key: cord-1000140-gnb8kwge
authors: James, Katherine; Muñoz-Muñoz, Jose
title: Computational Network Inference for Bacterial Interactomics
date: 2022-03-30
journal: mSystems
DOI: 10.1128/msystems.01456-21
sha: 3ec002d7dd0bfbf7cb1bb1ead224d2a16e85ab1e
doc_id: 1000140
cord_uid: gnb8kwge

Since the large-scale experimental characterization of protein–protein interactions (PPIs) is not possible for all species, several computational PPI prediction methods have been developed that harness existing data from other species. While PPI network prediction has been extensively used in eukaryotes, microbial network inference has lagged behind. However, bacterial interactomes can be built using the same principles and techniques; in fact, several methods are better suited to bacterial genomes. These predicted networks allow systems-level analyses in species that lack experimental interaction data. This review describes the current network inference and analysis techniques and summarizes the use of computationally-predicted microbial interactomes to date.

transcription from an operon in one species indicates functional relation and often interaction in another (Fig. 1F) (79, 81) .

Interacting protein pairs tend to evolve at the same rate (46, 87) , and so the distribution of protein pairs will therefore be co-conserved if they interact (78, (88) (89) (90) (91) . The phylogenetic profile method (Fig. 1G) infers interactions when two genes have a similar pattern of conservation (presence/absence) in multiple genomes (92, 93) . MirrorTree and ContextMirror algorithms extend this method beyond binary similarity to globally assess the phylogenies (94, 95) . Finally, in silico two-hybrid (i2H) method identifies interacting pairs from correlated mutations in multiple sequence alignments, since mutation in interacting proteins coevolve (96) . Several other extensions to these methods have been proposed (97) (98) (99) .

Machine learning can be used to infer PPI by training a classifier on positive (interacting) and negative (noninteracting) pairs of proteins (100) . Many data types can be included in the training set including sequence features, co-citation, protein annotation, phylogenetics, expression data, and physiochemical properties (101) (102) (103) (104) (105) (106) (107) (108) (109) , and multiple data types produce better accuracy than a single input (110, 111) . The resulting PPI networks often have confidence scores for putative interactions that allow thresholding and the use of network analysis algorithms that utilize these scores (112) . Microbial PPI prediction has been carried out using several algorithms including random forests (113) (114) (115) (116) (117) , support vector machines (118) (119) (120) , and Bayesian classifiers (121) (122) (123) . A related method is probabilistic functional integrated networks (PFINs), which combine multiple data types in a probabilistic framework to produce a network of confidence-weighted interactions (124) (125) (126) .

Once a predicted interactome has been built, it is essential to evaluate how accurately it represents real cellular biology and assess the level of false interactions that may be present. Evaluation of the quality of a predicted network is difficult due to the level of noise in the underlying data: often several validation methods are required. Small-scale experimental validation can be used on a subset of PPIs to give a level of confidence in the predicted network (20, 64, 122, 127, 128) . However, experimental validation is only possible for a small number of interactions. Expression data sets can be used to assess PPIs since interacting proteins are likely to have correlated expression (64, 119, (128) (129) (130) (131) , and evidence of support for predicted PPIs can be found in other experimental data sets or from text-mined small-scale studies (122, (132) (133) (134) .

Domain data can be used to assess networks predicted using other input data types (133) . Similarly, interaction predictions from other methodologies can be assessed using phylogenetics (119, 122, 129, 131) . Hub proteins in predicted networks often correspond to hubs in other species (135) . Clustering the network can be used to assess how well the network represents known protein complexes (136) . Comparison of the predicted network with random networks can also be used to assess its biological relevance (64, 137) .

Protein functional annotations provide useful validation tools as interacting proteins tend to have shared function (64, 119, 128, 131, 132, 138, 139) , shared cellular localization (128, 132, 138) , and related phenotype (119, 131, 133) . Protein functional prediction can provide an objective method of network evaluation by testing its ability to predict the known annotations, for example by a leave-one-out or partitioned crossvalidation (140) (141) (142) (143) . Data partitioning can also be used in the training/testing phase of machine learning to provide a measure of network accuracy (134) .

Several network parameters can be used to reveal aspects of network topology and identify key proteins (144) . The degree of a protein is its number of interactions; proteins with a high degree are considered hubs and tend to be essential and conserved (46, 145, 146) , and are often targets for pathogens (147, 148) . Identification of hubs in predicted networks can highlight important proteins for further study (129) . The degree distribution of a network, p(k), is the probability a selected protein has k links (149) . This distribution reflects the organization of cellular processes (150) , with many low degree proteins and a small number of hubs giving a scale-free distribution (4, 149, 151) . This topology makes networks resistant to random perturbation (5, 151) and has been found in several biological networks in a number of model species (17, (152) (153) (154) , although some do not have this topology (27, 155) .

Biological networks are considered small world since they have small diameters (longest shortest path between two proteins) and small characteristic path lengths (average shortest path) relative to equivalently-sized random networks (156) (157) (158) . The proteins of these networks are arranged in locally-dense regions interconnected by a small number of interactions, and, like scale-free networks, this topology is resistant to perturbation (5) .

Several network measures assess the importance of proteins and interactions in networks' information flow to identify bottlenecks. For instance, betweenness centrality (159) measures the proportion of shortest paths passing through a protein/interaction. Proteins with high betweenness centrality and low degree often link network modules (160) . Betweenness centrality can aid the identification of key proteins within a predicted network (116, 147) .

Biological networks tend to have a hierarchical structure of modules within modules (4, 161) . Dense network regions are believed to relate to the functional units of the cell (151, (162) (163) (164) (165) . Partitioning or clustering large networks can reveal the underlying mechanisms of cellular biology and assign protein function (137, 166, 167) . Module detection can use additional data, for instance gene expression data (168) , functional annotations (169) , or domain profiles (170) .

Predicted networks can be used to directly annotate proteins with function (171). Network-based annotation transfers known annotations between pairs of directly connected proteins (172, 173) , between proteins with shared interaction partners (174) , or more globally using network topology (175) . Interaction confidence weights, such as those produced by machine learning algorithms, are particularly useful for annotation transfer (173) . Annotation and other data can also be used to create process/condition-specific subnetworks (176, 177) .

available in a few species, and small-scale studies require considerable curation to analyze as a whole (195, 196) . However, several studies have produced a number of predicted interactomes (Table 1) , providing insights into several aspects of microbial biology. Additionally, the STRING database and server contains functional interaction data, including co-citation, co-expression and gene neighborhood, for multiple microbial species (197) . STRING data have been used as the initial data source for network studies in several species (115, 178, (198) (199) (200) (201) (202) (203) (204) .

In E. coli, an interactome study combined experimental data with genome context predictions to assign functions to proteins, including several involved in cell envelope biogenesis (20) . A phylogenetic profile-based network was later produced, which contained previously uncharacterized components of several complexes, including the ribosome (205) . Comparison of these networks (20, 205) using edge propagation, demonstrated that both identify complexes overlapping functional modules (206) . A support vector machine-derived E. coli network was shown to be scale-free and had good overlap with experimental data (134) . The EcID database incorporated genome context and phylogenetic evidence into a Bayesian classifier to predict protein function, in particular linking yeaG and yeaH to nitrogen metabolism (121) . EcoliNet is a probabilistic functional integrated network for E. coli comprising ;99% of the genome and has successfully predicted knockout phenotypes (124) . The interacting domain profile pair method, IDPP, was evaluated on E. coli before producing a network for C. jejuni from H. pylori interaction data (207, 208) . IDPP was shown to successfully predict interactions in the target species that were not found in the source species.

Incorporating expression data, interologs and DDIs, Bacillus licheniformis proteins were assigned to complexes and putative functions (139) . A probabilistic network for Klebsiella pneumoniae, derived from multiple data types, was used to identify antibiotic resistance genes (125) . Wuchty and colleagues expanded experimental networks of Streptococcus pneumoniae and H. pylori using interolog data to improve their functional predictive power (30, 209). Interologs and DDIs were combined with experimental data to study the metabolic modules of Pseudomonas putida (210) . In the related species P. aeruginosa, probabilistic functional network integration identified novel virulence and antibiotic resistance genes (126) .

SynechoNET is a predicted interactome for Synechocystis, focused on membrane biology (204) , while InteroPORC predicted an interactome for this species comprising 28% of the genome (51) . Later Naïve Bayesian network classification was applied to protein functional prediction and modular analysis in Synechocystis (122) . Interactome prediction has also been widely used to study Mycobacterium tuberculosis due to unavailability of accurate in vitro methods in this bacterium (211, 212) . Several predicted interactomes have been created to study network properties (119), evolution (205) , protein function (115), virulence (213) , and drug resistance (198) in this species. Finally, interolog networks for 22 bacterial species (the largest of which are included in Table 1 ) were produced by McDermott and colleagues and used to predict functions for a large number of unannotated bacterial proteins (142) demonstrating the potential of large-scale network studies to enhance our understanding of bacterial cellular physiology.

Computational prediction has also been used to understand the interplay between bacteriophages and bacteria (214) . Phylogenetic profiles using genomic/metagenomic data have identified host-virus and virus-virus relationships (215) (216) (217) (218) . Leite and colleagues used interactions, DDIs, and sequence properties to compare machine learning frameworks, concluding that predictive power will improve as input data increases (114) . More recently, a Markov random field framework of virus-host and virus-virus similarity measures has been developed (219).

Comparison of networks for different species can reveal insights into interactome evolution. Zitnik and colleagues used STRING data to create networks for 1,539 bacteria (178); comparison with those of eukaryotes revealed that interactomes have evolved to become more robust, and that bacterial interactome robustness is associated with more complex environments. Using a binary interaction data set for Treponema pallidum, interolog networks were created for 372 other genomes, ;28% of which were estimated to be true interactions (31). This study also revealed that bacterial proteins have higher degrees than eukaryotic proteins. These networks also revealed a central role for cell motility proteins in bacterial interactomes.

By comparing a Methanobrevibacter ruminantium network with those of Methanosarcina acetivorans, Methanosarcina barkeri and Methanococcus maripaludis biosynthetic subsystems involved in survival in the rumen were identified (202) . By comparing the Methanothermobacter thermautotrophicus metabolic interactome with those of metalloving bacteria, separate evolution of niche-specific cellular functions was revealed (201) . A comparison of 10 strains of Salmonella enterica identified distinct transcription factor targets conferring adaptation to gastrointestinal and extra-intestinal environments (220) . Similarly, comparison of host-pathogen interactomes between two strains of Burkholderia pseudomallei revealed several interactions unique to the virulent strain and highlighted the potential roles of chaperon and drug/carbohydrate binding proteins during infection (221) .

Interactome prediction can identify cross talk between pathogen and host (222, 223) . A DDI-based network suggested that human-M. tuberculosis PPIs tend to have more domains than intraspecies interactions (138) , and this trend was later observed in an interolog-based mapping study, which also revealed that hub proteins of intraspecies networks tend to be involved in host-pathogen PPI (148) . Using a random forest framework, the cancer pathway was involved in M. tuberculosis infection (117), while a DDI network implicated several PPIs involving heat shock, redox proteins (224) . Finally, a combination of interolog and DDI mapping associated several genes of the host immune responses to M. tuberculosis infection (65) .

In Fusobacterium nucleatum, a host-pathogen network implicated the Fap2 adhesin as a virulence protein (225) . Comparison of machine learning classifiers for Bacillus anthracishumans PPI prediction, suggested neural networks outperform SVMs (120); the resulting interactions revealed involvement of apoptosis and immune regulation pathways in infection. The predicted networks between humans and B. anthracis, Francisella tularensis, and Yersinia pestis indicated that hubs and bottlenecks of the intraspecies networks tend to interact (147, 226) . Thirteen membrane proteins of Leptospira interrogans were predicted to be involved in cellular disruption during infection, four of which were common between strains (203). Coelho and colleagues produced a human-microbial PPI network of the oral cavity using a Bayeisan classifier, which revealed Rothia mucilaginosa, Leptotrichia buccalis, and Actinomyces odontolyticus as having the most interactions with human proteins (123) .

In the plant pathogen Ralstonia solanacearum, interolog network analysis identified interactions between its transportation proteins and core proteins of the A. thaliana interactome (227) . The response to metal ions was linked to the host defense response during Pseudomonas syringae infection of A. thaliana (228) . Defense response proteins were also found to be enriched in random forest-derived networks between A. thaliana and the pathogens P. syringae, Hpaloperonospora arabidopsis, and Golovinomyces orontii (113) . Interolog and DDI mapping has also been used to study plant-bacterial symbiosis, suggesting a role of host 14-3-3 and heat shock proteins in the relationship between Bradyrhizobium diazoefficiens and Glycine max (229) .

Computational prediction has been applied to viral species including HIV (230-232), hepatitis C virus (233) , human papillomaviruses (234) , and Ebola (235) . In particular, the recent pandemic has highlighted the importance of understanding viral-host interaction, and having resources available to rapidly respond to new viral threats. Two studies have compared PPI prediction to emerging data from Sars-CoV-2: the first used interolog mapping, DDIs, and machine learning to link ACE2 and DPP4 to spike protein binding (236) ; the second used an ensemble machine learning algorithm based on experimental data and sequence features to predict .1,000 potential human protein targets (237) .

Interactome networks can aid in the identification of potential drug targets by revealing essential pathogen interactions (238) . A M. tuberculosis-human interactome was shown to be enriched in predicted drug targets (239) , and several studies have used computational prediction to identify putative drug targets (199, 213) and to understand the mechanisms of drug resistance in this important pathogen (198, 240) . In P. aeroginisa a random forest predicted network was used to prioritize drug targets based on their essentiality and topological importance (116) . Using interolog mapping, 12 putative drug targets were identified in methicillin-resistant Staphylococcus aureus, including a histone deacetylase (241) . A predicted Corynebacterium pseudotuberculosis interactome was used to identify 41 essential proteins as candidates for infection diagnosis in livestock, highlighting the tryptophan biosynthesis pathway as a potential drug target (200) . The network also revealed that this species may use multiple iron acquisition strategies in low iron environments. In the swine pathogen Actinobacillus pleuropneumoniae, nine drug target candidates were identified using interolog mapping (242) . Interolog analysis has also been applied to the gut microbiome to identify target species driving metabolic change during disease (243) .

Although PPI network prediction has been extensively used in eukaryotes, microbial network inference can be achieved using the same principles and analysis techniques. Bacterial interactomes share common hierarchical properties, such as modularity and robustness (244) . Many of the caveats to interactome prediction in eukaryotes, such as evolutionary distance, unequal conservation, and physiological context (245) (246) (247) (248) (249) are mitigated in prokaryotes due to their smaller genomes and single-celled nature. Several prediction methods, in particular gene neighborhood and gene cluster, are more suited to microbial than eukaryotic use (79, 83) , and phylogenetic profiles are powerful predictors, particularly when including inputs from the three domains of life (250) .

Interolog and DDI mapping can only detect interactions within conserved areas of the genome (48), and these methods rely on the quality of the underlying interaction data; stochastic activation of reporters can give false positives and low sensitivity leads to false negatives (251, 252) , and different methods have their own strengths (253) (254) (255) (256) . In eukaryotes, poor overlap has been observed between data sets of different types, and between those of the same type (10, 189) . Comparison of experimental data of C. jejuni, H. pylori, and E. coli suggests that these data sets have significant levels of overlap and similar rates of false results (27) . Meta-interactome analysis can be used to identify broadly-conserved biological systems, although levels of conservation remain low due to lack of experimental interactome coverage in many species (257) .

Current experimental interactome data are incomplete and biased toward well-studied proteins and species (178) . Using a combination of computational methods (51, 119, 139, 201, 210) , and experimental data if available (20, 124, 220) , gives a more complete predicted interactome, reduces some biases, and strengthens the evidence of true interactions. Integration of diverse data types is particularly effective when using a probabilistic (124) (125) (126) or machine learning (115, 116, 121, 134) framework, allowing thresholding of interaction confidence scores and therefore reduction of noise.

Filling in the gaps in bacterial interactomes is vital to our understanding of their biology, and computational prediction can help to pin down these areas and target further analyses by identifying areas of interest and providing putative protein functions. Microbial comparative interactomics is now possible on a large scale; for instance, the .1,500 predicted networks produced by Zitnik and colleagues revealed the evolutionary rewiring of interactomes through time (178) . While there are parts of some interactomes that cannot currently be predicted due to the complexity of the protein locations and the lack of the accurate annotations in the genome of new bacterial isolates, interactome accuracy will improve as coverage of diverse species increases, providing insights in several areas of biology, in particular the identification of PPIs for antibacterial discovery (258) , understanding of pathogenicity through host cell rewiring (256) , and in engineering of synthetic cellular systems (259) .

This work received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Graphic produced by Patrick Lane, ScEYEnce Studios. 

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Co-evolutionary analysis reveals insights into protein-protein interactions

Assessing protein co-evolution in the context of the tree of life assists in the prediction of the interactome

Identification of functional links between genes using phylogenetic profiles

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In silico two-hybrid system for the selection of physically interacting protein pairs

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Probabilistic model of the human protein-protein interaction network

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Inferring a protein interaction map of Mycobacterium tuberculosis based on sequences and interologs

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Stringent homology-based prediction of H. sapiens-M. tuberculosis H37Rv protein-protein interactions

Emergence of scaling in random networks

Getting connected: analysis and principles of biological networks

Functional and topological characterization of protein interaction networks

The largescale organization of metabolic networks

A protein interaction map of Drosophila melanogaster

Some protein interaction data do not exhibit power law statistics

Collective dynamics of "small-world" networks

Classes of smallworld networks

The small world of metabolism

High-betweenness proteins in the yeast protein interaction network

Finding and evaluating community structure in networks

Detecting hierarchical modularity in biological networks

From molecular to modular cell biology

Revealing modular organization in the yeast transcriptional network

Community structure in social and biological networks

Protein complexes and functional modules in molecular networks

Interlog protein network: an evolutionary benchmark of protein interaction networks for the evaluation of clustering algorithms

The use of edge-betweenness clustering to investigate biological function in protein interaction networks

A hub-attachment based method to detect functional modules from confidence-scored protein interactions and expression profiles

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Protein complex prediction via verifying and reconstructing the topology of domain-domain interactions

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Guilt-by-association goes global

High-precision high-coverage functional inference from integrated data sources

Using indirect protein interactions for the prediction of gene ontology functions

Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps

Context-sensitive data integration and prediction of biological networks

Edge-based scoring and searching method for identifying condition-responsive protein-protein interaction sub-network

Evolution of resilience in protein interactomes across the tree of life

Comparative interactomics: comparing apples and pears?

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Local graph alignment and motif search in biological networks

Integrative network alignment reveals large regions of global network similarity in yeast and human

SPINAL: scalable protein interaction network alignment

Discovering topological motifs using a compact notation

Choosing appropriate models for protein-protein interaction networks: a comparison study

Characterizing the community structure of complex networks

Biological network comparison using graphlet degree distribution

Align human interactome with phenome to identify causative genes and networks underlying disease families

Comparison of human protein-protein interaction maps

Global alignment of multiple protein interaction networks with application to functional orthology detection

IsoRankN: spectral methods for global alignment of multiple protein networks

Predicting and exploring network components involved in pathogenesis in the malaria parasite via novel subnetwork alignments

Module-based subnetwork alignments reveal novel transcriptional regulators in malaria parasite Plasmodium falciparum

Identification of functional modules from conserved ancestral protein-protein interactions

The current Salmonella-host interactome

Bacteriophage proteinprotein interactions

STRING: a database of predicted functional associations between proteins

Mycobacterium tuberculosis interactome analysis unravels potential pathways to drug resistance

Uncovering potential drug targets for Tuberculosis using protein networks

In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks

Analysis of modularity in proteome-wide protein interaction networks of Methanothermobacter thermautotrophicus strain DH and metal-loving bacteria

Comparative analysis of differential proteome-wide protein-protein interaction network of Methanobrevibacter ruminantium M1

Inferring pathogen-host interactions between Leptospira interrogans and Homo sapiens using network theory

SynechoNET: integrated protein-protein interaction database of a model cyanobacterium Synechocystis sp. PCC 6803

Protein interaction networks revealed by proteome coevolution

Identification of protein complexes and functional modules in E. coli PPI networks

Prediction, assessment and validation of protein interaction maps in bacteria

Protein-protein interaction map inference using interacting domain profile pairs

Proteome data improves protein function prediction in the interactome of Helicobacter pylori

PutidaNET: interactome database service and network analysis of Pseudomonas putida KT2440

Dissecting virulence pathways of Mycobacterium tuberculosis through protein-protein association

Comparative analysis and assessment of M. tuberculosis H37Rv protein-protein interaction datasets

Uncovering new signaling proteins and potential drug targets through the interactome analysis of Mycobacterium tuberculosis

Computational approaches to predict bacteriophage-host relationships

A highly abundant bacteriophage discovered in the unknown sequences of human faecal metagenomes

Gene co-occurrence networks reflect bacteriophage ecology and evolution

Reticulate representation of evolutionary and functional relationships between phage genomes

Uncovering Earth's virome

A network-based integrated framework for predicting virus-prokaryote interactions

SalmoNet, an integrated network of ten Salmonella enterica strains reveals common and distinct pathways to host adaptation

In silico prediction of host-pathogen protein interactions in melioidosis pathogen Burkholderia pseudomallei and human reveals novel virulence factors and their targets

Systems biology of pathogen-host interaction: networks of protein-protein interaction within pathogens and pathogen-human interactions in the post-genomic era

Computational analysis of protein interaction networks for infectious diseases

Using structural knowledge in the protein data bank to inform the search for potential host-microbe protein interactions in sequence space: application to Mycobacterium tuberculosis

Perturbed human sub-networks by Fusobacterium nucleatum candidate virulence proteins. Microbiome 5:89

Elucidating the network features and evolutionary attributes of intra-and interspecific protein-protein interactions between human and pathogenic bacteria

Prediction of protein-protein interactions between Ralstonia solanacearum and Arabidopsis thaliana

Predicting genome-scale Arabidopsis-Pseudomonas syringae interactome using domain and interologbased approaches

Bradyrhizobium diazoefficiens USDA 110-Glycine max interactome provides candidate proteins associated with symbiosis

Prediction of HIV-1 virushost protein interactions using virus and host sequence motifs

Prediction of virus-host protein-protein interactions mediated by short linear motifs

Sequence-and interactomebased prediction of viral protein hotspots targeting host proteins: a case study for HIV Nef

Predicting protein-protein interactions between human and hepatitis C virus via an ensemble learning method

Prediction of protein-protein interactions between viruses and human by an SVM model

Review of computational methods for virus-host protein interaction prediction: a case study on novel Ebola-human interactions

The MERS-CoV receptor DPP4 as a candidate binding target of the SARS-CoV-2 spike

Machine learning techniques for sequence-based prediction of viral-host interactions between SARS-CoV-2 and human proteins

Recent advances in the development of protein-protein interactions modulators: mechanisms and clinical trials

Predicting and analyzing interactions between Mycobacterium tuberculosis and its human host

In silico enhancing M. tuberculosis protein interaction networks in STRING to predict drug-resistance pathways and pharmacological risks

Identification of histone deacetylase (HDAC) as a drug target against MRSA via interolog method of protein-protein interaction prediction

Identification of drug target candidates of the swine pathogen Actinobacillus pleuropneumoniae by construction of protein-protein interaction network

Analysis of the infant gut microbiome reveals metabolic functional roles associated with healthy infants and infants with atopic dermatitis using metaproteomics

Bacterial protein networks: properties and functions

An atlas of protein-protein interactions across mammalian tissues

An improved interolog mapping-based computational prediction of protein-protein interactions with increased network coverage

Unequal evolutionary conservation of human protein interactions in interologous networks

Cross-species protein interactome mapping reveals species-specific wiring of stress response pathways

A proteomewide fission yeast interactome reveals network evolution principles from yeasts to human

Phylogenetic profiles for the prediction of proteinprotein interactions: how to select reference organisms?

Strategies towards high-quality binary protein interactome maps

Combining multiple positive training sets to generate confidence scores for protein-protein interactions

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

Bacterial interactomes: interacting protein partners share similar function and are validated in independent assays more frequently than previously reported

Bacterial interactomes: from interactions to networks

Elucidation of hostpathogen protein-protein interactions to uncover mechanisms of host cell rewiring

Bacterial protein meta-interactomes predict cross-species interactions and protein function

Protein-protein interactions in bacteria: a promising and challenging avenue towards the discovery of new antibiotics

Post-translational tools expand the scope of synthetic biology

Prediction and characterization of protein-protein interaction network in Xanthomonas oryzae pv. oryzae PXO99 A