BACKGROUND
sequence-derived structural and physicochemical descriptors have frequently been used in machine learning prediction of protein structural and functional classes  <cit> , protein-protein interactions  <cit> , subcellular locations  <cit> , peptides containing specific properties <cit> , microarray data  <cit>  and protein secondary structure prediction  <cit> .
these descriptors serve to represent and distinguish proteins or peptides of different structural, functional and interaction profiles by exploring their distinguished features in compositions, correlations, and distributions of the constituent amino acids and their structural and physicochemical properties  <cit> .
there is thus a need to comparatively evaluate the effectiveness of these descriptor-sets for predicting different functional problems by using the same machine learning method and parameter optimization algorithm.
moreover, it is of interest to examine whether combined use of these descriptor-sets help to improve predictive performance.
this work is intended to evaluate the effectiveness of a total of six individual descriptor-sets and four combination-sets  in the prediction of several protein functional families by using support vector machine .
six sets of individual descriptors and three combination-sets have been separately utilized in machine learning prediction of different protein functional and structural properties, all of which have shown impressive predictive performances  <cit> .
the six individual sets are amino acid compositions  <cit>  , dipeptide compositions  <cit>  , normalized moreau-broto autocorrelation  <cit>  , moran autocorrelation  <cit>  , geary autocorrelation  <cit>  , and the composition, transition and distribution of structural and physicochemical properties  <cit>  .
the three combination-sets are quasi sequence order formed by weighted sums of amino acid compositions and physicochemical coupling correlations  <cit>  , pseudo amino acid composition  formed by weighted sums of amino acid compositions and physicochemical square correlations  <cit>  , and combination of amino acid compositions and dipeptide compositions   <cit> .
in this work, we also considered a fourth combination-set that combines descriptor-sets d <dig> through d <dig> .
the protein functional families studied here include enzyme ec <dig>   <cit> , g protein-coupled receptors  <cit> , transporter tc <dig> a  <cit> , chlorophyll  <cit> , lipid synthesis proteins involved in lipid synthesis  <cit> , and rrna-binding proteins.
these six protein families were selected for testing the descriptor-sets based on their functional diversity, sample size and the range of reported family member prediction accuracies  <cit> .
the reported prediction accuracies for these families are generally lower than those of other families  <cit> , which are ideal for critically evaluating the effectiveness of these descriptor-sets; having a lower accuracy should enable a better differentiation of the performance of the various classes.
svm was used as the machine learning method for predicting these functional families because it is a popular method that has consistently been shown better performances than other machine learning methods  <cit> .
as this work is intended as a benchmarking study of the performance of various classes of descriptors, other than automatic optimization of results that is an integral part of the svm programs, such as sigma value scanning, no further attempt was made to optimize the prediction performance of any descriptor class or of any dataset by manually tuning the parameters.
hence, prediction results reported in this paper might differ from those of reported studies.
ec <dig>  includes glycosyltransferases that catalyze the synthesis of glycoconjugates and are involved in post-translational modification of proteins .
increased levels of glycosyltransferases have been found in disease states and inflammation  <cit> .
tc <dig> a consists of auxiliary transport proteins that facilitate transport across membranes, which play regulatory and structural roles  <cit> .
gpcr represents g-protein coupled receptors that transduct signals for inducing cellular responses, and members of gpcr are of great pharmacological importance, as 50–60% of approved drugs elicit their therapeutic effect by selectively addressing members of the gpcr family  <cit> .
chlorophyll proteins are essential for harvesting solar energy in photosynthetic antenna systems  <cit> .
lipid synthesis proteins play central roles in such processes as metabolism, and deficiencies or altered functioning of lipid binding proteins are associated with disease states such as obesity, diabetes, atherosclerosis, hyperlipidemia and insulin resistance  <cit> .
rrna-binding proteins play central roles in the post-transcriptional regulation of gene expression  <cit> , and their binding capabilities are mediated by certain rna binding domains and motifs  <cit> .
RESULTS
the statistics of the six datasets are given in table  <dig>  training and prediction statistics for each of the studied descriptor-sets are given in table  <dig>  independent validation datasets were used to test the prediction accuracies.
among the 5-fold cross-validation test, independent dataset test and jackknife test, the jackknife is deemed the most rigorous  <cit> ; however, it would have taken a lot of time to use svm to conduct the jackknife test, thus as a compromise, here we adopted the independent dataset test.
the program cdhit  <cit>  was used to remove redundancy at both 90% and 70% sequence identity so to avoid bias, subsequently, the datasets are tested again with the independent evaluation sets and the statistics are given in table  <dig>  it should be emphasized that the performance evaluation for the studied descriptor-sets are based only on the datasets studied in this work and the conclusions from this study might not be readily extended to other datasets.
the performance of the ten descriptor-sets were ranked by the matthews correlation coefficient  values of the respective svm prediction of the six functional families, which are given in table  <dig>  the computed mcc scores for these descriptor-sets are in the range of  <dig> ~ <dig>  for all protein families studied.
accordingly, the performance of these descriptor-sets is categorized into two groups based on their mcc values: 'exceptional'  and 'good' .
moreover, these descriptor-sets are aligned in the order of their mcc values with "=" being of equal values and ">" indicating that one is better than the other.
it is noted that, as the differences of many of these mcc values are rather small, such alignment is likely superficial to some extent and may not best reflect the real ranking of performance.
overall, the performances of these descriptor-sets are not significantly different, there is no overwhelmingly preferred descriptor-set, and svm prediction performance appears to be highly dependent on the dataset.
*hsr: homologous sequence removed
nr:  not removed
as shown in table  <dig> and table  <dig>  for many of the studied datasets, the differences in prediction accuracies and mcc values between different descriptor-sets are small.
in particular, for gpcr and rrna binding proteins, the results of almost all descriptor-sets are in the 'exceptional' category.
examining the range of mcc values of the descriptor-sets for each of the studied protein families , the differences between the largest and smallest mcc values are, in order of increasing magnitude:  <dig> ,  <dig> ,  <dig> ,  <dig> ,  <dig>  and  <dig>  for rrna binding proteins, gpcr, tc <dig> a, lipid synthesis proteins, chlorophyll proteins and ec.
<dig>  families respectively.
given that a difference of  <dig>  and  <dig>  in mcc values translates to an approximate 4% and 7% difference in overall prediction accuracy, this separation is not large indeed.
though the dataset is a more important determinant of prediction performance than the choice of descriptor class, a few general trends could be observed.
three out of four of the combination-sets tend to exhibit slightly but consistently higher mcc values for the protein families studied in this work.
these sets are sets d <dig>  d <dig> and d <dig>  in contrast, only one out of six individual sets, set d <dig>  tend to exhibit slightly but consistently higher mcc values for the protein families studied in this work.
therefore, statistically speaking, it appears that the use of combination-sets tend to give slightly better prediction performance than the use of individual-sets.
when each class was examined individually in this study, we find that the combination of amino acid composition and dipeptide composition  tends to give consistently better results than that of the individual descriptor-sets .
it has been reported that one drawback of amino acid composition descriptors is that the same amino acid composition may correspond to diverse sequences as sequence order is lost  <cit> .
this sequence order information can be partially covered by considering dipeptide composition .
on the other hand, dipeptide composition lacks information concerning the fraction of the individual residue in the sequence, thus, a combination-set is expected to give better prediction results  <cit> .
using all descriptor-sets  generally, but not always, gives the best result, which is consistent with the findings on the use of molecular descriptors for predicting compounds of specific properties.
<cit>  for instance, xue et al.
found that feature selection methods are capable of reducing the noise generated by the use of overlapping and redundant molecular descriptors, and in some cases, improving the accuracy of svm classification of pharmacokinetic behaviour of chemical agents  <cit> .
in our study, for example, the three autocorrelation descriptor-sets  all utilize the same physicochemical properties, only differing in the correlation algorithm.
the use of all available descriptors likely results in the inclusion of partially redundant information, some of which may to some extent become noise that interferes with the prediction results or obscures relevant information.
based on the results of previous studies  <cit> , it is possible that feature selection methods may be applied for selecting the optimal set of descriptors to improve prediction accuracy as well as computing efficiency for predicting protein functional families.
CONCLUSIONS
the effectiveness of ten protein descriptor-sets in six protein functional family prediction using svm was evaluated.
corroborating with previous work done on chemical descriptors  <cit>  and protein descriptors  <cit> , we found that the descriptor-sets evaluated in this paper, which comprise some of the commonly used descriptors, generally return good results and do not differ significantly.
in particular, the use of combination descriptor-sets tends to give slightly better prediction performance than the use of individual descriptor-sets.
while there seems to be no preferred descriptor-set that could be utilized for all datasets as prediction results is highly dependent on datasets, the performance of protein classification may be enhanced by selection of optimal combinations of descriptors using established feature-selection methods  <cit> .
incorporation of appropriate sets of physicochemical properties not covered by some of the existing descriptor-sets may also help improving the prediction performance.

 sequence-derived structural and physicochemical descriptors have frequently been used in machine learning prediction of protein structural and functional classes, protein-protein interactions, subcellular locations, peptides containing specific properties, microarray data and protein secondary structure prediction. these descriptors serve to represent and distinguish proteins or peptides of different structural, functional and interaction profiles by exploring their distinguished features in compositions, correlations, and distributions of the constituent amino acids and their structural and physicochemical properties. there is thus a need to comparatively evaluate the effectiveness of these descriptor-sets for predicting different functional problems by using the same machine learning method and parameter optimization algorithm. moreover, it is of interest to examine whether combined use of these descriptor-sets help to improve predictive performance. this work is intended to evaluate the effectiveness of a total of six individual descriptor-sets and four combination-sets in the prediction of several protein functional families by using support vector machine. six sets of individual descriptors and three combination-sets have been separately utilized in machine learning prediction of different protein functional and structural properties, all of which have shown impressive predictive performances. the six individual sets are amino acid compositions, dipeptide compositions, normalized moreau-broto autocorrelation, moran autocorrelation, geary autocorrelation, and the composition, transition and distribution of structural and physicochemical properties. the three combination-sets are quasi sequence order formed by weighted sums of amino acid compositions and physicochemical coupling correlations, pseudo amino acid composition formed by weighted sums of amino acid compositions and physicochemical square correlations, and combination of amino acid compositions and dipeptide compositions. in this work, we also considered a fourth combination-set that combines descriptor-sets d through d. the protein functional families studied here include enzyme ec, g protein-coupled receptors, transporter tc a, chlorophyll, lipid synthesis proteins involved in lipid synthesis, and rrna-binding proteins. these six protein families were selected for testing the descriptor-sets based on their functional diversity, sample size and the range of reported family member prediction accuracies. the reported prediction accuracies for these families are generally lower than those of other families, which are ideal for critically evaluating the effectiveness of these descriptor-sets; having a lower accuracy should enable a better differentiation of the performance of the various classes. svm was used as the machine learning method for predicting these functional families because it is a popular method that has consistently been shown better performances than other machine learning methods. as this work is intended as a benchmarking study of the performance of various classes of descriptors, other than automatic optimization of results that is an integral part of the svm programs, such as sigma value scanning, no further attempt was made to optimize the prediction performance of any descriptor class or of any dataset by manually tuning the parameters. hence, prediction results reported in this paper might differ from those of reported studies. ec includes glycosyltransferases that catalyze the synthesis of glycoconjugates and are involved in post-translational modification of proteins. increased levels of glycosyltransferases have been found in disease states and inflammation. tc a consists of auxiliary transport proteins that facilitate transport across membranes, which play regulatory and structural roles. gpcr represents g-protein coupled receptors that transduct signals for inducing cellular responses, and members of gpcr are of great pharmacological importance, as 50–60% of approved drugs elicit their therapeutic effect by selectively addressing members of the gpcr family. chlorophyll proteins are essential for harvesting solar energy in photosynthetic antenna systems. lipid synthesis proteins play central roles in such processes as metabolism, and deficiencies or altered functioning of lipid binding proteins are associated with disease states such as obesity, diabetes, atherosclerosis, hyperlipidemia and insulin resistance. rrna-binding proteins play central roles in the post-transcriptional regulation of gene expression, and their binding capabilities are mediated by certain rna binding domains and motifs.  the statistics of the six datasets are given in table training and prediction statistics for each of the studied descriptor-sets are given in table independent validation datasets were used to test the prediction accuracies. among the 5-fold cross-validation test, independent dataset test and jackknife test, the jackknife is deemed the most rigorous; however, it would have taken a lot of time to use svm to conduct the jackknife test, thus as a compromise, here we adopted the independent dataset test. the program cdhit was used to remove redundancy at both 90% and 70% sequence identity so to avoid bias, subsequently, the datasets are tested again with the independent evaluation sets and the statistics are given in table it should be emphasized that the performance evaluation for the studied descriptor-sets are based only on the datasets studied in this work and the conclusions from this study might not be readily extended to other datasets. the performance of the ten descriptor-sets were ranked by the matthews correlation coefficient values of the respective svm prediction of the six functional families, which are given in table the computed mcc scores for these descriptor-sets are in the range of ~ for all protein families studied. accordingly, the performance of these descriptor-sets is categorized into two groups based on their mcc values:'exceptional' and'good'. moreover, these descriptor-sets are aligned in the order of their mcc values with "=" being of equal values and ">" indicating that one is better than the other. it is noted that, as the differences of many of these mcc values are rather small, such alignment is likely superficial to some extent and may not best reflect the real ranking of performance. overall, the performances of these descriptor-sets are not significantly different, there is no overwhelmingly preferred descriptor-set, and svm prediction performance appears to be highly dependent on the dataset. *hsr: homologous sequence removed nr: not removed as shown in table and table for many of the studied datasets, the differences in prediction accuracies and mcc values between different descriptor-sets are small. in particular, for gpcr and rrna binding proteins, the results of almost all descriptor-sets are in the'exceptional' category. examining the range of mcc values of the descriptor-sets for each of the studied protein families, the differences between the largest and smallest mcc values are, in order of increasing magnitude:,,,, and for rrna binding proteins, gpcr, tc a, lipid synthesis proteins, chlorophyll proteins and ec. families respectively. given that a difference of and in mcc values translates to an approximate 4% and 7% difference in overall prediction accuracy, this separation is not large indeed. though the dataset is a more important determinant of prediction performance than the choice of descriptor class, a few general trends could be observed. three out of four of the combination-sets tend to exhibit slightly but consistently higher mcc values for the protein families studied in this work. these sets are sets d d and d in contrast, only one out of six individual sets, set d tend to exhibit slightly but consistently higher mcc values for the protein families studied in this work. therefore, statistically speaking, it appears that the use of combination-sets tend to give slightly better prediction performance than the use of individual-sets. when each class was examined individually in this study, we find that the combination of amino acid composition and dipeptide composition tends to give consistently better results than that of the individual descriptor-sets. it has been reported that one drawback of amino acid composition descriptors is that the same amino acid composition may correspond to diverse sequences as sequence order is lost. this sequence order information can be partially covered by considering dipeptide composition. on the other hand, dipeptide composition lacks information concerning the fraction of the individual residue in the sequence, thus, a combination-set is expected to give better prediction results. using all descriptor-sets generally, but not always, gives the best result, which is consistent with the findings on the use of molecular descriptors for predicting compounds of specific properties. for instance, xue et al. found that feature selection methods are capable of reducing the noise generated by the use of overlapping and redundant molecular descriptors, and in some cases, improving the accuracy of svm classification of pharmacokinetic behaviour of chemical agents. in our study, for example, the three autocorrelation descriptor-sets all utilize the same physicochemical properties, only differing in the correlation algorithm. the use of all available descriptors likely results in the inclusion of partially redundant information, some of which may to some extent become noise that interferes with the prediction results or obscures relevant information. based on the results of previous studies, it is possible that feature selection methods may be applied for selecting the optimal set of descriptors to improve prediction accuracy as well as computing efficiency for predicting protein functional families.  the effectiveness of ten protein descriptor-sets in six protein functional family prediction using svm was evaluated. corroborating with previous work done on chemical descriptors and protein descriptors, we found that the descriptor-sets evaluated in this paper, which comprise some of the commonly used descriptors, generally return good results and do not differ significantly. in particular, the use of combination descriptor-sets tends to give slightly better prediction performance than the use of individual descriptor-sets. while there seems to be no preferred descriptor-set that could be utilized for all datasets as prediction results is highly dependent on datasets, the performance of protein classification may be enhanced by selection of optimal combinations of descriptors using established feature-selection methods. incorporation of appropriate sets of physicochemical properties not covered by some of the existing descriptor-sets may also help improving the prediction performance. 
sequence-derived structural and physicochemical descriptors have frequently been used in machine learning prediction of protein functional families, thus there is a need to comparatively evaluate the effectiveness of these descriptor-sets by using the same method and parameter optimization algorithm, and to examine whether the combined use of these descriptor-sets help to improve predictive performance. six individual descriptor-sets and four combination-sets were evaluated in support vector machines prediction of six protein functional families. our study suggests that currently used descriptor-sets are generally useful for classifying proteins and the prediction performance may be enhanced by exploring combinations of descriptors. the combination-sets tend to give slightly but consistently higher mcc values and thus overall best performance such that three out of four combination-sets show slightly better performance compared to one out of six individual descriptor-sets.
 sequence-derived structural and physicochemical descriptors have frequently been used in machine learning prediction of protein functional families, thus there is a need to comparatively evaluate the effectiveness of these descriptor-sets by using the same method and parameter optimization algorithm, and to examine whether the combined use of these descriptor-sets help to improve predictive performance. six individual descriptor-sets and four combination-sets were evaluated in support vector machines prediction of six protein functional families.  the performance of these descriptor-sets were ranked by matthews correlation coefficient, and categorized into two groups based on their performance. while there is no overwhelmingly favourable choice of descriptor-sets, certain trends were found. the combination-sets tend to give slightly but consistently higher mcc values and thus overall best performance such that three out of four combination-sets show slightly better performance compared to one out of six individual descriptor-sets.  our study suggests that currently used descriptor-sets are generally useful for classifying proteins and the prediction performance may be enhanced by exploring combinations of descriptors. 