Protein interaction maps for model organisms

Co-fractionation-mass spectrometry to characterize native mitochondrial protein assemblies in mammalian neurons and brain

Human mitochondrial (mt) protein assemblies are vital for neuronal and brain function, and their alteration contributes to many human disorders, e.g., neurodegenerative diseases resulting from abnormal protein-protein interactions (PPIs). Knowledge of the composition of mt protein complexes is, however, still limited. Affinity purification mass spectrometry (MS) and proximity-dependent biotinylation MS have defined protein partners of some mt proteins, but are too technically challenging and laborious to be practical for analyzing large numbers of samples at the proteome level, e.g., for the study of neuronal or brain-specific mt assemblies, as well as altered mtPPIs on a proteome-wide scale for a disease of interest in brain regions, disease tissues or neurons derived from patients. To address this challenge, we adapted a co-fractionation-MS platform to survey native mt assemblies in adult mouse brain and in human NTERA-2 embryonal carcinoma stem cells or differentiated neuronal-like cells. The workflow consists of orthogonal separations of mt extracts isolated from chemically cross-linked samples to stabilize PPIs, data-dependent acquisition MS to identify co-eluted mt protein profiles from collected fractions and a computational scoring pipeline to predict mtPPIs, followed by network partitioning to define complexes linked to mt functions as well as those essential for neuronal and brain physiological homeostasis. We developed an R/CRAN software package, Macromolecular Assemblies from Co-elution Profiles for automated scoring of co-fractionation-MS data to define complexes from mtPPI networks. Presently, the co-fractionation-MS procedure takes 1.5-3.5 d of proteomic sample preparation, 31 d of MS data acquisition and 8.5 d of data analyses to produce meaningful biological insights.

Mapping the plant proteome: tools for surveying coordinating pathways

Plants rapidly respond to environmental fluctuations through coordinated, multi-scalar regulation, enabling complex reactions despite their inherently sessile nature. In particular, protein post-translational signaling and protein-protein interactions combine to manipulate cellular responses and regulate plant homeostasis with precise temporal and spatial control. Understanding these proteomic networks are essential to addressing ongoing global crises, including those of food security, rising global temperatures, and the need for renewable materials and fuels. Technological advances in mass spectrometry-based proteomics are enabling investigations of unprecedented depth, and are increasingly being optimized for and applied to plant systems. This review highlights recent advances in plant proteomics, with an emphasis on spatially and temporally resolved analysis of post-translational modifications and protein interactions. It also details the necessity for generation of a comprehensive plant cell atlas while highlighting recent accomplishments within the field.

A novel system to rapidly detect protein-protein interactions (PPIs) based on fluorescence co-localization

OBJECTIVE

Rapid and convenient detection of protein-protein interactions (PPIs) is of great significance for understanding function of protein.

RESULTS

For efficiently detecting PPIs, we used the changes of proteins fluorescence localization to design a novel system, fluorescence translocation co-localization (FTCL), based on nuclear localization signal (NLS) in living cells. Depending on the original state of protein localization (both in the cytoplasm, both in the nucleus, one in the nucleus and another in the cytoplasm), two target proteins can be partitioned into the cytoplasm and nucleus by adding a NLS or mutating an existing NLS. Three independent results display that the changes of protein fluorescence co-localization were observed following co-expression of the two target proteins. At the same time, we verified the accuracy of fluorescence co-localization by co-immunoprecipitation.

CONCLUSIONS

There FTCL system provided a novel detection method for PPIs, regardless of protein localization in the nucleus or cytoplasm. More importantly, this study provides a new strategy for future protein interaction studies through organelle localization (such as mitochondria, Golgi and cytomembrane, etc.).

A Pan-plant Protein Complex Map Reveals Deep Conservation and Novel Assemblies

Plants are foundational for global ecological and economic systems, but most plant proteins remain uncharacterized. Protein interaction networks often suggest protein functions and open new avenues to characterize genes and proteins. We therefore systematically determined protein complexes from 13 plant species of scientific and agricultural importance, greatly expanding the known repertoire of stable protein complexes in plants. By using co-fractionation mass spectrometry, we recovered known complexes, confirmed complexes predicted to occur in plants, and identified previously unknown interactions conserved over 1.1 billion years of green plant evolution. Several novel complexes are involved in vernalization and pathogen defense, traits critical for agriculture. We also observed plant analogs of animal complexes with distinct molecular assemblies, including a megadalton-scale tRNA multi-synthetase complex. The resulting map offers a cross-species view of conserved, stable protein assemblies shared across plant cells and provides a mechanistic, biochemical framework for interpreting plant genetics and mutant phenotypes.

A Bayesian hierarchical variable selection prior for pathway-based GWAS using summary statistics

While genome-wide association studies (GWASs) have been widely used to uncover associations between diseases and genetic variants, standard SNP-level GWASs often lack the power to identify SNPs that individually have a moderate effect size but jointly contribute to the disease. To overcome this problem, pathway-based GWASs methods have been developed as an alternative strategy that complements SNP-level approaches. We propose a Bayesian method that uses the generalized fused hierarchical structured variable selection prior to identify pathways associated with the disease using SNP-level summary statistics. Our prior has the flexibility to take in pathway structural information so that it can model the gene-level correlation based on prior biological knowledge, an important feature that makes it appealing compared to existing pathway-based methods. Using simulations, we show that our method outperforms competing methods in various scenarios, particularly when we have pathway structural information that involves complex gene-gene interactions. We apply our method to the Wellcome Trust Case Control Consortium Crohn's disease GWAS data, demonstrating its practical application to real data.

Network and Pathway Analysis of Toxicogenomics Data

Toxicogenomics is the study of the molecular effects of chemical, biological and physical agents in biological systems, with the aim of elucidating toxicological mechanisms, building predictive models and improving diagnostics. The vast majority of toxicogenomics data has been generated at the transcriptome level, including RNA-seq and microarrays, and large quantities of drug-treatment data have been made publicly available through databases and repositories. Besides the identification of differentially expressed genes (DEGs) from case-control studies or drug treatment time series studies, bioinformatics methods have emerged that infer gene expression data at the molecular network and pathway level in order to reveal mechanistic information. In this work we describe different resources and tools that have been developed by us and others that relate gene expression measurements with known pathway information such as over-representation and gene set enrichment analyses. Furthermore, we highlight approaches that integrate gene expression data with molecular interaction networks in order to derive network modules related to drug toxicity. We describe the two main parts of the approach, i.e., the construction of a suitable molecular interaction network as well as the conduction of network propagation of the experimental data through the interaction network. In all cases we apply methods and tools to publicly available rat in vivo data on anthracyclines, an important class of anti-cancer drugs that are known to induce severe cardiotoxicity in patients. We report the results and functional implications achieved for four anthracyclines (doxorubicin, epirubicin, idarubicin, and daunorubicin) and compare the information content inherent in the different computational approaches.

The maternal plasma proteome changes as a function of gestational age in normal pregnancy: a longitudinal study

OBJECTIVE

Pregnancy is accompanied by dramatic physiological changes in maternal plasma proteins. Characterization of the maternal plasma proteome in normal pregnancy is an essential step for understanding changes to predict pregnancy outcome. The objective of this study was to describe maternal plasma proteins that change in abundance with advancing gestational age and determine biological processes that are perturbed in normal pregnancy.

STUDY DESIGN

A longitudinal study included 43 normal pregnancies that had a term delivery of an infant who was appropriate for gestational age without maternal or neonatal complications. For each pregnancy, 3 to 6 maternal plasma samples (median, 5) were profiled to measure the abundance of 1125 proteins using multiplex assays. Linear mixed-effects models with polynomial splines were used to model protein abundance as a function of gestational age, and the significance of the association was inferred via likelihood ratio tests. Proteins considered to be significantly changed were defined as having the following: (1) >1.5-fold change between 8 and 40 weeks of gestation; and (2) a false discovery rate-adjusted value of P < .1. Gene ontology enrichment analysis was used to identify biological processes overrepresented among the proteins that changed with advancing gestation.

RESULTS

The following results were found: (1) Ten percent (112 of 1125) of the profiled proteins changed in abundance as a function of gestational age; (2) of the 1125 proteins analyzed, glypican-3, sialic acid-binding immunoglobulin-type lectin-6, placental growth factor, C-C motif-28, carbonic anhydrase 6, prolactin, interleukin-1 receptor 4, dual-specificity mitogen-activated protein kinase 4, and pregnancy-associated plasma protein-A had more than a 5-fold change in abundance across gestation (these 9 proteins are known to be involved in a wide range of both physiological and pathological processes, such as growth regulation, embryogenesis, angiogenesis immunoregulation, inflammation etc); and (3) biological processes associated with protein changes in normal pregnancy included defense response, defense response to bacteria, proteolysis, and leukocyte migration (false discovery rate, 10%).

CONCLUSION

The plasma proteome of normal pregnancy demonstrates dramatic changes in both the magnitude of changes and the fraction of the proteins involved. Such information is important to understand the physiology of pregnancy and the development of biomarkers to differentiate normal vs abnormal pregnancy and determine the response to interventions.

Multitask Matrix Completion for Learning Protein Interactions Across Diseases

Disease-causing pathogens such as viruses introduce their proteins into the host cells in which they interact with the host's proteins, enabling the virus to replicate inside the host. These interactions between pathogen and host proteins are key to understanding infectious diseases. Often multiple diseases involve phylogenetically related or biologically similar pathogens. Here we present a multitask learning method to jointly model interactions between human proteins and three different but related viruses: Hepatitis C, Ebola virus, and Influenza A. Our multitask matrix completion-based model uses a shared low-rank structure in addition to a task-specific sparse structure to incorporate the various interactions. We obtain between 7 and 39 percentage points improvement in predictive performance over prior state-of-the-art models. We show how our model's parameters can be interpreted to reveal both general and specific interaction-relevant characteristics of the viruses. Our code is available online.

Gene-gene Interaction Analyses for Atrial Fibrillation

Atrial fibrillation (AF) is a heritable disease that affects more than thirty million individuals worldwide. Extensive efforts have been devoted to the study of genetic determinants of AF. The objective of our study is to examine the effect of gene-gene interaction on AF susceptibility. We performed a large-scale association analysis of gene-gene interactions with AF in 8,173 AF cases, and 65,237 AF-free referents collected from 15 studies for discovery. We examined putative interactions between genome-wide SNPs and 17 known AF-related SNPs. The top interactions were then tested for association in an independent cohort for replication, which included more than 2,363 AF cases and 114,746 AF-free referents. One interaction, between rs7164883 at the HCN4 locus and rs4980345 at the SLC28A1 locus, was found to be significantly associated with AF in the discovery cohorts (interaction OR = 1.44, 95% CI: 1.27–1.65, P = 4.3 × 10^–8). Eight additional gene-gene interactions were also marginally significant (P < 5 × 10^–7). However, none of the top interactions were replicated. In summary, we did not find significant interactions that were associated with AF susceptibility. Future increases in sample size and denser genotyping might facilitate the identification of gene-gene interactions associated with AF.

Effect of Protein Repetitiveness on Protein-Protein Interaction Prediction Results Using Support Vector Machines

BACKGROUND

There are many computational approaches to predict the protein-protein interactions using support vector machines (SVMs) with high performance. In fact, performance of currently reported methods are significantly over-estimated and affected by the object repetitiveness in the datasets used.

OBJECTIVE

To study the effect of object repetitiveness of datasets on predicting results.

METHOD

We present novel methods to construct different positive datasets with or without repeating proteins using graph maximum matching in the protein-protein interaction datasets and corresponding series of negative datasets with different proteins repetitiveness are constructed using graph adjacency matrix. The relationship between the SVM prediction results and the repeated proteins (repeat numbers and repeat rates) and the distributions of repeated proteins in the datasets are analyzed.

RESULTS

Protein repetitiveness of positive and negative datasets can affect the prediction result: high protein repetitiveness of positive or negative datasets yield high performance prediction result.

CONCLUSION

This indicate that dealing with object repetitiveness of datasets is a key issue in protein-protein interactions prediction using SVMs since real world data contain certain degrees of repeat proteins.

Transcriptome- Assisted Label-Free Quantitative Proteomics Analysis Reveals Novel Insights into Piper nigrum-Phytophthora capsici Phytopathosystem

Black pepper (Piper nigrum L.), a tropical spice crop of global acclaim, is susceptible to Phytophthora capsici, an oomycete pathogen which causes the highly destructive foot rot disease. A systematic understanding of this phytopathosystem has not been possible owing to lack of genome or proteome information. In this study, we explain an integrated transcriptome-assisted label-free quantitative proteomics pipeline to study the basal immune components of black pepper when challenged with P. capsici. We report a global identification of 532 novel leaf proteins from black pepper, of which 518 proteins were functionally annotated using BLAST2GO tool. A label-free quantitation of the protein datasets revealed 194 proteins common to diseased and control protein datasets of which 22 proteins showed significant up-regulation and 134 showed significant down-regulation. Ninety-three proteins were identified exclusively on P. capsici infected leaf tissues and 245 were expressed only in mock (control) infected samples. In-depth analysis of our data gives novel insights into the regulatory pathways of black pepper which are compromised during the infection. Differential down-regulation was observed in a number of critical pathways like carbon fixation in photosynthetic organism, cyano-amino acid metabolism, fructose, and mannose metabolism, glutathione metabolism, and phenylpropanoid biosynthesis. The proteomics results were validated with real-time qRT-PCR analysis. We were also able to identify the complete coding sequences for all the proteins of which few selected genes were cloned and sequence characterized for further confirmation. Our study is the first report of a quantitative proteomics dataset in black pepper which provides convincing evidence on the effectiveness of a transcriptome-based label-free proteomics approach for elucidating the host response to biotic stress in a non-model spice crop like P. nigrum, for which genome information is unavailable. Our dataset will serve as a useful resource for future studies in this plant. Data are available via ProteomeXchange with identifier PXD003887.

Multi-OMICs and Genome Editing Perspectives on Liver Cancer Signaling Networks

The advent of the human genome sequence and the resulting ~20,000 genes provide a crucial framework for a transition from traditional biology to an integrative “OMICs” arena (Lander et al., 2001; Venter et al., 2001; Kitano, 2002). This brings in a revolution for cancer research, which now enters a big data era. In the past decade, with the facilitation by next-generation sequencing, there have been a huge number of large-scale sequencing efforts, such as The Cancer Genome Atlas (TCGA), the HapMap, and the 1000 genomes project. As a result, a deluge of genomic information becomes available from patients stricken by a variety of cancer types. The list of cancer-associated genes is ever expanding. New discoveries are made on how frequent and highly penetrant mutations, such as those in the telomerase reverse transcriptase (TERT) and TP53, function in cancer initiation, progression, and metastasis. Most genes with relatively frequent but weakly penetrant cancer mutations still remain to be characterized. In addition, genes that harbor rare but highly penetrant cancer-associated mutations continue to emerge. Here, we review recent advances related to cancer genomics, proteomics, and systems biology and suggest new perspectives in targeted therapy and precision medicine.

ABC transporter PEN3/PDR8/ABCG36 interacts with calmodulin that, like PEN3, is required for Arabidopsis nonhost resistance

Nonhost resistance (NHR) is the most prevalent form of plant immunity. In Arabidopsis, NHR requires membrane-localized ATP-binding cassette (ABC) transporter PENETRATION (PEN) 3. Upon perception of pathogen-associated molecular patterns, PEN3 becomes phosphorylated, suggestive of PEN3 regulation by post-translational modification. Here, we investigated the PEN3 protein interaction network. We probed the Arabidopsis protein microarray AtPMA-5000 with the N-terminal cytoplasmic domain of PEN3. Several of the proteins identified to interact with PEN3 in vitro represent cellular Ca(2+) sensors, including calmodulin (CaM) 3, CaM7 and several CaM-like proteins, pointing to the importance of Ca(2+) sensing to PEN3-mediated NHR. We demonstrated co-localization of PEN3 and CaM7, and we confirmed PEN3-CaM interaction in vitro and in vivo by PEN3 pull-down with CaM Sepharose, CaM overlay assay and bimolecular fluorescence complementation. We also show that just like in pen3, NHR to the nonadapted fungal pathogens Phakopsora pachyrhizi and Blumeria graminis f.sp. hordei is compromised in the Arabidopsis cam7 and pen3 cam7 mutants. Our study discloses CaM7 as a PEN3-interacting protein crucial to Arabidopsis NHR and emphasizes the importance of Ca(2+) sensing to plant immunity.

Gene Expression Analysis Through Network Biology: Bioinformatics Approaches

Following the availability of high-throughput technologies, vast amounts of biological data have been generated. Gene expression is one example of the popular data that has been utilized for studying cellular systems in the transcriptional level. Several bioinformatics approaches have been developed to analyze such data. A typical expression analysis identifies a ranked list of individual significant differentially expressed genes between two conditions of interest. However, it has been accepted that biomolecules in a living organism are working together and interacting with each other. Study through network analysis could be complementary to typical expression analysis and provides more contexts to understanding the biological systems. Conversely, expression data could provide clues to functional links between biomolecules in biological networks. In this chapter, bioinformatics approaches to analyze expression data in network levels including basic concepts of network biology are described. Different concepts to integrate expression data with interactome data and example studies are explained.

A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma

Recent advances in genetics have spurred rapid progress towards the systematic identification of genes involved in complex diseases. Still, the detailed understanding of the molecular and physiological mechanisms through which these genes affect disease phenotypes remains a major challenge. Here, we identify the asthma disease module, i.e. the local neighborhood of the interactome whose perturbation is associated with asthma, and validate it for functional and pathophysiological relevance, using both computational and experimental approaches. We find that the asthma disease module is enriched with modest GWAS P-values against the background of random variation, and with differentially expressed genes from normal and asthmatic fibroblast cells treated with an asthma-specific drug. The asthma module also contains immune response mechanisms that are shared with other immune-related disease modules. Further, using diverse omics (genomics, gene-expression, drug response) data, we identify the GAB1 signaling pathway as an important novel modulator in asthma. The wiring diagram of the uncovered asthma module suggests a relatively close link between GAB1 and glucocorticoids (GCs), which we experimentally validate, observing an increase in the level of GAB1 after GC treatment in BEAS-2B bronchial epithelial cells. The siRNA knockdown of GAB1 in the BEAS-2B cell line resulted in a decrease in the NFkB level, suggesting a novel regulatory path of the pro-inflammatory factor NFkB by GAB1 in asthma.

The role of protein interaction networks in systems biomedicine

The challenging task of studying and modeling complex dynamics of biological systems in order to describe various human diseases has gathered great interest in recent years. Major biological processes are mediated through protein interactions, hence there is a need to understand the chaotic network that forms these processes in pursuance of understanding human diseases. The applications of protein interaction networks to disease datasets allow the identification of genes and proteins associated with diseases, the study of network properties, identification of subnetworks, and network-based disease gene classification. Although various protein interaction network analysis strategies have been employed, grand challenges are still existing. Global understanding of protein interaction networks via integration of high-throughput functional genomics data from different levels will allow researchers to examine the disease pathways and identify strategies to control them. As a result, it seems likely that more personalized, more accurate and more rapid disease gene diagnostic techniques will be devised in the future, as well as novel strategies that are more personalized. This mini-review summarizes the current practice of protein interaction networks in medical research as well as challenges to be overcome.

Cell cycle population effects in perturbation studies

Growth condition perturbation or gene function disruption are commonly used strategies to study cellular systems. Although it is widely appreciated that such experiments may involve indirect effects, these frequently remain uncharacterized. Here, analysis of functionally unrelated Saccharyomyces cerevisiae deletion strains reveals a common gene expression signature. One property shared by these strains is slower growth, with increased presence of the signature in more slowly growing strains. The slow growth signature is highly similar to the environmental stress response (ESR), an expression response common to diverse environmental perturbations. Both environmental and genetic perturbations result in growth rate changes. These are accompanied by a change in the distribution of cells over different cell cycle phases. Rather than representing a direct expression response in single cells, both the slow growth signature and ESR mainly reflect a redistribution of cells over different cell cycle phases, primarily characterized by an increase in the G1 population. The findings have implications for any study of perturbation that is accompanied by growth rate changes. Strategies to counter these effects are presented and discussed.

Integrating in silico resources to map a signaling network

The abundance of publicly available life science databases offers a wealth of information that can support interpretation of experimentally derived data and greatly enhance hypothesis generation. Protein interaction and functional networks are not simply new renditions of existing data: they provide the opportunity to gain insights into the specific physical and functional role a protein plays as part of the biological system. In this chapter, we describe different in silico tools that can quickly and conveniently retrieve data from existing data repositories and we discuss how the available tools are best utilized for different purposes. While emphasizing protein-protein interaction databases (e.g., BioGrid and IntAct), we also introduce metasearch platforms such as STRING and GeneMANIA, pathway databases (e.g., BioCarta and Pathway Commons), text mining approaches (e.g., PubMed and Chilibot), and resources for drug-protein interactions, genetic information for model organisms and gene expression information based on microarray data mining. Furthermore, we provide a simple step-by-step protocol for building customized protein-protein interaction networks in Cytoscape, a powerful network assembly and visualization program, integrating data retrieved from these various databases. As we illustrate, generation of composite interaction networks enables investigators to extract significantly more information about a given biological system than utilization of a single database or sole reliance on primary literature.

Prediction of protein interaction types based on sequence and network features

BACKGROUND

Protein interactions mediate a wide spectrum of functions in various cellular contexts. Functional versatility of protein complexes is due to a broad range of structural adaptations that determine their binding affinity, the number of interaction sites, and the lifetime. In terms of stability it has become customary to distinguish between obligate and non-obligate interactions dependent on whether or not the protomers can exist independently. In terms of spatio-temporal control protein interactions can be either simultaneously possible (SP) or mutually exclusive (ME). In the former case a network hub interacts with several proteins at the same time, offering each of them a separate interface, while in the latter case the hub interacts with its partners one at a time via the same binding site. So far different types of interactions were distinguished based on the properties of the corresponding binding interfaces derived from known three-dimensional structures of protein complexes.

RESULTS

Here we present PiType, an accurate 3D structure-independent computational method for classifying protein interactions into simultaneously possible (SP) and mutually exclusive (ME) as well as into obligate and non-obligate. Our classifier exploits features of the binding partners predicted from amino acid sequence, their functional similarity, and network topology. We find that the constituents of non-obligate complexes possess a higher degree of structural disorder, more short linear motifs, and lower functional similarity compared to obligate interaction partners while SP and ME interactions are characterized by significant differences in network topology. Each interaction type is associated with a distinct set of biological functions. Moreover, interactions within multi-protein complexes tend to be enriched in one type of interactions.

CONCLUSION

PiType does not rely on atomic structures and is thus suitable for characterizing proteome-wide interaction datasets. It can also be used to identify sub-modules within protein complexes. PiType is available for download as a self-installing package from http://webclu.bio.wzw.tum.de/PiType/PiType.zip.

Network approaches to the functional analysis of microbial proteins

Large amounts of detailed biological data have been generated over the past few decades. Much of these data is freely available in over 1000 online databases; an enticing, but frustrating resource for microbiologists interested in a systems-level view of the structure and function of microbial cells. The frustration engendered by the need to trawl manually through hundreds of databases in order to accumulate information about a gene, protein, pathway, or organism of interest can be alleviated by the use of computational data integration to generated network views of the system of interest. Biological networks can be constructed from a single type of data, such as protein-protein binding information, or from data generated by multiple experimental approaches. In an integrated network, nodes usually represent genes or gene products, while edges represent some form of interaction between the nodes. Edges between nodes may be weighted to represent the probability that the edge exists in vivo. Networks may also be enriched with ontological annotations, facilitating both visual browsing and computational analysis via web service interfaces. In this review, we describe the construction, analysis of both single-data source and integrated networks, and their application to the inference of protein function in microbes.

An interaction network predicted from public data as a discovery tool: application to the Hsp90 molecular chaperone machine

Understanding the functions of proteins requires information about their protein-protein interactions (PPI). The collective effort of the scientific community generates far more data on any given protein than individual experimental approaches. The latter are often too limited to reveal an interactome comprehensively. We developed a workflow for parallel mining of all major PPI databases, containing data from several model organisms, and to integrate data from the literature for a protein of interest. We applied this novel approach to build the PPI network of the human Hsp90 molecular chaperone machine (Hsp90Int) for which previous efforts have yielded limited and poorly overlapping sets of interactors. We demonstrate the power of the Hsp90Int database as a discovery tool by validating the prediction that the Hsp90 co-chaperone Aha1 is involved in nucleocytoplasmic transport. Thus, we both describe how to build a custom database and introduce a powerful new resource for the scientific community.

Interactome networks and human disease

Complex biological systems and cellular networks may underlie most genotype to phenotype relationships. Here, we review basic concepts in network biology, discussing different types of interactome networks and the insights that can come from analyzing them. We elaborate on why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease.

Integrative features of the yeast phosphoproteome and protein-protein interaction map

Following recent advances in high-throughput mass spectrometry (MS)-based proteomics, the numbers of identified phosphoproteins and their phosphosites have greatly increased in a wide variety of organisms. Although a critical role of phosphorylation is control of protein signaling, our understanding of the phosphoproteome remains limited. Here, we report unexpected, large-scale connections revealed between the phosphoproteome and protein interactome by integrative data-mining of yeast multi-omics data. First, new phosphoproteome data on yeast cells were obtained by MS-based proteomics and unified with publicly available yeast phosphoproteome data. This revealed that nearly 60% of ∼6,000 yeast genes encode phosphoproteins. We mapped these unified phosphoproteome data on a yeast protein-protein interaction (PPI) network with other yeast multi-omics datasets containing information about proteome abundance, proteome disorders, literature-derived signaling reactomes, and in vitro substratomes of kinases. In the phospho-PPI, phosphoproteins had more interacting partners than nonphosphoproteins, implying that a large fraction of intracellular protein interaction patterns (including those of protein complex formation) is affected by reversible and alternative phosphorylation reactions. Although highly abundant or unstructured proteins have a high chance of both interacting with other proteins and being phosphorylated within cells, the difference between the number counts of interacting partners of phosphoproteins and nonphosphoproteins was significant independently of protein abundance and disorder level. Moreover, analysis of the phospho-PPI and yeast signaling reactome data suggested that co-phosphorylation of interacting proteins by single kinases is common within cells. These multi-omics analyses illuminate how wide-ranging intracellular phosphorylation events and the diversity of physical protein interactions are largely affected by each other.

Detection of protein-protein interactions in bacteria by GFP-fragment reconstitution

Protein-protein interaction is one of the most pivotal roles of proteins in living organisms. Association/dissociation of proteins reflects responses to intrinsic or extrinsic perturbations of signaling pathways, involved in gene expression, cell division, cell differentiation, and apoptosis. For further understanding of the biological processes, it is important to monitor protein-protein interactions in model organisms. In particular, Escherichia coli-based methods are suitable to assess large libraries of proteins. Many of these proteins cannot be used in yeast due to toxicity or poor expression. Herein we describe a general method based on an intein-mediated protein reconstitution system (PRS) to detect protein-protein interactions in bacterial cells. The PRS-based approach requires no other agents including enzymes, substrates, and ATP. Another advantage is that matured green fluorescent protein (GFP) accumulates in a targeted cell till degraded. These allow highly sensitive screening of protein-protein interactions.

Sequence motifs in MADS transcription factors responsible for specificity and diversification of protein-protein interaction

Protein sequences encompass tertiary structures and contain information about specific molecular interactions, which in turn determine biological functions of proteins. Knowledge about how protein sequences define interaction specificity is largely missing, in particular for paralogous protein families with high sequence similarity, such as the plant MADS domain transcription factor family. In comparison to the situation in mammalian species, this important family of transcription regulators has expanded enormously in plant species and contains over 100 members in the model plant species Arabidopsis thaliana. Here, we provide insight into the mechanisms that determine protein-protein interaction specificity for the Arabidopsis MADS domain transcription factor family, using an integrated computational and experimental approach. Plant MADS proteins have highly similar amino acid sequences, but their dimerization patterns vary substantially. Our computational analysis uncovered small sequence regions that explain observed differences in dimerization patterns with reasonable accuracy. Furthermore, we show the usefulness of the method for prediction of MADS domain transcription factor interaction networks in other plant species. Introduction of mutations in the predicted interaction motifs demonstrated that single amino acid mutations can have a large effect and lead to loss or gain of specific interactions. In addition, various performed bioinformatics analyses shed light on the way evolution has shaped MADS domain transcription factor interaction specificity. Identified protein-protein interaction motifs appeared to be strongly conserved among orthologs, indicating their evolutionary importance. We also provide evidence that mutations in these motifs can be a source for sub- or neo-functionalization. The analyses presented here take us a step forward in understanding protein-protein interactions and the interplay between protein sequences and network evolution.

Microarray-based analysis of cell-cycle gene expression during spermatogenesis in the mouse

Mammalian spermatogenesis is a continuum of cellular differentiation in a lineage that features three principal stages: 1) a mitotically active stage in spermatogonia, 2) a meiotic stage in spermatocytes, and 3) a postreplicative stage in spermatids. We used a microarray-based approach to identify changes in expression of cell-cycle genes that distinguish 1) mitotic type A spermatogonia from meiotic pachytene spermatocytes and 2) pachytene spermatocytes from postreplicative round spermatids. We detected expression of 550 genes related to cell-cycle function in one or more of these cell types. Although a majority of these genes were expressed during all three stages of spermatogenesis, we observed dramatic changes in levels of individual transcripts between mitotic spermatogonia and meiotic spermatocytes and between meiotic spermatocytes and postreplicative spermatids. Our results suggest that distinct cell-cycle gene regulatory networks or subnetworks are associated with each phase of the cell cycle in each spermatogenic cell type. In addition, we observed expression of different members of certain cell-cycle gene families in each of the three spermatogenic cell types investigated. Finally, we report expression of 221 cell-cycle genes that have not previously been annotated as part of the cell cycle network expressed during spermatogenesis, including eight novel genes that appear to be testis-specific.

Next-generation genomics: an integrative approach

Integrating results from diverse experiments is an essential process in our effort to understand the logic of complex systems, such as development, homeostasis and responses to the environment. With the advent of high-throughput methods--including genome-wide association (GWA) studies, chromatin immunoprecipitation followed by sequencing (ChIP-seq) and RNA sequencing (RNA-seq)--acquisition of genome-scale data has never been easier. Epigenomics, transcriptomics, proteomics and genomics each provide an insightful, and yet one-dimensional, view of genome function; integrative analysis promises a unified, global view. However, the large amount of information and diverse technology platforms pose multiple challenges for data access and processing. This Review discusses emerging issues and strategies related to data integration in the era of next-generation genomics.

Building macromolecular assemblies by information-driven docking: introducing the HADDOCK multibody docking server

Over the last years, large scale proteomics studies have generated a wealth of information of biomolecular complexes. Adding the structural dimension to the resulting interactomes represents a major challenge that classical structural experimental methods alone will have difficulties to confront. To meet this challenge, complementary modeling techniques such as docking are thus needed. Among the current docking methods, HADDOCK (High Ambiguity-Driven DOCKing) distinguishes itself from others by the use of experimental and/or bioinformatics data to drive the modeling process and has shown a strong performance in the critical assessment of prediction of interactions (CAPRI), a blind experiment for the prediction of interactions. Although most docking programs are limited to binary complexes, HADDOCK can deal with multiple molecules (up to six), a capability that will be required to build large macromolecular assemblies. We present here a novel web interface of HADDOCK that allows the user to dock up to six biomolecules simultaneously. This interface allows the inclusion of a large variety of both experimental and/or bioinformatics data and supports several types of cyclic and dihedral symmetries in the docking of multibody assemblies. The server was tested on a benchmark of six cases, containing five symmetric homo-oligomeric protein complexes and one symmetric protein-DNA complex. Our results reveal that, in the presence of either bioinformatics and/or experimental data, HADDOCK shows an excellent performance: in all cases, HADDOCK was able to generate good to high quality solutions and ranked them at the top, demonstrating its ability to model symmetric multicomponent assemblies. Docking methods can thus play an important role in adding the structural dimension to interactomes. However, although the current docking methodologies were successful for a vast range of cases, considering the variety and complexity of macromolecular assemblies, inclusion of some kind of experimental information (e.g. from mass spectrometry, nuclear magnetic resonance, cryoelectron microscopy, etc.) will remain highly desirable to obtain reliable results.

POINeT: protein interactome with sub-network analysis and hub prioritization

BACKGROUND

Protein-protein interactions (PPIs) are critical to every aspect of biological processes. Expansion of all PPIs from a set of given queries often results in a complex PPI network lacking spatiotemporal consideration. Moreover, the reliability of available PPI resources, which consist of low- and high-throughput data, for network construction remains a significant challenge. Even though a number of software tools are available to facilitate PPI network analysis, an integrated tool is crucial to alleviate the burden on querying across multiple web servers and software tools.

RESULTS

We have constructed an integrated web service, POINeT, to simplify the process of PPI searching, analysis, and visualization. POINeT merges PPI and tissue-specific expression data from multiple resources. The tissue-specific PPIs and the numbers of research papers supporting the PPIs can be filtered with user-adjustable threshold values and are dynamically updated in the viewer. The network constructed in POINeT can be readily analyzed with, for example, the built-in centrality calculation module and an integrated network viewer. Nodes in global networks can also be ranked and filtered using various network analysis formulas, i.e., centralities. To prioritize the sub-network, we developed a ranking filtered method (S3) to uncover potential novel mediators in the midbody network. Several examples are provided to illustrate the functionality of POINeT. The network constructed from four schizophrenia risk markers suggests that EXOC4 might be a novel marker for this disease. Finally, a liver-specific PPI network has been filtered with adult and fetal liver expression profiles.

CONCLUSION

The functionalities provided by POINeT are highly improved compared to previous version of POINT. POINeT enables the identification and ranking of potential novel genes involved in a sub-network. Combining with tissue-specific gene expression profiles, PPIs specific to selected tissues can be revealed. The straightforward interface of POINeT makes PPI search and analysis just a few clicks away. The modular design permits further functional enhancement without hampering the simplicity. POINeT is available at (http://poinet.bioinformatics.tw/).

Interaction networks: from protein functions to drug discovery. A review

Most genes, proteins and other components carry out their functions within a complex network of interactions and a single molecule can affect a wide range of other cell components. A global, integrative, approach has been developed for several years, including protein-protein interaction networks (interactomes). In this review, we describe the high-throughput methods used to identify new interactions and to build large interaction datasets. The minimum information required for reporting a molecular interaction experiment (MIMIx) has been defined as a standard for storing data in publicly available interaction databases. Several examples of interaction networks from molecular machines (proteasome) or organelles (phagosome, mitochondrion) to whole organisms (viruses, bacteria, yeast, fly, and worm) are given and attempts to cover the entire human interaction network are discussed. The methods used to perform the topological analysis of interaction networks and to extract biological information from them are presented. These investigations have provided clues on protein functions, signalling and metabolic pathways, and physiological processes, unraveled the molecular basis of some diseases (cancer, infectious diseases), and will be very useful to identify new therapeutic targets and for drug discovery. A major challenge is now to integrate data from different sources (interactome, transcriptome, phenome, localization) to switch from static to dynamic interaction networks. The merging of a viral interactome and the human interactome has been used to simulate viral infection, paving the way for future studies aiming at providing molecular basis of human diseases.

A protein domain-based interactome network for C. elegans early embryogenesis

Many protein-protein interactions are mediated through independently folding modular domains. Proteome-wide efforts to model protein-protein interaction or "interactome" networks have largely ignored this modular organization of proteins. We developed an experimental strategy to efficiently identify interaction domains and generated a domain-based interactome network for proteins involved in C. elegans early-embryonic cell divisions. Minimal interacting regions were identified for over 200 proteins, providing important information on their domain organization. Furthermore, our approach increased the sensitivity of the two-hybrid system, resulting in a more complete interactome network. This interactome modeling strategy revealed insights into C. elegans centrosome function and is applicable to other biological processes in this and other organisms.

The ORFeome of Staphylococcus aureus v 1.1

Background

The bacterium Staphylococcus aureus causes significant morbidity and mortality in humans, primarily due to the emergence of strains that are resistant to antibiotics – notably methicillin-resistant S. aureus (MRSA) isolates. Development of effective strategies for the control and treatment of MRSA infections may best be achieved through 'omics' approaches, which first requires cloning the entire set of S. aureus' protein-encoding open reading frames (ORFs), or ORFeome.

Results

The complete genome sequence of S. aureus strain Mu50 has 2697 predicted protein-coding ORFs. Based on the sequence of this strain we designed PCR primers to construct from an S. aureus (non-MRSA) clinical isolate an ORFeome library that contains 2562 unique Gateway^® entry clones (95% coverage), each corresponding to a defined ORF. The high quality of the ORFeome library was verified by DNA sequencing and PCR amplification, and its functionality was demonstrated by expressing recombinant proteins and observing protein interactions in a yeast 2-hybrid homodimerization screen.

Conclusion

This first ORFeome library for S. aureus provides an essential new tool for investigating the systems biology of this important pathogen.

AtPID: Arabidopsis thaliana protein interactome database--an integrative platform for plant systems biology

Arabidopsis thaliana Protein Interactome Database (AtPID) is an object database that integrates data from several bioinformatics prediction methods and manually collected information from the literature. It contains data relevant to protein-protein interaction, protein subcellular location, ortholog maps, domain attributes and gene regulation. The predicted protein interaction data were obtained from ortholog interactome, microarray profiles, GO annotation, and conserved domain and genome contexts. This database holds 28,062 protein-protein interaction pairs with 23,396 pairs generated from prediction methods. Among the rest 4666 pairs, 3866 pairs of them involving 1875 proteins were manually curated from the literature and 800 pairs were from enzyme complexes in KEGG. In addition, subcellular location information of 5562 proteins is available. AtPID was built via an intuitive query interface that provides easy access to the important features of proteins. Through the incorporation of both experimental and computational methods, AtPID is a rich source of information for system-level understanding of gene function and biological processes in A. thaliana. Public access to the AtPID database is available at http://atpid.biosino.org/.

A recombinant CHSE-214 cell line expressing an Mx1 promoter-reporter system responds to both interferon type I and type II from salmonids and represents a versatile tool to study the IFN-system in teleost fish

A transgenic cell line for the detection of salmon interferons (IFNs) has been established. It is based on a CHSE-214 cell line containing a reporter construct expressing firefly luciferase under the control of the rainbow trout promoter for the IFN-induced Mx1 gene. This cell line, named CHSE-Mx10, showed IFN-induced luciferase expression after more than 80 passages, confirming the stability of this cell line. Interestingly, the Mx promoter was shown to respond to both salmon IFN-alpha/beta and trout IFN-gamma in a dose-dependent manner, while there was no response to TNF-alpha and IL-1beta. IFN-alpha/beta activity could be measured at a range of 9-150 U/ml, and IFN-gamma showed activity between 10 and 100 ng/ml. The reproducibility of both responses was good. The CHSE-Mx10 reporter system constitutes a versatile tool to study the induction and regulation of IFN signaling in teleost fish. A preliminary study presented herein suggests that both infectious pancreas necrosis virus (IPNV) and salmon pancreas disease virus (SPDV) may block activation of the Mx promoter in CHSE-Mx10 stimulated with IFN-alpha/beta.

InPrePPI: an integrated evaluation method based on genomic context for predicting protein-protein interactions in prokaryotic genomes

Background

Although many genomic features have been used in the prediction of protein-protein interactions (PPIs), frequently only one is used in a computational method. After realizing the limited power in the prediction using only one genomic feature, investigators are now moving toward integration. So far, there have been few integration studies for PPI prediction; one failed to yield appreciable improvement of prediction and the others did not conduct performance comparison. It remains unclear whether an integration of multiple genomic features can improve the PPI prediction and, if it can, how to integrate these features.

Results

In this study, we first performed a systematic evaluation on the PPI prediction in Escherichia coli (E. coli) by four genomic context based methods: the phylogenetic profile method, the gene cluster method, the gene fusion method, and the gene neighbor method. The number of predicted PPIs and the average degree in the predicted PPI networks varied greatly among the four methods. Further, no method outperformed the others when we tested using three well-defined positive datasets from the KEGG, EcoCyc, and DIP databases. Based on these comparisons, we developed a novel integrated method, named InPrePPI. InPrePPI first normalizes the AC value (an integrated value of the accuracy and coverage) of each method using three positive datasets, then calculates a weight for each method, and finally uses the weight to calculate an integrated score for each protein pair predicted by the four genomic context based methods. We demonstrate that InPrePPI outperforms each of the four individual methods and, in general, the other two existing integrated methods: the joint observation method and the integrated prediction method in STRING. These four methods and InPrePPI are implemented in a user-friendly web interface.

Conclusion

This study evaluated the PPI prediction by four genomic context based methods, and presents an integrated evaluation method that shows better performance in E. coli.

Integrated data analysis for genome-wide research

Integrated data analysis is introduced as the intermediate level of a systems biology approach to analyse different 'omics' datasets, i.e., genome-wide measurements of transcripts, protein levels or protein-protein interactions, and metabolite levels aiming at generating a coherent understanding of biological function. In this chapter we focus on different methods of correlation analyses ranging from simple pairwise correlation to kernel canonical correlation which were recently applied in molecular biology. Several examples are presented to illustrate their application. The input data for this analysis frequently originate from different experimental platforms. Therefore, preprocessing steps such as data normalisation and missing value estimation are inherent to this approach. The corresponding procedures, potential pitfalls and biases, and available software solutions are reviewed. The multiplicity of observations obtained in omics-profiling experiments necessitates the application of multiple testing correction techniques.

A search engine to identify pathway genes from expression data on multiple organisms

Background

The completion of several genome projects showed that most genes have not yet been characterized, especially in multicellular organisms. Although most genes have unknown functions, a large collection of data is available describing their transcriptional activities under many different experimental conditions. In many cases, the coregulatation of a set of genes across a set of conditions can be used to infer roles for genes of unknown function.

Results

We developed a search engine, the Multiple-Species Gene Recommender (MSGR), which scans gene expression datasets from multiple organisms to identify genes that participate in a genetic pathway. The MSGR takes a query consisting of a list of genes that function together in a genetic pathway from one of six organisms: Homo sapiens, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, Arabidopsis thaliana, and Helicobacter pylori. Using a probabilistic method to merge searches, the MSGR identifies genes that are significantly coregulated with the query genes in one or more of those organisms. The MSGR achieves its highest accuracy for many human pathways when searches are combined across species. We describe specific examples in which new genes were identified to be involved in a neuromuscular signaling pathway and a cell-adhesion pathway.

Conclusion

The search engine can scan large collections of gene expression data for new genes that are significantly coregulated with a pathway of interest. By integrating searches across organisms, the MSGR can identify pathway members whose coregulation is either ancient or newly evolved.

Dependence network modeling for biomarker identification

MOTIVATION

Our purpose is to develop a statistical modeling approach for cancer biomarker discovery and provide new insights into early cancer detection. We propose the concept of dependence network, apply it for identifying cancer biomarkers, and study the difference between the protein or gene samples from cancer and non-cancer subjects based on mass-spectrometry (MS) and microarray data.

RESULTS

Three MS and two gene microarray datasets are studied. Clear differences are observed in the dependence networks for cancer and non-cancer samples. Protein/gene features are examined three at one time through an exhaustive search. Dependence networks are constructed by binding triples identified by the eigenvalue pattern of the dependence model, and are further compared to identify cancer biomarkers. Such dependence-network-based biomarkers show much greater consistency under 10-fold cross-validation than the classification-performance-based biomarkers. Furthermore, the biological relevance of the dependence-network-based biomarkers using microarray data is discussed. The proposed scheme is shown promising for cancer diagnosis and prediction.

AVAILABILITY

See supplements: http://dsplab.eng.umd.edu/~genomics/dependencenetwork/

Toward a global picture of development: lessons from genome-scale analysis in Caenorhabditis elegans embryonic development

Development is the result of complex events, including cascades of transcriptional programs and numerous molecular interactions. Traditionally, research focus has been given to the characterization of individual mutants, regulators, or interactions. With the availability of complete genome sequences and high-throughput (HT) experimental techniques, probing development on a system level has become feasible. Pioneering work initiated in invertebrate model systems such as Caenorhabditis elegans has provided first drafts of catalogs of essential components, transcriptional regulatory diagrams and molecular interaction networks underlying developmental processes. Integrating these drafts approximates a system-level picture of development and provides local models for protein/gene functions. Here we summarize the progress toward elucidating developmental processes on a system level, including the applications of genomic technologies and computational analyses. We discuss C. elegans embryonic development in case studies to illustrate how various HT approaches can be integrated and how biological insights can be gained from these approaches.

Integrating forward and reverse proteomics to unravel protein function

To date, proteomics approaches have aimed to either identify novel proteins or change in protein expression/modification in various organisms under normal or disease conditions. One major aspect of functional proteomics is to identify protein biological properties in a given context, however, forward proteomics approaches alone cannot complete this goal. Indeed, with the increasing successes of such proteomics-based research strategies and the subsequent increasing amounts of proteins identified with unknown molecular functions, approaches allowing for systematic analyses of protein functions are desired. In this review, we propose to depict the complementarities of forward and reverse proteomics approaches in the definite understanding of protein functions. This dual strategy requires a data integration loop which allows for systematic characterization of protein function(s). The details of the integrative process combining both in silico and experimental resources and tools are presented. Altogether, we believe that the integration of forward and reverse proteomics approaches supported by bioinformatics will provide an efficient path towards systems biology.

VIRGO: computational prediction of gene functions

Dramatic advances in sequencing technology and sophisticated experimental assays that interrogate the cell, combined with the public availability of the resulting data, herald the era of systems biology. However, the biological functions of more than 40% of the genes in sequenced genomes are unknown, posing a fundamental barrier to progress in systems biology. The large scale and diversity of available data requires the development of techniques that can automatically utilize these datasets to make quantified and robust predictions of gene function that can be experimentally verified. We present a service called the VIRtual Gene Ontology (VIRGO) that (i) constructs a functional linkage network (FLN) from gene expression and molecular interaction data, (ii) labels genes in the FLN with their functional annotations in the Gene Ontology and (iii) systematically propagates these labels across the FLN in order to precisely predict the functions of unlabelled genes. VIRGO assigns confidence estimates to predicted functions so that a biologist can prioritize predictions for further experimental study. For each prediction, VIRGO also provides an informative ‘propagation diagram’ that traces the flow of information in the FLN that led to the prediction. VIRGO is available at .

Computational approaches for predicting protein-protein interactions: a survey

Discovery of the protein interactions that take place within a cell can provide a starting point for understanding biological regulatory pathways. Global interaction patterns among proteins, for example, can suggest new drug targets and aid the design of new drugs by providing a clearer picture of the biological pathways in the neighborhoods of the drug targets. High-throughput experimental screens have been developed to detect protein-protein interactions, however, they show high rates of errors in terms of false positives and false negatives. Many computational approaches have been proposed to tackle the problem of protein-protein interaction prediction. They range from comparative genomics based methods to data integration based approaches. Challenging properties of protein-protein interaction data have to be addressed appropriately before a higher quality interaction map with better coverage can be achieved. This paper presents a survey of major works in computational prediction of protein-protein interactions, explaining their assumptions, main ideas, and limitations.

Protein-protein interactions more conserved within species than across species

Experimental high-throughput studies of protein–protein interactions are beginning to provide enough data for comprehensive computational studies. Today, about ten large data sets, each with thousands of interacting pairs, coarsely sample the interactions in fly, human, worm, and yeast. Another about 55,000 pairs of interacting proteins have been identified by more careful, detailed biochemical experiments. Most interactions are experimentally observed in prokaryotes and simple eukaryotes; very few interactions are observed in higher eukaryotes such as mammals. It is commonly assumed that pathways in mammals can be inferred through homology to model organisms, e.g. the experimental observation that two yeast proteins interact is transferred to infer that the two corresponding proteins in human also interact. Two pairs for which the interaction is conserved are often described as interologs. The goal of this investigation was a large-scale comprehensive analysis of such inferences, i.e. of the evolutionary conservation of interologs. Here, we introduced a novel score for measuring the overlap between protein–protein interaction data sets. This measure appeared to reflect the overall quality of the data and was the basis for our two surprising results from our large-scale analysis. Firstly, homology-based inferences of physical protein–protein interactions appeared far less successful than expected. In fact, such inferences were accurate only for extremely high levels of sequence similarity. Secondly, and most surprisingly, the identification of interacting partners through sequence similarity was significantly more reliable for protein pairs within the same organism than for pairs between species. Our analysis underlined that the discrepancies between different datasets are large, even when using the same type of experiment on the same organism. This reality considerably constrains the power of homology-based transfer of interactions. In particular, the experimental probing of interactions in distant model organisms has to be undertaken with some caution. More comprehensive images of protein–protein networks will require the combination of many high-throughput methods, including in silico inferences and predictions. http://www.rostlab.org/results/2006/ppi_homology/

The IntAct database contains about ten large-scale data sets of protein–protein interactions. Each set contains thousands of experimentally observed pair interactions. Most pairs were observed in yeast (Saccharomyces cerevisiae), fly (Drosophila melanogaster), and worm (Caenorhabditis elegans). These interactions are often perceived as model organisms in the sense that one can infer that two mouse proteins interact if one experimentally observes the two corresponding proteins in worm to interact. Here, the authors analyzed in detail how the sequence signals of physical protein–protein interactions are conserved. It is a common assumption that protein–protein interactions can easily be inferred through homology transfer from one model organism to another organism of interest. Here, the authors demonstrated that such homology transfers are only accurate at unexpectedly high levels of sequence identity. Even more surprisingly, homology transfers of protein–protein interactions are significantly more reliable for protein pairs from the same species than for two protein pairs from different organisms. The observation that interactions were much more conserved within than across species was valid for all levels of sequence similarity, i.e. for very similar as well as for more diverged interologs.