g - protein coupled receptors ( gpcrs ) represent one of the most important classes of drug targets for pharmaceutical industry and play important roles in cellular signal transduction as cell surface receptor proteins 1 .
, 2 .. a gpcr only has a single polypeptide that consists of seven transmembrane -helices , three extracellular and three intracellular loops connecting the transmembrane domains .
the n - terminal of the polypeptide is located on the extracellular side and the c - terminal extends to the cytoplasm .
gpcrs can be activated by various extracellular signaling molecules that bind to the receptors and trigger their conformational changes .
the activated receptors will then couple with g - proteins ( gi / o , gq/11 , gs , and g12/13 ) and further activate different signaling pathways ( 3 ) . since most gpcrs are coupled with one single subtype of g - proteins , they are broadly categorized into gi / o- , gq/11- , gs- , and g12/13-coupled receptors based on their g - protein coupling preference . predicting the coupling specificity of gpcrs to g - proteins is vital for further understanding the mechanism of signal transduction and the function of the receptors within a cell , which may provide new clues for pharmaceutical research and development .
many computational methods such as the pattern discovery - based method ( 4 ) , the naive bayes model ( 5 ) , the chemometric approach ( 6 ) , and the hidden markov model ( hmm ) 7 . , 8 . , 9 . , have been developed .
furthermore , since previous experimental researches have demonstrated that the coupling specificity of gpcrs to g - proteins is mainly determined by the intracellular domains of the receptor 10 . , 11 . , the reported computational methods only used the intracellular regions of gpcr sequences as the information for the development of the prediction models . in this study , we hypothesize that , besides the intracellular regions of gpcrs , other factors such as the physiochemical properties of amino acid residues , the extracellular and transmembrane regions of gpcrs , might also have some influence on the coupling specificity , and therefore can be used as additional information to further improve the prediction accuracy . accordingly , the features of amino acid compositions and the physiochemical properties of the full - length gpcr sequences have been analyzed and extracted , and classifiers have been developed to predict the coupling specificity based on these features and support vector machines ( svms ) .
in this study , 124 human gpcr sequences ( 62 , 33 , and 29 sequences for gi / o- , gq/11- , gs - class , respectively ) were collected from the gpdb database ( 12 ) , which were used to develop the svm classifiers for three classes of gpcrs .
ten - fold cross - validation process was used to evaluate the performance of the proposed method , that is , nine tenths of the sequences were used as the training set to extract classification features and train svm classifiers , and one tenth were used as the test set to verify the prediction results .
the training and test sets were segregated at random and the process was repeated 30 times .
three measures , sensitivity ( sn ) , specificity ( sp ) , and overall performance accuracy ( acc ) , were used to evaluate the prediction performance .
sn and sp are defined as sn = tp/(tp+fn ) and sp = tn/(tn+fp ) , respectively , where tp is the number of correctly predicted positive samples for a specific class of gpcrs , fn is the number of incorrectly predicted positive samples , and fp is the number of incorrectly predicted negative samples . the overall performance accuracy , which measures the average accuracy of predicting the three classes of gpcrs , is defined as the percentage of all correctly predicted number of positive samples to the total number of positive samples . during the development of svm classifiers ,
different kernel functions were tried , and it was found that using the linear kernel function could get the best results . besides the commonly used feature of amino acid compositions , additional features such as hydrophobicity , normalized van der waals volume , polarity , polarizability , and the charge of amino acids were also used in this study for better prediction results . for each of these additional features
, it is defined as the feature frequency in the form of k - mers ( see materials and methods ) and the value of parameter k ( k  1 ) has a great influence on the prediction results .
the results of predicting the coupling specificity for the three classes of gpcrs with different k ( k = 1 , 2 , and 3 ) are given in table 1 , which were obtained by using the linear kernel function and the features extracted from the full - length gpcr sequences .
it can be seen that , when k = 2 , the overall performance accuracy is higher than others , while the sn and sp for all three classes also achieve a relatively high level . in order to compare the prediction performance based on the information of the full - length sequences of gpcrs with that only based on the intracellular part as adopted by other researches
, we predicted the transmembrane helices of these gpcrs with the programs conpred ii ( 13 ) and hmmtop 2.0 ( 14 ) , then extracted the intracellular sequences and converted them into fixed - length feature vectors with the same method .
the prediction results with k = 2 and k = 3 are given in table 2 .
the primary aim of this study is to improve the accuracy of predicting the coupling specificity of gpcrs to g - proteins by such measures as making use of the information of full - length gpcr sequences , integrating the compositional features and physiochemical properties of amino acids , and using the svm method . by comparing table 1 with table 2 , it is distinct that the prediction accuracy based on the full - length sequences of gpcrs is better than that only based on the intracellular part , meaning that the full - length sequences do contain more information about the coupling specificity of gpcrs to g - proteins than the intracellular sequences .
in addition , the methods based on the intracellular sequences need to extract them from the full - length sequences of gpcrs using transmembrance topology prediction programs , which may bring some errors as the accuracy of predicting the seven transmembrance -helices of gpcrs is currently only about 80% . on the contrary
, this problem does not exist in the proposed method that uses the information of full - length gpcr sequences directly .
furthermore , the additional features used in this study , including hydrophobicity , normalized van der waals volume , polarity , polarizability , and the charge of amino acids , can contribute to better prediction accuracy ( data not shown ) , implying that the physiochemical properties of amino acids might also play important roles in determining the coupling specificity of gpcrs to g - proteins .
it is worth noticing that the overall prediction accuracy of our method is still affected by the following factors .
firstly , the size of dataset and the imbalance among different gpcr classes have a great influence on the prediction accuracy . secondly ,
although the collected dataset has been filtrated to only include single coupled gpcrs , it may still contain some potential promiscuous receptors , which could couple to more than one class of g - proteins and have not been validated by biological experiments . on one hand , if the training set contains the promiscuous receptors , it will influence the feature extraction of the single coupled receptors , therefore , the test set can not be classified effectively . on the other hand ,
if the test set has promiscuous ones , it will not obtain the overall prediction results of their coupling specificity to g - proteins .
fortunately , with the accumulation of more experimental data , the influence of the above factors on the prediction accuracy will decrease gradually . in a word , the testing results demonstrate that the method proposed in this study could obtain better prediction accuracy .
future work of this study will focus on exploring and integrating more features of gpcrs to further improve the accuracy of predicting the coupling specificity of gpcrs to g - proteins and to develop novel approaches to distinguish single coupled receptors from promiscuous ones .
a set of human gpcr sequences with known coupling specificity was collected from the gpdb database ( http://bioinformatics.biol.uoa.gr/gpdb ) .
this database , which is useful for the study of gpcr / g - protein interactions , contains 469 species of g - proteins and gpcr sequences .
we selected 124 human gpcrs that meet the following criteria : firstly , only single coupled receptors ( only couple to one class of g - proteins ) were included in the dataset .
g12/13-coupled receptors were not included because of insufficient data . secondly , those gpcr sequences labeled with 
finally , the gpcrs were divided into three groups according to their coupling specificity , and the gi / o- , gq/11- , and gs - coupled receptors contained 62 , 33 , and 29 sequences , respectively .
furthermore , all of these sequences were verified with the tips ( 15 ) and swiss - prot database .
generally , in the process of constructing the positive and negative samples for developing classifiers , two kinds of methods are wildly used , one is the one - against - other (
1-v - n ) method , and the other is the one - against - one ( 1-v-1 ) method . supposing there are n classes to be classified , the one - against - other method means to pick up the samples in one class as positive and the ones in remnant classes as negative .
another method , one - against - one , uses the samples in one class as positive and the ones in another class as negative . in this study
, the numbers of gpcr sequences for the classes gq/11 and gs are not large enough , which may cause the imbalance problem in the dataset .
the one - against - other method will further aggravate the imbalance problem , while the one - against - one method can improve the prediction accuracy for classes with fewer samples , and therefore was adopted in this study .
for each gpcr sequence , the feature vector was assembled from the encoded representations of amino acid compositions , hydrophobicity , normalized van der waals volume , polarity , polarizability , and charge .
two different coding schemes were adopted for the amino acid compositions and the five physiochemical properties , respectively . according to the amino acid compositions ,
a protein sequence is represented by a vector in a 20-dimensional space : xa=[f1,f2,  ,f20]t where fi ( i = 1 , 2 ,  , 20 ) is the occurrence frequency of the twenty amino acids in the sequence . for each of the five physiochemical properties ,
the twenty amino acids are grouped into three classes represented by pi ( i = 1 , 2 , and 3 ) according to their different values .
so a primal sequence is transformed into five different physiochemical sequences consisting of p1 , p2 , and p3 , which are called pcseq .
then , the k - mer vector c and the distribution vector d for the pcseq corresponding to each of the five physiochemical properties are calculated , respectively 16 . , 17 .. given an integer k  1 , k - mers are composed of all the continuous subsequences with length k. there are possibly 3 permutation and combination of the subsequences .
so the k - mer vector c is a vector with 3 dimensions and the value of each dimension is the occurrence frequency of every subsequence in the pcseq .
the distribution vector d , which is used to describe the global distribution of the property in the pcseq , is described by five chain lengths ( in percent ) , within which 25% , 50% , 75% , and 100% of the amino acids with a certain class pi in each of the five properties are contained .
it is defined as:(2)d=i=13j=14pos(l(i)j25%)l(seq)100% where l(i ) is the number of amino acids in the special class pi , and l(seq ) is the length of pcseq . for each of the chosen physiochemical properties ,
its k - mer vector c and the distribution vector d were calculated and combined .
finally , a protein sequence was converted into a vector with [ 20 + ( 3 + 4  3 )  5 ) ] dimensions as the input of the svm classifiers .
svm is a standard supervised learning algorithm based on recent developments in the statistical learning theory 18 .
, 19 .. it builds a hyperplane separating the positive and negative examples in multiple - dimensional space .
the svm calculation was implemented by using the libsvm 2.8 ( 20 ) software package .
the software enables the user to choose different parameters and kernel functions including linear kernel function , radial basis function , and polynomial kernel function to obtain the best effect . in this study ,
three svms were constructed for classifying the gi / o- , gq/11- , and gs - gpcrs .
the comparison results of using different kernel functions show that the linear kernel function can achieve the best accuracy , which is therefore used in the developed svm classifiers . in addition
, the penalty factor c is an important parameter of svm , which has a great influence on the prediction results . in this study ,
the optimal value of this parameter was searched in the range of 1 to 200 by comparing the prediction results .
a set of human gpcr sequences with known coupling specificity was collected from the gpdb database ( http://bioinformatics.biol.uoa.gr/gpdb ) .
this database , which is useful for the study of gpcr / g - protein interactions , contains 469 species of g - proteins and gpcr sequences .
we selected 124 human gpcrs that meet the following criteria : firstly , only single coupled receptors ( only couple to one class of g - proteins ) were included in the dataset .
g12/13-coupled receptors were not included because of insufficient data . secondly , those gpcr sequences labeled with 
finally , the gpcrs were divided into three groups according to their coupling specificity , and the gi / o- , gq/11- , and gs - coupled receptors contained 62 , 33 , and 29 sequences , respectively .
furthermore , all of these sequences were verified with the tips ( 15 ) and swiss - prot database .
generally , in the process of constructing the positive and negative samples for developing classifiers , two kinds of methods are wildly used , one is the one - against - other (
1-v - n ) method , and the other is the one - against - one ( 1-v-1 ) method . supposing there are n classes to be classified , the one - against - other method means to pick up the samples in one class as positive and the ones in remnant classes as negative .
another method , one - against - one , uses the samples in one class as positive and the ones in another class as negative . in this study
, the numbers of gpcr sequences for the classes gq/11 and gs are not large enough , which may cause the imbalance problem in the dataset .
the one - against - other method will further aggravate the imbalance problem , while the one - against - one method can improve the prediction accuracy for classes with fewer samples , and therefore was adopted in this study .
for each gpcr sequence , the feature vector was assembled from the encoded representations of amino acid compositions , hydrophobicity , normalized van der waals volume , polarity , polarizability , and charge .
two different coding schemes were adopted for the amino acid compositions and the five physiochemical properties , respectively . according to the amino acid compositions ,
a protein sequence is represented by a vector in a 20-dimensional space : xa=[f1,f2,  ,f20]t where fi ( i = 1 , 2 ,  , 20 ) is the occurrence frequency of the twenty amino acids in the sequence . for each of the five physiochemical properties ,
the twenty amino acids are grouped into three classes represented by pi ( i = 1 , 2 , and 3 ) according to their different values .
so a primal sequence is transformed into five different physiochemical sequences consisting of p1 , p2 , and p3 , which are called pcseq .
then , the k - mer vector c and the distribution vector d for the pcseq corresponding to each of the five physiochemical properties are calculated , respectively 16 . , 17 .. given an integer k  1 , k - mers are composed of all the continuous subsequences with length k. there are possibly 3 permutation and combination of the subsequences .
so the k - mer vector c is a vector with 3 dimensions and the value of each dimension is the occurrence frequency of every subsequence in the pcseq .
the distribution vector d , which is used to describe the global distribution of the property in the pcseq , is described by five chain lengths ( in percent ) , within which 25% , 50% , 75% , and 100% of the amino acids with a certain class pi in each of the five properties are contained .
it is defined as:(2)d=i=13j=14pos(l(i)j25%)l(seq)100% where l(i ) is the number of amino acids in the special class pi , and l(seq ) is the length of pcseq . for each of the chosen physiochemical properties ,
its k - mer vector c and the distribution vector d were calculated and combined .
finally , a protein sequence was converted into a vector with [ 20 + ( 3 + 4  3 )  5 ) ] dimensions as the input of the svm classifiers .
svm is a standard supervised learning algorithm based on recent developments in the statistical learning theory 18 .
, 19 .. it builds a hyperplane separating the positive and negative examples in multiple - dimensional space .
the svm calculation was implemented by using the libsvm 2.8 ( 20 ) software package .
the software enables the user to choose different parameters and kernel functions including linear kernel function , radial basis function , and polynomial kernel function to obtain the best effect . in this study ,
three svms were constructed for classifying the gi / o- , gq/11- , and gs - gpcrs .
the comparison results of using different kernel functions show that the linear kernel function can achieve the best accuracy , which is therefore used in the developed svm classifiers .
in addition , the penalty factor c is an important parameter of svm , which has a great influence on the prediction results . in this study ,
the optimal value of this parameter was searched in the range of 1 to 200 by comparing the prediction results .
 g - protein coupled receptors ( gpcrs ) represent one of the most important classes of drug targets for pharmaceutical industry and play important roles in cellular signal transduction . 
 predicting the coupling specificity of gpcrs to g - proteins is vital for further understanding the mechanism of signal transduction and the function of the receptors within a cell , which can provide new clues for pharmaceutical research and development . in this study , 
 the features of amino acid compositions and physiochemical properties of the full - length gpcr sequences have been analyzed and extracted . 
 based on these features , classifiers have been developed to predict the coupling specificity of gpcrs to g - proteins using support vector machines . 
 the testing results show that this method could obtain better prediction accuracy . 
Introduction
Results
Discussion
Materials and Methods
Dataset
Coding of GPCR sequences
SVM