2064

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https://doi.org/10.1021/acs.jproteome.8b00927

Published April 1, 2019

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Abstract
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Figure 1
Figure 1. Schematic overview of the new network functionality in Perseus. PerseusNet implements a number of processing and analysis steps facilitated by the network collection data type. While including proteomics centric analyses, such as for the analysis of interaction screens, the network module also provides a number of general purpose tools, as, for instance, for network annotation, filtering, and topology determination. With the extension of the Perseus plugin API to networks and furthermore to other programing languages, it becomes possible to integrate existing network analysis tools in Perseus. Networks are easily imported to and exported from Perseus, due to its support for standard formats.
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Figure 2
Figure 2. Schematic representation of the network collection data type. User-facing information is displayed in tabular form with tables listing the networks in the collection, as well as providing detailed information on the nodes and edges of each network. Internally an auxiliary graph data structure aids in the implementation of graph algorithms. Node- and edge-mapping provide the required cross-references between the tabular and graph representation.
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Figure 3
Figure 3. Schematic of the Perseus plugin system. Plugins written in C# are native to Perseus and implement their functionality directly on top of the application programming interfaces and data structures provided by the application framework. PluginInterop enables the execution of scripts in the Python and R languages, as well as other external programs. By communicating via the file system, data are transferred between Perseus and the external program. The companion libraries ‘perseuspy’ and ‘PerseusR’ enable developers to access the data science ecosystem in their language of choice. For custom graphical user interface elements and an improved user experience of external tools, developers can implement a thin C# wrapper class that extends the generic functionality of PluginInterop.
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Figure 4
Figure 4. AP-MS. (a) The Hawaii plot provides an overview over an entire dataset, in this case consisting of three baits in two conditions (Table S1 and Experimental Section). (14) Each volcano plot displays the results of a pull-down of a specific bait (Bap1, Eed, Ring1b) in one of the ESC or NPC cell lines. Significant interactors are determined using a permutation-based FDR and the resulting high-confidence Class A (solid line) and low-confidence Class B (dashed lines) thresholds are displayed in the plot. In this case only in the Bap1 ESC pull-down, Class B interactions could be found. Class A interactors are displayed in dark gray, other proteins are shown in light gray. (b) Enrichment plot comparing the Eed pull-downs in ESC and NPC cell lines. Significant interactors in any of the two conditions are displayed in black, nonsignificant proteins are displayed in light gray. Proteins differentially enriched in one of the two conditions will be located far from the diagonal and can be identified visually. (c) Visualization of the resulting protein interaction network for both cell lines. Bait proteins are colored in green, and their interactors are colored in blue. Thick lines represent Class A interactions, thinner lines Class B. Interactions which were already annotated in the human CORUM database are highlighted in red.
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Figure 5
Figure 5. Handling large-scale protein interaction databases in Perseus. (a) Interactions in PPI databases are often annotated with confidence scores derived from various sources. Perseus provides tools to load and combine confidence scores derived a variety of data sources, including dynamic confidence adjustments based on condition-specific data. High-confidence networks can be obtained by removing edges below a given hard threshold or alternatively, confidence scores can be utilized directly as so-called edge weights, thereby allowing for the inclusion of lower-confidence interactions. (b) Histogram of the combined confidence score from the human STRING PPI network. Superimposed in orange is the number of interactions in the filtered network if the edges with scores lower than the current value were removed. Filtering out low confidence edges leads to a significant reduction in the number of edges in the final network. (c) Log–log plot of the node degree against the degree frequency generated from the human STRING PPI network. The R² value of the linear fit (orange) to these data represents the scale-free fit index.
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Figure 6
Figure 6. Network analysis of an EGF stimulation phosphoproteomics study. (a) Comparison of network topologies used for the analysis of phosphoproteomics data. Nodes in the network are represented as gray circles or pie charts where each slice represents the observed phosphorylation changes at a specific site on the protein. Physical protein–protein interactions (left side) are present between all classes of proteins and are by definition undirected. In order to capture the enzymatic action of kinases more accurately, directed interactions (right side) from kinase to substrate are defined in a site-specific manner. (b) KSEA Z-score and PHOTON signaling functionality scores derived from phosphoproteomics data measured after EGF stimulation (Table S2) only weakly correlate to each other (Pearson correlation 0.52). Kinases annotated in GO with the term ‘Epidermal growth factor receptor signaling pathway’ are highlighted in red. Both methods assign high scores to central members of the expected pathway. (c) Signaling network reconstructed by PHOTON from the 100 highest scoring proteins anchored at EGF. The interactive visualization has an automatic layout and phosphorylation data overlay.
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Figure 7
Figure 7. Co-expression network analysis on clinical data. (a) The correlation matrix is an equivalent representation of a fully connected network with edge weights corresponding to the correlation between the proteins. (b) Co-expression clustering and identified co-expression modules annotate the original expression matrix. Phenotype data can be correlated with representative co-expression module profiles and provide a high-level interpretation of the modules. (c) Parameter selection of the power parameter for the Yanovich et al. (19) dataset (Table S3 and Experimental Section). The lowest power reaching close to a high scale-free fit index of 0.9 (red line) was selected. (d) Co-expression cluster dendrogram. Each color corresponds to one co-expression module. (e) Correlation heat map between module eigengenes and clinical parameters.
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  PhosphoSitePlus (PSP), a knowledge-base dedicated to mammalian post-translational modifications (PTMs), contains over 330000 non-redundant PTMs, including phospho, acetyl, ubiquityl and Me groups. Over 95% of the sites are from mass spectrometry (MS) expts. In order to improve data reliability, early MS data have been reanalyzed, applying a common std. of anal. across over 1000000 spectra. Site assignments with P > 0.05 were filtered out. Two new downloads are available from PSP. The 'Regulatory sites' dataset includes curated information about modification sites that regulate downstream cellular processes, mol. functions and protein-protein interactions. The 'PTMVar' dataset, an intersect of missense mutations and PTMs from PSP, identifies over 25000 PTMVars (PTMs Impacted by Variants) that can rewire signaling pathways. The PTMVar data include missense mutations from UniPROTKB, TCGA and other sources that cause over 2000 diseases or syndromes (MIM) and polymorphisms, or are assocd. with hundreds of cancers. PTMVars include 18548 phosphorlyation sites, 3412 ubiquitylation sites, 2316 acetylation sites, 685 methylation sites and 245 succinylation sites.
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19. 19
  Yanovich, G. Clinical Proteomics of Breast Cancer Reveals a Novel Layer of Breast Cancer Classification. Cancer Res. 2018, 78, 6001– 6010, DOI: 10.1158/0008-5472.CAN-18-1079
  19
  Clinical proteomics of breast cancer reveals a novel layer of breast cancer classification
  Yanovich, Gali; Agmon, Hadar; Harel, Michal; Sonnenblick, Amir; Peretz, Tamar; Geiger, Tamar
  Cancer Research (2018), 78 (20), 6001-6010CODEN: CNREA8; ISSN:0008-5472. (American Association for Cancer Research)
  Breast cancer classification has been the focus of numerous worldwide efforts, analyzing the mol. basis of breast cancer subtypes and aiming to assoc. them with clin. outcome and to improve the current diagnostic routine. Genomic and transcriptomic profiles of breast cancer have been well established, however the proteomic contribution to these profiles has yet to be elucidated. In this work, we utilized mass spectrometry-based proteomic anal. on more than 130 clin. breast samples to demonstrate intertumor heterogeneity across three breast cancer subtypes and healthy tissue. Unsupervised anal. identified four proteomic clusters, among them, one that represents a novel luminal subtype characterized by increased PI3K signaling. This subtype was further validated using an independent protein-based dataset, but not in two independent transcriptome cohorts. These results demonstrate the importance of deep proteomic anal., which may affect cancer treatment decision making.
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20. 20
  Szklarczyk, D. The STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 2017, 45, D362– D368, DOI: 10.1093/nar/gkw937
  20
  The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible
  Szklarczyk, Damian; Morris, John H.; Cook, Helen; Kuhn, Michael; Wyder, Stefan; Simonovic, Milan; Santos, Alberto; Doncheva, Nadezhda T.; Roth, Alexander; Bork, Peer; Jensen, Lars J.; von Mering, Christian
  Nucleic Acids Research (2017), 45 (D1), D362-D368CODEN: NARHAD; ISSN:1362-4962. (Oxford University Press)
  A system-wide understanding of cellular function requires knowledge of all functional interactions between the expressed proteins. The STRING database aims to collect and integrate this information, by consolidating known and predicted protein-protein assocn. data for a large no. of organisms. The assocns. in STRING include direct (phys.) interactions, as well as indirect (functional) interactions, as long as both are specific and biol. meaningful. Apart from collecting and reassessing available exptl. data on protein-protein interactions, and importing known pathways and protein complexes from curated databases, interaction predictions are derived from the following sources: (i) systematic coexpression anal., (ii) detection of shared selective signals across genomes, (iii) automated text-mining of the scientific literature and (iv) computational transfer of interaction knowledge between organisms based on gene orthol. In the latest version 10.5 of STRING, the biggest changes are concerned with data dissemination: the web front-end has been completely redesigned to reduce dependency on outdated browser technologies, and the database can now also be queried from inside the popular Cytoscape software framework. Further improvements include automated background anal. of user inputs for functional enrichments, and streamlined download options. The STRING resource is available online, at http://string-db.org/.
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21. 21
  Chatr-Aryamontri, A. The BioGRID interaction database: 2017 update. Nucleic Acids Res. 2017, 45, D369– D379, DOI: 10.1093/nar/gkw1102
  21
  The BioGRID interaction database: 2017 update
  Chatr-aryamontri, Andrew; Oughtred, Rose; Boucher, Lorrie; Rust, Jennifer; Chang, Christie; Kolas, Nadine K.; O'Donnell, Lara; Oster, Sara; Theesfeld, Chandra; Sellam, Adnane; Stark, Chris; Breitkreutz, Bobby-Joe; Dolinski, Kara; Tyers, Mike
  Nucleic Acids Research (2017), 45 (D1), D369-D379CODEN: NARHAD; ISSN:1362-4962. (Oxford University Press)
  The Biol. General Repository for Interaction Datasets (BioGRID: https://thebiogrid.org) is an open access database dedicated to the annotation and archival of protein, genetic and chem. interactions for all major model organism species and humans. As of Sept. 2016 (build 3.4.140), the BioGRID contains 1 072 173 genetic and protein interactions, and 38 559 post-translational modifications, as manually annotated from 48 114 publications. This dataset represents interaction records for 66 model organisms and represents a 30% increase compared to the previous 2015 BioGRID update. BioGRID curates the biomedical literature for major model organism species, including humans, with a recent emphasis on central biol. processes and specific human diseases. To facilitate network-based approaches to drug discovery, BioGRID now incorporates 27 501 chem.-protein interactions for human drug targets, as drawn from the DrugBank database. A new dynamic interaction network viewer allows the easy navigation and filtering of all genetic and protein interaction data, as well as for bioactive compds. and their established targets. BioGRID data are directly downloadable without restriction in a variety of standardized formats and are freely distributed through partner model organism databases and meta-databases.
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22. 22
  Orchard, S. The MIntAct project - IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res. 2014, 42, D358– 63, DOI: 10.1093/nar/gkt1115
  22
  The MIntAct project-IntAct as a common curation platform for 11 molecular interaction databases
  Orchard, Sandra; Ammari, Mais; Aranda, Bruno; Breuza, Lionel; Briganti, Leonardo; Broackes-Carter, Fiona; Campbell, Nancy H.; Chavali, Gayatri; Chen, Carol; del-Toro, Noemi; Duesbury, Margaret; Dumousseau, Marine; Galeota, Eugenia; Hinz, Ursula; Iannuccelli, Marta; Jagannathan, Sruthi; Jimenez, Rafael; Khadake, Jyoti; Lagreid, Astrid; Licata, Luana; Lovering, Ruth C.; Meldal, Birgit; Melidoni, Anna N.; Milagros, Mila; Peluso, Daniele; Perfetto, Livia; Porras, Pablo; Raghunath, Arathi; Ricard-Blum, Sylvie; Roechert, Bernd; Stutz, Andre; Tognolli, Michael; van Roey, Kim; Cesareni, Gianni; Hermjakob, Henning
  Nucleic Acids Research (2014), 42 (D1), D358-D363CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)
  IntAct (freely available at http://www.ebi.ac.uk/intact) is an open-source, open data mol. interaction database populated by data either curated from the literature or from direct data depositions. IntAct has developed a sophisticated web-based curation tool, capable of supporting both IMEx- and MIMIx-level curation. This tool is now utilized by multiple addnl. curation teams, all of whom annotate data directly into the IntAct database. Members of the IntAct team supply appropriate levels of training, perform quality control on entries and take responsibility for long-term data maintenance. Recently, the MINT and IntAct databases decided to merge their sep. efforts to make optimal use of limited developer resources and maximize the curation output. All data manually curated by the MINT curators have been moved into the IntAct database at EMBL-EBI and are merged with the existing IntAct dataset. Both IntAct and MINT are active contributors to the IMEx consortium (http://www.imexconsortium.org).
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23. 23
  Ruepp, A. CORUM: The comprehensive resource of mammalian protein complexes-2009. Nucleic Acids Res. 2010, 38, D497– 501, DOI: 10.1093/nar/gkp914
  23
  CORUM: the comprehensive resource of mammalian protein complexes-2009
  Ruepp, Andreas; Waegele, Brigitte; Lechner, Martin; Brauner, Barbara; Dunger-Kaltenbach, Irmtraud; Fobo, Gisela; Frishman, Goar; Montrone, Corinna; Mewes, H.-Werner
  Nucleic Acids Research (2010), 38 (Database Iss), D497-D501CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)
  CORUM is a database that provides a manually curated repository of exptl. characterized protein complexes from mammalian organisms, mainly human (64%), mouse (16%) and rat (12%). Protein complexes are key mol. entities that integrate multiple gene products to perform cellular functions. The new CORUM 2.0 release encompasses 2837 protein complexes offering the largest and most comprehensive publicly available dataset of mammalian protein complexes. The CORUM dataset is built from 3198 different genes, representing ∼16% of the protein coding genes in humans. Each protein complex is described by a protein complex name, subunit compn., function as well as the literature ref. that characterizes the resp. protein complex. Recent developments include mapping of functional annotation to Gene Ontol. terms as well as cross-refs. to Entrez Gene identifiers. In addn., a Phylogenetic Conservation' anal. tool was implemented that analyses the potential occurrence of orthologous protein complex subunits in mammals and other selected groups of organisms. This allows one to predict the occurrence of protein complexes in different phylogenetic groups. CORUM is freely accessible at (http://mips.helmholtz-muenchen.de/genre/proj/corum/index.html).
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24. 24
  Gingras, A. C.; Gstaiger, M.; Raught, B.; Aebersold, R. Analysis of protein complexes using mass spectrometry. Nat. Rev. Mol. Cell Biol. 2007, 8, 645– 654, DOI: 10.1038/nrm2208
  24
  Analysis of protein complexes using mass spectrometry
  Gingras, Anne-Claude; Gstaiger, Matthias; Raught, Brian; Aebersold, Ruedi
  Nature Reviews Molecular Cell Biology (2007), 8 (8), 645-654CODEN: NRMCBP; ISSN:1471-0072. (Nature Publishing Group)
  A review. The versatile combination of affinity purifn. and mass spectrometry (AP-MS) has recently been applied to the detailed characterization of many protein complexes and large protein-interaction networks. The combination of AP-MS with other techniques, such as biochem. fractionation, intact mass measurement and chem. crosslinking, can help to decipher the supramol. organization of protein complexes. AP-MS can also be combined with quant. proteomics approaches to better understand the dynamics of protein-complex assembly.
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25. 25
  Dunham, W. H.; Mullin, M.; Gingras, A. C. Affinity-purification coupled to mass spectrometry: Basic principles and strategies. Proteomics 2012, 12, 1576, DOI: 10.1002/pmic.201100523
  25
  Affinity-purification coupled to mass spectrometry: Basic principles and strategies
  Dunham, Wade H.; Mullin, Michael; Gingras, Anne-Claude
  Proteomics (2012), 12 (10), 1576-1590CODEN: PROTC7; ISSN:1615-9853. (Wiley-VCH Verlag GmbH & Co. KGaA)
  A review. Identifying the interactions established by a protein of interest can be a crit. step in understanding its function. This is esp. true when an unknown protein of interest is demonstrated to phys. interact with proteins of known function. While many techniques have been developed to characterize protein-protein interactions, one strategy that has gained considerable momentum over the past decade for identification and quantification of protein-protein interactions, is affinity-purifn. followed by mass spectrometry (AP-MS). Here, we briefly review the basic principles used in affinity-purifn. coupled to mass spectrometry, with an emphasis on tools (both biochem. and computational), which enable the discovery and reporting of high quality protein-protein interactions.
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26. 26
  Hein, M. Y. A Human Interactome in Three Quantitative Dimensions Organized by Stoichiometries and Abundances. Cell 2015, 163, 712– 723, DOI: 10.1016/j.cell.2015.09.053
  26
  A Human Interactome in Three Quantitative Dimensions Organized by Stoichiometries and Abundances
  Hein, Marco Y.; Hubner, Nina C.; Poser, Ina; Cox, Juergen; Nagaraj, Nagarjuna; Toyoda, Yusuke; Gak, Igor A.; Weisswange, Ina; Mansfeld, Joerg; Buchholz, Frank; Hyman, Anthony A.; Mann, Matthias
  Cell (Cambridge, MA, United States) (2015), 163 (3), 712-723CODEN: CELLB5; ISSN:0092-8674. (Cell Press)
  The organization of a cell emerges from the interactions in protein networks. The interactome is critically dependent on the strengths of interactions and the cellular abundances of the connected proteins, both of which span orders of magnitude. However, these aspects have not yet been analyzed globally. Here, the authors have generated a library of HeLa cell lines expressing 1125 GFP-tagged proteins under near-endogenous control, which the authors used as input for a next-generation interaction survey. Using quant. proteomics, the authors detect specific interactions, est. interaction stoichiometries, and measure cellular abundances of interacting proteins. These three quant. dimensions reveal that the protein network is dominated by weak, substoichiometric interactions that play a pivotal role in defining network topol. The minority of stable complexes can be identified by their unique stoichiometry signature. This study provides a rich interaction dataset connecting thousands of proteins and introduces a framework for quant. network anal.
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27. 27
  Huttlin, E. L. The BioPlex Network: A Systematic Exploration of the Human Interactome. Cell 2015, 162, 425– 440, DOI: 10.1016/j.cell.2015.06.043
  27
  The BioPlex Network: A Systematic Exploration of the Human Interactome
  Huttlin, Edward L.; Ting, Lily; Bruckner, Raphael J.; Gebreab, Fana; Gygi, Melanie P.; Szpyt, John; Tam, Stanley; Zarraga, Gabriela; Colby, Greg; Baltier, Kurt; Dong, Rui; Guarani, Virginia; Vaites, Laura Pontano; Ordureau, Alban; Rad, Ramin; Erickson, Brian K.; Wuhr, Martin; Chick, Joel; Zhai, Bo; Kolippakkam, Deepak; Mintseris, Julian; Obar, Robert A.; Harris, Tim; Artavanis-Tsakonas, Spyros; Sowa, Mathew E.; De Camilli, Pietro; Paulo, Joao A.; Harper, J. Wade; Gygi, Steven P.
  Cell (Cambridge, MA, United States) (2015), 162 (2), 425-440CODEN: CELLB5; ISSN:0092-8674. (Cell Press)
  Protein interactions form a network whose structure drives cellular function and whose organization informs biol. inquiry. Using high-throughput affinity-purifn. mass spectrometry, the authors identify interacting partners for 2594 human proteins in HEK293T cells. The resulting network (BioPlex) contains 23,744 interactions among 7668 proteins with 86% previously undocumented. BioPlex accurately depicts known complexes, attaining 80%-100% coverage for most CORUM complexes. The network readily subdivides into communities that correspond to complexes or clusters of functionally related proteins. More generally, network architecture reflects cellular localization, biol. process, and mol. function, enabling functional characterization of thousands of proteins. Network structure also reveals assocns. among thousands of protein domains, suggesting a basis for examg. structurally related proteins. Finally, BioPlex, in combination with other approaches, can be used to reveal interactions of biol. or clin. significance. For example, mutations in the membrane protein VAPB implicated in familial amyotrophic lateral sclerosis perturb a defined community of interactors.
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28. 28
  Hubner, N. C. Quantitative proteomics combined with BAC TransgeneOmics reveals in vivo protein interactions. J. Cell Biol. 2010, 189, 739– 754, DOI: 10.1083/jcb.200911091
  28
  Quantitative proteomics combined with BAC TransgeneOmics reveals in vivo protein interactions
  Hubner, Nina C.; Bird, Alexander W.; Cox, Juergen; Splettstoesser, Bianca; Bandilla, Peter; Poser, Ina; Hyman, Anthony; Mann, Matthias
  Journal of Cell Biology (2010), 189 (4), 739-754CODEN: JCLBA3; ISSN:0021-9525. (Rockefeller University Press)
  Protein interactions are involved in all cellular processes. Their efficient and reliable characterization is therefore essential for understanding biol. mechanisms. In this study, we show that combining bacterial artificial chromosome (BAC) TransgeneOmics with quant. interaction proteomics, which we call quant. BAC-green fluorescent protein interactomics (QUBIC), allows specific and highly sensitive detection of interactions using rapid, generic, and quant. procedures with minimal material. We applied this approach to identify known and novel components of well-studied complexes such as the anaphase-promoting complex. Furthermore, we demonstrate second generation interaction proteomics by incorporating directed mutational transgene modification and drug perturbation into QUBIC. These methods identified domain/isoform-specific interactors of pericentrin- and phosphorylation-specific interactors of TACC3, which are necessary for its recruitment to mitotic spindles. The scalability, simplicity, cost effectiveness, and sensitivity of this method provide a basis for its general use in small-scale expts. and in mapping the human protein interactome.
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29. 29
  Noble, W. S. How does multiple testing correction work?. Nat. Biotechnol. 2009, 27, 1135– 1137, DOI: 10.1038/nbt1209-1135
  29
  How does multiple testing correction work?
  Noble, William S.
  Nature Biotechnology (2009), 27 (12), 1135-1137CODEN: NABIF9; ISSN:1087-0156. (Nature Publishing Group)
  A review. When prioritizing hits from a high-throughput expt., it is important to correct for random events that falsely appear significant. How is this done and what methods should be used. Techniques for doing this are discussed here.
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30. 30
  Tusher, V. G.; Tibshirani, R.; Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. U. S. A. 2001, 98, 5116– 5121, DOI: 10.1073/pnas.091062498
  30
  Significance analysis of microarrays applied to the ionizing radiation response
  Tusher, Virginia Goss; Tibshirani, Robert; Chu, Gilbert
  Proceedings of the National Academy of Sciences of the United States of America (2001), 98 (9), 5116-5121CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
  Microarrays can measure the expression of thousands of genes to identify changes in expression between different biol. states. Methods are needed to det. the significance of these changes while accounting for the enormous no. of genes. We describe a method, Significance Anal. of Microarrays (SAM), that assigns a score to each gene on the basis of change in gene expression relative to the std. deviation of repeated measurements. For genes with scores greater than an adjustable threshold, SAM uses permutations of the repeated measurements to est. the percentage of genes identified by chance, the false discovery rate (FDR). When the transcriptional response of human cells to ionizing radiation was measured by microarrays, SAM identified 34 genes that changed at least 1.5-fold with an estd. FDR of 12%, compared with FDRs of 60 and 84% by using conventional methods of anal. Of the 34 genes, 19 were involved in cell cycle regulation and 3 in apoptosis. Surprisingly, four nucleotide excision repair genes were induced, suggesting that this repair pathway for UV-damaged DNA might play a previously unrecognized role in repairing DNA damaged by ionizing radiation.
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31. 31
  Franz, M. Cytoscape.js: A graph theory library for visualisation and analysis. Bioinformatics 2015, 32, 309– 311, DOI: 10.1093/bioinformatics/btv557
  There is no corresponding record for this reference.
32. 32
  Dogrusoz, U.; Giral, E.; Cetintas, A.; Civril, A.; Demir, E. A layout algorithm for undirected compound graphs. Inf. Sci. (N. Y.) 2009, 179, 980– 994, DOI: 10.1016/j.ins.2008.11.017
  There is no corresponding record for this reference.
33. 33
  Pedamallu, C. S.; Ozdamar, L. A Review on protein-protein interaction network databases. In Springer Proceedings in Mathematics and Statistics; Springer, 2014; Vol. 73, pp 511– 519.
  There is no corresponding record for this reference.
34. 34
  Alanis-Lobato, G.; Andrade-Navarro, M. A.; Schaefer, M. H. HIPPIE v2.0: Enhancing meaningfulness and reliability of protein-protein interaction networks. Nucleic Acids Res. 2017, 45, D408– 14, DOI: 10.1093/nar/gkw985
  34
  HIPPIE v2.0: enhancing meaningfulness and reliability of protein-protein interaction networks
  Alanis-Lobato, Gregorio; Andrade-Navarro, Miguel A.; Schaefer, Martin H.
  Nucleic Acids Research (2017), 45 (D1), D408-D414CODEN: NARHAD; ISSN:1362-4962. (Oxford University Press)
  The increasing no. of exptl. detected interactions between proteins makes it difficult for researchers to ext. the interactions relevant for specific biol. processes or diseases. This makes it necessary to accompany the large-scale detection of protein-protein interactions (PPIs) with strategies and tools to generate meaningful PPI subnetworks. To this end, we generated the Human Integrated Protein-Protein Interaction ref. or HIPPIE (http://cbdm.uni-mainz.de/hippie/). HIPPIE is a one-stop resource for the generation and interpretation of PPI networks relevant to a specific research question. We provide means to generate highly reliable, context-specific PPI networks and to make sense out of them. We just released the second major update of HIPPIE, implementing various new features. HIPPIE grew substantially over the last years and now contains more than 270 000 confidence scored and annotated PPIs. We integrated different types of exptl. information for the confidence scoring and the construction of context-specific networks. We implemented basic graph algorithms that highlight important proteins and interactions. HIPPIE's graphical interface implements several ways for wet lab and computational scientists alike to access the PPI data.
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35. 35
  Gene Ontology Consortium, C. Gene Ontology Consortium: going forward. Nucleic Acids Res. 2015, 43, D1049– 56, DOI: 10.1093/nar/gku1179
  There is no corresponding record for this reference.
36. 36
  Clauset, A.; Shalizi, C. R.; Newman, M. E. J. Power-law distributions in empirical data. SIAM Rev. 2009, 51, 661, DOI: 10.1137/070710111
  There is no corresponding record for this reference.
37. 37
  Albert, R. Scale-free networks in cell biology. J. Cell Sci. 2005, 118, 4947, DOI: 10.1242/jcs.02714
  37
  Scale-free networks in cell biology
  Albert, Reka
  Journal of Cell Science (2005), 118 (21), 4947-4957CODEN: JNCSAI; ISSN:0021-9533. (Company of Biologists Ltd.)
  A review. A cell's behavior is a consequence of the complex interactions between its numerous constituents, such as DNA, RNA, proteins and small mols. Cells use signaling pathways and regulatory mechanisms to coordinate multiple processes, allowing them to respond to and adapt to an ever-changing environment. The large no. of components, the degree of interconnectivity and the complex control of cellular networks are becoming evident in the integrated genomic and proteomic analyses that are emerging. It is increasingly recognized that the understanding of properties that arise from whole-cell function require integrated, theor. descriptions of the relationships between different cellular components. Recent theor. advances allow us to describe cellular network structure with graph concepts and have revealed organizational features shared with numerous non-biol. networks. We now have the opportunity to describe quant. a network of hundreds or thousands of interacting components. Moreover, the obsd. topologies of cellular networks give us clues about their evolution and how their organization influences their function and dynamic responses.
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38. 38
  Riley, N. M.; Coon, J. J. Phosphoproteomics in the Age of Rapid and Deep Proteome Profiling. Anal. Chem. 2016, 88, 74– 94, DOI: 10.1021/acs.analchem.5b04123
  38
  Phosphoproteomics in the Age of Rapid and Deep Proteome Profiling
  Riley, Nicholas M.; Coon, Joshua J.
  Analytical Chemistry (Washington, DC, United States) (2016), 88 (1), 74-94CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)
  A review. Protein phosphorylation is a post-translational modification (PTM) that orchestrates a diverse array of cellular processes. Because this modification serves as a rapid and reversible means to modulate protein activity and transduce signals, the regulation of phosphorylation is a central mechanism in cell health and disease. The addn. and removal of phosphoryl modifications via kinases and phosphates, resp., makes the landscape of phosphorylation particularly dynamic. Understanding the complex networks and functions coordinated by phosphorylation requires knowledge of specific amino acid modifications with both spatial and temporal resoln. - a task that remains a challenging anal. endeavor. Mass spectrometry (MS) has emerged as the premier tool for global PTM anal., boasting high sensitivity, considerable throughput, and the capacity to localize modifications to a single residue. Indeed, MS-centric phosphoproteomics has become a std. approach for investigating protein phosphorylation in labs. worldwide. Anal. Chem. last reviewed the contribution of MS and related technologies to phosphoproteomics in 2011. Since that time, MS methodol. has developed at an impressive pace. While routine proteomic expts. can now analyze thousands of proteins in just a few hours, rather than days or weeks, characterizing the global phosphoproteome is significantly more challenging than measuring non-modified proteins. The relative low abundance of phosphorylated peptides and the need for residue-specific information require special considerations in sample handling, data acquisition, and post-acquisition processing that constrain reproducibility, quant. efficacy, throughput, and depth in phosphoproteomic workflows. Advances in MS-based approaches have remarkably improved our abilities to investigate the many roles of protein phosphorylation across a diverse set of biol. contexts, but many tech. obstacles still exist. Poor run-to-run overlap, challenges in confident phosphosite assignment, and complications inherent to various quant. strategies limit biol. insight, despite ever-increasing nos. of detected phosphopeptides. Focusing on work from the past two years (2013-2015), this review examines major developments in MS technol. that have enabled the characterization of tens of thousands of phosphopeptides in a given expt., and considers the contribution of this anal. power to translational research. We also discuss how future innovation can address tech. challenges of today's methods, and we offer our perspective on how phosphoproteomics will continue to mature.
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39. 39
  Casado, P. Kinase-substrate enrichment analysis provides insights into the heterogeneity of signaling pathway activation in leukemia cells. Sci. Signaling 2013, 6, rs6– rs6, DOI: 10.1126/scisignal.2003573
  There is no corresponding record for this reference.
40. 40
  Hernandez-Armenta, C.; Ochoa, D.; Gonçalves, E.; Saez-Rodriguez, J.; Beltrao, P. Benchmarking substrate-based kinase activity inference using phosphoproteomic data. Bioinformatics 2017, 33, 1845– 1851, DOI: 10.1093/bioinformatics/btx082
  40
  Benchmarking substrate-based kinase activity inference using phosphoproteomic data
  Hernandez-Armenta, Claudia; Ochoa, David; Goncalves, Emanuel; Saez-Rodriguez, Julio; Beltrao, Pedro
  Bioinformatics (2017), 33 (12), 1845-1851CODEN: BOINFP; ISSN:1460-2059. (Oxford University Press)
  Motivation: Phosphoproteomic expts. are increasingly used to study the changes in signaling occurring across different conditions. It has been proposed that changes in phosphorylation of kinase target sites can be used to infer when a kinase activity is under regulation. However, these approaches have not yet been benchmarked due to a lack of appropriate benchmarking strategies. Results: We used curated phosphoproteomic expts. and a gold std. dataset contg. a total of 184 kinase-condition pairs where regulation is expected to occur to benchmark and compare different kinase activity inference strategies: Z-test, Kolmogorov Smirnov test, Wilcoxon rank sum test, gene set enrichment anal. (GSEA), and a multiple linear regression model. We also tested weighted variants of the Z-test and GSEA that include information on kinase sequence specificity as proxy for affinity. Finally, we tested how the no. of known substrates and the type of evidence (in vivo, in vitro or in silico) supporting these influence the predictions. Conclusions: Most models performed well with the Z-test and the GSEA performing best as detd. by the area under the ROC curve (Mean AUC 1/4 0.722). Weighting kinase targets by the kinase target sequence preference improves the results marginally. However, the no. of known substrates and the evidence supporting the interactions has a strong effect on the predictions.
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41. 41
  Herranz, N. mTOR regulates MAPKAPK2 translation to control the senescence-associated secretory phenotype. Nat. Cell Biol. 2015, 17, 1205, DOI: 10.1038/ncb3225
  41
  mTOR regulates MAPKAPK2 translation to control the senescence-associated secretory phenotype
  Herranz, Nicolas; Gallage, Suchira; Mellone, Massimiliano; Wuestefeld, Torsten; Klotz, Sabrina; Hanley, Christopher J.; Raguz, Selina; Acosta, Juan Carlos; Innes, Andrew J.; Banito, Ana; Georgilis, Athena; Montoya, Alex; Wolter, Katharina; Dharmalingam, Gopuraja; Faull, Peter; Carroll, Thomas; Martinez-Barbera, Juan Pedro; Cutillas, Pedro; Reisinger, Florian; Heikenwalder, Mathias; Miller, Richard A.; Withers, Dominic; Zender, Lars; Thomas, Gareth J.; Gil, Jesus
  Nature Cell Biology (2015), 17 (9), 1205-1217CODEN: NCBIFN; ISSN:1465-7392. (Nature Publishing Group)
  Senescent cells secrete a combination of factors collectively known as the senescence-assocd. secretory phenotype (SASP). The SASP reinforces senescence and activates an immune surveillance response, but it can also show pro-tumorigenic properties and contribute to age-related pathologies. In a drug screen to find new SASP regulators, we uncovered the mTOR inhibitor rapamycin as a potent SASP suppressor. Here we report a mechanism by which mTOR controls the SASP by differentially regulating the translation of the MK2 (also known as MAPKAPK2) kinase through 4EBP1. In turn, MAPKAPK2 phosphorylates the RNA-binding protein ZFP36L1 during senescence, inhibiting its ability to degrade the transcripts of numerous SASP components. Consequently, mTOR inhibition or constitutive activation of ZFP36L1 impairs the non-cell-autonomous effects of senescent cells in both tumor-suppressive and tumor-promoting contexts. Altogether, our results place regulation of the SASP as a key mechanism by which mTOR could influence cancer, age-related diseases and immune responses.
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42. 42
  Wilkes, E. H.; Terfve, C.; Gribben, J. G.; Saez-Rodriguez, J.; Cutillas, P. R. Empirical inference of circuitry and plasticity in a kinase signaling network. Proc. Natl. Acad. Sci. U. S. A. 2015, 112, 7719, DOI: 10.1073/pnas.1423344112
  42
  Empirical inference of circuitry and plasticity in a kinase signaling network
  Wilkes, Edmund H.; Terfve, Camille; Gribben, John G.; Saez-Rodriguez, Julio; Cutillas, Pedro Rodriguez
  Proceedings of the National Academy of Sciences of the United States of America (2015), 112 (25), 7719-7724CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
  Our understanding of physiol. and disease is hampered by the difficulty of measuring the circuitry and plasticity of signaling networks that regulate cell biol., and how these relate to phenotypes. Here, using mass spectrometry-based phosphoproteomics, we systematically characterized the topol. of a network comprising the PI3K/Akt/mTOR and MEK/ERK signaling axes and confirmed its biol. relevance by assessing its dynamics upon EGF and IGF1 stimulation. Measuring the activity of this network in models of acquired drug resistance revealed that cells chronically treated with PI3K or mTORC1/2 inhibitors differed in the way their networks were remodeled. Unexpectedly, we also obsd. a degree of heterogeneity in the network state between cells resistant to the same inhibitor, indicating that even identical and carefully controlled exptl. conditions can give rise to the evolution of distinct kinase network statuses. These data suggest that the initial conditions of the system do not necessarily det. the mechanism by which cancer cells become resistant to PI3K/mTOR targeted therapies. The patterns of signaling network activity obsd. in the resistant cells mirrored the patterns of response to several drug combination treatments, suggesting that the activity of the defined signaling network truly reflected the evolved phenotypic diversity.
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43. 43
  Linding, R. NetworKIN: A resource for exploring cellular phosphorylation networks. Nucleic Acids Res. 2007, 36, D695– 99, DOI: 10.1093/nar/gkm902
  There is no corresponding record for this reference.
44. 44
  Wiredja, D. D.; Koyutürk, M.; Chance, M. R. The KSEA App: a web-based tool for kinase activity inference from quantitative phosphoproteomics. Bioinformatics 2017, 33, 3489, DOI: 10.1093/bioinformatics/btx415
  44
  The KSEA app: A web-based tool for kinase activity inference from quantitative phosphoproteomics
  Wiredja, Danica D.; Koyuturk, Mehmet; Chance, Mark R.
  Bioinformatics (2017), 33 (21), 3489-3491CODEN: BOINFP; ISSN:1367-4811. (Oxford University Press)
  Summary: Computational characterization of differential kinase activity from phosphoproteomics datasets is crit. for correctly inferring cellular circuitry and how signaling cascades are altered in drug treatment and/or disease. Kinase-Substrate Enrichment Anal. (KSEA) offers a powerful approach to estg. changes in a kinase's activity based on the collective phosphorylation changes of its identified substrates. However, KSEA has been limited to programmers who are able to implement the algorithms. Thus, to make it accessible to the larger scientific community, we present a web-based application of this method: the KSEA App. Overall, we expect that this tool will offer a quick and user-friendly way of generating kinase activity ests. from high-throughput phosphoproteomics datasets.
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45. 45
  Zhang, B.; Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 2005, 4, 17, DOI: 10.2202/1544-6115.1128
  There is no corresponding record for this reference.
46. 46
  Cox, J.; Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 2008, 26, 1367– 1372, DOI: 10.1038/nbt.1511
  46
  MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification
  Cox, Juergen; Mann, Matthias
  Nature Biotechnology (2008), 26 (12), 1367-1372CODEN: NABIF9; ISSN:1087-0156. (Nature Publishing Group)
  Efficient anal. of very large amts. of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resoln., quant. MS data. Using correlation anal. and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over std. techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome expt. and allows statistically robust identification and quantification of >4000 proteins in mammalian cell lysates.
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47. 47
  Sinitcyn, P. MaxQuant goes Linux. Nat. Methods 2018, 15, 401, DOI: 10.1038/s41592-018-0018-y
  47
  MaxQuant goes Linux
  Sinitcyn, Pavel; Tiwary, Shivani; Rudolph, Jan; Gutenbrunner, Petra; Wichmann, Christoph; Yilmaz, Sule; Hamzeiy, Hamid; Salinas, Favio; Cox, Juergen
  Nature Methods (2018), 15 (6), 401CODEN: NMAEA3; ISSN:1548-7091. (Nature Research)
  There is no expanded citation for this reference.
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48. 48
  Kristensen, A. R.; Foster, L. J. Protein correlation profiling-SILAC to study protein-protein interactions. Methods Mol. Biol. 2014, 1188, 263, DOI: 10.1007/978-1-4939-1142-4_18
  48
  Protein correlation profiling-SILAC to study protein-protein interactions
  Kristensen Anders R; Foster Leonard J
  Methods in molecular biology (Clifton, N.J.) (2014), 1188 (), 263-70 ISSN:.
  An interactome describes the global organization of protein interactions within a cell and is typically generated using affinity purification-mass spectrometry (AP-MS), yeast two-hybrid screening, or protein-fragment complementation assays (Gavin et al. Nature 440: 631-636, 2006; Krogan et al. Nature 440: 637-643, 2006; Uetz et al. Nature 403: 623-627, 2000; Tarassov et al. Science 320: 1465-1470, 2008). These techniques have been widely used to depict the interactome as we know it today but current models of interactomes do not contain stoichiometric or temporal information. In this chapter we describe size-exclusion chromatography (SEC) combined with protein correlation profiling-stable isotope labeling by amino acids in cell culture (PCP-SILAC) to generate dynamic chromatographs for thousands of proteins (Kristensen et al. Nat Methods 9: 907-909, 2012). Using the precise co-elution of two proteins as evidence that they interact, it is possible to identify similar numbers of protein interactions without overexpression or creating fusion proteins as other high-throughput techniques require. In addition, triplex SILAC allows us to quantify protein stoichiometry and temporal changes to the interactome following perturbation. Finally, SEC-PCP-SILAC is very time efficient since it generates two orders of magnitude fewer samples for LC-MS analysis and avoids the tedious tagging and purification steps, making it possible for everyone with a single mass spectrometer to study the interactome.
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49. 49
  Liu, F.; Lössl, P.; Scheltema, R.; Viner, R.; Heck, A. J. R. Optimized fragmentation schemes and data analysis strategies for proteome-wide cross-link identification. Nat. Commun. 2017, 8, 15473, DOI: 10.1038/ncomms15473
  49
  Optimized fragmentation schemes and data analysis strategies for proteome-wide cross-link identification
  Liu, Fan; Loessl, Philip; Scheltema, Richard; Viner, Rosa; Heck, Albert J. R.
  Nature Communications (2017), 8 (), 15473CODEN: NCAOBW; ISSN:2041-1723. (Nature Publishing Group)
  We describe optimized fragmentation schemes and data anal. strategies substantially enhancing the depth and accuracy in identifying protein cross-links using non-restricted whole proteome databases. These include a novel hybrid data acquisition strategy to sequence cross-links at both MS2 and MS3 level and a new algorithmic design XlinkX v2.0 for data anal. As proof-of-concept we investigated proteome-wide protein interactions in E. coli and HeLa cell lysates, resp., identifying 1,158 and 3,301 unique cross-links at ~ 1% false discovery rate. These protein interaction repositories provide meaningful structural information on many endogenous macromol. assemblies, as we showcase on several protein complexes involved in translation, protein folding and carbohydrate metab.
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Supporting Information
Supporting Information
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jproteome.8b00927.
- Figure S1, Graphical workflow combining matrix and network activities; Figure S2, Organization of the new Perseus plugin API for networks; Figure S3, Context-specific documentation; and explanations of Tables S1–S3 (PDF)
- Table S1, AP-MS pull screen (TXT)
- Table S2, phosphoproteomics of EGF stimulation (TXT)
- Table S3, clinical proteomics dataset (TXT)
- Supplementary Data 1, Perseus network collection data format: example of a network collection describing three small, randomly generated networks (ZIP)
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A Network Module for the Perseus Software for Computational Proteomics Facilitates Proteome Interaction Graph AnalysisClick to copy article linkArticle link copied!

Journal of Proteome Research

Abstract

Introduction

Experimental Section

Creating Interaction Networks from Pulldown Experiments

Approximately Scale-Free Topology of the STRING Interaction Network

Network Analysis of a Phosphoproteomic Dataset of EGF Stimulation

Co-expression Analysis of a Clinical Proteomics Dataset

PluginInterop Provides a Central Entry Point for All External Plugins

Library Support for Scripting Languages

Implementation of PluginPHOTON

Implementation of PluginCoExpression

Implementation of KSEA in Perseus

Results and Discussion

Workflow-Based Biological Network Analysis

Figure 1

Figure 2

Multilingual Plugin Activities

Figure 3

Affinity Enrichment MS Interactomics

Figure 4

Importing, Curating, and Probing Large-Scale PPI Networks

Figure 5

Network Analysis of PTM Data

Figure 6

Co-expression Clustering and Clinical Data

Figure 7

Software Implementation, Download, and Maintenance

Conclusions

Supporting Information

Terms & Conditions

Author Information

Acknowledgments

References

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Abstract

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Figure 3

Figure 4

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Supporting Information

Supporting Information

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A Network Module for the Perseus Software for Computational Proteomics Facilitates Proteome Interaction Graph Analysis
Click to copy article linkArticle link copied!