Visualization and Analysis of the Complexome Network of Saccharomyces cerevisiae

Deeper investigation into the utility of functional class scoring in missing protein prediction from proteomics data

Functional Class Scoring (FCS) is a network-based approach previously demonstrated to be powerful in missing protein prediction (MPP). We update its performance evaluation using data derived from new proteomics technology (SWATH) and also checked for reproducibility using two independent datasets profiling kidney tissue proteome. We also evaluated the objectivity of the FCS p-value, and followed up on the value of MPP from predicted complexes. Our results suggest that (1) FCS [Formula: see text]-values are non-objective, and are confounded strongly by complex size, (2) best recovery performance do not necessarily lie at standard [Formula: see text]-value cutoffs, (3) while predicted complexes may be used for augmenting MPP, they are inferior to real complexes, and are further confounded by issues relating to network coverage and quality and (4) moderate sized complexes of size 5 to 10 still exhibit considerable instability, we find that FCS works best with big complexes. While FCS is a powerful approach, blind reliance on its non-objective [Formula: see text]-value is ill-advised.

A homologous mapping method for three-dimensional reconstruction of protein networks reveals disease-associated mutations

BACKGROUND

One of the crucial steps toward understanding the associations among molecular interactions, pathways, and diseases in a cell is to investigate detailed atomic protein-protein interactions (PPIs) in the structural interactome. Despite the availability of large-scale methods for analyzing PPI networks, these methods often focused on PPI networks using genome-scale data and/or known experimental PPIs. However, these methods are unable to provide structurally resolved interaction residues and their conservations in PPI networks.

RESULTS

Here, we reconstructed a human three-dimensional (3D) structural PPI network (hDiSNet) with the detailed atomic binding models and disease-associated mutations by enhancing our PPI families and 3D-domain interologs from 60,618 structural complexes and complete genome database with 6,352,363 protein sequences across 2274 species. hDiSNet is a scale-free network (γ = 2.05), which consists of 5177 proteins and 19,239 PPIs with 5843 mutations. These 19,239 structurally resolved PPIs not only expanded the number of PPIs compared to present structural PPI network, but also achieved higher agreement with gene ontology similarities and higher co-expression correlation than the ones of 181,868 experimental PPIs recorded in public databases. Among 5843 mutations, 1653 and 790 mutations involved in interacting domains and contacting residues, respectively, are highly related to diseases. Our hDiSNet can provide detailed atomic interactions of human disease and their associated proteins with mutations. Our results show that the disease-related mutations are often located at the contacting residues forming the hydrogen bonds or conserved in the PPI family. In addition, hDiSNet provides the insights of the FGFR (EGFR)-MAPK pathway for interpreting the mechanisms of breast cancer and ErbB signaling pathway in brain cancer.

CONCLUSIONS

Our results demonstrate that hDiSNet can explore structural-based interactions insights for understanding the mechanisms of disease-associated proteins and their mutations. We believe that our method is useful to reconstruct structurally resolved PPI networks for interpreting structural genomics and disease associations.

PTMOracle: A Cytoscape App for Covisualizing and Coanalyzing Post-Translational Modifications in Protein Interaction Networks

Post-translational modifications of proteins (PTMs) act as key regulators of protein activity and of protein-protein interactions (PPIs). To date, it has been difficult to comprehensively explore functional links between PTMs and PPIs. To address this, we developed PTMOracle, a Cytoscape app for coanalyzing PTMs within PPI networks. PTMOracle also allows extensive data to be integrated and coanalyzed with PPI networks, allowing the role of domains, motifs, and disordered regions to be considered. For proteins of interest, or a whole proteome, PTMOracle can generate network visualizations to reveal complex PTM-associated relationships. This is assisted by OraclePainter for coloring proteins by modifications, OracleTools for network analytics, and OracleResults for exploring tabulated findings. To illustrate the use of PTMOracle, we investigate PTM-associated relationships and their role in PPIs in four case studies. In the yeast interactome and its rich set of PTMs, we construct and explore histone-associated and domain-domain interaction networks and show how integrative approaches can predict kinases involved in phosphodegrons. In the human interactome, a phosphotyrosine-associated network is analyzed but highlights the sparse nature of human PPI networks and lack of PTM-associated data. PTMOracle is open source and available at the Cytoscape app store: http://apps.cytoscape.org/apps/ptmoracle .

Global profiling of protein complexes: current approaches and their perspective in biomedical research

INTRODUCTION

Despite the rapid evolution of proteomic methods, protein interactions and their participation in protein complexes - an important aspect of their function - has rarely been investigated on the proteome-wide level. Disease states, such as muscular dystrophy or viral infection, are induced by interference in protein-protein interactions within complexes. The purpose of this review is to describe the current methods for global complexome analysis and to critically discuss the challenges and opportunities for the application of these methods in biomedical research. Areas covered: We discuss advancements in experimental techniques and computational tools that facilitate profiling of the complexome. The main focus is on the separation of native protein complexes via size exclusion chromatography and gel electrophoresis, which has recently been combined with quantitative mass spectrometry, for a global protein-complex profiling. The development of this approach has been supported by advanced bioinformatics strategies and fast and sensitive mass spectrometers that have allowed the analysis of whole cell lysates. The application of this technique to biomedical research is assessed, and future directions are anticipated. Expert commentary: The methodology is quite new, and has already shown great potential when combined with complementary methods for detection of protein complexes.

Detecting Protein Complexes from Signed Protein-Protein Interaction Networks

Identification of protein complexes is fundamental for understanding the cellular functional organization. With the accumulation of physical protein-protein interaction (PPI) data, computational detection of protein complexes from available PPI networks has drawn a lot of attentions. While most of the existing protein complex detection algorithms focus on analyzing the physical protein-protein interaction network, none of them take into account the "signs"  (i.e., activation-inhibition relationships) of physical interactions. As the "signs"  of interactions reflect the way proteins communicate, considering the "signs"  of interactions can not only increase the accuracy of protein complex identification, but also deepen our understanding of the mechanisms of cell functions. In this study, we proposed a novel Signed Graph regularized Nonnegative Matrix Factorization (SGNMF) model to identify protein complexes from signed PPI networks. In our experiments, we compared the results collected by our model on signed PPI networks with those predicted by the state-of-the-art complex detection techniques on the original unsigned PPI networks. We observed that considering the "signs"  of interactions significantly benefits the detection of protein complexes. Furthermore, based on the predicted complexes, we predicted a set of signed complex-complex interactions for each dataset, which provides a novel insight of the higher level organization of the cell. All the experimental results and codes can be downloaded from http://mail.sysu.edu.cn/home/stsddq@mail.sysu.edu.cn/dai/others/SGNMF.zip.

From proteomes to complexomes in the era of systems biology

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

Predicting physical interactions between protein complexes

Protein complexes enact most biochemical functions in the cell. Dynamic interactions between protein complexes are frequent in many cellular processes. As they are often of a transient nature, they may be difficult to detect using current genome-wide screens. Here, we describe a method to computationally predict physical interactions between protein complexes, applied to both humans and yeast. We integrated manually curated protein complexes and physical protein interaction networks, and we designed a statistical method to identify pairs of protein complexes where the number of protein interactions between a complex pair is due to an actual physical interaction between the complexes. An evaluation against manually curated physical complex-complex interactions in yeast revealed that 50% of these interactions could be predicted in this manner. A community network analysis of the highest scoring pairs revealed a biologically sensible organization of physical complex-complex interactions in the cell. Such analyses of proteomes may serve as a guide to the discovery of novel functional cellular relationships.

Proteomics study on the hepatoprotective effects of traditional Chinese medicine formulae Yin-Chen-Hao-Tang by a combination of two-dimensional polyacrylamide gel electrophoresis and matrix-assisted laser desorption/ionization-time of flight mass spectrometry

Proteomics can bring breakthroughs in the study of traditional Chinese medicine (TCM). Yin-Chen-Hao-Tang (YCHT), a famous TCM formulae, has been used to alleviate various types of liver injury. However, the underlying mechanisms and drug targets of YCHT associated with the hepatic injury are largely unknown. To identify the possible target proteins of YCHT, two-dimensional gel electrophoresis (2-DE)-based proteomics was performed and proteins altered after YCHT treatment were identified by MALDI-TOF/TOF-MS. Interestingly, 15 modulated proteins were identified, out of which 7 were found to be significantly altered by YCHT. YCHT treatment caused a statistically significant down-regulation of zinc finger protein 407, haptoglobin, macroglobulin, alpha-1-antitrypsin; significant up-regulation of transthyretin, vitamin D-binding protein, and prothrombin, appear to be involved in metabolism, energy generation, chaperone, antioxidation, signal transduction, protein folding and apoptosis. Finally, interaction network from 7 differentially expressed protein to the signal-related proteins was established using bioinformatic analysis. Of note, these signal-related proteins could be included in a network together with 7 proteins through direct interaction or only one intermediate partner. Functional pathway analysis suggested that these proteins were closely related in the protein-protein interaction network and the modulation of multiple vital physiological pathways. Thus, our data will help to understand the molecular mechanisms of hepatoprotective effects of YCHT.

A multidimensional matrix for systems biology research and its application to interaction networks

A multidimensional matrix containing 76 parameters from 21 transcriptomics, proteomics, interactomics, phenotypic and sequence-based data sets, in which each data set covered most of the Saccharomyces cerevisiae proteome, was compiled for systems biology research. The maximal information coefficient (MIC) was used to measure correlations between every pair of parameters. Out of 2850 possible comparisons, 340 pairs of variables (12%) showed statistically significant MIC scores. There were 321 relationships that were expected; these included relationships within physicochemical parameters of proteins, between abundance levels of genes/proteins and expression noise, and between different types of intracellular networks. We found 19 potentially novel relationships between different types of "-omics" data. The strongest of these involved genetic interaction networks, which were correlated with pleiotropy and cell-to-cell variability in protein expression. Protein disorder also showed a number of significant relationships with protein abundance, signaling and regulatory networks. Significant cross-talk was seen between the signaling and kinase interaction networks. Investigation of this revealed densely connected kinase clusters and significant signaling between them, along with signaling centers that act as integrators or broadcasters of intracellular information. These centers may allow for redundancy and a means of dampening noise in networks under a variety of genetic or environmental perturbations.

MoNetFamily: a web server to infer homologous modules and module-module interaction networks in vertebrates

A module is a fundamental unit forming with highly connected proteins and performs a certain kind of biological functions. Modules and module–module interaction (MMI) network are essential for understanding cellular processes and functions. The MoNetFamily web server can identify the modules, homologous modules (called module family) and MMI networks across multiple species for the query protein(s). This server first finds module candidates of the query by using BLASTP to search the module template database (1785 experimental and 1252 structural templates). MoNetFamily then infers the homologous modules of the selected module candidate using protein–protein interaction (PPI) families. According to homologous modules and PPIs, we statistically calculated MMIs and MMI networks across multiple species. For each module candidate, MoNetFamily identifies its neighboring modules and their MMIs in module networks of Homo sapiens, Mus musculus and Danio rerio. Finally, MoNetFamily shows the conserved proteins, PPI profiles and functional annotations of the module family. Our results indicate that the server can be useful for MMI network (e.g. 1818 modules and 9678 MMIs in H. sapiens) visualizations and query annotations using module families and neighboring modules. We believe that the server is able to provide valuable insights to determine homologous modules and MMI networks across multiple species for studying module evolution and cellular processes. The MoNetFamily sever is available at http://monetfamily.life.nctu.edu.tw.