BACKGROUND
pseudomonas syringae is a gram-negative bacterium causing economically important diseases in a wide range of plant species leading to severe agricultural losses worldwide.
each strain of pseudomonas shows a high degree of host specificity and infects only a limited number of plant species or even a few cultivars of a single plant species  <cit> .
among them, pathovar tomato strain dc <dig>  has been asserted to infect the plant host arabidopsis thaliana and tomato causing bacterial spec and brown spot.
thus, arabidopsis-pseudomonas has been accepted as a model system for experimental characterization of the molecular dynamics of plant-pathogen interactions in both resistance and susceptible interactions  <cit> .
the whole genome sequence of pst dc <dig> revealed that it has ~ <dig> virulence-related genes  <cit> .
one of the major classes of virulence factors includes effector proteins that are delivered into the host through a type iii protein secretion system  to suppress plant immune responses, and also to facilitate disease development  <cit> .
basically, pseudomonas syringae pathogenesis is dependent on effector proteins and to date, nearly  <dig> different type iii effector proteins encoded by hop genes have been identified .
in addition, pst dc <dig> also produces non-proteinaceous virulence effectors, including coronatine , which are crucial for pathogenesis.
however, the virulence function of a large number of potential effectors encoded by the pst dc <dig> genome and their mode of action is still unknown.
similarly, in arabidopsis it has been seen that approximately  <dig> proteins are directly related to plant defense  <cit> .
many of these proteins interact directly with the pathogen proteins and some of them initiate plant defense responses to the infection.
recently, mukhtar et al.
<cit>  reported an experimental protein interaction network  containing  <dig> arabidopsis proteins and  <dig> pathogen effectors including very few interactions with pst dc <dig>  till now, only nearly  <dig> % of the full genome of arabidopsis has been evidenced for interaction.
therefore, to functionally characterize the dynamic interactions of plants with bacterial pathogens, there is a need for genome-wide study of the host-pathogen interactions.
knowledge of such novel resistance interactions provides the backbone of the understanding of plant resistance mechanisms and will aid in the further analysis of plant immunity  <cit> .
generally, pathogen attacks host tissues, secreting degradation enzymes and toxin release.
many of such mechanisms involve the protein-protein interactions .
ppis are essential process in all living cells and play a crucial role in the infection process, and initiating a defense response.
in this context, understanding the ppi network  between plant proteins and pathogen proteins is a critical step for studying the molecular basis of pathogenesis  <cit> .
in particular, computational approaches ameliorate the study of host-pathogen protein interactions in a genome-wide range.
in the past decade, a series of ppi prediction methods have been elegantly developed and are playing an increasingly important role in complementing experimental approaches.
diverse data types or properties, such as gene ontology  annotations  <cit> , protein sequence similarity  <cit> , protein domain interactions  <cit> , and protein structural information  <cit>  have been frequently utilized to construct ppi prediction methods.
among these computational methods, the interolog and the domain-based methods  <cit>  are widely used approaches for ppis prediction.
in this work, we used the interolog and the domain-based methods to jointly predict the protein-protein interactions between pseudomonas syringae and arabidopsis thaliana.
the domain-based approach infers inter-species protein-protein interactions by known domain-domain interactions from various databases and the interolog approach identifies protein-protein interactions based on homologous pairs of protein interactions across different organisms.
we present the prediction pipeline in detail and the functional analysis of the predicted results.
materials and methods
data sources
the whole proteome of pseudomonas syringae pv.
tomato dc <dig> is downloaded from pseudomonas genome database  which contains  <dig> protein sequences.
similarly, the full genome of arabidopsis thaliana containing  <dig> protein sequences is extracted from the tair <dig> database .
to infer the prediction from the interolog, we have used two types of datasets: the hpidb dataset and dip dataset.
database of interacting proteins  is a collection of experimental determined interactions between proteins in intra-species  <cit> .
as of jan  <dig>  dip database contains  <dig> sequences of  <dig> protein-protein interactions.
host pathogen interaction database  is a database of experimental determined interactions between  <dig> host and  <dig> pathogens  <cit> .
as of jan  <dig>  hpidb database contains  <dig> sequences of  <dig> unique protein-protein interactions.
to implement the domain based model, the domain-domain interaction databases, ipfam and 3did are used.
the ipfam database is a catalog of protein family interactions, including domain and ligand interactions, calculated from known structures in protein data bank .
as of jan  <dig>  the ipfam <dig>  database contains  <dig> domain-domain interactions.
the database of three-dimensional interacting domains  is a collection of high-resolution three-dimensional structural templates for domain-domain interactions.
it contains templates for interactions between two globular domains as well as novel domain-peptide interactions.
as of jan.  <dig>  the 3did database contains  <dig> domain-domain interactions.
identification of secreted proteins in pseudomonas syringae
all proteins of pseudomonas are processed through the psortb <dig>   and those predicted as cytoplasmic or cytoplasmic membrane are discarded as these proteins have less chance of involvement in interaction.
the rest proteins annotated with extracellular, outer membrane and unknown are considered to be positive candidates for interaction.
again we search the whole proteome of pseudomonas through the effector database   <cit> , which is an integrated database for secreted type proteins for bacteria.
those identified as secreted are considered as positive candidates for interaction.
combining these two steps,  <dig> potential candidate proteins of pstdc <dig> are filtered for interaction prediction.
prediction of ppis between arabidopsis and pseudomonas
in this study, the probability of interaction between an arabidopsis and a pseudomonas protein is inferred from two approaches: the domain based and the interolog method individually.
the prediction framework is shown in figure  <dig>
domain based protein-protein interaction prediction
the domain-based method uses domain interaction information, which is derived from known protein 3d structures, to infer the potential ppis.
if two proteins contain an interacting domain pair, it is expected that these two proteins may interact with each other.
to get the domains in arabidopsis and pseudomonas, hmmpfam is used in interproscan <dig>  <cit> .
in total,  <dig> domains are extracted for all the arabidopsis proteins and  <dig> domains are collected for pstdc <dig>  if a protein pair between pseudomonas and arabidopsis contains an interacting domain pair from ipfam and 3did, then the pair is expected to interact with each other.
interolog based protein-protein interaction prediction
the interolog method relies on protein sequence similarity to conduct the ppi prediction.
an interolog is a conserved interaction between a pair of proteins which have interacting homologs in another organism  <cit> .
the illustration of interolog is shown in figure  <dig>  consider that a and b are two different interacting proteins of one organism, and a' and b' are two different interacting proteins of another organism.
then the interaction between a and b is an interolog of the interaction between a' and b', if a is a homolog of a', b is a homolog of b', a and b interact, and a' and b' interact.
thus, interologs are homologous pairs of protein interactions across different organisms.
each protein in arabidopsis and pseudomonas is blasted against all the protein sequences in the dip and hpidb database to identify the homologs with e-value, sequence identity and aligned sequence length coverage of  <dig> e- <dig>   <dig> and 80% respectively.
each protein pair between pseudomonas and arabidopsis is predicted to interact if an experimentally verified interaction exists between their respective homologous proteins in dip or hpidb databases.
RESULTS
prediction of interactions
to predict the genome wide interactions, all proteins of arabidopsis and pseudomonas are paired up, which constitute ~97m ppis.
the interaction probability of each pair is assessed through the domain-based model and interolog-based model separately.
the predicted interactions from these methods are reported in table  <dig>  a total of ~ <dig> m probable ppis are predicted from both the methods, which include ~ <dig> arabidopsis proteins and  <dig> pesudomonas proteins.
out of these,  <dig> ppis are predicted by domain based method involving  <dig> arabidopsis and  <dig> pseudomonas proteins.
similarly, the interolog method predicted ~ <dig> m ppis including  <dig> arabidopsis and  <dig> pseudomonas proteins.
nearly,  <dig> ppis are consistently predicted by both methods as consensus which comprises  <dig> arabidopsis and  <dig> pseudomonas proteins.
the interaction network of the consensus predicted ppi is shown in figure  <dig>  on average, a pseudomonas protein has around  <dig> arabidopsis interacting partners, whereas an arabidopsis protein interact with around  <dig> pseudomonas proteins.
the reported results are coherent with the previous studies in which it is demonstrated that a few pathogen proteins involved in interaction in the host interactome  <cit> .
all predicted interactions from the domain based method, interolog method and the consensus predictions are available in tables s1-s <dig> respectively in additional files  <dig>   <dig> and  <dig>
predicted effector hubs
the effectors of pseudomonas with the highest number of edges  are pspto_ <dig>  pspto_ <dig>  pspto_ <dig>  pspto_ <dig>  pspto_ <dig>  pspto_ <dig>  pspto_ <dig>  pspto_ <dig>  pspto_ <dig>  pspto_ <dig>  pspto_ <dig>  pspto_ <dig> and pspto_ <dig> with more than  <dig> ppis in the arabidopsis-pseudomonas interactome.
there are also several effectors with more than  <dig> predicted ppis.
these are pspto_ <dig>  pspto_ <dig>  pspto_ <dig>  pspto_ <dig>  pspto_ <dig>  pspto_ <dig>  pspto_ <dig>  pspto_ <dig>  pspto_ <dig>  pspto_ <dig>  pspto_ <dig>  pspto_ <dig>  pspto_ <dig> and pspto_ <dig>  in contrast to these hub proteins, several effectors are predicted to interact with very few proteins.
these hub proteins play important role in pathogenesis, hence can be further investigated for deciphering virulence mechanism.
functional enrichment analysis of proteins involved in the interaction
functional enrichment analysis is an important assessment for elucidating the functional relevance of the host and pathogen proteins involved in the ppis.
the presence of enriched  functional categories that are closely related to host defense and pathogen infection support the validity of the predicted ppis of the prediction models.
gene ontology  is a comprehensive functional system to annotate the gene products.
we used the biological process go term enrichment to see the relevance of the predicted proteins.
the database for annotation, visualization and integrated discovery  is used to conduct the enrichment analysis <cit> .
the over represented biological processes of arabidopsis and pseudomonas proteins in the predicted ppis are listed in tables  <dig> and  <dig> respectively.
the enrichment analysis in arabidopsis shows that many proteins involved in the biological process, response to cadmium ion and metal ion.
in literature, it has been shown that metal ions are required for pathogen virulence and plant defense  <cit> .
fones et al.
demonstrated zn, ni or cd are accumulated when thlaspi caerule resist to a leaf spot caused by pseudomonas syringae pv.
maculicola  <cit> .
block and james reveal that the plant immune responses include deposition of lignin and callose in the cell wall and production of reactive oxygen species and anti-microbial compounds  <cit> .
qiu et al.
<cit>  show that mapk/erk kinase may directly or indirectly act through another signaling cascade to activate a transcription factor.
the transcription factor will then bind a particular region of dna, resulting in the recruitment of rna polymerase to transcribe a gene that will ultimately contribute to altering the function of the cell and cause pathogenesis <cit> .
these evidences in literature support our predicted results.
subcellular localization of arabidopsis proteins targeted by the predicted pseudomonas proteins
pathogens suppress host immunity by directing a range of secreted proteins or effectors, to the cytoplasm of host cells.
once these effector proteins traversed the host plasma-membrane, are transported to many subcellular locations where they subvert the host immune system to enable pathogen growth and reproduction.
the knowledge of cellular compartments of the arabidopsis proteins targeted by the predicted pseudomonas will be helpful in deciphering the mechanism of host-pathogen interactions.
if the targeted arabidopsis proteins are located in cellular compartments that are very relevant to the pathogen's infection or very likely to be involved in interactions with the pathogen, then the prediction result supports the host-pathogen predictions.
to have a clear understanding the location of the interactions in host, we extracted the subcellular localization of the predicted arabidopsis proteins from both the domain based and interolog methods using the atsubp  <cit>  available in tair database.
to date, atsubp is the only tool for subcellular location prediction of arabidopsis proteins on a genome-scale with high accuracy for seven locations.
the subcellular locations of all predicted arabidopsis proteins are listed in table  <dig>  we found that 29% host proteins are localized in nucleus, 9% in extracellular, 10% in chloroplast, 16% in cytoplasm, 10% in cell membrane, 1% in golgi, 5% in mitochondrion and 20% as unknown.
it reveals that major of the interactions occur in nucleus, cytoplasm, chloroplast and plasma membrane region.
in a recent review by block and james  <cit>  shows that the effectors of pseudomonas syringae target the plant proteins mostly in plasma membrane, chloroplast and mitochondrion.
citovsky et al.
<cit>  showed that when agrobacterium tumefaciens interact with a. thaliana, it hijacks vip <dig> protein and use it to shuttle transfer-dna  into the nucleus for its reproduction.
tao et al.
investigated that tip, an arabidopsis protein, interacts with the coat protein  of turnip crinkle virus  in yeast cells in nuclei  <cit> .
thus, the predicted locations of the interacting arabidopsis proteins by our approach are in close agreement with the earlier findings.
also the localizations for a large number of proteins are still unknown which need a special attention for experimental characterization.
* predictions were generated using arabidopsis-specific subcellular localization predictor, atsubp .
CONCLUSIONS
in this study, we have demonstrated that the sequence and domain similarity to known interactions are valuable information in predicting the host-pathogen interactions.
we identified ~ <dig> ppis between arabidopsis thaliana and pseudomonas syringae pv.
tomato dc <dig> based on the domain-based and interolog approaches.
the functional annotations of both arabidopsis and pseudomonas proteins involved in the predicted ppi are analyzed and it shows the relevance of the proteins for host defense and pathogen infections.
the present work may provide some useful information and resource to the plant community to understand the molecular mechanism of the plant immunity system against pathogen virulence.
the quality of the predicted interactome could further be improved by combining these methods with other computational approaches and biological data sources.
the reliability of the predicted interactions can be further assessed through experimental validations.
list of abbreviations used
pst dc <dig>  pseudomonas syringae pathovar tomato dc3000; dip, database of interacting proteins; hpidb, host pathogen interaction database; ppi, protein-protein interaction; go, gene ontology; ttss, type iii protein secretion system; 3did, three-dimensional interacting domains; ipfam, protein family interactions; david, database for annotation, visualization and integrated discovery.
competing interests
the authors declare that they have no competing financial interests.
authors' contributions
sss collected the host and pathogen proteome datasets, developed algorithms and models, performed the calculations, figures and tables, and wrote the draft manuscript.
tw helped in data analysis and setting up the pipelines on high-performance computing center.
rk conceived the study, participated in its design and coordination, and edited the final manuscript.
all authors read and approved the final manuscript.
supplementary material
additional file 1
predicted protein-protein interaction pairs for arabidopsis-pseudomonas syringae based on the domain model.
click here for file
additional file 2
predicted protein-protein interaction pairs for arabidopsis-pseudomonas syringae based on the interolog model.
click here for file
additional file 3
consensus protein-protein interaction pairs predicted based on both the domain and interolog models in arabidopsis-pseudomonas syringae.
click here for file
acknowledgements
the authors duly acknowledge the funding support to this study from oklahoma center for advancement of science and technology , grant number ps12- <dig>  we also thank the anonymous referees for critical review of the manuscript that helped improving the research article.
declaration
funding for the publication of this article has come from ocast funds account ab-5- <dig>  osu.
this article has been published as part of bmc bioinformatics volume  <dig> supplement  <dig>  2014: proceedings of the 11th annual mcbios conference.
the full contents of the supplement are available online at http://www.biomedcentral.com/bmcbioinformatics/supplements/15/s <dig>

 pseudomonas syringae is a gram-negative bacterium causing economically important diseases in a wide range of plant species leading to severe agricultural losses worldwide. each strain of pseudomonas shows a high degree of host specificity and infects only a limited number of plant species or even a few cultivars of a single plant species. among them, pathovar tomato strain dc has been asserted to infect the plant host arabidopsis thaliana and tomato causing bacterial spec and brown spot. thus, arabidopsis-pseudomonas has been accepted as a model system for experimental characterization of the molecular dynamics of plant-pathogen interactions in both resistance and susceptible interactions. the whole genome sequence of pst dc revealed that it has ~ virulence-related genes. one of the major classes of virulence factors includes effector proteins that are delivered into the host through a type iii protein secretion system to suppress plant immune responses, and also to facilitate disease development. basically, pseudomonas syringae pathogenesis is dependent on effector proteins and to date, nearly different type iii effector proteins encoded by hop genes have been identified. in addition, pst dc also produces non-proteinaceous virulence effectors, including coronatine, which are crucial for pathogenesis. however, the virulence function of a large number of potential effectors encoded by the pst dc genome and their mode of action is still unknown. similarly, in arabidopsis it has been seen that approximately proteins are directly related to plant defense. many of these proteins interact directly with the pathogen proteins and some of them initiate plant defense responses to the infection. recently, mukhtar et al. reported an experimental protein interaction network containing arabidopsis proteins and pathogen effectors including very few interactions with pst dc till now, only nearly % of the full genome of arabidopsis has been evidenced for interaction. therefore, to functionally characterize the dynamic interactions of plants with bacterial pathogens, there is a need for genome-wide study of the host-pathogen interactions. knowledge of such novel resistance interactions provides the backbone of the understanding of plant resistance mechanisms and will aid in the further analysis of plant immunity. generally, pathogen attacks host tissues, secreting degradation enzymes and toxin release. many of such mechanisms involve the protein-protein interactions. ppis are essential process in all living cells and play a crucial role in the infection process, and initiating a defense response. in this context, understanding the ppi network between plant proteins and pathogen proteins is a critical step for studying the molecular basis of pathogenesis. in particular, computational approaches ameliorate the study of host-pathogen protein interactions in a genome-wide range. in the past decade, a series of ppi prediction methods have been elegantly developed and are playing an increasingly important role in complementing experimental approaches. diverse data types or properties, such as gene ontology annotations, protein sequence similarity, protein domain interactions, and protein structural information have been frequently utilized to construct ppi prediction methods. among these computational methods, the interolog and the domain-based methods are widely used approaches for ppis prediction. in this work, we used the interolog and the domain-based methods to jointly predict the protein-protein interactions between pseudomonas syringae and arabidopsis thaliana. the domain-based approach infers inter-species protein-protein interactions by known domain-domain interactions from various databases and the interolog approach identifies protein-protein interactions based on homologous pairs of protein interactions across different organisms. we present the prediction pipeline in detail and the functional analysis of the predicted results. materials and methods data sources the whole proteome of pseudomonas syringae pv. tomato dc is downloaded from pseudomonas genome database which contains protein sequences. similarly, the full genome of arabidopsis thaliana containing protein sequences is extracted from the tair database. to infer the prediction from the interolog, we have used two types of datasets: the hpidb dataset and dip dataset. database of interacting proteins is a collection of experimental determined interactions between proteins in intra-species. as of jan dip database contains sequences of protein-protein interactions. host pathogen interaction database is a database of experimental determined interactions between host and pathogens. as of jan hpidb database contains sequences of unique protein-protein interactions. to implement the domain based model, the domain-domain interaction databases, ipfam and 3did are used. the ipfam database is a catalog of protein family interactions, including domain and ligand interactions, calculated from known structures in protein data bank. as of jan the ipfam database contains domain-domain interactions. the database of three-dimensional interacting domains is a collection of high-resolution three-dimensional structural templates for domain-domain interactions. it contains templates for interactions between two globular domains as well as novel domain-peptide interactions. as of jan. the 3did database contains domain-domain interactions. identification of secreted proteins in pseudomonas syringae all proteins of pseudomonas are processed through the psortb and those predicted as cytoplasmic or cytoplasmic membrane are discarded as these proteins have less chance of involvement in interaction. the rest proteins annotated with extracellular, outer membrane and unknown are considered to be positive candidates for interaction. again we search the whole proteome of pseudomonas through the effector database, which is an integrated database for secreted type proteins for bacteria. those identified as secreted are considered as positive candidates for interaction. combining these two steps, potential candidate proteins of pstdc are filtered for interaction prediction. prediction of ppis between arabidopsis and pseudomonas in this study, the probability of interaction between an arabidopsis and a pseudomonas protein is inferred from two approaches: the domain based and the interolog method individually. the prediction framework is shown in figure domain based protein-protein interaction prediction the domain-based method uses domain interaction information, which is derived from known protein 3d structures, to infer the potential ppis. if two proteins contain an interacting domain pair, it is expected that these two proteins may interact with each other. to get the domains in arabidopsis and pseudomonas, hmmpfam is used in interproscan. in total, domains are extracted for all the arabidopsis proteins and domains are collected for pstdc if a protein pair between pseudomonas and arabidopsis contains an interacting domain pair from ipfam and 3did, then the pair is expected to interact with each other. interolog based protein-protein interaction prediction the interolog method relies on protein sequence similarity to conduct the ppi prediction. an interolog is a conserved interaction between a pair of proteins which have interacting homologs in another organism. the illustration of interolog is shown in figure consider that a and b are two different interacting proteins of one organism, and a' and b' are two different interacting proteins of another organism. then the interaction between a and b is an interolog of the interaction between a' and b', if a is a homolog of a', b is a homolog of b', a and b interact, and a' and b' interact. thus, interologs are homologous pairs of protein interactions across different organisms. each protein in arabidopsis and pseudomonas is blasted against all the protein sequences in the dip and hpidb database to identify the homologs with e-value, sequence identity and aligned sequence length coverage of e- and 80% respectively. each protein pair between pseudomonas and arabidopsis is predicted to interact if an experimentally verified interaction exists between their respective homologous proteins in dip or hpidb databases.  prediction of interactions to predict the genome wide interactions, all proteins of arabidopsis and pseudomonas are paired up, which constitute ~97m ppis. the interaction probability of each pair is assessed through the domain-based model and interolog-based model separately. the predicted interactions from these methods are reported in table a total of ~ m probable ppis are predicted from both the methods, which include ~ arabidopsis proteins and pesudomonas proteins. out of these, ppis are predicted by domain based method involving arabidopsis and pseudomonas proteins. similarly, the interolog method predicted ~ m ppis including arabidopsis and pseudomonas proteins. nearly, ppis are consistently predicted by both methods as consensus which comprises arabidopsis and pseudomonas proteins. the interaction network of the consensus predicted ppi is shown in figure on average, a pseudomonas protein has around arabidopsis interacting partners, whereas an arabidopsis protein interact with around pseudomonas proteins. the reported results are coherent with the previous studies in which it is demonstrated that a few pathogen proteins involved in interaction in the host interactome. all predicted interactions from the domain based method, interolog method and the consensus predictions are available in tables s1-s respectively in additional files and predicted effector hubs the effectors of pseudomonas with the highest number of edges are pspto_ pspto_ pspto_ pspto_ pspto_ pspto_ pspto_ pspto_ pspto_ pspto_ pspto_ pspto_ and pspto_ with more than ppis in the arabidopsis-pseudomonas interactome. there are also several effectors with more than predicted ppis. these are pspto_ pspto_ pspto_ pspto_ pspto_ pspto_ pspto_ pspto_ pspto_ pspto_ pspto_ pspto_ pspto_ and pspto_ in contrast to these hub proteins, several effectors are predicted to interact with very few proteins. these hub proteins play important role in pathogenesis, hence can be further investigated for deciphering virulence mechanism. functional enrichment analysis of proteins involved in the interaction functional enrichment analysis is an important assessment for elucidating the functional relevance of the host and pathogen proteins involved in the ppis. the presence of enriched functional categories that are closely related to host defense and pathogen infection support the validity of the predicted ppis of the prediction models. gene ontology is a comprehensive functional system to annotate the gene products. we used the biological process go term enrichment to see the relevance of the predicted proteins. the database for annotation, visualization and integrated discovery is used to conduct the enrichment analysis. the over represented biological processes of arabidopsis and pseudomonas proteins in the predicted ppis are listed in tables and respectively. the enrichment analysis in arabidopsis shows that many proteins involved in the biological process, response to cadmium ion and metal ion. in literature, it has been shown that metal ions are required for pathogen virulence and plant defense. fones et al. demonstrated zn, ni or cd are accumulated when thlaspi caerule resist to a leaf spot caused by pseudomonas syringae pv. maculicola. block and james reveal that the plant immune responses include deposition of lignin and callose in the cell wall and production of reactive oxygen species and anti-microbial compounds. qiu et al. show that mapk/erk kinase may directly or indirectly act through another signaling cascade to activate a transcription factor. the transcription factor will then bind a particular region of dna, resulting in the recruitment of rna polymerase to transcribe a gene that will ultimately contribute to altering the function of the cell and cause pathogenesis. these evidences in literature support our predicted results. subcellular localization of arabidopsis proteins targeted by the predicted pseudomonas proteins pathogens suppress host immunity by directing a range of secreted proteins or effectors, to the cytoplasm of host cells. once these effector proteins traversed the host plasma-membrane, are transported to many subcellular locations where they subvert the host immune system to enable pathogen growth and reproduction. the knowledge of cellular compartments of the arabidopsis proteins targeted by the predicted pseudomonas will be helpful in deciphering the mechanism of host-pathogen interactions. if the targeted arabidopsis proteins are located in cellular compartments that are very relevant to the pathogen's infection or very likely to be involved in interactions with the pathogen, then the prediction result supports the host-pathogen predictions. to have a clear understanding the location of the interactions in host, we extracted the subcellular localization of the predicted arabidopsis proteins from both the domain based and interolog methods using the atsubp available in tair database. to date, atsubp is the only tool for subcellular location prediction of arabidopsis proteins on a genome-scale with high accuracy for seven locations. the subcellular locations of all predicted arabidopsis proteins are listed in table we found that 29% host proteins are localized in nucleus, 9% in extracellular, 10% in chloroplast, 16% in cytoplasm, 10% in cell membrane, 1% in golgi, 5% in mitochondrion and 20% as unknown. it reveals that major of the interactions occur in nucleus, cytoplasm, chloroplast and plasma membrane region. in a recent review by block and james shows that the effectors of pseudomonas syringae target the plant proteins mostly in plasma membrane, chloroplast and mitochondrion. citovsky et al. showed that when agrobacterium tumefaciens interact with a. thaliana, it hijacks vip protein and use it to shuttle transfer-dna into the nucleus for its reproduction. tao et al. investigated that tip, an arabidopsis protein, interacts with the coat protein of turnip crinkle virus in yeast cells in nuclei. thus, the predicted locations of the interacting arabidopsis proteins by our approach are in close agreement with the earlier findings. also the localizations for a large number of proteins are still unknown which need a special attention for experimental characterization. * predictions were generated using arabidopsis-specific subcellular localization predictor, atsubp.  in this study, we have demonstrated that the sequence and domain similarity to known interactions are valuable information in predicting the host-pathogen interactions. we identified ~ ppis between arabidopsis thaliana and pseudomonas syringae pv. tomato dc based on the domain-based and interolog approaches. the functional annotations of both arabidopsis and pseudomonas proteins involved in the predicted ppi are analyzed and it shows the relevance of the proteins for host defense and pathogen infections. the present work may provide some useful information and resource to the plant community to understand the molecular mechanism of the plant immunity system against pathogen virulence. the quality of the predicted interactome could further be improved by combining these methods with other computational approaches and biological data sources. the reliability of the predicted interactions can be further assessed through experimental validations. list of abbreviations used pst dc pseudomonas syringae pathovar tomato dc3000; dip, database of interacting proteins; hpidb, host pathogen interaction database; ppi, protein-protein interaction; go, gene ontology; ttss, type iii protein secretion system; 3did, three-dimensional interacting domains; ipfam, protein family interactions; david, database for annotation, visualization and integrated discovery. competing interests the authors declare that they have no competing financial interests. authors' contributions sss collected the host and pathogen proteome datasets, developed algorithms and models, performed the calculations, figures and tables, and wrote the draft manuscript. tw helped in data analysis and setting up the pipelines on high-performance computing center. rk conceived the study, participated in its design and coordination, and edited the final manuscript. all authors read and approved the final manuscript. supplementary material additional file 1 predicted protein-protein interaction pairs for arabidopsis-pseudomonas syringae based on the domain model. click here for file additional file 2 predicted protein-protein interaction pairs for arabidopsis-pseudomonas syringae based on the interolog model. click here for file additional file 3 consensus protein-protein interaction pairs predicted based on both the domain and interolog models in arabidopsis-pseudomonas syringae. click here for file acknowledgements the authors duly acknowledge the funding support to this study from oklahoma center for advancement of science and technology, grant number ps12- we also thank the anonymous referees for critical review of the manuscript that helped improving the research article. declaration funding for the publication of this article has come from ocast funds account ab-5- osu. this article has been published as part of bmc bioinformatics volume supplement 2014: proceedings of the 11th annual mcbios conference. the full contents of the supplement are available online at http://www.biomedcentral.com/bmcbioinformatics/supplements/15/s 
the domain-based method uses domain interaction information, which is derived from known protein 3d structures, to infer the potential ppis. the experimental study of ppis at a large scale is very scarce and also the high throughput experimental results show high false positive rate. the present work predicts the protein-protein interaction network between arabidopsis thaliana and pseudomonas syringae pv. pseudomonas syringae is a major bacterial leaf pathogen that causes diseases in a wide range of plant species. among its various strains, pathovar tomato strain dc is asserted to infect the plant host arabidopsis thaliana and thus, has been accepted as a model system for experimental characterization of the molecular dynamics of plant-pathogen interactions. this can be a useful resource to the plant community to characterize the host-pathogen interaction in arabidopsis and pseudomonas system. a pseudomonas protein and an arabidopsis protein are predicted to interact with each other if an experimentally verified interaction exists between their respective homologous proteins in another organism. if a pseudomonas and an arabidopsis protein contain an interacting domain pair, one can expect the two proteins to interact with each other. in agriculture, study of plant-microbe interactions is demanding a special attention to develop management strategies for the destructive pathogen induced diseases that cause huge crop losses every year worldwide. in this study, we used two computational approaches, the interolog and the domain-based to predict the interactions between arabidopsis and pstdc in genome scale. the interolog method relies on protein sequence similarity to conduct the ppi prediction. protein-protein interactions play a critical role in initiating pathogenesis and maintaining infection. understanding the ppi network between a host and pathogen is a critical step for studying the molecular basis of pathogenesis. although the predicted ppis may contain some false positives, the computational methods provide reasonable amount of interactions which can be further validated by high throughput experiments. the interolog-based method predicts ~ m ppis involving around arabidopsis and pseudomonas proteins in the full genome. the domain-based method predicts ppis comprising arabidopsis and pseudomonas proteins. plant-pathogen interactionsbioinformaticsunsupervised learningarabidopsispseudomonas syringaeinteractomecomputational prediction6- march 11th annual mcbios conference stillwater, ok, usa further, these prediction models can be applied to the agriculturally relevant crops.
 every year pathogenic organisms cause billions of dollars' worth damage to crops and livestock. in agriculture, study of plant-microbe interactions is demanding a special attention to develop management strategies for the destructive pathogen induced diseases that cause huge crop losses every year worldwide. pseudomonas syringae is a major bacterial leaf pathogen that causes diseases in a wide range of plant species. among its various strains, pathovar tomato strain dc is asserted to infect the plant host arabidopsis thaliana and thus, has been accepted as a model system for experimental characterization of the molecular dynamics of plant-pathogen interactions. protein-protein interactions play a critical role in initiating pathogenesis and maintaining infection. understanding the ppi network between a host and pathogen is a critical step for studying the molecular basis of pathogenesis. the experimental study of ppis at a large scale is very scarce and also the high throughput experimental results show high false positive rate. hence, there is a need for developing efficient computational models to predict the interaction between host and pathogen in a genome scale, and find novel candidate effectors and/or their targets.  in this study, we used two computational approaches, the interolog and the domain-based to predict the interactions between arabidopsis and pstdc in genome scale. the interolog method relies on protein sequence similarity to conduct the ppi prediction. a pseudomonas protein and an arabidopsis protein are predicted to interact with each other if an experimentally verified interaction exists between their respective homologous proteins in another organism. the domain-based method uses domain interaction information, which is derived from known protein 3d structures, to infer the potential ppis. if a pseudomonas and an arabidopsis protein contain an interacting domain pair, one can expect the two proteins to interact with each other. the interolog-based method predicts ~ m ppis involving around arabidopsis and pseudomonas proteins in the full genome. the domain-based method predicts ppis comprising arabidopsis and pseudomonas proteins. further, around ppis have been identified as interacting from both the methods as a consensus.  the present work predicts the protein-protein interaction network between arabidopsis thaliana and pseudomonas syringae pv. tomato dc in a genome wide scale with a high confidence. although the predicted ppis may contain some false positives, the computational methods provide reasonable amount of interactions which can be further validated by high throughput experiments. this can be a useful resource to the plant community to characterize the host-pathogen interaction in arabidopsis and pseudomonas system. further, these prediction models can be applied to the agriculturally relevant crops. plant-pathogen interactionsbioinformaticsunsupervised learningarabidopsispseudomonas syringaeinteractomecomputational prediction6- march 11th annual mcbios conference stillwater, ok, usa 