Abstract

Prediction of protein function is of significance in studying biological processes. One approach for function prediction is to classify a protein into functional family. Support vector machine (SVM) is a useful method for such classification, which may involve proteins with diverse sequence distribution. We have developed a web-based software, SVMProt, for SVM classification of a protein into functional family from its primary sequence. SVMProt classification system is trained from representative proteins of a number of functional families and seed proteins of Pfam curated protein families. It currently covers 54 functional families and additional families will be added in the near future. The computed accuracy for protein family classification is found to be in the range of 69.1–99.6%. SVMProt shows a certain degree of capability for the classification of distantly related proteins and homologous proteins of different function and thus may be used as a protein function prediction tool that complements sequence alignment methods. SVMProt can be accessed at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.

Received February 14, 2003; Revised March 19, 2003. Accepted April 2, 2003

INTRODUCTION

Knowledge about protein function is essential in the understanding of biological processes (1,2). As the gap between the amount of sequence information and functional characterization widens, increasing efforts are being directed at the development of computational tools for protein function prediction (25). Various methods have been developed, which include sequence similarity (68), evolutionary analysis (9,10), structure-based approach (11), protein/gene fusion (12,13), protein interaction (14,15) and family classification by sequence clustering (16,17).

In the absence of clear sequence or structural similarities, the criteria for comparison of distantly-related proteins become increasingly difficult to formulate (17). Moreover, not all homologous proteins have analogous functions (9). The presence of a shared domain within a group of proteins does not necessarily imply that these proteins perform the same function (18). Many proteins sharing promiscuous domains (e.g. SH2, WD40, DnaJ) are known to have very different functions (12). These problems often hinder some of the clustering-based methods (16). In addition to the development of algorithms to overcome these problems (16), different approaches that combine or complement existing methods are being explored (3,9,17,19).

It is of interest to consider protein functional family classification as a method for facilitating protein function prediction, which is expected to be particularly useful in the cases described above and may thus be used as a protein function prediction tool to complement sequence alignment methods. Functional families of various proteins have been documented (2023). A method for the classification of proteins with diverse sequence distribution is also available. A statistical learning method, support vector machines (SVM) (24), has recently been used for classification of G-protein coupled receptors (25) and DNA-binding proteins (26). It has also been employed in a number of other protein studies including protein–protein interaction prediction (15), fold recognition (27), solvent accessibility (28) and structure prediction (29,30). The prediction accuracy ranges from 65 to 91.4% in these studies. Thus SVM classification of protein functional family may be potentially developed into a protein function prediction tool to complement methods based on sequence similarity and clustering.

Instead of direct comparison or clustering of sequences, SVM classification is based on the analysis of physicochemical properties of a protein generated from its sequence (2530). Samples of proteins known to be in a functional class (positive samples) and those not in the class (negative samples) are used to train a SVM system to recognize specific features and classify proteins into either the functional class or outside of the class. Such an approach may be applied to functional prediction for both distantly-related and closely-related proteins. Proteins of specific functional class share common structural and chemical features essential for performing similar functions (2022). Given sufficient samples of proteins of specific function, SVM can be trained and used to recognize proteins with characteristics for a particular function (15,25,26).

We have developed a web-based software, SVMProt, for the classification of a protein into functional class from its primary sequence. The functionally distinguished classes of proteins are collected from several databases (2023,31,32) that include all major classes of enzymes, receptors, transporters, channels, DNA-binding proteins and RNA-binding proteins. The core SVM program used in SVMProt is SVM★ which has recently been developed and tested for the classification of DNA-binding proteins (26). SVMProt is specifically trained and tested on each of the functional classes currently collected. Its usefulness on protein functional classification is evaluated. Its capability in the classification of distantly related proteins and homologous proteins of different function is also studied.

SOFTWARE ACCESS

The SVMProt web page is at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi and it is shown in Figure 1. The sequence of a protein, in RAW format and containing no non-amino acid letters, can be input in a window provided. A sequence of less than 50 amino acids is not accepted. The computed result is displayed in a separate window as shown in Figure 2. Depending on the computed result, one of the following four outcomes is displayed. If the input protein is predicted to belong to one or more functional families, then the name of each family is displayed. For some protein families, a cross-link to the respective protein family database is provided and that of more families will be added. If the input protein is predicted to not belong to any of the functional classes currently included in SVMProt, then a message of ‘Your input protein is not in any of the functional classes currently covered by SVMProt’ is displayed. If the input sequence contains invalid characters or abnormal composition such as a long stretch of consecutive single letters, then a message of ‘invalid character …’ or ‘your input sequence is not a valid sequence’ is displayed. If the input sequence is less than 50 amino acids, then a message of ‘your input sequence is less than 50 amino acids’ is displayed.

METHODS

Table 1 lists the protein functional families currently covered by SVMProt. These include 46 families of enzymes from BRENDA (20), G-protein coupled receptors from GPCRDB (21), nuclear receptors from NucleaRDB (21), tyrosine receptor kinases derived from NCBI (31), five families of channels and one family of transporters from TCDB (22) and LGICdb (23) and DNA- and RNA-binding proteins derived from SWISS-PROT (32). Additional families of transporters will be added very soon. Other families of proteins are being searched and collected. The updated list of functional classes is provided in the SVMProt web page.

SVMProt is trained for protein classification in the following manner. First, every protein sequence is represented by specific feature vector assembled from encoded representations of tabulated residue properties including amino acid composition, hydrophobicity, normalized Van der Waals volume, polarity, polarizability, charge, surface tension, secondary structure and solvent accessibility for each residue in the sequence (15,2530). Three descriptors, composition (C), transition (T) and distribution (D), are used to describe global composition of each of these properties (33). C is the number of amino acids of a particular property (such as hydrophobicity) divided by the total number of amino acids. T characterizes the percent frequency with which amino acids of a particular property is followed by amino acids of a different property. D measures the chain length within which the first, 25, 50, 75 and 100% of the amino acids of a particular property is located respectively.

A hypothetical protein sequence AEAAAEAEEAAAAAEAEEEAAEEAEEEAAE, as shown in Figure 3, has 16 alanines (n1=16) and 14 glutamic acids (n2=14). The composition for these two amino acids are n1×100.00/(n1+n2)=53.33 and n2×100.00/(n1+n2)=46.67 respectively. There are 15 transitions from A to E or from E to A in this sequence and the percent frequency of these transitions is (15/29)×100.00= 51.72. The first, 25, 50, 75 and 100% of As are located within the first 1, 5, 12, 20 and 29 residues, respectively. The D descriptor for As is thus 1/30×100.00=3.33, 5/30× 100.00=16.67, 12/30×100.00=40.0, 20/30×100.00= 66.67, 29/30×100.00=96.67. Likewise, the D descriptor for Es is 6.67, 26.67, 60.0, 76.67, 100.0. Overall, the amino acid composition descriptors for this sequence are C=(53.33, 46.67), T=(51.72) and D=(3.33, 16.67, 40.0, 66.67, 96.67, 6.67, 26.67, 60.0, 76.67, 100.0), respectively.

Descriptors for other properties can be computed by a similar procedure and all the descriptors are combined to form the feature vector. In most studies, amino acids are divided into three classes for each property and thus the three descriptors for each property consist of 21 elements: three for C, three for T and 15 for D (15,2530,33).

SVMProt is fed and trained with examples of proteins of a particular functional family (positive samples) and those that do not belong to this family (negative samples). The feature vectors of these positive and negative samples are input into the SVMProt system. The trained SVMProt system can then be used to classify a protein into either the positive group (protein is predicted to be in the family) or the negative group (protein is predicted to not belong to the family). Because protein feature vectors describe global composition of various physicochemical properties, SVMProt cannot address such questions as which part of a protein sequence is likely to match with a protein family.

All distinct protein members in each family found by us are used to construct positive samples for training SVMProt. More proteins are being searched which will be added in training and testing SVMProt. The negative samples for training are selected from seed proteins of the curated protein families in the Pfam database (34) excluding those that belong to the family under study. Training sets of both positive and negative samples are further screened so that only essential proteins that optimally represent each class are retained. The SVMProt training system for each family is optimized and tested by using separate testing sets of both positive and negative samples. While possible, all the remaining distinct proteins in each functional family (not in the training set of that family) are used as positive samples and all the remaining representative seed proteins in Pfam curated families are used to construct negative samples in a testing set. The performance of SVMProt classification is further evaluated by using independent sets of both positive and negative samples. There is no duplicate protein in each training, testing or independent evaluation set. The number of both positive and negative samples of proteins for the training, testing and independent evaluation sets of every functional class is given in Table 1.

The theory of SVM had been described in the literature (15,2430). Thus only a brief description is given here. SVM is based on the structural risk minimization (SRM) principle from statistical learning theory (24). In linearly separable cases, SVM constructs a hyperplane which separates two different groups of feature vectors with a maximum margin. A feature vector is represented by xi, with physicochemical descriptors of a protein as its components. The hyperplane is constructed by finding another vector w and a parameter b that minimizes ‖w2 and satisfies the following conditions: 

\[\eqalignno{ {\bf w} \cdot {\bf x}_i + b \geq + 1, \hbox{ for } y_i = + 1 \quad \hbox{Group } 1\ (\hbox{positive}) \bf{1}\cr \]
 
\[ {\bf w} \cdot {\bf x}_i + b \leq - 1, \hbox{ for }y_i = - 1 \quad \hbox{Group } 2\ (\hbox{negative}) \bf{2}}\]
where yi is the group index, w is a vector normal to the hyperplane, |b|/‖w‖ is the perpendicular distance from the hyperplane to the origin and ‖w2 is the Euclidean norm of w. After the determination of w and b, a given vector x can be classified by: 
\[\hbox{sign}[({\bf w} \cdot {\bf x}) + b] \eqno\bf{3}\]
In non-linearly separable cases, SVM maps the input variable into a high dimensional feature space using a kernel function K(xi, xj). An example of a kernel function is the Gaussian kernel which has been extensively used in different studies (15,2430): 
\[K({\bf x}_i, {\bf x}_j) = \hbox{e}^{ - \Vert {{\bf x}_j - {\bf x}_i }\Vert^{2} /2\sigma^2 } \eqno\bf{4 }\]
Linear support vector machine is applied to this feature space and then the decision function is given by: 
\[f({\bf x}) = \hbox{sign}\left(\sum_{i = 1}^{l} \alpha_i^0 y_i K( {\bf x}, {\bf x}_i) + b\right) \eqno\bf{5}\]
where the coefficients αi0 and b are determined by maximizing the following Langrangian expression: 
\[\sum_{i = 1}^{l} \alpha_i - {1 \over 2}\sum_{i = 1}^{l}\sum_{j = 1}^{l}\alpha_i \alpha_j y_i y_j K({\bf x}_i, {\bf x}_j) \eqno\bf{6}\]
under conditions: 
\[a_i \geq 0\quad \hbox{and}\quad \sum_{i = 1}^l \alpha_i y_i = 0 \eqno\bf{7}\]
A positive or negative value from Eq. 3 or Eq. 5 indicates that the vector x belongs to the positive or negative group, respectively. To further reduce the complexity of parameter selection, hard margin SVM with threshold instead of soft margin SVM with threshold is used in SVMProt.

Scoring of SVM classification of proteins has been estimated by a reliability index and its usefulness has been demonstrated by statistical analysis (29). A slightly modified reliability score, R-value, is used in SVMProt: 

\[\openup3pt R \hbox{-value} = \left\{\matrix{1\hfill \hbox{if } d < 0.2\hfill\cr {\displaystyle {d\over 0.2}} + 1 \hbox{if } 0.2 \leq d < 1.8\cr 10\hfill \hbox{if } d \geq 1.8\hfill\cr}\right. \eqno\bf{8}\]
where d is the distance between the position of the vector of a classified protein and the optimal separating hyperplane in the hyperspace. There is a statistical correlation between R-value and expected classification accuracy (probability of correct classification) (29). Thus another quantity, P-value, is introduced to indicate the expected classification accuracy. P-value is derived from the statistical relationship, shown in Figure 4, between the R-value and actual classification accuracy based on the analysis of 9932 positive and 45 999 negative samples of proteins.

As in the case of all discriminative methods (24,35), the performance of SVMProt classification can be measured by the quantity of true positives (TP), true negatives (TN), false positives (FP), false negatives (FN) and the overall accuracy (Q) given below: 

\[ Q = {{TP + TN} \over {TP + TN + FP + FN}} \eqno\bf{9}\]

RESULTS AND REMARKS

The results for the classification of each of the functional classes are given in Table 1. All the computed TP, TN, FP, FN and Q are given in the table. The overall accuracy Q of protein classification ranges from 69.1 to 99.6%, which is on average slightly improved from that obtained in other SVM studies of proteins (15,2430). One possible reason for this improvement is the use of representative proteins of Pfam curated families as negative samples for SVM classification, which provides a more comprehensive sampling of proteins not in a functional class.

Some low sequence similarity proteins share similar function (3638). Efforts have been directed at exploration of various novel approaches in predicting the function of these distantly related proteins (16,37,39). SVMProt is tested on 24 randomly selected distantly related proteins in seven families. Sequence similarity E-value for each of these proteins from BLAST search against most members of its family is significantly higher than the commonly accepted value of 0.05 for similarity proteins. Thus alignment methods may not work well for these proteins. Fourteen proteins are correctly classified by SVMProt, which accounts for 58.3% of all distantly related proteins studied. This suggests that, to a certain extent, SVMProt is useful for the classification of distantly related proteins.

Homologous proteins do not necessarily have analogous function (9) and there are certain levels of difficulty to distinguish them using sequence alignment methods. SVMProt is tested to four pairs of homologous proteins of different families and the results are shown in Table 2. While all eight proteins are correctly classified into their respective family, only five of them are not classified into the family of their respective homolog, representing 62.5% of all the homologous proteins examined. This limited study seems to indicate that SVMprot has a certain degree of capability for classification of homologous proteins of different functions. Further analysis is needed to provide a more objective assessment.

The ability of SVMProt in the classification of some distantly related proteins and homologous proteins of different functions probably results from the use of a combination of physicochemical properties to represent a protein. Protein function is determined by specific structural and chemical features at substrate binding sites (20). Some of these function-related features might be captured by the residue properties such as hydrophobicity, normalized Van der Waals volume, polarity, polarizability, charge, surface tension, secondary structure and solvent accessibility which are used in the construction of the SVMProt feature vectors for proteins.

As shown in Table 1, there are several families with substantially high Q score (∼90%) but relatively modest TP : FN ratio (<100 : 37). Generally, SVMProt gives an accurate prediction of TNs. The imbalance between the number of proteins in a family and those outside of the family may thus lead to cases of high Q score with modest TP : FN ratio. Examination of FN proteins of these families shows that many of these proteins either belong to more than one family or contain a domain shared by proteins in another family. These proteins are often classified into the related family. An analysis of a broad range of families indicates that a substantial portion (61.3%) of incorrectly classified proteins are of low sequence similarity to most of the other members in its family (i.e. the sequence similarity score E value of each of these proteins against most members of its family is significantly higher than 0.05). The percentage of low sequence similarity proteins in a family is not expected to be very high. Therefore, our study seems to suggest that sequence distance has a certain level of influence on the accuracy of SVM classification.

Several factors may affect the prediction accuracy. One is the diversity of protein samples. It is likely that not all possible types of proteins are adequately represented in some functional classes. This can be improved along with the availability of more protein data. SVM prediction may be further improved by using more comprehensive and refined set of protein descriptors. The SVM optimization procedure and feature vector selection algorithm may also be improved by adding additional constraints and by incorporating independent component analysis and kernel PCA in the preprocessing steps.

Our study suggests that SVM has potential in the classification of proteins into functional families. SVMProt appears to have a certain level of capability for classification of distantly related proteins and homologous proteins of different functions and, thus, potentially may be used as a protein function prediction tool that complements sequence alignment methods. Further improvements on protein functional family coverage, sample collection and SVM algorithm may enable the development of SVMProt into a useful protein function prediction tool.

Figure 1. SVMProt web page.

Figure 1. SVMProt web page.

Figure 2. Example of the SVMProt output returned to the user.

Figure 2. Example of the SVMProt output returned to the user.

Figure 3. Hypothetical sequence for illustration of derivation of the feature vector of a protein.

Figure 3. Hypothetical sequence for illustration of derivation of the feature vector of a protein.

Figure 4. Statistical relationship between the R-value and P-value (probability of correct classification) derived from analysis of 9932 positive and 45 999 negative samples of proteins.

Figure 4. Statistical relationship between the R-value and P-value (probability of correct classification) derived from analysis of 9932 positive and 45 999 negative samples of proteins.

Table 1.

List of protein families currently covered by SVMProt, statistics of datasets and prediction results. Predicted results are given in TP (true positive), FN (false negative), TN (true negative), FP (false positive), and Q (overall accuracy). Number of positive or negative samples in testing and independent evaluation sets is TP+FN or TN+FP, respectively

Protein family Training set Testing set Independent evaluation set Q (%) 
 Positive Negative Positive Negative Positive Negative  
   TP FN TN FP TP FN TN FP  
EC 1.1 Oxidoreductases acting on the CH-OH group of donors 383 896 743 23 1384 452 54 932 60 92.4 
EC 1.2 Oxidoreductases acting on the aldehyde or oxo group of donors 256 1127 233 1156 13 200 32 972 23 95.5 
EC 1.3 Oxidoreductases acting on the CH-CH group of donors 170 871 91 1429 75 33 985 15 95.7 
EC 1.4 Oxidoreductases acting on the CH-NH2 group of donors 80 459 60 1836 44 13 992 10 97.8 
EC 1.5 Oxidoreductases acting on the CH-NH group of donors 129 1129 42 1117 35 33 983 21 95.0 
EC 1.6 Oxidoreductases acting on NADH or NADPH 434 776 729 1516 15 531 42 971 33 95.2 
EC 1.7 Oxidoreductases acting on other nitrogenous compounds as donors 86 1088 24 1224 36 10 1003 98.8 
EC 1.8 Oxidoreductases acting on a sulfur group of donors 106 734 74 1580 56 30 1005 97.1 
EC 1.9 Oxidoreductases acting on a heme group of donors 122 480 712 1817 400 18 995 98.4 
EC 1.10 Oxidoreductases acting on diphenols and related substances as donors 48 431 23 1879 22 10 1005 99.0 
EC 1.11 Oxidoreductases acting on a peroxide as acceptor 89 569 95 1740 73 14 997 98.1 
EC 1.13 Oxidoreductases acting on single donors with incorporation of molecular oxygen (oxygenases) 83 721 52 1581 46 10 1001 98.7 
EC 1.14 Oxidoreductases acting on paired donors with incorporation or reduction of molecular oxygen 201 1146 157 1166 127 24 993 13 96.8 
EC 1.15 Oxidoreductases acting on superoxide as acceptor 60 1196 58 1119 54 1007 99.3 
EC 1.17 Oxidoreductases acting on CH2 groups 65 1197 58 1121 46 12 1006 98.7 
EC 1.18 Oxidoreductases acting on iron-sulfur proteins as donors 64 814 47 1501 41 11 1006 99.0 
EC 2.1 Transferases transferring one-carbon groups 486 1184 330 1103 287 76 920 74 88.9 
EC 2.2 Transferases transferring aldehyde or ketone residues 35 1197 30 1121 26 1005 99.2 
EC 2.3 Acyltransferases 302 1001 246 1284 196 44 966 27 94.2 
EC 2.4 Glycosyltransferases 427 1180 264 1110 245 58 933 64 90.6 
EC 2.5 Transferases transferring alkyl or aryl groups, other than methyl groups 320 1024 225 1284 197 53 964 39 92.7 
EC 2.6 Transferases transferring nitrogenous groups 132 1109 79 1206 71 19 995 12 97.2 
EC 2.7 Transferases transferring phosphorus-containing groups 1133 1334 1024 581 1217 195 759 202 83.3 
EC 2.8 Transferases transferring sulfur-containing groups 60 541 22 1772 19 14 1003 98.5 
EC 3.1 Hydrolases acting on ester bonds 760 1295 453 966 13 97 439 954 31 69.1 
EC 3.2 Glycosylases 337 867 379 1397 13 268 49 939 51 92.3 
EC 3.3 Hydrolases acting on ether bonds 54 843 29 1474 35 1008 99.5 
EC 3.4 Hydrolases acting on peptide bonds (Peptidases) 436 1188 240 1112 217 59 959 43 92.0 
EC 3.5 Hydrolases acting on carbon-nitrogen bonds, other than peptide bonds 414 1145 181 1137 199 73 931 60 89.5 
EC 3.6 Hydrolases acting on acid anhydrides 693 1089 770 1196 646 75 951 42 93.2 
EC 4.1 Carbon-carbon lyases 546 1145 776 1113 17 547 62 881 105 89.5 
EC 4.2 Carbon-oxygen lyases 505 1231 382 1047 324 79 915 77 88.8 
EC 4.3 Carbon-nitrogen lyases 96 803 86 1514 67 12 999 98.1 
EC 4.4 Carbon-sulfur lyases 40 1194 18 11 1118 15 15 1004 98.5 
EC 4.6 Phosphorus-oxygen lyases 63 989 26 1319 23 21 1002 97.8 
EC 5.1 Racemases and Epimerases 144 830 72 1464 65 29 981 19 95.6 
EC 5.2 Cis-trans-Isomerases 78 673 24 1643 32 17 1005 98.2 
EC 5.3 Intramolecular oxidoreductases 230 950 174 1355 159 21 982 25 96.1 
EC 5.4 Intramolecular transferases 144 1172 55 1132 65 26 997 97.0 
EC 5.5 Intramolecular lyases 22 1196 14 1121 14 1006 99.7 
EC 5.99 Other Isomerases 68 705 73 1597 58 994 98.4 
EC 6.1 Ligases forming carbon-oxygen bonds 281 1115 381 1185 13 286 29 980 27 95.8 
EC 6.2 Ligases forming carbon-sulfur bonds 81 947 71 1362 53 18 1001 98.0 
EC 6.3 Ligases forming carbon-nitrogen bonds 381 1133 358 1148 294 57 946 45 92.4 
EC 6.4 Ligases forming carbon-carbon bonds 48 963 26 1347 29 1003 99.5 
EC 6.5 Ligases forming phosphoric ester bonds 30 1198 16 10 1095 18 979 98.9 
G-protein coupled receptors 680 586 2694 1704 836 933 66 95.9 
Nuclear receptors 334 538 601 1755 221 26 962 24 95.9 
Tyrosine kinase receptors 14 1197 1121 1006 99.6 
α-type channels 96 1037 14 1232 967 98.6 
β-barrel porins 83 1076 19 1237 11 1003 99.1 
Pore-forming toxins (proteins and peptides) 105 948 24 1344 16 12 997 98.8 
Electrochemical potential-driven transporters porters (symporters, uniporters, antiporters) 201 450 274 1815 94 12 942 40 95.2 
DNA-binding proteins 1943 1353 2308 10 799 13 1938 188 683 239 86.0 
RNA-binding proteins 871 1120 610 1153 613 127 898 80 88.0 
Protein family Training set Testing set Independent evaluation set Q (%) 
 Positive Negative Positive Negative Positive Negative  
   TP FN TN FP TP FN TN FP  
EC 1.1 Oxidoreductases acting on the CH-OH group of donors 383 896 743 23 1384 452 54 932 60 92.4 
EC 1.2 Oxidoreductases acting on the aldehyde or oxo group of donors 256 1127 233 1156 13 200 32 972 23 95.5 
EC 1.3 Oxidoreductases acting on the CH-CH group of donors 170 871 91 1429 75 33 985 15 95.7 
EC 1.4 Oxidoreductases acting on the CH-NH2 group of donors 80 459 60 1836 44 13 992 10 97.8 
EC 1.5 Oxidoreductases acting on the CH-NH group of donors 129 1129 42 1117 35 33 983 21 95.0 
EC 1.6 Oxidoreductases acting on NADH or NADPH 434 776 729 1516 15 531 42 971 33 95.2 
EC 1.7 Oxidoreductases acting on other nitrogenous compounds as donors 86 1088 24 1224 36 10 1003 98.8 
EC 1.8 Oxidoreductases acting on a sulfur group of donors 106 734 74 1580 56 30 1005 97.1 
EC 1.9 Oxidoreductases acting on a heme group of donors 122 480 712 1817 400 18 995 98.4 
EC 1.10 Oxidoreductases acting on diphenols and related substances as donors 48 431 23 1879 22 10 1005 99.0 
EC 1.11 Oxidoreductases acting on a peroxide as acceptor 89 569 95 1740 73 14 997 98.1 
EC 1.13 Oxidoreductases acting on single donors with incorporation of molecular oxygen (oxygenases) 83 721 52 1581 46 10 1001 98.7 
EC 1.14 Oxidoreductases acting on paired donors with incorporation or reduction of molecular oxygen 201 1146 157 1166 127 24 993 13 96.8 
EC 1.15 Oxidoreductases acting on superoxide as acceptor 60 1196 58 1119 54 1007 99.3 
EC 1.17 Oxidoreductases acting on CH2 groups 65 1197 58 1121 46 12 1006 98.7 
EC 1.18 Oxidoreductases acting on iron-sulfur proteins as donors 64 814 47 1501 41 11 1006 99.0 
EC 2.1 Transferases transferring one-carbon groups 486 1184 330 1103 287 76 920 74 88.9 
EC 2.2 Transferases transferring aldehyde or ketone residues 35 1197 30 1121 26 1005 99.2 
EC 2.3 Acyltransferases 302 1001 246 1284 196 44 966 27 94.2 
EC 2.4 Glycosyltransferases 427 1180 264 1110 245 58 933 64 90.6 
EC 2.5 Transferases transferring alkyl or aryl groups, other than methyl groups 320 1024 225 1284 197 53 964 39 92.7 
EC 2.6 Transferases transferring nitrogenous groups 132 1109 79 1206 71 19 995 12 97.2 
EC 2.7 Transferases transferring phosphorus-containing groups 1133 1334 1024 581 1217 195 759 202 83.3 
EC 2.8 Transferases transferring sulfur-containing groups 60 541 22 1772 19 14 1003 98.5 
EC 3.1 Hydrolases acting on ester bonds 760 1295 453 966 13 97 439 954 31 69.1 
EC 3.2 Glycosylases 337 867 379 1397 13 268 49 939 51 92.3 
EC 3.3 Hydrolases acting on ether bonds 54 843 29 1474 35 1008 99.5 
EC 3.4 Hydrolases acting on peptide bonds (Peptidases) 436 1188 240 1112 217 59 959 43 92.0 
EC 3.5 Hydrolases acting on carbon-nitrogen bonds, other than peptide bonds 414 1145 181 1137 199 73 931 60 89.5 
EC 3.6 Hydrolases acting on acid anhydrides 693 1089 770 1196 646 75 951 42 93.2 
EC 4.1 Carbon-carbon lyases 546 1145 776 1113 17 547 62 881 105 89.5 
EC 4.2 Carbon-oxygen lyases 505 1231 382 1047 324 79 915 77 88.8 
EC 4.3 Carbon-nitrogen lyases 96 803 86 1514 67 12 999 98.1 
EC 4.4 Carbon-sulfur lyases 40 1194 18 11 1118 15 15 1004 98.5 
EC 4.6 Phosphorus-oxygen lyases 63 989 26 1319 23 21 1002 97.8 
EC 5.1 Racemases and Epimerases 144 830 72 1464 65 29 981 19 95.6 
EC 5.2 Cis-trans-Isomerases 78 673 24 1643 32 17 1005 98.2 
EC 5.3 Intramolecular oxidoreductases 230 950 174 1355 159 21 982 25 96.1 
EC 5.4 Intramolecular transferases 144 1172 55 1132 65 26 997 97.0 
EC 5.5 Intramolecular lyases 22 1196 14 1121 14 1006 99.7 
EC 5.99 Other Isomerases 68 705 73 1597 58 994 98.4 
EC 6.1 Ligases forming carbon-oxygen bonds 281 1115 381 1185 13 286 29 980 27 95.8 
EC 6.2 Ligases forming carbon-sulfur bonds 81 947 71 1362 53 18 1001 98.0 
EC 6.3 Ligases forming carbon-nitrogen bonds 381 1133 358 1148 294 57 946 45 92.4 
EC 6.4 Ligases forming carbon-carbon bonds 48 963 26 1347 29 1003 99.5 
EC 6.5 Ligases forming phosphoric ester bonds 30 1198 16 10 1095 18 979 98.9 
G-protein coupled receptors 680 586 2694 1704 836 933 66 95.9 
Nuclear receptors 334 538 601 1755 221 26 962 24 95.9 
Tyrosine kinase receptors 14 1197 1121 1006 99.6 
α-type channels 96 1037 14 1232 967 98.6 
β-barrel porins 83 1076 19 1237 11 1003 99.1 
Pore-forming toxins (proteins and peptides) 105 948 24 1344 16 12 997 98.8 
Electrochemical potential-driven transporters porters (symporters, uniporters, antiporters) 201 450 274 1815 94 12 942 40 95.2 
DNA-binding proteins 1943 1353 2308 10 799 13 1938 188 683 239 86.0 
RNA-binding proteins 871 1120 610 1153 613 127 898 80 88.0 
Table 2.

Assessment of SVMProt classification of homologous proteins of different functions

Protein 1 (P1) Family1 (F1) Protein 2 (P2) Family2 (F2) Similarity score E-value Classification 
Glycolate oxidase (P05414) EC1.1 IPP isomerase (Q8PW37) EC5.3 3.00E-07 P1→F1; P2→F2 
Creatinase (P38488) EC3.5 Xaa-Pro dipeptidase (O58885) EC3.4 3.00E-15 P1→F1 ; P2→F1, F2 
Cystathionine gamma-synthase (P38675) EC4.2 Methionine gamma-lyase (P13254) EC4.4 2.00E-15 P1→F1; P2→F1, F2 
Cystathionine gamma-synthase (P38676) EC4.2 Cystathionine gamma-lyase (Q8VCN5) EC4.4 1.00E-12 P1→F1; P2→F1, F2 
Protein 1 (P1) Family1 (F1) Protein 2 (P2) Family2 (F2) Similarity score E-value Classification 
Glycolate oxidase (P05414) EC1.1 IPP isomerase (Q8PW37) EC5.3 3.00E-07 P1→F1; P2→F2 
Creatinase (P38488) EC3.5 Xaa-Pro dipeptidase (O58885) EC3.4 3.00E-15 P1→F1 ; P2→F1, F2 
Cystathionine gamma-synthase (P38675) EC4.2 Methionine gamma-lyase (P13254) EC4.4 2.00E-15 P1→F1; P2→F1, F2 
Cystathionine gamma-synthase (P38676) EC4.2 Cystathionine gamma-lyase (Q8VCN5) EC4.4 1.00E-12 P1→F1; P2→F1, F2 

P1→F1 indicates classification of protein P1 into family F1.

P2→F1, F2 indicates classification of protein P2 into both family F1 and family F2.

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