BACKGROUND
many biological processes involve different types of interactions among proteins.
listing the proteins in the cell is not enough to fully understand the cellular machinery and all the interactions between them need to be delineated as well  <cit> .
recently, systematic identification of protein-protein interactions  had been proposed by high throughput experimental methods  and computational methods  complexes  <cit> , and interologs mapping  <cit> ).
the ppis for diverse organisms obtained from these methods were being collected by many databases, such as dip  <cit> , bind  <cit> , mips  <cit>  and string  <cit> .
as an increasing number of reliable ppis become available, a new concept and method to identify homologous ppis across multiple species has been proposed to understand a newly determined ppi in the post-genomic era  <cit> .
in the interactions between proteins, the protein domains interact physically with one another to perform the necessary functions.
the domain-domain interaction  can recruit the formation of multi-protein signalling complexes, and control the conformation, activity, and substrate specificity of enzymes  <cit> .
however, almost all large scale methods to explore interacting proteins cannot respond how a protein interacts with another one in molecular detail , whether experimental or computational methods.
in addition, identification of interacting domain through 3d structural information provides interacting domains and atomic details for thousands of direct physical interactions between proteins  <cit> .
for example, the membrane fusion adaptor p <dig> forms a tight complex with p <dig> and mediates p <dig> binding to its t-snare  syntaxin <dig> for another round of membrane fusion  <cit>  .
according to the result of psi-blast, both the two homologous proteins in saccharomyces cerevisiae  are very similar  to the template in mus musculus and might interact with each other based on the definition of homologous ppis  <cit> .
however, the rix7p lacks the important binding domain  and not involved in the process of membrane fusion although both p <dig> and rix7p belongs to type ii aaa+ proteins which containing two atpase domains.
therefore, the protein pair of rix7p and shp1p should be not a homologous ppi of p <dig> and p <dig> due to lack of the interacting domains.
the statistical interfacial pair potentials have been used to score how well the query protein pair fit the template structure by previous methods  <cit> .
this is a general empirical matrix for all the dimers of known structures to model the pair of query proteins  <cit> .
however, although binding sites are mainly hydrophobic, protruding, and electrostatic complementary, no general patterns are observed  <cit> .
it had been found that the free energy of binding is not evenly distributed across interfaces; instead, there are hot spots of binding energy made up of a small subset of residues in the interface of complexes  <cit> .
there is a correspondence between the experimental identified energy hot spots and the structurally conserved residues  <cit> .
recently, many studies had been proven that conservative residues may perform specific functional  role  <cit> .
therefore, we consider the general empirical matrix cannot characterize all binding site correctly.
to address these issues, we proposed a new method "pair position specific scoring matrix " to estimate the probabilities with which residue pairs occur at various contact positions by evolutionary profiles, leading to a more sensitive scoring system.
according to our knowledge, pairpssm is the first method for searching homologous ppis across multiple species using a 3d dimer as a template and automatically mapping interacting domains for these ppis.
the experimental results demonstrate that pairpssm can successfully identify the homologous ppis with 90% accuracy.
moreover, pairpssm could be applied to search the homologous ppi across seven organisms commonly used in molecular research, including homo sapiens, m. musculus, rattus norvegicus, caenorhabditis elegans, drosophila melanogaster, s. cerevisiae and escherichia coli.
in these seven organisms, our method infers ~ <dig>  homologous ppis in which the interacting domains and residues  are automatically modelled.
based on these homologous ppis, we believe that pairpssm is able to provide valuable insights for ppi evolution and networks across multiple species.
materials and methods
pair position specific scoring matrix
scoring matrix architecture
for a 3d-dimer with the number of contact residue pairs r, the empirical matrix of dimension  <dig> ×  <dig> is replaced by a protein-protein interaction position-specific matrix  of dimension r ×  <dig>  a typical pairpssm with  <dig>  contact residue pairs was shown in figure s <dig> in additional file  <dig>  the residue pair in a contact position is considered as a single symbol.
the advantage of this matrix is estimation of the probabilities with which residue pairs occur at various contact positions, leading to a more sensitive scoring system.
construction of evolutionary profile
to obtain the evolutionary profiles from multiple sequence alignment, our alignment result should be come from a comprehensive and non-redundant protein database.
the protein database is obtained from the ncbi reference sequences database .
although refseq aims to provide a non-redundant set of sequences for users, two major sources  of redundancy occur in refseq.
therefore, we used blastcluster to remove redundancy with 90% identical over an area covering 90% of the length of each sequence in the same species.
finally,  <dig> , <dig> protein sequences were selected into our non-redundant protein database.
to produce a multiple sequence alignment from the psi-blast, we collected all refseq sequence segments aligned to the two proteins of 3d-dimer with e-value ≤ 10- <dig>  the two proteins of 3d-dimer are used as a template for constructing two multiple sequence alignments, respectively.
any sequence that is > 95% identical to 3d-dimer  is purged.
target frequency estimation
given two multiple sequence alignment derived from a 3d-dimer, we generated score matrices with the theoretical foundation for the score of a specific contact position using the form log , where qij is the estimated probability for the contact residue pair i&j to be found in the column; pij is the expect probability of i&j to be found in the column.
the estimate of qij for a specific contact position should converse simply to the observed frequency of the residue pair i&j in that column.
to estimate the qij, we implemented the data-dependent the pseudocount method  <cit>  which is relative simplicity and often performs nearly as well comparing the dirichlet mixtures  <cit> .
the qij is given as qij=αfij+βgijα+β, where fij is observed frequency and gij=pipjesij is the residue pair pseudocount frequency.
the sij is the interacting energy of the residues i&j in empirical matrix.
pi and pj are the background probabilities of residues i and j, respectively.
the α and β are the relative weights given to observed and pseudocount residue frequency.
in our study, α is set to the number of different residue-pair types in column - <dig> and β =  <dig>  the pij is defined as pij = pi × pj.
the residue composition of the protein interface is obtained from lu et al.
<cit>  .
amino acid classification
the sequence variability at each contact position can be estimated based on two multiple sequence alignments of a dimer template.
unlike unconservative mutations, conservative ones preserve the essential nature of the side chain.
therefore, we make some tolerances for such mutations.
saha et al.
<cit>  made a classification based on the similarity of the environment of each amino acid residue in protein structures to the nine groups:  ala and val;  met, leu and ile;  gly, ser and thr;  pro, phe, tyr and trp;  cys;  his;  arg and lys;  asp and glu;  asn and gln.
we examine this classification of amino acid whether suitable for access the contact residue potential by calculating the standard deviation of contact residue potential in the cluster of amino acid.
figure 2a shows that the three groups ,  and  have high standard deviations of intra contact residue potential.
therefore, we slightly modify the group as follows:  ala and gly;  val, met, leu and ile;  pro, ser and thr;  phe, tyr and trp;  cys;  his and arg;  lys;  asp and glu;  asn and gln.
in this way, all the standard deviations of intra-contact residue potential are smaller than  <dig>  .
we considered this amino acid classification is more reasonable for measuring the contact residue potential.
3d-dimers and related 3d-dimers
here, we select the co-crystallized proteins from the pdb  <cit>  with using the following criteria.
the resolution of the co-crystallized protein should be smaller than  <dig> Å;  each chain of the co-crystallized proteins should be comprised more than  <dig> amino acids.
if the protein consists of the cross-chain domain defined by scop, we regard it;  the number of interacting residue pairs is set to be greater than  <dig> and each chain must contain more than  <dig> contact residues to make sure that the dimer is reasonably extensive  <cit> .
interacting residue pairs are defined as a pair of residues from different chains that have at least one pair of heavy atoms within  <dig> Å with each other;  elimination of artificial packing complexes rather than biologically functional multimers by using pqs server  <cit> ;  we remove redundancy by the sequence identity > 50%.
finally, the non-redundant template set, termed nrt, consists of  <dig>  heterodimers and  <dig>  homodimers.
to model ppis, we explored whether the two similar dimers possess the similar protein interaction type.
here, we defined that two 3d-dimers contain the same interacting scop domain  are "related-dimer pairs".
among  <dig>  dimers derived from pdb, we first remove the dimers without annotations of scop domain then selected  <dig>  heterodimers and  <dig>  homodimers.
second, the dimers are clustered by blastcluster  <cit>  according 80% identical over an area covering 80% of the length of each sequence.
we choose one representative dimer from each cluster if the number of interacting residue pairs more than the mean of the cluster and the resolution of crystallization is smallest.
third, the representative dimers are grouped based on the domain definition in scop.
we group the dimers which possess the same interacting domain pair in family level.
totally, there are  <dig> groups of heterodimer and  <dig> groups of homodimer with ≥  <dig> member.
we choose one representative member for each group and pair the representative one for the all other members in the group.
these pairs of dimers are considered as related dimer pairs.
in this way, we derived a related 3d-dimer set, termed rd, with  <dig> and  <dig>  related dimer pairs from the  <dig> groups of heterodimer and  <dig> groups of homodimer, respectively.
true protein complexes and unreasonable protein pairs
our method utilized a 3d-dimer to model all candidates of homologous ppis and used a specific pairpssm to determine whether two proteins interact.
here, we constructed two data sets, including the protein pairs form complexes  and the protein pairs cannot interact with each other .
from  <dig> representative 3d-dimers , we used the psi-blast to remove the similar dimers with > 80% sequence identity and get protein pair with e-value ≤  <dig> .
if the protein pair is co-crystallized in pdb and it contains the same interacting scop domains for the query dimer, we consider the protein pair as the positive  as the homologous ppi of the 3d-dimer.
in the other word, if the protein pair is not co-crystallized in pdb and it does not contain the same interacting scop domain for the query dimer, we considered the protein pair as negative cases .
among  <dig> representative 3d-dimers, we selected  <dig> pairs of positive homologous ppis and  <dig> pairs of negative protein pairs.
gold standard positives and negatives of ppis in s. cerevisiae
to examine the pairpssm for identifying homologous ppis in s. cerevisiae, the s. cerevisiae proteome was obtained from the saccharomyces genome database   <cit> .
the corresponding amino acid sequences of total  <dig>  open reading frames  were collected.
<dig>  reliable pps, considered as gold positive set, were downloaded from the core set of database of interacting protein   <cit> .
for gold negative set, we followed the previous study  <cit>  assumed that the proteins in different compartments do not interact with each other and generated  <dig> , <dig> non-interacting protein pairs according to the proteins in separate subcellular compartments  <cit> .
yeast gene expression
the gene expression profiles of two interacting proteins were also used to access the accuracy of our method according to the basic assumption: "the gene pair with similar expression profiles is likely to encode an interacting protein pair"  <cit> .
the rosetta compendium set consisting of the expression profiles of  <dig> deletion mutants and under chemical treatments  <cit>  was used to measure the similarity of gene expression profiles of two genes.
performance criteria
to assess the quality of our method, the mean average precision  and mean false positive rate  are used in this study.
the map is defined as map=∑j=1mapj/m, where ap=∑i=1ai/ti/a;ti is the number of protein pair candidates in a hit list containing i positive ppis; a is total numbers of positive ppis in the template t; m is the total numbers of template.
the mfp is defined as mfp=∑j=1mfpj/m where fp=∑i=1a//a;k is the total numbers of protein pair candidates in template t from psi-blast.
in addition, the similarity of two gene-expression data is defined by the pearson correlation coefficient between the two gene-expression profiles.
to test whether the mean of correlation coefficient for candidates of protein-protein interactions higher than that of non-interacting protein pairs, we calculate the t-score and the p-value for the null hypothesis of the sample mean  smaller than the mean of gold negative set.
RESULTS
we first explore the relationship between sequence similarity and interacting similarity for homologous ppis.
modelling ppis by homology is reasonable only when the correlation is high enough.
second, the pairpssm is verified in two data sets:  we examined whether the energy calculated from pairpssm could distinguish the true protein complexes and unreasonable protein pairs;  for identified homologous ppis in yeast proteome through pairpssm, we used two common metrics  to assess the performance and compared with the empirical matrix used by previous method.
third, the similarity of gene expression profiles for the candidates of protein-protein interactions is examined.
finally, we applied pairpssm to identify homologous ppis for seven organisms and used two biological examples to illustrate the operation and power of pairpssm.
similar 3d-dimers imply similar interacting types
the rd set  described above provides all instances of a particular interaction type occurring within different complex structures, that we then wish to compare to each other and correlate with sequence similarity.
to compare the binding of different instances of the two dimers with the same interacting domains, we used pair coverage  to calculate binding site overlap from the number of shared interacting residue pairs.
given a pair of related dimers a-b and a'-b', where a-a' and b-b' contain same scop domains.
we use a structural alignment tool, ce  <cit> , to align the a-a' and b-b', respectively.
the pc is defined as pc=ncpm2ncpab×ncpa′b′, where the ncpm is the matching number of contact residue pairs between the structural alignment of a-a' and b-b'; ncpab and ncpa'b' are the number contact residues pairs of dimer a-b and a'-b', respectively.
the interacting types of two dimers are very alike when the pc of a related dimer pair is greater than  <dig> .
in following discussion, the sequence identity between two dimers is defined as the minimum sequence identity in a&a' and b&b'.
the rationale is that the interacting partners with the lower sequence identity would tend to be the better indicator for the diversity of the interaction.
in the pairs of related homodimers, the trend which gives a guide to the degree of sequence similarity needed to be confident in a similar interaction is also observed.
however, there are more exceptions  in the pairs of homodimers than heterodimers .
in addition, the pcs of heterodimers are higher than that of homodimers in difference sequence identity .
the result means that the specificity of interaction in heterodimer is more conserved than homodimers.
for this reason, we think that heterodimers are more suitable for the template to model ppis than homodimers.
in summary, we find the related dimers indeed keep similar interacting type.
sequence similarity needed to be confident in a similar ppi.
we suggest one must be careful with identity below than 30% to model ppis by homology.
because the specificity of ppi in heterodimer is more conserved than homodimers, we consider the heterodimers are more suitable for the templates to model interactions.
finally, we find that the interacting types of antigen-antibody complexes are often diverse, they may be not suitable for used to as templates.
verification in true protein complexes and unreasonable protein pairs
to verify pairpssm, we would likely to study whether the specific interfacial energy calculated from pairpssm could distinguish the true protein complexes  and unreasonable protein pairs .
in  <dig> pairs of positive homologous ppi  and  <dig> pairs of negative protein pair , we used one dimer to score the other one by our specific empirical matrix  and general empirical matrix.
based on the frequency of positive set and negative set occurred in different specific interfacial energy intervals, we found that the interfacial energy of positive set is significant higher than that of negative set .
next, the error rate is calculated by averaging the number of false positive divided by number of positive set and the number of false negative divided by number of negative set.
experimental result shows that the minimum error rate is 18% when the specific interfacial energy  is set to  <dig> .
in addition, we also applied the general interfacial energy based on empirical matrix to the positive set and negative set .
when the general interfacial energy is set to  <dig>  we can obtain the minimum error rate 17%.
interestingly, the distributions of positive and negative dataset are not high concentrated in the two sides  when using the specific interfacial energy .
that is why the error rate higher in using specific interfacial energy than in using general interfacial energy.
in addition, the correlation  between the specific interfacial energy and the number of contact residues  is higher than the correlation  between general interfacial energy and the number of contact residues .
because specific interfacial energy is highly dependent on the characteristic of dimer template, we design a method to normalize the specific interfacial energy.
when a protein pair is modelled by a 3d-dimer and gets a specific interfacial energy scored by pairpssm, we normalize the energy defined as the specific interfacial energy of the protein pair divided by the specific interfacial energy of the dimer template.
by using the normalized interfacial energy, we can find the distribution of positive and negative dataset is much more concentrated in the two sides  than the unnormalized  and the error rate reduce from 18% to 13% .
for this reason, we consider the normalized specific interfacial energy equal set to  <dig>  is a good threshold for identifying homologous ppis.
verification in yeast proteome
here, we identified homologous ppis in s. cerevisiae and used the average precisions and false positive rate, commonly used to evaluate the quality of database searching, to verify our pairpssm.
in the nrt set,  <dig>  representative heterodimers were considered as queries to search database of yeast proteome by psi-blast.
we defined the proteins searched out with e-value ≤ 10- <dig> are homologous to the query protein.
given a query of heterodimer a-b, a' and b' are the homologous proteins of a and b, respectively.
all the homologous protein pairs a'-b' are considered as candidates of homologous ppis.
among these candidates, the known interacting protein pairs and the others are considered as positives and negatives, respectively.
among  <dig> queries,  <dig> queries have both positive candidates and negative candidates, and then these queries could calculate the average precisions and false positive rate .
figure  <dig> shows the mean average precisions  and mean false positive rate  of the  <dig> queries with different sequence identity limit.
the map and mfp are  <dig>  and  <dig>  by using specific interfacial energy, respectively.
on the other hand, the map is  <dig>  and the mfp is  <dig>  by using the general interfacial energy.
in order to avoid our method merely identify homologous ppis with high sequence identity, we set the sequence identity limit to remove the candidates if one protein of candidates with sequence identity > sequence identity limit.
these experimental results indicate the pairpssm is much better than the general empirical matrix even though in identifying remote homologous ppis .
in the above results, the candidates which are not included in the known interacting protein pairs are considered as negatives.
however, it may be somewhat unreasonable because many candidates are indeed interacting proteins in nature but have not proven by experimental methods in the past.
here, we considered the candidates overlapping with  <dig> , <dig> non-interacting protein pairs defined by jasen et al.
as negatives  <cit> .
the candidates without any annotations are removed for calculate average precisions and false positive rates.
in this way, our method using pairpssm is about 10% improvement than using general empirical matrix .
in the future, we will compare our pairpssm with previous works  <cit>  carefully to judge the utilities and advantages for predicting ppis.
example analysis: pdb code 1a2kad
we give an example using the 3d-dimer, pdb code 1a2kad, to search database of yeast proteome and illustrate the accuracy and operation of pairpssm.
the a chain of 1a2k is a rat nuclear transport factor  <dig>  and the d chain of 1a2k is a dog gtp binding protein ran   <cit> .
the transportation between nucleus requires to the nuclear pore complexes  in the nuclear envelope and several key factors including importin α and β, which recognize proteins with a nuclear localization sequences , the small gtp binding protein ran and nuclear transport factor   <cit> .
for this 3d-dimer  as query , we obtain  <dig> protein pair candidates .
the two protein pair candidates, ntf2&gsp <dig> and ntf2&gsp <dig>  were proven to bind with each other by yeast two hybrid test  <cit>  and the other twelve protein pair candidates are non-interacting proteins due to locate in different compartments.
interestingly, the ubp3-associated protein bre <dig> , one of the proteins in these negative protein pairs, and the ntf <dig> have the same domain annotation   <cit> .
based on two-hybrid assay, the ntf <dig> domain of bre <dig> is necessary and sufficient to interact with ubiquitin carboxyl-terminal hydrolase  <dig>  <cit> .
in addition, ntf <dig> and bre <dig> perform nucleocytoplasmic transport and coregulate vesicle transport in cytoplasm and nuclear envelope, respectively.
these results show that bre <dig> with transport functions may be the paralogous protein of ntf <dig> but does not interact with the proteins , because bre <dig> and the proteins of these negative protein pairs are in different subcellular compartments .
the normalized specific interfacial energies calculated from pairpssm  of the two positive protein pairs are both above the threshold  <dig>  and the twelve negative protein pairs are below the threshold .
the result shows that pairpssm is useful for identifying homologous ppis.
however, the general interfacial energies of the two positive protein pairs and ten out of twelve negative protein pairs are both above the threshold - <dig>   <cit> .
in summary,  <dig> out of  <dig> protein pairs are identified incorrectly with general interfacial energy and all the  <dig> ppis are identified correctly by our specific interfacial energy.
the multiple sequence alignment results between the  <dig> candidates and their corresponding template, such as a chain of 1a2k  and d chain of 1a2k .
the interface involves primarily the putative switch ii loop of ran   and the hydrophobic cavity surrounding surface of ntf <dig>  <cit> .
the interaction made by the switch ii loops accounts for the ability of ntf <dig> to discriminate between gdp and gtp bounds forms of ran.
a striking feature of the interacting interface was the aromatic ring of phe <dig> of ran .
it inserts into the hydrophobic cavity of ntf <dig> where it was surrounded by the hydrophobic side chains of trp <dig>  leu <dig>  phe <dig>  ile <dig>  leu <dig>  ala <dig>  met <dig>  phe <dig> and leu <dig>  the positive protein pairs  are both conservative in this important site .
on the other hand, the interacting interface on ntf <dig> involved this molecule's characteristic hydrophobic cavity.
hydrophobic residues in the upper portion of the ntf <dig> cavity, together with negatively charged residues, glu <dig>  asp <dig> and asp <dig>  are surrounding the cavity  made significant contributions to the interface with gdp-ran.
the three important negatives residues are conservative from a chain of 1a2k  to the yeast ntf <dig>  however, the three important sites are mutated to threonine in bre <dig> .
the bre <dig> is an ubiquitin protease cofactor which forms deubiquitination complex with ubp3p that coregulates anterograde and retrograde transport between the endoplasmic reticulum and golgi compartments.
the three important residues mutated may be resulted in bre <dig> does not interact with gsp <dig> and gsp <dig>  encouragingly, we give poor score to these two candidates  and successfully identify the true homologous ppis .
verification in yeast expression profiles
recently, many scientists consider that genes with similar expression profiles are likely to encode interacting proteins  <cit> .
therefore, we compare the distribution of gene expression profiles for the two gold standard sets and our identified homologous ppis by pairpssm with normalized specific interfacial energies ≥  <dig>  and  <dig>  .
the homologous ppis composed of the same protein are not used to calculate the gene expression profiles because their expression profiles must be identical and should not be taken account of.
experimental result shows that the distribution of the correlation coefficients of our identified homologous ppis is similar to the core set of dip  and right shift to non-interacting protein pairs  .
then, we used standard two sample t-test to test the mean of correlation coefficient for our identified homologous ppis to non-interacting protein pairs.
the e-values of the two sets are 10- <dig> and 10- <dig>  respectively.
these results indicate that the mean of correlation coefficient for identified homologous ppis by pairpssm is significant higher than that for non-interacting protein pairs.
identification of homologous protein-protein interactions in seven common organisms
in the above section, we have verified our pairpssm in two data sets and obtained a reasonable threshold for normalized specific interfacial energy about  <dig> ~ <dig> .
here, we apply pairpssm to identify homologous ppis in seven organisms commonly used in molecular research, including h. sapiens, m. musculus, r. norvegicus, c. elegans, d. melanogaster, s. cerevisiae and e. coli.
by set the threshold to  <dig> , we obtain about  <dig>  homologous ppis from the seven common organisms .
comparing our identified homologous ppis and the ppis deposited in dip database, there is a large difference for number of interactions in the same organism.
for example, we identify  <dig>  ppis in s. cerevisiae but dip collects  <dig>  ppis.
on the other hand, we identify  <dig>  ppis in homo sapiens but dip collects only  <dig>  ppis.
there are two reason for large drop, one is the large-scale experimental method  is hard to apply in mammalian organisms and results in the interactions deposited in dip are few in h. sapiens, m. musculus, or r. norvegicus.
the other reason is gene duplication and alternative splicing often occurred in the mammalian organisms and result in some redundancy protein in protein database.
in these organisms, we may over estimate the number of identified ppis.
structural genomics projects are generating new structures at an unprecedented rate--a benefit of recent developments in high-throughput technologies  <cit> .
as a result, the number of protein structures in the protein data bank  is increasing rapidly.
for each new determined 3d-dimer, we can apply our method to identify all the candidates in thousands of organisms quickly.
it helps the biologists to further detail analysis the ppi network.
model homologous protein-protein interactions in h. sapiens by 1evtbd
the pairpssm can apply not only to yeast proteome but also to any other organisms.
ppis between the fibroblast growth factors  and their receptors had been intensive studied  <cit> .
fgfs play key roles in morphogenesis, development, angiogenesis, and wound healing.
these fgf-simulated processes are mediated by for fgf receptor tyrosine kinase.
there are more than  <dig> human protein fgfs that bind to one or more of  <dig> fgf receptors , where the c and b denote isoforms iiic & iiib formed by alternative splicing.
the complex of fgf1/fgfr <dig>  had been dissolved by plotnikov et al.
<cit> .
ornitz et al.
perform a study of fgfr specificity by measuring mitogenic activity of fgfr-inducible baf <dig> cell-line  <cit> .
based on the binding affinity of the seven fgf/receptor complexes   <cit>  , we determined that low and high binding affinity relative to the fgf <dig> are < 10% and > 10%, respectively.
in this study, we used the pairpssm of 1evtbd to model homologous ppis for the seven fgf/receptor complexes.
among seven fgf/receptor complexes, six complexes are high affinity and our pairpssm also give high interfacial energy.
however, the other one, fgf4/fgfr3b complexes, with very low binding affinity  but our pairpssm give a high normalized interfacial energy .
for the detailed sequence analysis , we find that most contact positions in fgfr3b are much conservative except some residues in d <dig> immunoglobulin -like domains .
this result may mean some other factors involved in determining the strength of the fgfr interactions.
in conclusion, we successfully identify  <dig> out of  <dig> fgf/receptor complexes.
there is a good agreement between the specific interfacial energy and binding affinity even though still with an incorrect case.
CONCLUSIONS
we have developed a new method "pairpssm", a more sensitive scoring system for estimating the probabilities with residue pairs occurred at various contact positions by evolutionary profiles, to infer domain annotated homologous ppis across multiple species.
the specific interfacial energy calculated from pairpssm can successfully distinguish the true protein complexes and non-reasonable protein pair with about 90% accuracy.
experimental results show that the pairpssm outperforms general empirical matrix about 10% improvements even though for the distantly related protein sequences.
moreover, we applied pairpssm to identify ~ <dig>  homologous ppis, automatically modelled the interacting domains and residues , in seven organisms, including h. sapiens, m. musculus, r. norvegicus, c. elegans, d. melanogaster, s. cerevisiae and e. coli.
based on these homologous ppis, we believe that pairpssm is able to provide valuable insights for ppi evolution and networks across multiple species.
competing interests
the authors declare that they have no competing interests.
authors' contributions
cyl, ycc, and jmy conceived and designed the experiments.
ycc, ysl, and jmy implemented the program.
cyl, ysl, and jmy performed the experiments and analyzed the data.
cyl, ycc, and jmy wrote the paper.
declarations
the publication costs for this article were funded by national science council, partial supports of ministry of education and national health research institutes .
this article has been published as part of bmc bioinformatics volume  <dig> supplement  <dig>  2013: selected articles from the eleventh asia pacific bioinformatics conference : bioinformatics.
the full contents of the supplement are available online at http://www.biomedcentral.com/bmcbioinformatics/supplements/14/s <dig>
supplementary material
additional file 1
the supplementary information.
click here for file
acknowledgements
this paper was supported by national science council, partial supports of ministry of education and national health research institutes ; 'center for bioinformatics research of aiming for the top university program' of the national chiao tung university and ministry of education, taiwan.
we also thanks core facility for protein structural analysis supported by national core facility.

 many biological processes involve different types of interactions among proteins. listing the proteins in the cell is not enough to fully understand the cellular machinery and all the interactions between them need to be delineated as well. recently, systematic identification of protein-protein interactions had been proposed by high throughput experimental methods and computational methods complexes, and interologs mapping ). the ppis for diverse organisms obtained from these methods were being collected by many databases, such as dip, bind, mips and string. as an increasing number of reliable ppis become available, a new concept and method to identify homologous ppis across multiple species has been proposed to understand a newly determined ppi in the post-genomic era. in the interactions between proteins, the protein domains interact physically with one another to perform the necessary functions. the domain-domain interaction can recruit the formation of multi-protein signalling complexes, and control the conformation, activity, and substrate specificity of enzymes. however, almost all large scale methods to explore interacting proteins cannot respond how a protein interacts with another one in molecular detail, whether experimental or computational methods. in addition, identification of interacting domain through 3d structural information provides interacting domains and atomic details for thousands of direct physical interactions between proteins. for example, the membrane fusion adaptor p forms a tight complex with p and mediates p binding to its t-snare syntaxin for another round of membrane fusion. according to the result of psi-blast, both the two homologous proteins in saccharomyces cerevisiae are very similar to the template in mus musculus and might interact with each other based on the definition of homologous ppis. however, the rix7p lacks the important binding domain and not involved in the process of membrane fusion although both p and rix7p belongs to type ii aaa+ proteins which containing two atpase domains. therefore, the protein pair of rix7p and shp1p should be not a homologous ppi of p and p due to lack of the interacting domains. the statistical interfacial pair potentials have been used to score how well the query protein pair fit the template structure by previous methods. this is a general empirical matrix for all the dimers of known structures to model the pair of query proteins. however, although binding sites are mainly hydrophobic, protruding, and electrostatic complementary, no general patterns are observed. it had been found that the free energy of binding is not evenly distributed across interfaces; instead, there are hot spots of binding energy made up of a small subset of residues in the interface of complexes. there is a correspondence between the experimental identified energy hot spots and the structurally conserved residues. recently, many studies had been proven that conservative residues may perform specific functional role. therefore, we consider the general empirical matrix cannot characterize all binding site correctly. to address these issues, we proposed a new method "pair position specific scoring matrix " to estimate the probabilities with which residue pairs occur at various contact positions by evolutionary profiles, leading to a more sensitive scoring system. according to our knowledge, pairpssm is the first method for searching homologous ppis across multiple species using a 3d dimer as a template and automatically mapping interacting domains for these ppis. the experimental results demonstrate that pairpssm can successfully identify the homologous ppis with 90% accuracy. moreover, pairpssm could be applied to search the homologous ppi across seven organisms commonly used in molecular research, including homo sapiens, m. musculus, rattus norvegicus, caenorhabditis elegans, drosophila melanogaster, s. cerevisiae and escherichia coli. in these seven organisms, our method infers ~ homologous ppis in which the interacting domains and residues are automatically modelled. based on these homologous ppis, we believe that pairpssm is able to provide valuable insights for ppi evolution and networks across multiple species. materials and methods pair position specific scoring matrix scoring matrix architecture for a 3d-dimer with the number of contact residue pairs r, the empirical matrix of dimension × is replaced by a protein-protein interaction position-specific matrix of dimension r × a typical pairpssm with contact residue pairs was shown in figure s in additional file the residue pair in a contact position is considered as a single symbol. the advantage of this matrix is estimation of the probabilities with which residue pairs occur at various contact positions, leading to a more sensitive scoring system. construction of evolutionary profile to obtain the evolutionary profiles from multiple sequence alignment, our alignment result should be come from a comprehensive and non-redundant protein database. the protein database is obtained from the ncbi reference sequences database. although refseq aims to provide a non-redundant set of sequences for users, two major sources of redundancy occur in refseq. therefore, we used blastcluster to remove redundancy with 90% identical over an area covering 90% of the length of each sequence in the same species. finally,, protein sequences were selected into our non-redundant protein database. to produce a multiple sequence alignment from the psi-blast, we collected all refseq sequence segments aligned to the two proteins of 3d-dimer with e-value ≤ 10- the two proteins of 3d-dimer are used as a template for constructing two multiple sequence alignments, respectively. any sequence that is > 95% identical to 3d-dimer is purged. target frequency estimation given two multiple sequence alignment derived from a 3d-dimer, we generated score matrices with the theoretical foundation for the score of a specific contact position using the form log, where qij is the estimated probability for the contact residue pair i&j to be found in the column; pij is the expect probability of i&j to be found in the column. the estimate of qij for a specific contact position should converse simply to the observed frequency of the residue pair i&j in that column. to estimate the qij, we implemented the data-dependent the pseudocount method which is relative simplicity and often performs nearly as well comparing the dirichlet mixtures. the qij is given as qij=αfij+βgijα+β, where fij is observed frequency and gij=pipjesij is the residue pair pseudocount frequency. the sij is the interacting energy of the residues i&j in empirical matrix. pi and pj are the background probabilities of residues i and j, respectively. the α and β are the relative weights given to observed and pseudocount residue frequency. in our study, α is set to the number of different residue-pair types in column - and β = the pij is defined as pij = pi × pj. the residue composition of the protein interface is obtained from lu et al.. amino acid classification the sequence variability at each contact position can be estimated based on two multiple sequence alignments of a dimer template. unlike unconservative mutations, conservative ones preserve the essential nature of the side chain. therefore, we make some tolerances for such mutations. saha et al. made a classification based on the similarity of the environment of each amino acid residue in protein structures to the nine groups: ala and val; met, leu and ile; gly, ser and thr; pro, phe, tyr and trp; cys; his; arg and lys; asp and glu; asn and gln. we examine this classification of amino acid whether suitable for access the contact residue potential by calculating the standard deviation of contact residue potential in the cluster of amino acid. figure 2a shows that the three groups, and have high standard deviations of intra contact residue potential. therefore, we slightly modify the group as follows: ala and gly; val, met, leu and ile; pro, ser and thr; phe, tyr and trp; cys; his and arg; lys; asp and glu; asn and gln. in this way, all the standard deviations of intra-contact residue potential are smaller than. we considered this amino acid classification is more reasonable for measuring the contact residue potential. 3d-dimers and related 3d-dimers here, we select the co-crystallized proteins from the pdb with using the following criteria. the resolution of the co-crystallized protein should be smaller than Å; each chain of the co-crystallized proteins should be comprised more than amino acids. if the protein consists of the cross-chain domain defined by scop, we regard it; the number of interacting residue pairs is set to be greater than and each chain must contain more than contact residues to make sure that the dimer is reasonably extensive. interacting residue pairs are defined as a pair of residues from different chains that have at least one pair of heavy atoms within Å with each other; elimination of artificial packing complexes rather than biologically functional multimers by using pqs server; we remove redundancy by the sequence identity > 50%. finally, the non-redundant template set, termed nrt, consists of heterodimers and homodimers. to model ppis, we explored whether the two similar dimers possess the similar protein interaction type. here, we defined that two 3d-dimers contain the same interacting scop domain are "related-dimer pairs". among dimers derived from pdb, we first remove the dimers without annotations of scop domain then selected heterodimers and homodimers. second, the dimers are clustered by blastcluster according 80% identical over an area covering 80% of the length of each sequence. we choose one representative dimer from each cluster if the number of interacting residue pairs more than the mean of the cluster and the resolution of crystallization is smallest. third, the representative dimers are grouped based on the domain definition in scop. we group the dimers which possess the same interacting domain pair in family level. totally, there are groups of heterodimer and groups of homodimer with ≥ member. we choose one representative member for each group and pair the representative one for the all other members in the group. these pairs of dimers are considered as related dimer pairs. in this way, we derived a related 3d-dimer set, termed rd, with and related dimer pairs from the groups of heterodimer and groups of homodimer, respectively. true protein complexes and unreasonable protein pairs our method utilized a 3d-dimer to model all candidates of homologous ppis and used a specific pairpssm to determine whether two proteins interact. here, we constructed two data sets, including the protein pairs form complexes and the protein pairs cannot interact with each other. from representative 3d-dimers, we used the psi-blast to remove the similar dimers with > 80% sequence identity and get protein pair with e-value ≤. if the protein pair is co-crystallized in pdb and it contains the same interacting scop domains for the query dimer, we consider the protein pair as the positive as the homologous ppi of the 3d-dimer. in the other word, if the protein pair is not co-crystallized in pdb and it does not contain the same interacting scop domain for the query dimer, we considered the protein pair as negative cases. among representative 3d-dimers, we selected pairs of positive homologous ppis and pairs of negative protein pairs. gold standard positives and negatives of ppis in s. cerevisiae to examine the pairpssm for identifying homologous ppis in s. cerevisiae, the s. cerevisiae proteome was obtained from the saccharomyces genome database. the corresponding amino acid sequences of total open reading frames were collected. reliable pps, considered as gold positive set, were downloaded from the core set of database of interacting protein. for gold negative set, we followed the previous study assumed that the proteins in different compartments do not interact with each other and generated, non-interacting protein pairs according to the proteins in separate subcellular compartments. yeast gene expression the gene expression profiles of two interacting proteins were also used to access the accuracy of our method according to the basic assumption: "the gene pair with similar expression profiles is likely to encode an interacting protein pair". the rosetta compendium set consisting of the expression profiles of deletion mutants and under chemical treatments was used to measure the similarity of gene expression profiles of two genes. performance criteria to assess the quality of our method, the mean average precision and mean false positive rate are used in this study. the map is defined as map=∑j=1mapj/m, where ap=∑i=1ai/ti/a;ti is the number of protein pair candidates in a hit list containing i positive ppis; a is total numbers of positive ppis in the template t; m is the total numbers of template. the mfp is defined as mfp=∑j=1mfpj/m where fp=∑i=1a//a;k is the total numbers of protein pair candidates in template t from psi-blast. in addition, the similarity of two gene-expression data is defined by the pearson correlation coefficient between the two gene-expression profiles. to test whether the mean of correlation coefficient for candidates of protein-protein interactions higher than that of non-interacting protein pairs, we calculate the t-score and the p-value for the null hypothesis of the sample mean smaller than the mean of gold negative set.  we first explore the relationship between sequence similarity and interacting similarity for homologous ppis. modelling ppis by homology is reasonable only when the correlation is high enough. second, the pairpssm is verified in two data sets: we examined whether the energy calculated from pairpssm could distinguish the true protein complexes and unreasonable protein pairs; for identified homologous ppis in yeast proteome through pairpssm, we used two common metrics to assess the performance and compared with the empirical matrix used by previous method. third, the similarity of gene expression profiles for the candidates of protein-protein interactions is examined. finally, we applied pairpssm to identify homologous ppis for seven organisms and used two biological examples to illustrate the operation and power of pairpssm. similar 3d-dimers imply similar interacting types the rd set described above provides all instances of a particular interaction type occurring within different complex structures, that we then wish to compare to each other and correlate with sequence similarity. to compare the binding of different instances of the two dimers with the same interacting domains, we used pair coverage to calculate binding site overlap from the number of shared interacting residue pairs. given a pair of related dimers a-b and a'-b', where a-a' and b-b' contain same scop domains. we use a structural alignment tool, ce, to align the a-a' and b-b', respectively. the pc is defined as pc=ncpm2ncpab×ncpa′b′, where the ncpm is the matching number of contact residue pairs between the structural alignment of a-a' and b-b'; ncpab and ncpa'b' are the number contact residues pairs of dimer a-b and a'-b', respectively. the interacting types of two dimers are very alike when the pc of a related dimer pair is greater than. in following discussion, the sequence identity between two dimers is defined as the minimum sequence identity in a&a' and b&b'. the rationale is that the interacting partners with the lower sequence identity would tend to be the better indicator for the diversity of the interaction. in the pairs of related homodimers, the trend which gives a guide to the degree of sequence similarity needed to be confident in a similar interaction is also observed. however, there are more exceptions in the pairs of homodimers than heterodimers. in addition, the pcs of heterodimers are higher than that of homodimers in difference sequence identity. the result means that the specificity of interaction in heterodimer is more conserved than homodimers. for this reason, we think that heterodimers are more suitable for the template to model ppis than homodimers. in summary, we find the related dimers indeed keep similar interacting type. sequence similarity needed to be confident in a similar ppi. we suggest one must be careful with identity below than 30% to model ppis by homology. because the specificity of ppi in heterodimer is more conserved than homodimers, we consider the heterodimers are more suitable for the templates to model interactions. finally, we find that the interacting types of antigen-antibody complexes are often diverse, they may be not suitable for used to as templates. verification in true protein complexes and unreasonable protein pairs to verify pairpssm, we would likely to study whether the specific interfacial energy calculated from pairpssm could distinguish the true protein complexes and unreasonable protein pairs. in pairs of positive homologous ppi and pairs of negative protein pair, we used one dimer to score the other one by our specific empirical matrix and general empirical matrix. based on the frequency of positive set and negative set occurred in different specific interfacial energy intervals, we found that the interfacial energy of positive set is significant higher than that of negative set. next, the error rate is calculated by averaging the number of false positive divided by number of positive set and the number of false negative divided by number of negative set. experimental result shows that the minimum error rate is 18% when the specific interfacial energy is set to. in addition, we also applied the general interfacial energy based on empirical matrix to the positive set and negative set. when the general interfacial energy is set to we can obtain the minimum error rate 17%. interestingly, the distributions of positive and negative dataset are not high concentrated in the two sides when using the specific interfacial energy. that is why the error rate higher in using specific interfacial energy than in using general interfacial energy. in addition, the correlation between the specific interfacial energy and the number of contact residues is higher than the correlation between general interfacial energy and the number of contact residues. because specific interfacial energy is highly dependent on the characteristic of dimer template, we design a method to normalize the specific interfacial energy. when a protein pair is modelled by a 3d-dimer and gets a specific interfacial energy scored by pairpssm, we normalize the energy defined as the specific interfacial energy of the protein pair divided by the specific interfacial energy of the dimer template. by using the normalized interfacial energy, we can find the distribution of positive and negative dataset is much more concentrated in the two sides than the unnormalized and the error rate reduce from 18% to 13%. for this reason, we consider the normalized specific interfacial energy equal set to is a good threshold for identifying homologous ppis. verification in yeast proteome here, we identified homologous ppis in s. cerevisiae and used the average precisions and false positive rate, commonly used to evaluate the quality of database searching, to verify our pairpssm. in the nrt set, representative heterodimers were considered as queries to search database of yeast proteome by psi-blast. we defined the proteins searched out with e-value ≤ 10- are homologous to the query protein. given a query of heterodimer a-b, a' and b' are the homologous proteins of a and b, respectively. all the homologous protein pairs a'-b' are considered as candidates of homologous ppis. among these candidates, the known interacting protein pairs and the others are considered as positives and negatives, respectively. among queries, queries have both positive candidates and negative candidates, and then these queries could calculate the average precisions and false positive rate. figure shows the mean average precisions and mean false positive rate of the queries with different sequence identity limit. the map and mfp are and by using specific interfacial energy, respectively. on the other hand, the map is and the mfp is by using the general interfacial energy. in order to avoid our method merely identify homologous ppis with high sequence identity, we set the sequence identity limit to remove the candidates if one protein of candidates with sequence identity > sequence identity limit. these experimental results indicate the pairpssm is much better than the general empirical matrix even though in identifying remote homologous ppis. in the above results, the candidates which are not included in the known interacting protein pairs are considered as negatives. however, it may be somewhat unreasonable because many candidates are indeed interacting proteins in nature but have not proven by experimental methods in the past. here, we considered the candidates overlapping with, non-interacting protein pairs defined by jasen et al. as negatives. the candidates without any annotations are removed for calculate average precisions and false positive rates. in this way, our method using pairpssm is about 10% improvement than using general empirical matrix. in the future, we will compare our pairpssm with previous works carefully to judge the utilities and advantages for predicting ppis. example analysis: pdb code 1a2kad we give an example using the 3d-dimer, pdb code 1a2kad, to search database of yeast proteome and illustrate the accuracy and operation of pairpssm. the a chain of 1a2k is a rat nuclear transport factor and the d chain of 1a2k is a dog gtp binding protein ran. the transportation between nucleus requires to the nuclear pore complexes in the nuclear envelope and several key factors including importin α and β, which recognize proteins with a nuclear localization sequences, the small gtp binding protein ran and nuclear transport factor. for this 3d-dimer as query, we obtain protein pair candidates. the two protein pair candidates, ntf2&gsp and ntf2&gsp were proven to bind with each other by yeast two hybrid test and the other twelve protein pair candidates are non-interacting proteins due to locate in different compartments. interestingly, the ubp3-associated protein bre, one of the proteins in these negative protein pairs, and the ntf have the same domain annotation. based on two-hybrid assay, the ntf domain of bre is necessary and sufficient to interact with ubiquitin carboxyl-terminal hydrolase. in addition, ntf and bre perform nucleocytoplasmic transport and coregulate vesicle transport in cytoplasm and nuclear envelope, respectively. these results show that bre with transport functions may be the paralogous protein of ntf but does not interact with the proteins, because bre and the proteins of these negative protein pairs are in different subcellular compartments. the normalized specific interfacial energies calculated from pairpssm of the two positive protein pairs are both above the threshold and the twelve negative protein pairs are below the threshold. the result shows that pairpssm is useful for identifying homologous ppis. however, the general interfacial energies of the two positive protein pairs and ten out of twelve negative protein pairs are both above the threshold -. in summary, out of protein pairs are identified incorrectly with general interfacial energy and all the ppis are identified correctly by our specific interfacial energy. the multiple sequence alignment results between the candidates and their corresponding template, such as a chain of 1a2k and d chain of 1a2k. the interface involves primarily the putative switch ii loop of ran and the hydrophobic cavity surrounding surface of ntf. the interaction made by the switch ii loops accounts for the ability of ntf to discriminate between gdp and gtp bounds forms of ran. a striking feature of the interacting interface was the aromatic ring of phe of ran. it inserts into the hydrophobic cavity of ntf where it was surrounded by the hydrophobic side chains of trp leu phe ile leu ala met phe and leu the positive protein pairs are both conservative in this important site. on the other hand, the interacting interface on ntf involved this molecule's characteristic hydrophobic cavity. hydrophobic residues in the upper portion of the ntf cavity, together with negatively charged residues, glu asp and asp are surrounding the cavity made significant contributions to the interface with gdp-ran. the three important negatives residues are conservative from a chain of 1a2k to the yeast ntf however, the three important sites are mutated to threonine in bre. the bre is an ubiquitin protease cofactor which forms deubiquitination complex with ubp3p that coregulates anterograde and retrograde transport between the endoplasmic reticulum and golgi compartments. the three important residues mutated may be resulted in bre does not interact with gsp and gsp encouragingly, we give poor score to these two candidates and successfully identify the true homologous ppis. verification in yeast expression profiles recently, many scientists consider that genes with similar expression profiles are likely to encode interacting proteins. therefore, we compare the distribution of gene expression profiles for the two gold standard sets and our identified homologous ppis by pairpssm with normalized specific interfacial energies ≥ and. the homologous ppis composed of the same protein are not used to calculate the gene expression profiles because their expression profiles must be identical and should not be taken account of. experimental result shows that the distribution of the correlation coefficients of our identified homologous ppis is similar to the core set of dip and right shift to non-interacting protein pairs. then, we used standard two sample t-test to test the mean of correlation coefficient for our identified homologous ppis to non-interacting protein pairs. the e-values of the two sets are 10- and 10- respectively. these results indicate that the mean of correlation coefficient for identified homologous ppis by pairpssm is significant higher than that for non-interacting protein pairs. identification of homologous protein-protein interactions in seven common organisms in the above section, we have verified our pairpssm in two data sets and obtained a reasonable threshold for normalized specific interfacial energy about ~. here, we apply pairpssm to identify homologous ppis in seven organisms commonly used in molecular research, including h. sapiens, m. musculus, r. norvegicus, c. elegans, d. melanogaster, s. cerevisiae and e. coli. by set the threshold to, we obtain about homologous ppis from the seven common organisms. comparing our identified homologous ppis and the ppis deposited in dip database, there is a large difference for number of interactions in the same organism. for example, we identify ppis in s. cerevisiae but dip collects ppis. on the other hand, we identify ppis in homo sapiens but dip collects only ppis. there are two reason for large drop, one is the large-scale experimental method is hard to apply in mammalian organisms and results in the interactions deposited in dip are few in h. sapiens, m. musculus, or r. norvegicus. the other reason is gene duplication and alternative splicing often occurred in the mammalian organisms and result in some redundancy protein in protein database. in these organisms, we may over estimate the number of identified ppis. structural genomics projects are generating new structures at an unprecedented rate--a benefit of recent developments in high-throughput technologies. as a result, the number of protein structures in the protein data bank is increasing rapidly. for each new determined 3d-dimer, we can apply our method to identify all the candidates in thousands of organisms quickly. it helps the biologists to further detail analysis the ppi network. model homologous protein-protein interactions in h. sapiens by 1evtbd the pairpssm can apply not only to yeast proteome but also to any other organisms. ppis between the fibroblast growth factors and their receptors had been intensive studied. fgfs play key roles in morphogenesis, development, angiogenesis, and wound healing. these fgf-simulated processes are mediated by for fgf receptor tyrosine kinase. there are more than human protein fgfs that bind to one or more of fgf receptors, where the c and b denote isoforms iiic & iiib formed by alternative splicing. the complex of fgf1/fgfr had been dissolved by plotnikov et al.. ornitz et al. perform a study of fgfr specificity by measuring mitogenic activity of fgfr-inducible baf cell-line. based on the binding affinity of the seven fgf/receptor complexes, we determined that low and high binding affinity relative to the fgf are < 10% and > 10%, respectively. in this study, we used the pairpssm of 1evtbd to model homologous ppis for the seven fgf/receptor complexes. among seven fgf/receptor complexes, six complexes are high affinity and our pairpssm also give high interfacial energy. however, the other one, fgf4/fgfr3b complexes, with very low binding affinity but our pairpssm give a high normalized interfacial energy. for the detailed sequence analysis, we find that most contact positions in fgfr3b are much conservative except some residues in d immunoglobulin -like domains. this result may mean some other factors involved in determining the strength of the fgfr interactions. in conclusion, we successfully identify out of fgf/receptor complexes. there is a good agreement between the specific interfacial energy and binding affinity even though still with an incorrect case.  we have developed a new method "pairpssm", a more sensitive scoring system for estimating the probabilities with residue pairs occurred at various contact positions by evolutionary profiles, to infer domain annotated homologous ppis across multiple species. the specific interfacial energy calculated from pairpssm can successfully distinguish the true protein complexes and non-reasonable protein pair with about 90% accuracy. experimental results show that the pairpssm outperforms general empirical matrix about 10% improvements even though for the distantly related protein sequences. moreover, we applied pairpssm to identify ~ homologous ppis, automatically modelled the interacting domains and residues, in seven organisms, including h. sapiens, m. musculus, r. norvegicus, c. elegans, d. melanogaster, s. cerevisiae and e. coli. based on these homologous ppis, we believe that pairpssm is able to provide valuable insights for ppi evolution and networks across multiple species. competing interests the authors declare that they have no competing interests. authors' contributions cyl, ycc, and jmy conceived and designed the experiments. ycc, ysl, and jmy implemented the program. cyl, ysl, and jmy performed the experiments and analyzed the data. cyl, ycc, and jmy wrote the paper. declarations the publication costs for this article were funded by national science council, partial supports of ministry of education and national health research institutes. this article has been published as part of bmc bioinformatics volume supplement 2013: selected articles from the eleventh asia pacific bioinformatics conference: bioinformatics. the full contents of the supplement are available online at http://www.biomedcentral.com/bmcbioinformatics/supplements/14/s supplementary material additional file 1 the supplementary information. click here for file acknowledgements this paper was supported by national science council, partial supports of ministry of education and national health research institutes;'center for bioinformatics research of aiming for the top university program' of the national chiao tung university and ministry of education, taiwan. we also thanks core facility for protein structural analysis supported by national core facility.
the protein-protein interaction is one of the most important features to understand biological processes. moreover, we applied pairpssm to identify ~ homologous ppis with their interacting domains and residues in seven common organisms. our pairpssm is able to provide statistical significance of residue pairs using evolutionary profiles and a scoring system for inferring homologous ppis. the pairpssm can successfully distinguish the true protein complexes from unreasonable protein pairs with about 90% accuracy. in this study, we proposed "pair position specific scoring matrix " to identify homologous ppis. according to our best knowledge, the pairpssm is the first method for searching homologous ppis across multiple species using pair position specific scoring matrix and a 3d dimer as the template to map interacting domain pairs of these ppis. in the post-genomic era, to rapidly identify homologous ppis for analyzing the contact residue pairs of their interfaces within ddis on a genomic scale is essential to determine ppi networks and the ppi interface evolution across multiple species. for the test set including representative heterodimers and, non-interacting protein pairs, the mean average precision and mean false positive rate of pairpssm were and, respectively. we believe that pairpssm is able to provide valuable insights for the ppi evolution and networks across multiple species.
 the protein-protein interaction is one of the most important features to understand biological processes. for a ppi, the physical domain-domain interaction plays the key role for biology functions. in the post-genomic era, to rapidly identify homologous ppis for analyzing the contact residue pairs of their interfaces within ddis on a genomic scale is essential to determine ppi networks and the ppi interface evolution across multiple species.  in this study, we proposed "pair position specific scoring matrix " to identify homologous ppis. the pairpssm can successfully distinguish the true protein complexes from unreasonable protein pairs with about 90% accuracy. for the test set including representative heterodimers and, non-interacting protein pairs, the mean average precision and mean false positive rate of pairpssm were and, respectively. moreover, we applied pairpssm to identify ~ homologous ppis with their interacting domains and residues in seven common organisms.  our pairpssm is able to provide statistical significance of residue pairs using evolutionary profiles and a scoring system for inferring homologous ppis. according to our best knowledge, the pairpssm is the first method for searching homologous ppis across multiple species using pair position specific scoring matrix and a 3d dimer as the template to map interacting domain pairs of these ppis. we believe that pairpssm is able to provide valuable insights for the ppi evolution and networks across multiple species. 21- january the eleventh asia pacific bioinformatics conference vancouver, canada 