Computational analysis of human protein interaction networks

Large amounts of human protein interaction data have been produced by experiments and prediction methods. However, the experimental coverage of the human interactome is still low in contrast to predicted data. To gain insight into the value of publicly available human protein network data, we compared predicted datasets, high-throughput results from yeast two-hybrid screens, and literature-curated protein-protein interactions. This evaluation is not only important for further methodological improvements, but also for increasing the confidence in functional hypotheses derived from predictions. Therefore, we assessed the quality and the potential bias of the different datasets using functional similarity based on the Gene Ontology, structural iPfam domain-domain interactions, likelihood ratios, and topological network parameters. This analysis revealed major differences between predicted datasets, but some of them also scored at least as high as the experimental ones regarding multiple quality measures. Therefore, since only small pair wise overlap between most datasets is observed, they may be combined to enlarge the available human interactome data. For this purpose, we additionally studied the influence of protein length on data quality and the number of disease proteins covered by each dataset. We could further demonstrate that protein interactions predicted by more than one method achieve an elevated reliability.     

Interactome mapping for analysis of complex phenotypes: insights from benchmarking binary interaction assays

Protein interactions mediate essentially all biological processes and analysis of protein-protein interactions using both large-scale and small-scale approaches has contributed fundamental insights to the understanding of biological systems. In recent years, interactome network maps have emerged as an important tool for analyzing and interpreting genetic data of complex phenotypes. Complementary experimental approaches to test for binary, direct interactions, and for membership in protein complexes are used to explore the interactome. The two approaches are not redundant but yield orthogonal perspectives onto the complex network of physical interactions by which proteins mediate biological processes. In recent years, several publications have demonstrated that interactions from high-throughput experiments can be equally reliable as the high quality subset of interactions identified in small-scale studies. Critical for this insight was the introduction of standardized experimental benchmarking of interaction and validation assays using reference sets. The data obtained in these benchmarking experiments have resulted in greater appreciation of the limitations and the complementary strengths of different assays. Moreover, benchmarking is a central element of a conceptual framework to estimate interactome sizes and thereby measure progress toward near complete network maps. These estimates have revealed that current large-scale data sets, although often of high quality, cover only a small fraction of a given interactome. Here, I review the findings of assay benchmarking and discuss implications for quality control, and for strategies toward obtaining a near-complete map of the interactome of an organism.     

The Interactorium: Visualising proteins,complexes and interaction networks in a virtual 3‐D cell

Here, we describe the Interactorium, a tool in which a Virtual Cell is used as the context for the seamless visualisation of the yeast protein interaction network, protein complexes and protein 3‐D structures. The tool has been designed to display very complex networks of up to 40 000 proteins or 6000 multiprotein complexes and has a series of toolboxes and menus to allow real‐time data manipulation and control the manner in which data are displayed. It incorporates new algorithms that reduce the complexity of the visualisation by the generation of putative new complexes from existing data and by the reduction of edges through the use of protein “twins” when they occur in multiple locations. Since the Interactorium permits multi‐level viewing of the molecular biology of the cell, it is a considerable advance over existing approaches. We illustrate its use for Saccharomyces cerevisiae but note that it will also be useful for the analysis of data from simpler prokaryotes and higher eukaryotes, including humans. The Interactorium is available for download at http://www.interactorium.net .     

A domain level interaction network of amyloid precursor protein and Aβ of Alzheimer's disease

The primary constituent of the amyloid plaque, β‐amyloid (Aβ), is thought to be the causal “toxic moiety” of Alzheimer's disease. However, despite much work focused on both Aβ and its parent protein, amyloid precursor protein (APP), the functional roles of APP and its cleavage products remain to be fully elucidated. Protein–protein interaction networks can provide insight into protein function, however, high‐throughput data often report false positives and are in frequent disagreement with low‐throughput experiments. Moreover, the complexity of the CNS is likely to be under represented in such databases. Therefore, we curated the published work characterizing both APP and Aβ to create a protein interaction network of APP and its proteolytic cleavage products, with annotation, where possible, to the level of APP binding domain and isoform. This is the first time that an interactome has been refined to domain level, essential for the interpretation of APP due to the presence of multiple isoforms and processed fragments. Gene ontology and network analysis were used to identify potentially novel functional relationships among interacting proteins.     

Four-dimensional visualisation and analysis of protein-protein interaction networks

Protein-protein interaction networks are typically built with interactions collated from many experiments. These networks are thus composite and show all interactions that are currently known to occur in a cell. However, these representations are static and ignore the constant changes in protein-protein interactions. Here we present software for the generation and analysis of dynamic, four-dimensional (4-D) protein interaction networks. In this, time-course-derived abundance data are mapped onto three-dimensional networks to generate network movies. These networks can be navigated, manipulated and queried in real time. Two types of dynamic networks can be generated: a 4-D network that maps expression data onto protein nodes and one that employs 'real-time rendering' by which protein nodes and their interactions appear and disappear in association with temporal changes in expression data. We illustrate the utility of this software by the analysis of singlish interface date hub interactions during the yeast cell cycle. In this, we show that proteins MLC1 and YPT52 show strict temporal control of when their interaction partners are expressed. Since these proteins have one and two interaction interfaces, respectively, it suggests that temporal control of gene expression may be used to limit competition at the interaction interfaces of some hub proteins. The software and movies of the 4-D networks are available at http://www.systemsbiology.org.au/downloads_geomi.html.     

Experimental and computational tools useful for (re)construction of dynamic kinase–substrate networks

The explosion of site‐ and context‐specific in vivo phosphorylation events presents a potentially rich source of biological knowledge and calls for novel data analysis and modeling paradigms. Perhaps the most immediate challenge is delineating detected phosphorylation sites to their effector kinases. This is important for (re)constructing transient kinase–substrate interaction networks that are essential for mechanistic understanding of cellular behaviors and therapeutic intervention, but has largely eluded high‐throughput protein‐interaction studies due to their transient nature and strong dependencies on cellular context. Here, we surveyed some of the computational approaches developed to dissect phosphorylation data detected in systematic proteomic experiments and reviewed some experimental and computational approaches used to map phosphorylation sites to their effector kinases in efforts aimed at reconstructing biological signaling networks.     

Proteomic analysis of interactors for yeast protein arginine methyltransferase Hmt1 reveals novel substrate and insights into additional biological roles

Protein arginine methylation is a PTM catalyzed by an evolutionarily conserved family of enzymes called protein arginine methyltransferases (PRMTs), with PRMT1 being the most conserved member of this enzyme family. This modification has emerged to be an important regulator of protein functions. To better understand the role of PRMTs in cellular pathways and functions, we have carried out a proteomic profiling experiment to comprehensively identify the physical interactors of Hmt1, the budding yeast homolog for human PRMT1. Using a dual‐enzymatic digestion linear trap quadrupole/Orbitrap proteomic strategy, we identified a total of 108 proteins that specifically copurify with Hmt1 by tandem affinity purification. A reverse coimmunoprecipitation experiment was used to confirm Hmt1's physical association with Bre5, Mtr4, Snf2, Sum1, and Ssd1, five proteins that were identified as Hmt1‐specific interactors in multiple biological replicates. To determine whether the identified Hmt1‐interactors had the potential to act as an Hmt1 substrate, we used published bioinformatics algorithms that predict the presence and location of potential methylarginines for each identified interactor. One of the top hits from this analysis, Snf2, was experimentally confirmed as a robust substrate of Hmt1 in vitro. Overall, our data provide a feasible proteomic approach that aid in the better understanding of PRMT1's roles within a cell.     

PPI spider: A tool for the interpretation of proteomics data in the context of protein–protein interaction networks

Recent advances in experimental technologies allow for the detection of a complete cell proteome. Proteins that are expressed at a particular cell state or in a particular compartment as well as proteins with differential expression between various cells states are commonly delivered by many proteomics studies. Once a list of proteins is derived, a major challenge is to interpret the identified set of proteins in the biological context. Protein–protein interaction (PPI) data represents abundant information that can be employed for this purpose. However, these data have not yet been fully exploited due to the absence of a methodological framework that can integrate this type of information. Here, we propose to infer a network model from an experimentally identified protein list based on the available information about the topology of the global PPI network. We propose to use a Monte Carlo simulation procedure to compute the statistical significance of the inferred models. The method has been implemented as a freely available web‐based tool, PPI spider ( http://mips.helmholtz‐muenchen.de/proj/ppispider ). To support the practical significance of PPI spider, we collected several hundreds of recently published experimental proteomics studies that reported lists of proteins in various biological contexts. We reanalyzed them using PPI spider and demonstrated that in most cases PPI spider could provide statistically significant hypotheses that are helpful for understanding of the protein list.     

Systematic prediction of human membrane receptor interactions

Membrane receptor‐activated signal transduction pathways are integral to cellular functions and disease mechanisms in humans. Identification of the full set of proteins interacting with membrane receptors by high‐throughput experimental means is difficult because methods to directly identify protein interactions are largely not applicable to membrane proteins. Unlike prior approaches that attempted to predict the global human interactome, we used a computational strategy that only focused on discovering the interacting partners of human membrane receptors leading to improved results for these proteins. We predict specific interactions based on statistical integration of biological data containing highly informative direct and indirect evidences together with feedback from experts. The predicted membrane receptor interactome provides a system‐wide view, and generates new biological hypotheses regarding interactions between membrane receptors and other proteins. We have experimentally validated a number of these interactions. The results suggest that a framework of systematically integrating computational predictions, global analyses, biological experimentation and expert feedback is a feasible strategy to study the human membrane receptor interactome.     

Mapping the in vitro interactome of cardiac sodium (Na+)‐calcium (Ca2+) exchanger 1 (NCX1)

The sodium (Na⁺)‐calcium (Ca²⁺) exchanger 1 (NCX1) is an antiporter membrane protein encoded by the SLC8A1 gene. In the heart, it maintains cytosolic Ca²⁺ homeostasis, serving as the primary mechanism for Ca²⁺ extrusion during relaxation. Dysregulation of NCX1 is observed in end‐stage human heart failure. In this study, we used affinity purification coupled with MS in rat left ventricle lysates to identify novel NCX1 interacting proteins in the heart. Two screens were conducted using: (1) anti‐NCX1 against endogenous NCX1 and (2) anti‐His (where His is histidine) with His‐trigger factor‐NCX1_cyt recombinant protein as bait. The respective methods identified 112 and 350 protein partners, of which several were known NCX1 partners from the literature, and 29 occurred in both screens. Ten novel protein partners (DYRK1A, PPP2R2A, SNTB1, DMD, RABGGTA, DNAJB4, BAG3, PDE3A, POPDC2, STK39) were validated for binding to NCX1, and two partners (DYRK1A, SNTB1) increased NCX1 activity when expressed in HEK293 cells. A cardiac NCX1 protein–protein interaction map was constructed. The map was highly connected, containing distinct clusters of proteins with different biological functions, where “cell communication” and “signal transduction” formed the largest clusters. The NCX1 interactome was also significantly enriched with proteins/genes involved in “cardiovascular disease” which can be explored as novel drug targets in future research.     

Analysis of the interactome of ribosomal protein S19 mutants

Diamond–Blackfan anemia, characterized by defective erythroid progenitor maturation, is caused in one‐fourth of cases by mutations of ribosomal protein S19 (RPS19), which is a component of the ribosomal 40S subunit. Our previous work described proteins interacting with RPS19 with the aim to determine its functions. Here, two RPS19 mutants, R62W and R101H, have been selected to compare their interactomes versus the wild‐type protein one, using the same functional proteomic approach that we employed to characterize RPS19 interactome. Mutations R62W and R101H impair RPS19 ability to associate with the ribosome. Results presented in this paper highlight the striking differences between the interactomes of wild‐type and mutant RPS19 proteins. In particular, mutations abolish interactions with proteins having splicing, translational and helicase activity, thus confirming the role of RPS19 in RNA processing/metabolism and translational control. The data have been deposited to the ProteomeXchange with identifier PXD000640 ( http://proteomecentral.proteomexchange.org/dataset/PXD000640 ).     

Splitting the dynamics of large biochemical interaction networks

This article is inscribed in the general motivation of understanding the dynamics on biochemical networks including metabolic and genetic interactions. Our approach is continuous modeling by differential equations. We address the problem of the huge size of those systems. We present a mathematical tool for reducing the size of the model, master-slave synchronization, and fit it to the biochemical context.     

Pathway Palette: A rich internet application for peptide‐, protein‐ and network‐oriented analysis of MS data

Recent improvements in proteomic technologies have collectively yielded data sets that far exceed the capabilities of typical low‐throughput interpretation strategies. Unfortunately, tools designed to leverage the “peptide‐centric” content of MS‐based proteomics lag the current rate of data production. Here, we describe Pathway Palette ( http://blaispathways.dfci.harvard.edu ), a freely accessible internet application that enables researchers to easily transition from peptides to biological pathways, while simultaneously retaining the qualitative and quantitative aspects of the underlying MS data.     

Identification and characterization of novel ERC‐55 interacting proteins: Evidence for the existence of several ERC‐55 splicing variants; including the cytosolic ERC‐55‐C

ERC‐55, encoded from RCN2, is localized in the ER and belongs to the CREC protein family. ERC‐55 is involved in various diseases and abnormal cell behavior, however, the function is not well defined and it has controversially been reported to interact with a cytosolic protein, the vitamin D receptor. We have used a number of proteomic techniques to further our functional understanding of ERC‐55. By affinity purification, we observed interaction with a large variety of proteins, including those secreted and localized outside of the secretory pathway, in the cytosol and also in various organelles. We confirm the existence of several ERC‐55 splicing variants including ERC‐55‐C localized in the cytosol in association with the cytoskeleton. Localization was verified by immunoelectron microscopy and sub‐cellular fractionation. Interaction of lactoferrin, S100P, calcyclin (S100A6), peroxiredoxin‐6, kininogen and lysozyme with ERC‐55 was further studied in vitro by SPR experiments. Interaction of S100P requires [Ca²⁺] of ～10^?7 M or greater, while calcyclin interaction requires [Ca²⁺] of >10^?5 M. Interaction with peroxiredoxin‐6 is independent of Ca²⁺. Co‐localization of lactoferrin, S100P and calcyclin with ERC‐55 in the perinuclear area was analyzed by fluorescence confocal microscopy. The functional variety of the interacting proteins indicates a broad spectrum of ERC‐55 activities such as immunity, redox homeostasis, cell cycle regulation and coagulation.     

Proteomic,functional and motif‐based analysis of C‐terminal Src kinase‐interacting proteins

C‐terminal Src kinase (Csk) that functions as an essential negative regulator of Src family tyrosine kinases (SFKs) interacts with tyrosine‐phosphorylated molecules through its Src homology 2 (SH2) domain, allowing it targeting to the sites of SFKs and concomitantly enhancing its kinase activity. Identification of additional Csk‐interacting proteins is expected to reveal potential signaling targets and previously undescribed functions of Csk. In this study, using a direct proteomic approach, we identified 151 novel potential Csk‐binding partners, which are associated with a wide range of biological functions. Bioinformatics analysis showed that the majority of identified proteins contain one or several Csk‐SH2 domain‐binding motifs, indicating a potentially direct interaction with Csk. The interactions of Csk with four proteins (partitioning defective 3 (Par3), DDR1, SYK and protein kinase C iota) were confirmed using biochemical approaches and phosphotyrosine 1127 of Par3 C‐terminus was proved to directly bind to Csk‐SH2 domain, which was consistent with predictions from in silico analysis. Finally, immunofluorescence experiments revealed co‐localization of Csk with Par3 in tight junction (TJ) in a tyrosine phosphorylation‐dependent manner and overexpression of Csk, but not its SH2‐domain mutant lacking binding to phosphotyrosine, promoted the TJ assembly in Madin‐Darby canine kidney cells, implying the involvement of Csk‐SH2 domain in regulating cellular TJs. In conclusion, the newly identified potential interacting partners of Csk provided new insights into its functional diversity in regulation of numerous cellular events, in addition to controlling the SFK activity.     

The structural analysis of protein–protein interactions by NMR spectroscopy

A comprehensive understanding of protein–protein interactions is an important next step in our quest to understand how the information contained in a genome is put into action. Although a number of experimental techniques can report on the existence of a protein– protein interaction, very few can provide detailed structural information. NMR spectroscopy is one of these, and in recent years several complementary NMR approaches, including residual dipolar couplings and the use of paramagnetic effects, have been developed that can provide insight into the structure of protein–protein complexes. In this article, we review these approaches and comment on their strengths and weaknesses.     

Expression and purification of the D4 region of PLD1 and characterization of its interaction with PED-PEA15

PLD’s (Phospholipases D) are ubiquitously expressed proteins involved in many transphosphatidylation reactions. They have a bi-lobed structure composed by two similar domains which at their interface reconstitute the catalytic site through the association of the two conserved HxKx₄Dx₆GSxN motifs. PLD1 interacts with the small phosphoprotein PED-PEA15 by an unknown mechanism that, by enhancing PLD1 stability, apparently increases its enzymatic activity; the minimum interacting region of PLD1 was previously identified as spanning residues 712–1074 (D4 region). Since the D4/PED-PEA15 interaction has been claimed to be one of the multiple molecular events that can trigger type 2 diabetes, we purified the two recombinant proteins to study in vitro this binding by both ELISA and SPR techniques. Whilst PED-PEA15 was easily expressed and purified, expression of recombinant D4 was more problematic and only the fusion protein with Thioredoxin A and a six Histidine Tag (Trx-His₆-D4) demonstrated sufficient stability for further characterization. We have found that Trx-His₆-D4 is present as two different oligomeric forms, though only the monomeric variant is able to interact with PED-PEA15. All these findings may have important implications for both the mechanisms of phospholipase activity and PED-PEA15 regulative functions.