proteins rarely perform their biological functions independently ,   since they usually interact with each other . in fact , most of the biological activities require the creation of protein complexes . as a consequence , the different levels of complexity of the biological systems are not exclusively determined by the number of proteins of an organism , but also by the number of their interactions . many experimental methods have been developed to study protein interactions , such as the two hybrid system in yeast , the affinity purification followed by mass spectrometry and the phage display libraries [ 14 ] . the use of these techniques led to the creation of many databases containing a great number of protein - protein interactions , such as the database of interacting proteins ( dips ) , the general repository for interaction datasets ( biogrids ) , the human protein reference database ( hprd ) , and the biomolecular interaction network database ( bind ) [ 58 ] . because of the high amount of false positives and false negatives resulting from the application of these experimental approaches on a large scale , such data repositories must be cautiously used . once a list of candidates is obtained , it is necessary to analyze in vivo every possible interactor by expensive , time - consuming , and labour - intensive experimental techniques in order to validate the in vitro experimental result.for this reason , in silico methods for the prediction of protein interactions are considered valid tools to reduce the number of candidates . two main computational approaches , based on sequence similarity and structural modelling , have been applied for the prediction of protein interactions . the former is based on the selection of potential interacting partners on the basis of the sequence similarity with confirmed interactors [ 1013 ] . this approach is based on the  homology modelling  principle : similar protein sequences , sharing similar structures , should also share similar interactants . among sequence - similarity based methods , approach , based on the assumption that interacting protein pairs are likely to evolve in a correlated fashion [ 15 , 16 ] . the latter set of approaches is based on the properties of the three - dimensional structures of the proteins , generally referred to as docking methods ; these methods exploit surface complementarity and electrostatics properties to predict reliable structural complexes [ 1725 ] . although both these approaches are based on strong theoretical and experimental data , they exhibit some limitations . the first approach might fail to predict protein interactions , since a protein complex might be subjected to a different selection pressure than each single constituting protein during evolution [ 26 , 27 ] . for this reason , all the structural details of a protein interaction become important to determine the affinity and the specificity of protein interactions . docking methods , in fact , analyze the interactions at a three - dimensional level and are therefore considered to be more accurate , also evaluating the biophysical parameters of the interaction sites . however , docking methods are generally limited by the lack of the structures for the majority of the proteins and by incomplete bio - physical interaction knowledge [ 29 , 30 ] . for these reasons , at present , an integration of the two approaches is essential to better predict putative interacting proteins [ 3133 ] . the aim of this work is to design a knowledge - based tool that can integrate the two approaches described above . in particular , considering a target protein , we first select a set of the candidate interactors already obtained from other experimental results , but not yet in vivo validated . then , we exploit the sequence and the structural information on in vivo confirmed interacting proteins and complexes , to finally select the most reliable partners of the target protein . the algorithm predicts the protein interactors of a specific target protein ( tp ) relying on the following information : ( i ) a list of potential interactors , already obtained from other experimental results , but not in vivo validated ; ( ii ) in vivo confirmed interactors ; and ( iii ) three - dimensional complex structures involving tp . the approach follows five steps : known structures complexes involving tp are searched in public available databases , specifically the protein data bank ( pdb ) and the protein quaternary structure ( pqs ) . first , tp interactors are searched in the human protein reference database ( hprd ) [ 7 , available at http://www.hprd.org/ ] . then , two collections of tp interactors are generated : confirmed and validated in vivo ( cint ) and potential in vitro discovered ( pint ) . the cint group also includes tp interacting chain sequences , extracted from pdb and pqs databases . pint and cint related sequences are globally aligned using the needleman and wunsch algorithm with the matrix blosum50 and fixing the penalties for row and column gaps both equal to 8 . the score of the best alignment of each i  pint is the first element of the score function ( score1(i ) ) . pint sequences are then aligned with the conserved regions of cint members , using the needleman and wunsch algorithm , thus computing the second component of the score ( score2(i ) ) . the aminoacids at the interface binding sites belonging to chains interacting with tp are retrieved on the basis of the information on known protein complexes . these interacting aminoacids are exploited to build a set of interacting motifs , which are then searched within the pint members for the calculation of the third and fourth components of the score function ( score3(i ) and score4(i ) ) . using the cartesian coordinates of the complexes involving tp , reported in pdb files , we identify putative tp interacting proteins . for every interactor , we find the interacting aminoacids between tp and the interactor chains : these residues are defined as centres of bond  . in order to reduce the computational burden involved in the identification of the interacting residues , we primarily select the aminoacids at the protein interfaces . in detail , we first look for the amino acids that were less than 15     far from one residue of the target chain . then , for each interactor , we consider as interface residues also those closer than 10    to the aminoacids already found . in this step , the distance between two aminoacids is calculated as the distance between the  carbon atoms . once the interfaces are defined , we search for the interacting aminoacids , deriving from disulfur bridges , hydrogen bonds and salt bridges electrostatic interactions . the following aspects are taken into consideration : a cysteine sulfur bridge satisfies the following geometrical constraints : the two sulfur atoms ( sg , according to pdb nomenclature ) must be 22.1    far and the distance between the  carbon of a cysteine and the sulfur atom of the other cysteine is set to 33.1   .   for hydrogen bonds , the distances between the acceptor and donor are computed . as shown in figure 2 , the distance ( d ) between a donor ( d ) and an acceptor ( a ) should be less than 3.5    and the angle  should be less than /2 . because the majority of the pdb files do not contain the coordinates of the hydrogen atoms , we do not consider the other three geometrical features of the bond ( r < 2.5   ,  > /2 and  > /2 ) [ 37 , 38 ] . moreover , we define the maximum number of hydrogen bonds that an atom can form ( as showed in table 1 ) , following the results of a statistical analysis reported by mcdonald and thornton . if a residue overtook the maximum number of bonds , we considered only those with the lowest distances between acceptor and donor . the negatively charged atoms at physiological ph are od1 and od2 of asparagine , and od1 and oe2 of glutamic acid . the positively charged ones are nh1 , nh2 of arginine and nz of lysine . for a salt bridge between two residues with opposite charges , the distance between the charged atoms is set to be less than 3.5   . because of the folding of the primary structure , two residues that are neighbours on the surface of the three - dimensional structure can be far apart in the protein sequence . this explains why the interacting amino acids are often spread in the protein linear sequence , so that we can find them completely isolated . moreover , although the interacting amino acids are the most important components of the interaction , also the neighbouring residues may effectively contribute . for this reason , we enlarge the  centre of bond  considering the neighbouring residues with the same hydropathy of the center of bond and then adding the so called proximal amino acids . we calculate the hydrophobic and the hydrophilic regions of the proteins , using the hydropathy scale of kyte - doolittle . in particular , we set to 1 every amino acid with a positive value in the scale ( hydrophobic residues ) and to 0 those having negative values ( hydrophilic residues ) . then , in addition to directly interacting residues , we also consider those that do not interact , but are closer than 4.2    to the corresponding ones of the tp chain , defined as  proximal aminoacids  . to calculate the distances , we consider the most external atoms of the backbone of each amino acid . table 2 reported an example of the centre of bond enlargement process . if the center of bond ( i.e. , sn ) is in a hydrophilic region , we perform a symmetrical enlargement , until a hydrophobic amino acid is found ( i.e. , pk and rk ) . then the proximal amino acids adjacent to the result of the enlargement ( i.e. , pksnrk ) is added ( i.e. , v at position 48 ) as a result , it is possible to group two or more adjacent centers of bond . we define a set of one or more grouped centers of bond as a  binding site . the set of binding sites belonging to an interface between two chains is denoted as  interacting site  . for each binding site , we extract  interacting motif  through the analysis of secondary and tertiary structures of the proteins ; these motifs are then searched within the pint sequences . to analyse the sequence motifs , we divide the aminoacids into six classes looking at their hydropathy and charge , as shown in table 3 . every amino acid belonging to the center of bond is assumed to be invariant in the motif , while the others residues ( except proline ) belonging to the binding site are variable within their class , as defined in table 3 . in case of proline , its structure do not allow the movement of the bond between the  carbon and the nitrogen of the backbone , blocking the rotation of the chain ; as a consequence , the substitution of the proline with any other aminoacid could influence the stability of the interaction . the secondary structures of the interactors , as extracted from the pdb files of the protein complexes , are also considered to compute of the structural motifs . the obtained motifs of the example sequence ( table 2 ) is therefore 
										 ( 1)[i / v / l]p[k / r]sn[r / k][k / r]hhlllll 
									 where   loops ( l ) and alpha chains ( h ) are reported . finally , we assign a score to every motif according to the following rules : every unchanged residue is scored 20 , while every variable amino acid is scored as the ratio between 20 and the number of allowed variations . then , the final score of the motif ( motif score , scorem ) is computed by multiplying the single scores of the aminoacids . for example , the score of the sequence motif [ ivl]p[kr]sn[rk][rk ] is : ( 20/3 )  20  ( 20/2 )  20  20  ( 20/2)(20/2 ) = 5.33  10 . we search for both the sequence and structural motifs in all the predicted interactors . because the secondary structure is unknown for the majority of the potential interactors , three of the most used tools for secondary structure prediction , that is , predator , nnpredict and nps [ 4143 ] , are employed . all these algorithms assign to every amino acid a secondary structure as loop ( l ) , alpha chain ( h ) and beta sheet ( e ) . therefore , we compute a score for every interactor i of the list pint as 
								 ( 2)score3(i)=max  ( scoremilength(sequence(i ) ) ) , 
							 where scoremi is the list of motif scores of the interactor i. finally , assuming that a single motif is not sufficient to determine an interaction , it is also important to take into account how many complete interaction sites ( sets of binding sites belonging to an interface between two chains ) are present . for this reason , we define another score related to the subset of the motifs of the interactor i belonging to an interaction site 
								 ( 3)score4(i)=(scoremilength(sequence(i ) ) ) , 
							 where scoremi is the list of motif scores of the interactor i belonging to an interaction site that includes all the motifs . once the four scores are computed , we calculate a normalised final score , which expresses a measure of the likelihood that a potential interactor is a real interactor of the target protein . the proposed procedure was tested by using the growth factor receptor - bound protein 2 ( grb2 ) as the target protein . moreover , a validation approach was applied ( seesection3.6 ) to evaluate the accuracy and precision of the procedure and to choose a suitable score threshold to predict new interactors . we initially retrieved 21 known structures of protein complexes containing grb2 main domains ( i.e. , sh2 and sh3 ) . the list of potential interactors ( pint ) was then obtained by retrieving from hprd database 46 in vitro discovered interactors . we also retrieved from the same database 141 interactors in vivo validated , which together with the sequences of the proteins extracted from the 21 grb2 complexes , formed a list of 247 confirmed interactors ( cint ) . each pint member was aligned with cint counterparts , evaluating score1 for the best alignments . next , every potential interactor was aligned with the conserved regions of the confirmed ones , computed with consurf , thus obtaining the values of score2 ( table 4 ) . we extracted binding sites for every interface between grb2 and the different chains of each of the 21 retrieved complexes . we found 190 different interaction motifs , with scores ranging from a minimum of 20 ( single aminoacid motifs ) to a maximum of 1.58  10 ( r[hqn]qq[ivl][fcma][ivl][kr][ed][ivl]e motif ) . we searched for the interacting motifs in the pint list and we selected , for each potential interactor , the one with the highest score . table 4 highlighted the sequence , the structural configuration and the score ( score3 ) of the motif with the highest ranking . by grouping the motifs belonging to the same interface between two chains table 4 showed the number and the sum of the calculated scores only for the motifs belonging to an interaction site ( score4 ) . the final score of each of the 46 possible grb2 interactors was computed as the normalized sum of all the scores , as illustrated in table 4 . to test the methodology , a dataset made of 10 grb2 true ( confirmed ) and 10 false ( not confirmed , randomly chosen proteins ) interactors was employed ; the final scores were computed for each simulated interaction . to perform an unbiased comparison leave - one - out  cross - validation procedure by considering each time a different protein as the unique  test  case and the remaining as  training set  . we then applied a simple classifier , obtained by computing the best threshold ( th ) on the scores of the training set to   maximize the information gain ( ig ) : 
								 ( 5)ig=c=12p(c)log2p(c )  + tscore=12p(tscore)c=12p(ctscore)log2(ctscore ) , 
							 where c was the class of the protein ( confirmed interactors / not interacting proteins ) and tscore a binary variable such that 
								 ( 6)tscore=  0 ,  if  scoreth , tscore  =  1 ,  if  score > th . 
							 as a result , the mean accuracy of the leave - one - out procedure was 75% , and the mean precision was 86% . finally , we calculated the median value of the thresholds obtained in the leave - one - out process ( i.e. , 1.63 ) ; this value was then used to select 21 proteins with the highest probability to be grb2 interactors : among these , the isoform 1 of the mitogen - activated protein kinase 14 ( mapk14 ) had the highest probability score . we have developed a novel algorithm for prediction of protein - protein interactions that combines structure similarity and sequence conservation of protein complex interfaces . the performance of the algorithm was tested on the ability to predict grb2-interacting proteins . this protein plays a very important role in the process of signal transduction , as a mediator between the growth factors receptors at the cellular membrane level and the cytosolic ras proteins . in particular , the mitosis promoting signal , stimulated by the epidermal growth factor ( egf ) , requires the tyrosine kinase activity , originated by specific trans membrane receptors ( egrfs ) . starting from these receptors , the activation of ras consists in a cascade of protein interactions that involves the grb2/sos-1 complex . different sites of auto phosphorylation in the c - terminal region of egfrs are binding sites for the sh2 domain of grb2 , while its sh3 domains mediate the recruitment of the exchange factor sos - l , inducing the subsequent activation of ras proteins . then , ras lead to a cascade that ends with the nuclear translocation of phosphorylated map kinase , which then activates transcription factors . after our bioinformatics prediction , we ranked a set of 46 potential grb2 interactors according to their scores , which we assumed to be putative interactors with the target protein . resorting to a score threshold chosen by means of a cross - validation strategy , we further screened   21 of the most probable interactors . among these , mapk14 had the highest probability score of interaction with grb2 . mapks are a group of serine / threonine kinases , activated in response to many extracellular stimuli and mediating different signal transduction pathways . four different mapks families were identified in mammalian cells : extracellular signal - regulated kinase ( erk ) , c - jun n - terminal kinase / stress - activated protein kinase ( jnk / sapk ) , erk5/big map kinase 1 ( bmk1 ) and mapk p38 . in particular , mapk p38 proteins are involved in growth regulation , cellular differentiation , apoptosis , cellular response to inflammation and stress . this subfamily is composed of four members (  ,  ,  and  ) and mapk14 is the isoform  , that together with  , is ubiquitary expressed . to further confirm our in silico prediction , the mapk14-grb2 interaction was previously in vitro observed using the gst pull - down technique . in particular , this study hypothesized that in platelets p38 bind to the sh2 domain of grb2 in response to stimulation mediated by activation of fcriia ( cd32 ) receptor . this association could act by carrying the cytosolic grb2 , with its complexed proteins , towards specific subcellular topologies , driving the complex to specific substrates . at a structural level , the best motif found for mapk14-grb2 interaction was ppp[ivl ] ; this motif was also identified as an interacting site , because in the in silico extracted complex ( 1gbq ) , it allowed the sh3 domain to bind to sos-1 protein . however , global alignments between grb2 and mapk14 protein sequences did not directly reveal a contribution of the above mentioned motif : differently , three motifs , extracted from complexes in which the sh2 domain of grb2 was involved , were aligned ( i.e. , pf , [ ed]n[ivl ] and [ ivl]k ) . these motifs were localized in pro58-phe59 , glu81-asn82-val83 , and leu151-lys152 in mapk14 protein tertiary structure ; as reported in figure 3 , a similar structural topology of the residues pro56-phe57 , glu79-asn80-ile81 , and leu148-lys149 was highlighted for erk2 , a tyrosine kinase which has been in vivo confirmed to interact with grb2 . for this reason , it was possible to predict that mapk14 , as well as erk2 , can interact with the sh2 domain of grb2 , probably through the above mentioned amino acids .   in summary , the method herein proposed is a first step to the definition of a bioinformatics tool to support experimental studies on protein interactions . according to the validation procedure performed , the accuracy and precision of this method were 75% and 86% , respectively . these results might suggest that the proposed bioinformatics approach can be effectively applied to preliminary screen a wide set of protein interactants , such as those deriving from two hybrids systems , to select those to be primarily investigated .   currently , the main limitations of our method are the small number of complexes with known structures and the relatively poor knowledge on confirmed interactors . to overcome these limits , we are working on future refinements of the method , in particular on exploiting the available bioinformatics and database knowledge to define different levels of prediction .
protein interactions are crucial in most biological processes . 
 several in silico methods have been recently developed to predict them . 
 this paper describes a bioinformatics method that combines sequence similarity and structural information to support experimental studies on protein interactions . 
 given a target protein , the approach selects the most likely interactors among the candidates revealed by experimental techniques , but not yet in vivo validated . 
 the sequence and the structural information of the in vivo confirmed proteins and complexes are exploited to evaluate the candidate interactors . 
 finally , a score is calculated to suggest the most likely interactors of the target protein . as an example , we searched for grb2 interactors . 
 we ranked a set of 46 candidate interactors by the presented method . 
 these candidates were then reduced to 21 , through a score threshold chosen by means of a cross - validation strategy . among them , the isoform 1 of mapk14 was in silico confirmed as a grb2 interactor . 
 finally , given a set of already confirmed interactors of grb2 , the accuracy and the precision of the approach were 75% and 86% , respectively . in conclusion , the proposed method can be conveniently exploited to select the proteins to be experimentally investigated within a set of potential interactors .