BACKGROUND
protein-protein interactions  regulate a wide range of biological processes, involved in almost every cellular function.
majority of the proteins in living cells interact with partner proteins and form a complex to regulate proper functions.
ppi employs transport mechanisms, muscle contractions, regulations of gene expression and signal transductions  <cit> .
ppis are classified into different types based on their functional and structural characteristics.
according to their stability, interaction surface and involvement, ppis are classified into obligate and non-obligate, homo and hetero, or permanent and transient  <cit> .
binding affinity defines the strength of ppis, and is represented by a dissociation constant .
binding affinity is crucial in drug developments and therapeutics, and thus, many approaches have been developed to measure the binding affinity.
generally, these approaches are categorized into two groups.
the first group identifies the binding affinity using scoring functions and two hybrid systems, surface plasmon resonance and forster resonance energy transfer  <cit> .
these experimental methods for estimating the binding affinity are costly and time consuming.
the second group uses computational methods to predict protein binding affinity, such as binding site prediction studies  <cit> , empirical scoring function, knowledge based and quantitative structural methods  <cit> .
machine learning models have been developed with structure- and sequence-based features to predict and classify the binding affinities.
yugandhar et al.
using sequence descriptors to develop a prediction method smo using support vector machines  to discriminate high and low binding affinity of heterodimeric protein complexes  <cit> .
additionally, the works  <cit>  used support vector regression  models with structure-based features to predict binding affinities for different sets of protein complexes.
alternatively, the work  <cit>  used functional features with a svr to represent the strength of interactions and observed physicochemical and conformational changes.
for existing studies of predicting binding affinities, the prediction models work using small datasets.
only few sequence based studies on predicting the binding affinities.
this work aims to predict the binding affinities of heterodimeric complexes and characterize the used sequence-based features.
nearly  <dig>  ppis exist and the growth of ppis in size increases speedily.
it is a challenging task to accurately predict the binding affinities of ppis based on sequence information only.
this work proposes a svm-based binding affinity classifier, called svm-bac, to classify heterodimeric protein complexes by predicting their binding affinity.
svm-bac using svm with an optimal feature selection method, an inheritable bi-objective combinatorial genetic algorithm   <cit> , can identify a small set of features to determine the binding affinity of protein complexes from  <dig> sequence descriptors including  <dig> physicochemical properties from the aaindex database  <cit>  and  <dig> selected physicochemical, energetic and conformational properties of the  <dig> amino acids from the literature  <cit> .
a dataset with  <dig> heterodimeric protein-protein complexes is established from the work  <cit> .
svm-bac identified  <dig> sequence descriptors to classify the high and low binding affinity of protein complexes and obtained 10-fold cross validation and independent test accuracies of  <dig> % and  <dig> %, respectively.
using these  <dig> features selected by svm-bac with svr, we estimated the binding affinity in terms of dissociation constant  for  <dig> heterodimeric protein complexes and obtained correlation coefficient of  <dig>  and a mean absolute error of  <dig> .
contribution analysis of prediction has been used to select top-ranked features.
the top-two physicochemical properties apparent partition energy  <cit>  and principal component analysis iv  <cit> , and an important secondary structure based feature, i.e.
normalized frequency of beta turn  <cit>  are effective in predicting the binding affinity of heterodimeric protein complexes.
RESULTS
prediction performance of svm-bac
we have classified heterodimeric protein complexes by predicting their binding affinities.
a dataset consisting of  <dig> and  <dig> complexes with high and low binding affinity was used, respectively.
all the sequences were encoded into  <dig> sequence descriptors.
svm-bac incorporating with the optimal feature selection algorithm ibcga selected a set of  <dig> informative sequence descriptors to discriminate the high and low binding affinity complexes.
svm-bac achieved the training , test accuracies and matthews correlation coefficient  of  <dig> %,  <dig> % and  <dig>  respectively, slightly better than the smo method  <cit>  with  <dig> %,  <dig> % and  <dig> , shown in table  <dig>  svm-bac predicted high and low binding affinity complexes with training sensitivity and specificity of  <dig>  and  <dig> , and test sensitivity and specificity of  <dig>  and  <dig> , respectively.
to avoid the biased results due to the fix partition of training and test datasets, we also evaluated the performance of svm-bac using the whole dataset of  <dig> complexes in terms of 10-fold and 5-fold cross validations .
the sensitivity, specificity, and accuracy of 10-cv were  <dig> ,  <dig> , and  <dig> %, respectively.
the sensitivity, specificity, and accuracy of 5-cv were  <dig> ,  <dig> , and  <dig> %, respectively.
the accuracies of 10-cv and 5-cv using  <dig> complexes were slightly smaller than the test accuracy  on  <dig> complexes.
sen , spe  and auc
classifier performance of using the roc curve is shown in figure  <dig>  the areas under the roc curve  were  <dig>  and  <dig>  for svm-bac and the smo method, respectively.
the smo method is better than several machine learning algorithms such as bayesian logistic regression, naïve bayes, multilayer perception, k-nearest neighbors, j <dig> decision tree, and random forest  <cit> .
the  <dig> sequence descriptors identified by svm-bac are given in table  <dig>  the results suggest that the  <dig> features selected by the optimization method ibcga were effective in predicting the binding affinity of complexes.
furthermore, we evaluated individual effect of these  <dig> features on prediction accuracy using knock-out analysis.
removing of an informative feature makes a significant decrease between  <dig> and 18% in terms of prediction accuracy, shown in figure  <dig>  these results suggest that the  <dig> features selected by ibcga have substantial effects on discriminating high and low binding affinity of protein complexes.
estimating binding affinities
binding affinity of a heterodimeric protein-protein complex is estimated in terms of dissociation constant .
the binding affinity dissociation constant depends on many factors, such as structural features, interface properties and physiological factors, which are not easily obtained from primary sequences only.
we made an attempt to estimate the binding affinities using the promising features of amino acids that were used to predict high and low binding affinity complexes.
there were  <dig> heterodimeric protein complexes used to estimate the binding affinities, which covered various ranges of binding affinity values  and functions.
support vector regression  was used as a prediction model to estimate binding affinities.
our model was trained using the  <dig> sequence descriptors and the pkd value.
the proposed sequence based model using svr yielded the correlation coefficient of  <dig>  and mean absolute error of  <dig>  .
the correlation result between estimated binding affinities and actual binding ones is shown in figure  <dig>  mean absolute error for  <dig> heterodimeric complexes is shown in figure  <dig>  the  <dig> protein-protein complexes and their respective pkd values were reported in additional file 1: table s <dig>
although we have used an effective set of sequence features, the estimation result of binding affinity was not good enough for the whole dataset, irrespective of their function types.
the result was consistent with the recently published prediction method of binding affinity using amino acid sequence feature  <cit>  that they predicted the binding affinity using  <dig> sequence-based features and obtained poor performance in terms of correlation coefficient on  <dig> protein complexes.
it is noted that protein-protein binding affinities also rely on their function types.
interestingly, when we observed the estimation error of  <dig> complexes, we found nearly  <dig> out of  <dig> complexes that the mean absolute error was  <dig> .
the result revealed that amino acid properties are also influential factors to estimate the binding affinities for heterodimeric complexes with specific functional types.
however, considering all the  <dig> complexes with various functional types, the estimation performance was not satisfactory, consistent with the work  <cit>  for the prediction of binding affinity on diverse protein-protein interactions.
although the promising properties of amino acids can predict high and low binding affinity complexes with satisfactory results, yet they cannot be used to accurately estimate the actual binding affinity dissociation constant.
to advance the estimation ability, structural features, interface properties, physiological factors, and partner residues information are useful which are not available from the primary sequences themselves.
partner-aware prediction of interacting residues in protein-protein complexes from sequence information has significance in characterizing the interaction  <cit> .
physicochemical property analysis
the top-two physicochemical properties according to the main effect difference  are apparent partition energies calculated from chothia index   <cit>  and principal component iv   <cit> .
large value of med means the great contribution to prediction performance.
an influential secondary structure related property, normalized frequency of beta turn   <cit>  was at rank  <dig>  the three physicochemical properties are further analyzed and discussed below.
table  <dig> presents the values of  <dig> amino acids for the three physicochemical properties, the amino acid compositions in high and low binding affinity complexes, and amino acid compositional difference between the two classes.
aguyh <dig> = apparent partition energies.
bsnep <dig> = principal component analysis iv.
cchop <dig> = normalized frequencies of beta turn.
the property of apparent partition energies
the property of guyh <dig> is described as "apparent partition energies calculated from chothia index  <cit> ".
chothia index is based on calculating the ratio of buried molar fractions for each amino acid in globular proteins.
guy proposed salvation energies calculated from vapour pressure of side chain analogues r  which are highly correlated  with chothia apparent transfer energy scale  <cit> .
this property describes the buried hydrophobicity in proteins.
the buried hydrophobicity nature of protein-protein interactions has been extensively studied.
protein-protein binding directly correlates with total buried hydrophobic surface area and the binding energy increases with the increment of interfacial buried surface area  <cit> .
mutational studies on free energy change on mutants Δ  correlated with hydrophobic buried area.
upon adding hydrophobic buried surface at their interface leads to gaining of free energy Δ  = - <dig> ±  <dig>  cal/mola <dig>  statistical and experimental estimations state that the increase of hydrophobic buried surface enhances the protein binding affinity  <cit> .
we thus calculated apparent partition energies for hydrophobic amino acids in our dataset according to the property  <cit> .
we found that the average apparent partition energies for high binding affinity complexes are slightly larger than those for low binding affinity complexes that mean of apparent partition energies obtained for high and low binding affinities were - <dig>  and - <dig> , respectively.
this property analysis declared the importance of hydrophobic amino acid residues at buried region which is one of the major influential factors to increase the binding strength of an interaction.
hydrophobic core in high binding affinity complex pdb_id: 1mah as an example is shown in figure  <dig>  the analysis results are consistent with the previous studies of binding affinity.
the property of principal component analysis iv
the property of snep <dig> was described as "relations between chemical structure and biological activity in principal component analysis iv".
sneath calculated the correlations of amino acids for the use in principal component analysis.
four vectors  were derived from the  <dig> amino acid correlations  <cit> .
these four vectors were interpreted as different properties, in which vector iv represents hydroxythiolation.
hydroxythiolation property has an ability to form hydrogen bonds.
hydrogen bonds and salt bridges are one of the major contributors to protein-protein interactions.
polar and non-polar side chains significantly contribute to stabilization of the complexes.
polar side chains stabilize the protein complexes through hydrogen bonds.
in general, protein interfaces are more hydrophilic than interior residues and form more hydrogen bonds at interfaces  <cit> .
in trypsin-pancreatic trypsin inhibitor, insulin dimer and hemoglobin alpha beta dimer complexes, most of the hydrogen bonds are charged; opposite charges are more favorable to hydrogen bond formation, and 86% of buried polar atoms are favorable to form hydrogen bonds.
chothia et al.
reported mean of hydrogen bonds per  <dig> a <dig> Δasa, and maximum and minimum numbers of hydrogen bonds in heterodimeric complexes were  <dig>  and  <dig> , respectively.
xu-et al analyzed hydrogen bond and salt bridge specificity, and charge distribution at protein-protein interfaces  <cit> .
we measured the numbers of hydrogen bonds at protein-protein interfaces in high and low binding affinity complexes.
the average numbers of hydrogen bonds in high and low binding affinity complexes were  <dig>  ±  <dig>  and  <dig>  ±  <dig> , respectively.
the hydrogen bonds at their interfaces were more enriched in high binding affinity complexes than in low binding affinity complexes.
contribution of these hydrogen bonds in overall protein-protein interactions is various and crucial to pinpoint.
the numbers of hydrogen bonds at interfaces in high and low binding affinity complexes are shown in figure  <dig>
the property of normalized frequencies of beta turns
the property of chop <dig> is described as "normalized frequencies of beta turn".
beta bends/ turn is formed by the polypeptide chain folds back on itself by  <dig> degrees.
beta turns shows three conformations based on their phi, psi values, and two major types exists i.e., types  <dig> and  <dig>  <cit> .
amino acid preferences are different in each type.
in type  <dig> beta turns gly possess a major preferences at position i+ <dig> and i+ <dig>  usually, beta turns promote antiparallel beta sheets, which can stabilize the secondary structure and these beta sheets are involved in protein interactions.
beta sheet interactions are involved in the binding of ras oncoproteins to their receptors, significant part occurred in cell signaling pathway  <cit> , immune system, and hiv- <dig> proteases and inhibitors  <cit> .
non-regular structures such as turns, helix and loops at interfaces are large groups in heterodimeric complexes and also have large percentages of interface residues at protein-protein complexes belonging to non-regular regions only  <cit> .
to examine the beta turn participation in heterodimeric complexes, we calculated the number of beta turns in the used dataset.
totally,  <dig>  beta turns participate in the  <dig> heterodimeric complexes.
on average, every high and low binding affinity complex possesses  <dig>  ±  <dig>  and  <dig>  ±  <dig>  beta turns, respectively.
notably, the mean number of beta turns in high binding affinity complexes is significantly larger than that in low binding affinity complexes where the p-value of student's t-test is  <dig> .
the difference accuracy of the beta turns property chop <dig> was  <dig> % using the knock-out analysis .
though, there are several factors influencing the protein binding affinities, beta turn is one of the most important factors in binding affinity prediction.
a better insight into beta turn would have the potential to improve our current protein structure analysis and prediction methods.
beta turn formation in the example complex prommp-2/timp- <dig> is shown in figure  <dig>
all  <dig> physicochemical properties and their amino acid composition preferences were calculated for high and low binding affinity complexes, reported in additional file 2: table s <dig>
significance of h-bonds and beta turn properties in predicting high and low binding affinity complexes
we estimated the individual effects of h-bonds and beta turn properties in the binding affinity classification by knocking out one of the two corresponding properties, snep <dig> and chop <dig>  the difference accuracies for the h-bonds and beta turn properties are  <dig> % and  <dig> %, respectively .
elimination of these two features decreases the overall prediction accuracy of  <dig> %.
the result suggests that the h-bond and beta turn properties equally contribute to predict high and low binding affinity complexes.
CONCLUSIONS
characterizing the physicochemical properties influencing the protein binding affinity has a significant role in protein-protein interaction studies.
we developed amino acid based predictor named as svm-bac to classify the high and low binding affinity complexes by identifying  <dig> informative properties.
moreover, the svr-based prediction method using the  <dig> features was investigated to examine the ability of predicting the binding affinities for the whole set of complexes of various functional types.
our model estimated the binding affinities  of  <dig> heterodimeric complexes with mean absolute error of  <dig>  and it can be further refined by considering the categorization of functional types.
further physicochemical analysis revealed that buried hydrophobicity, beta turns, and hydrogen bonds are influential factors in protein binding affinity.
the property analysis would be helpful to understand the underlying mechanism in the protein binding affinities.
though, protein binding affinities depend on various factors, we attempted to find out the contribution of sequence properties in binding affinity prediction.

 protein-protein interactions regulate a wide range of biological processes, involved in almost every cellular function. majority of the proteins in living cells interact with partner proteins and form a complex to regulate proper functions. ppi employs transport mechanisms, muscle contractions, regulations of gene expression and signal transductions. ppis are classified into different types based on their functional and structural characteristics. according to their stability, interaction surface and involvement, ppis are classified into obligate and non-obligate, homo and hetero, or permanent and transient. binding affinity defines the strength of ppis, and is represented by a dissociation constant. binding affinity is crucial in drug developments and therapeutics, and thus, many approaches have been developed to measure the binding affinity. generally, these approaches are categorized into two groups. the first group identifies the binding affinity using scoring functions and two hybrid systems, surface plasmon resonance and forster resonance energy transfer. these experimental methods for estimating the binding affinity are costly and time consuming. the second group uses computational methods to predict protein binding affinity, such as binding site prediction studies, empirical scoring function, knowledge based and quantitative structural methods. machine learning models have been developed with structure- and sequence-based features to predict and classify the binding affinities. yugandhar et al. using sequence descriptors to develop a prediction method smo using support vector machines to discriminate high and low binding affinity of heterodimeric protein complexes. additionally, the works used support vector regression models with structure-based features to predict binding affinities for different sets of protein complexes. alternatively, the work used functional features with a svr to represent the strength of interactions and observed physicochemical and conformational changes. for existing studies of predicting binding affinities, the prediction models work using small datasets. only few sequence based studies on predicting the binding affinities. this work aims to predict the binding affinities of heterodimeric complexes and characterize the used sequence-based features. nearly ppis exist and the growth of ppis in size increases speedily. it is a challenging task to accurately predict the binding affinities of ppis based on sequence information only. this work proposes a svm-based binding affinity classifier, called svm-bac, to classify heterodimeric protein complexes by predicting their binding affinity. svm-bac using svm with an optimal feature selection method, an inheritable bi-objective combinatorial genetic algorithm, can identify a small set of features to determine the binding affinity of protein complexes from sequence descriptors including physicochemical properties from the aaindex database and selected physicochemical, energetic and conformational properties of the amino acids from the literature. a dataset with heterodimeric protein-protein complexes is established from the work. svm-bac identified sequence descriptors to classify the high and low binding affinity of protein complexes and obtained 10-fold cross validation and independent test accuracies of % and %, respectively. using these features selected by svm-bac with svr, we estimated the binding affinity in terms of dissociation constant for heterodimeric protein complexes and obtained correlation coefficient of and a mean absolute error of. contribution analysis of prediction has been used to select top-ranked features. the top-two physicochemical properties apparent partition energy and principal component analysis iv, and an important secondary structure based feature, i.e. normalized frequency of beta turn are effective in predicting the binding affinity of heterodimeric protein complexes.  prediction performance of svm-bac we have classified heterodimeric protein complexes by predicting their binding affinities. a dataset consisting of and complexes with high and low binding affinity was used, respectively. all the sequences were encoded into sequence descriptors. svm-bac incorporating with the optimal feature selection algorithm ibcga selected a set of informative sequence descriptors to discriminate the high and low binding affinity complexes. svm-bac achieved the training, test accuracies and matthews correlation coefficient of %, % and respectively, slightly better than the smo method with %, % and, shown in table svm-bac predicted high and low binding affinity complexes with training sensitivity and specificity of and, and test sensitivity and specificity of and, respectively. to avoid the biased results due to the fix partition of training and test datasets, we also evaluated the performance of svm-bac using the whole dataset of complexes in terms of 10-fold and 5-fold cross validations. the sensitivity, specificity, and accuracy of 10-cv were,, and %, respectively. the sensitivity, specificity, and accuracy of 5-cv were,, and %, respectively. the accuracies of 10-cv and 5-cv using complexes were slightly smaller than the test accuracy on complexes. sen, spe and auc classifier performance of using the roc curve is shown in figure the areas under the roc curve were and for svm-bac and the smo method, respectively. the smo method is better than several machine learning algorithms such as bayesian logistic regression, naïve bayes, multilayer perception, k-nearest neighbors, j decision tree, and random forest. the sequence descriptors identified by svm-bac are given in table the results suggest that the features selected by the optimization method ibcga were effective in predicting the binding affinity of complexes. furthermore, we evaluated individual effect of these features on prediction accuracy using knock-out analysis. removing of an informative feature makes a significant decrease between and 18% in terms of prediction accuracy, shown in figure these results suggest that the features selected by ibcga have substantial effects on discriminating high and low binding affinity of protein complexes. estimating binding affinities binding affinity of a heterodimeric protein-protein complex is estimated in terms of dissociation constant. the binding affinity dissociation constant depends on many factors, such as structural features, interface properties and physiological factors, which are not easily obtained from primary sequences only. we made an attempt to estimate the binding affinities using the promising features of amino acids that were used to predict high and low binding affinity complexes. there were heterodimeric protein complexes used to estimate the binding affinities, which covered various ranges of binding affinity values and functions. support vector regression was used as a prediction model to estimate binding affinities. our model was trained using the sequence descriptors and the pkd value. the proposed sequence based model using svr yielded the correlation coefficient of and mean absolute error of. the correlation result between estimated binding affinities and actual binding ones is shown in figure mean absolute error for heterodimeric complexes is shown in figure the protein-protein complexes and their respective pkd values were reported in additional file 1: table s although we have used an effective set of sequence features, the estimation result of binding affinity was not good enough for the whole dataset, irrespective of their function types. the result was consistent with the recently published prediction method of binding affinity using amino acid sequence feature that they predicted the binding affinity using sequence-based features and obtained poor performance in terms of correlation coefficient on protein complexes. it is noted that protein-protein binding affinities also rely on their function types. interestingly, when we observed the estimation error of complexes, we found nearly out of complexes that the mean absolute error was. the result revealed that amino acid properties are also influential factors to estimate the binding affinities for heterodimeric complexes with specific functional types. however, considering all the complexes with various functional types, the estimation performance was not satisfactory, consistent with the work for the prediction of binding affinity on diverse protein-protein interactions. although the promising properties of amino acids can predict high and low binding affinity complexes with satisfactory results, yet they cannot be used to accurately estimate the actual binding affinity dissociation constant. to advance the estimation ability, structural features, interface properties, physiological factors, and partner residues information are useful which are not available from the primary sequences themselves. partner-aware prediction of interacting residues in protein-protein complexes from sequence information has significance in characterizing the interaction. physicochemical property analysis the top-two physicochemical properties according to the main effect difference are apparent partition energies calculated from chothia index and principal component iv. large value of med means the great contribution to prediction performance. an influential secondary structure related property, normalized frequency of beta turn was at rank the three physicochemical properties are further analyzed and discussed below. table presents the values of amino acids for the three physicochemical properties, the amino acid compositions in high and low binding affinity complexes, and amino acid compositional difference between the two classes. aguyh = apparent partition energies. bsnep = principal component analysis iv. cchop = normalized frequencies of beta turn. the property of apparent partition energies the property of guyh is described as "apparent partition energies calculated from chothia index ". chothia index is based on calculating the ratio of buried molar fractions for each amino acid in globular proteins. guy proposed salvation energies calculated from vapour pressure of side chain analogues r which are highly correlated with chothia apparent transfer energy scale. this property describes the buried hydrophobicity in proteins. the buried hydrophobicity nature of protein-protein interactions has been extensively studied. protein-protein binding directly correlates with total buried hydrophobic surface area and the binding energy increases with the increment of interfacial buried surface area. mutational studies on free energy change on mutants Δ correlated with hydrophobic buried area. upon adding hydrophobic buried surface at their interface leads to gaining of free energy Δ = - ± cal/mola statistical and experimental estimations state that the increase of hydrophobic buried surface enhances the protein binding affinity. we thus calculated apparent partition energies for hydrophobic amino acids in our dataset according to the property. we found that the average apparent partition energies for high binding affinity complexes are slightly larger than those for low binding affinity complexes that mean of apparent partition energies obtained for high and low binding affinities were - and -, respectively. this property analysis declared the importance of hydrophobic amino acid residues at buried region which is one of the major influential factors to increase the binding strength of an interaction. hydrophobic core in high binding affinity complex pdb_id: 1mah as an example is shown in figure the analysis results are consistent with the previous studies of binding affinity. the property of principal component analysis iv the property of snep was described as "relations between chemical structure and biological activity in principal component analysis iv". sneath calculated the correlations of amino acids for the use in principal component analysis. four vectors were derived from the amino acid correlations. these four vectors were interpreted as different properties, in which vector iv represents hydroxythiolation. hydroxythiolation property has an ability to form hydrogen bonds. hydrogen bonds and salt bridges are one of the major contributors to protein-protein interactions. polar and non-polar side chains significantly contribute to stabilization of the complexes. polar side chains stabilize the protein complexes through hydrogen bonds. in general, protein interfaces are more hydrophilic than interior residues and form more hydrogen bonds at interfaces. in trypsin-pancreatic trypsin inhibitor, insulin dimer and hemoglobin alpha beta dimer complexes, most of the hydrogen bonds are charged; opposite charges are more favorable to hydrogen bond formation, and 86% of buried polar atoms are favorable to form hydrogen bonds. chothia et al. reported mean of hydrogen bonds per a Δasa, and maximum and minimum numbers of hydrogen bonds in heterodimeric complexes were and, respectively. xu-et al analyzed hydrogen bond and salt bridge specificity, and charge distribution at protein-protein interfaces. we measured the numbers of hydrogen bonds at protein-protein interfaces in high and low binding affinity complexes. the average numbers of hydrogen bonds in high and low binding affinity complexes were ± and ±, respectively. the hydrogen bonds at their interfaces were more enriched in high binding affinity complexes than in low binding affinity complexes. contribution of these hydrogen bonds in overall protein-protein interactions is various and crucial to pinpoint. the numbers of hydrogen bonds at interfaces in high and low binding affinity complexes are shown in figure the property of normalized frequencies of beta turns the property of chop is described as "normalized frequencies of beta turn". beta bends/ turn is formed by the polypeptide chain folds back on itself by degrees. beta turns shows three conformations based on their phi, psi values, and two major types exists i.e., types and. amino acid preferences are different in each type. in type beta turns gly possess a major preferences at position i+ and i+ usually, beta turns promote antiparallel beta sheets, which can stabilize the secondary structure and these beta sheets are involved in protein interactions. beta sheet interactions are involved in the binding of ras oncoproteins to their receptors, significant part occurred in cell signaling pathway, immune system, and hiv- proteases and inhibitors. non-regular structures such as turns, helix and loops at interfaces are large groups in heterodimeric complexes and also have large percentages of interface residues at protein-protein complexes belonging to non-regular regions only. to examine the beta turn participation in heterodimeric complexes, we calculated the number of beta turns in the used dataset. totally, beta turns participate in the heterodimeric complexes. on average, every high and low binding affinity complex possesses ± and ± beta turns, respectively. notably, the mean number of beta turns in high binding affinity complexes is significantly larger than that in low binding affinity complexes where the p-value of student's t-test is. the difference accuracy of the beta turns property chop was % using the knock-out analysis. though, there are several factors influencing the protein binding affinities, beta turn is one of the most important factors in binding affinity prediction. a better insight into beta turn would have the potential to improve our current protein structure analysis and prediction methods. beta turn formation in the example complex prommp-2/timp- is shown in figure all physicochemical properties and their amino acid composition preferences were calculated for high and low binding affinity complexes, reported in additional file 2: table s significance of h-bonds and beta turn properties in predicting high and low binding affinity complexes we estimated the individual effects of h-bonds and beta turn properties in the binding affinity classification by knocking out one of the two corresponding properties, snep and chop the difference accuracies for the h-bonds and beta turn properties are % and %, respectively. elimination of these two features decreases the overall prediction accuracy of %. the result suggests that the h-bond and beta turn properties equally contribute to predict high and low binding affinity complexes.  characterizing the physicochemical properties influencing the protein binding affinity has a significant role in protein-protein interaction studies. we developed amino acid based predictor named as svm-bac to classify the high and low binding affinity complexes by identifying informative properties. moreover, the svr-based prediction method using the features was investigated to examine the ability of predicting the binding affinities for the whole set of complexes of various functional types. our model estimated the binding affinities of heterodimeric complexes with mean absolute error of and it can be further refined by considering the categorization of functional types. further physicochemical analysis revealed that buried hydrophobicity, beta turns, and hydrogen bonds are influential factors in protein binding affinity. the property analysis would be helpful to understand the underlying mechanism in the protein binding affinities. though, protein binding affinities depend on various factors, we attempted to find out the contribution of sequence properties in binding affinity prediction. 
results reveal that the following properties are effective in predicting the binding affinity of heterodimeric protein complexes: apparent partition energy based on buried molar fractions, relations between chemical structure and biological activity in principal component analysis iv, and normalized frequency of beta turn. svm-bac yielded the training accuracy, sensitivity, specificity, auc and test accuracy of %,,, and %, respectively, better than existing machine learning algorithms. the characterization analysis revealed that the average numbers of beta turns and hydrogen bonds at protein-protein interfaces in high binding affinity complexes are more than those in low binding affinity complexes. this work proposes a support vector machine based binding affinity classifier, called svm-bac, to classify heterodimeric protein complexes based on the prediction of their binding affinity. most machine learning approaches have been developed for predicting the binding affinity of protein-protein complexes based on structure and functional information. the features and support vector regression were further used to estimate the binding affinities of heterodimeric protein complexes. this work aims to predict the binding affinity of heterodimeric protein complexes from sequences only. svm-bac identified of sequence descriptors to classify heterodimeric protein complexes into low and high binding affinity. the proposed sequence-based prediction method svm-bac uses an optimal feature selection method to identify informative features to classify and predict binding affinity of heterodimeric protein complexes. prediction performance of a jackknife test was the correlation coefficient of and mean absolute error of. protein-protein interactions are involved in various biological processes, and underlying mechanism of the interactions plays a crucial role in therapeutics and protein engineering. we further analyze three informative physicochemical properties according to their contribution to prediction performance.
 protein-protein interactions are involved in various biological processes, and underlying mechanism of the interactions plays a crucial role in therapeutics and protein engineering. most machine learning approaches have been developed for predicting the binding affinity of protein-protein complexes based on structure and functional information. this work aims to predict the binding affinity of heterodimeric protein complexes from sequences only.  this work proposes a support vector machine based binding affinity classifier, called svm-bac, to classify heterodimeric protein complexes based on the prediction of their binding affinity. svm-bac identified of sequence descriptors to classify heterodimeric protein complexes into low and high binding affinity. svm-bac yielded the training accuracy, sensitivity, specificity, auc and test accuracy of %,,, and %, respectively, better than existing machine learning algorithms. the features and support vector regression were further used to estimate the binding affinities of heterodimeric protein complexes. prediction performance of a jackknife test was the correlation coefficient of and mean absolute error of. we further analyze three informative physicochemical properties according to their contribution to prediction performance. results reveal that the following properties are effective in predicting the binding affinity of heterodimeric protein complexes: apparent partition energy based on buried molar fractions, relations between chemical structure and biological activity in principal component analysis iv, and normalized frequency of beta turn.  the proposed sequence-based prediction method svm-bac uses an optimal feature selection method to identify informative features to classify and predict binding affinity of heterodimeric protein complexes. the characterization analysis revealed that the average numbers of beta turns and hydrogen bonds at protein-protein interfaces in high binding affinity complexes are more than those in low binding affinity complexes. 9- september joint 26th genome informatics workshop and asia pacific bioinformatics network 14th international conference on bioinformatics tokyo, japan 