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![]() J. Biomedical Science and Engineering, 2009, 2, 51-56 Published Online February 2009 in SciRes. http://www.scirp.org/journal/jbise JBiSE 51 Using position specific scoring matrix and auto covariance to predict protein subnuclear localization Rong-Quan Xiao1, Yan-Zhi Guo2, Yu-Hong Zeng2, Hai-Feng Tan1, Xue-Mei Pu2, Meng-Long Li1,2* 1College of Life Sciences, Sichuan University, Chengdu 610064. 2College of Chemistry, Sichuan University, Chengdu 610064, P.R. China. Correspondence should be addressed to Meng-Long Li(liml@scu.edu.cn). Tel: +86 28 89005151; Fax: +86 28 85412356. Received September 8th, 2008; revised November 13th, 2008; accepted November 20th, 2008 ABSTRACT The knowledge of subnuclear localization in eukaryotic cells is indispensable for under- standing the biological function of nucleus, genome regulation and drug discovery. In this study, a new feature representation was pro- posed by combining position specific scoring matrix (PSSM) and auto covariance (AC). The AC variables describe the neighboring effect between two amino acids, so that they incorpo- rate the sequence-order information; PSSM de- scribes the information of biological evolution of proteins. Based on this new descriptor, a support vector machine (SVM) classifier was built to predict subnuclear localization. To evaluate the power of our predictor, the benchmark dataset that contains 714 proteins localized in nine subnuclear compartments was utilized. The total jackknife cross validation ac- curacy of our method is 76.5%, that is higher than those of the Nuc-PLoc (67.4%), the OET- KNN (55.6%), AAC based SVM (48.9%) and ProtLoc (36.6%). The prediction software used in this article and the details of the SVM parameters are freely available at http://chemlab.scu.edu.cn/ predict_SubNL/index.htm and the dataset used in our study is from Shen and Chou’s work by downloading at http://chou.med.harvard.edu/ bioinf/Nuc-PLoc/Data.htm. Keywords: Position Specific Scoring Matrix; Auto Covariance; Support Vector Machine; Pro- tein Subnuclear Localization Prediction 1. INTRODUCTION The cell nucleus is complex, important subcellular or- ganelle in eukaryotes cell. It organizes the comprehen- sive assembly of our genes and their corresponding regulatory factors [1]. Meanwhile, it also reflects various intricate biological activities, and controls various kinds of biologic processes [2]. Many proteins, from outside a nuclear, trend to be localized into specific subnuclear locations of the nucleus [3]. If proteins can not be cor- rectly localized into its specific subnuclear locations in human, it will lead to genetic disease [4], cancer [5] or virally infected cells [6]. Thus, it’s desirable to get the knowledge of protein subnuclear localization for in- depth understanding cell biological processes and ge- nomic regulation. However, it is costly and time-con- suming to assay the subnuclear localization of proteins by biology experiments [7]. The number of protein se- quences is increasing more rapidly than that of identified proteins [7]. So it is of great practical significance to develop computational approaches for identifying the protein subnuclear localizations in cell nucleus. At the same time, many lines of evidences have indicated that computational approaches, such as structural bioinfor- matics [8], molecular docking [9], pharmacophore mod- elling [10], QSAR [11,12,13], protein subcellular loca- tion prediction [7,14], identification of membrane pro- teins and their types [15], identification of enzymes and their functional classes [16], identification of proteases and their types [17], protein cleavage site prediction [18,19], and signal peptide prediction [20,21] can pro- vide very useful information for both basic research and drug discovery in a timely manner. The present study is devoted to develop a new method for predicting protein subnuclear localization in hope to stimulate the devel- opment of the relevant areas. Recently, many algorithms have already been devel- oped for predicting protein subcellular localizations [22, 23,24,25,26,27,28,29,30,31,32,33], as reviewed by Chou [7]. Even several web severs have been constructed for predicting subcellular localization of various organisms [14,34,35,36,37]. However, there are only a few compu- tational methods for predicting protein subnuclear local- ization [38,39,40,41], such as OET-KNN [42], ProLoc [43], Nuc-PLoc [44], and AdaBoost classifiers [45]. Compared to the conventional amino acid composition (AAC), pseudo amino acid (PseAA) composition [46], originally introduced by Chou [47,48], can include the sequence-order information of sequences. Similarly, the PsePSSM was also proposed by Shen and Chou in order to incorporate the evolution information of proteins [44]. They built a new web server called Nuc-PLoc for pre- dicting protein subnuclear localization by fusing PseAA composition and PsePSSM with a promising prediction result. In this study, we developed a new method by fus- SciRes Copyright © 2009 ![]() 52 R. Q. Xiao et al. / J. Biomedical Science and Engineering 2 (2009) 51-56 SciRes Copyright © 2009 JBiSE ing position specific scoring matrix (PSSM) and auto covariance (AC), so that this method can incorporate sequence-order information by AC and the evolutionary information by PSSM. A classifier based on SVM was constructed to predict protein subnuclear localization using jackknife test. The result indicates that our method has successfully enhanced accuracies of the existing methods for predicting protein subnuclear localization. 2. MATERIALS AND METHODS 2.1. Data Sets In this paper, our dataset is obtained from article by Shen and Chou [44]. And anyone can freely download it at this page (http://chou.med.harvard.edu/bioinf/Nuc-PLoc/ Data.htm). This dataset consists of nine classes and 714 proteins in total. Details of this benchmark dataset are shown in Table 1. S i (i=1, 2… 9) is used to represent each of nine subsets and S represents the total dataset. 2.2. Feature Representations 2.2.1. Auto Covariance (AC) We selected three common physicochemical properties, hydrophobicity [49], volumes of side chains of amino acids [50], and polarity [51], to represent the structure and function [52], the stereospecific blockade [53] and the electronic property [54] of residues in a protein re- spectively. These original values were taken from Guo et al. [55] and were first normalized to zero mean value and unit standard deviation (SD) by Equation (1): , ' , ij j ij j PP PS − = (1) (i=1, 2, 3; j=1, 2, 3…, 20.) Where Pi,j is the i-th descriptor value for j-th amino acid, Pj is the mean of the j-t h descriptor of the 20 amino acids and Sj is the value of SD. So each protein sequence was translated into three vectors with each amino acid represented by the normalized values. There are many approaches to convert the protein se- quences into numerical order sequences, including auto- correlations and auto covariance (AC). Autocorrelations, quite similar to AC, has been used in the prediction of secondary structure content [56,57,58] and structural class [59,60,61,62]; however, AC as a statistical tool for Table 1. The benchmark dataset consists of 714 nuclear proteins classified into nine subnuclear localizations Subnuclear localization Subset No. of proteins Chromatin S1 99 Heterochromatin S2 22 Nuclear envelope S3 61 Nuclear matrix S4 29 Nuclear pore complex S5 79 Nuclear speckle S6 67 Nucleolus S7 307 Nucleoplasm S8 37 Nuclear PML body S9 13 Total S 714 analyzing sequences of vectors has also been success- fully adopted by our research group for protein classifi- cations [55,63] from primary sequence. So in our study, AC was selected to transform these numerical vectors into uniform matrices in order to take the neighboring effect of the sequences into account. Here, lag is the dis- tance between one residue and its neighbour, a certain number of residues away. The AC variables are calcu- lated by the Equation (2) [55]. ,, , ,(), 11 1 11 1 ()( ) − + == = − =−×− ∑∑ ∑ Llag L L ij ijij lag jilagj ii i Llag A CPPPP L L (2) Where i is the position in the sequence P, j is one de- scriptor, L is the length of the sequence P and lag is the value of the lag. In this way, the number of AC variables, D, can be calculated according to Equation (3) [55]. Dlgp = × (3) Where lg is the maximum lag (lag=1, 2, 3…, lg) and p represents the number of descriptors. 2.2.2. Position Specific Scoring Matrix (PSSM) A PSSM is a Position Specific Scoring Matrix and is a commonly used representation of motifs (patterns) in biological sequences [64]. So far, this method has been used for predicting protein subcellular localization [65] and subnuclear localization [40,44]. For a protein sequence P with L amino acid residues, PSSM is obtained according to the following Equation [44]. 111 211 20 212 22220 12 20 12 20 j j PSSM iiij i LLLj L PPPP PP P P PPP P P PPPP →→ → → →→→→ →→ → → →→ → → ⎡ ⎤ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ = ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎣ ⎦ LL LL MMMMM M LL MMMMM M LL (4) In Equation (4), where i→j describes i-th amino acid residue of the protein sequence P being mutated to amino acid type j in the biology evolution process, Pi→j is the score of this mutation and L is the length of the sequence P. Here we used the numerical codes 1, 2, 3… 20 to represent the single character of ordered 20 native amino acid types in Equation (4). To get the 20L × scores of the PPSSM in the Equation (4), we used three iterations of PSI-BLAST [66] with default threshold (the default E-value is 0.001) to search the Swiss-Prot database (version 54.4, released on 25 Oct. 2007) for multiple sequence alignment against the protein P. Then, the value of Pi→j is standardized by Equation (5), as given below. 20 1 1 20 )) max( min( o ij j ij o ij oo ij ij PP PPP → = → → →→ −∑ =− (5) ![]() R. Q. Xiao et al. / J. Biomedical Science and Engineering 2 (2009) 51-56 53 SciRes Copyright © 2009 JBiSE (i= 1, 2, 3… L; j= 1, 2, 3…20) Where o ij→ Pis the original scores generated by PSI-BLAST, ij→ P is a zero mean value over the 20 native amino acids and the value is between -1 and 1. However, because of proteins with different lengths L, the matrices of the PSSM descriptor in Equation (4) have different numbers of rows. To gain the uniform matrix for protein sequences of different lengths, we converted the PSSM of protein P to a uniform vector through the Equation (6) [44]. 12 20 (1,2, ,20) PSSM jj PPP P P Τ ⎡⎤ ⎢⎥ ⎣⎦ ==LL L (6) Where T is the transpose operator, j P is the average score over j-th column in Equation (4). Finally, the P SSM P describes the evolutionary infor- mation of a protein sample, and AC variables contain the interaction information between two amino acid residues of a sequence. So each protein sequence was converted into a numerical vector by concatenating PSSM and AC. Here, each AC variable was appended a weight factor of 0.05. 2.2.3. Accuracy and Matthew's Correlation Coef- ficient (MCC) To evaluate the performance of this method, two pa- rameters, accuracy and Matthew's correlation coefficient (MCC), were selected in this article. They are calculated by Equation (7) and Equation (8), respectively. TP Accuracy TP FN =+ (7) )()()()( FNTNFPTNFNTPFPTP FNFPTNTP MCC +×+×+×+ × − × = (8) Where TP represents the true positive; TN, the true nega- tive; FP, the false positive and FN, the false negative. 3. RESULTS AND DISCUSSION In statistical prediction, the following three cross-vali- dation methods are often used to examine a predictor for its effectiveness in practical application: independ- ent dataset test, subsampling test, and jackknife test [67]. However, as elucidated in [14] and demonstrated by Eq.50 of [7], among the three cross-validation methods, the jackknife test is deemed the most objective that can always yield a unique result for a given bench- mark dataset, and hence has been increasingly used by tigators to examine the accuracy of various predictors (see, e.g., [7,33,68,69,70,71,72,73,74,75,76,77,78,79,80, 81,82]). So in this paper, the jackknife test was chosen to validate the current algorithm. Because the benchmark dataset used has nine subsets, the one-to-one multiclass classification system led to 9*(9-1)/2=36 SVM models for one single encoding methods. Meanwhile, for AC variables, the value of lg was optimized as 13 through a series of control experiments, and the value of p is 3. So, the number of AC variables, D, is 39 (Dlgp = × 13 339 = ×= ) according to Equation (3). Amino acid composition (AAC) has been widely used for predicting subcellular localizations [7,14,22,23,24, 25,26,27,28,30,31,32,34,35,36,37,83,84,85], so it was also used as a substitution model in our study. And thus, three SVM models based on AAC, AC and PSSM, were respectively constructed. The results according to jackknife test are listed in Table 2. As can be seen from Table 2, the prediction accuracy of PSSM based model is nearly equal to that of AAC based model. However, AC based model gives the lower accuracy of 64.13%. Then we constructed models by fusing the three substitution models, so four fused classifier were built. Table 2 shows that the accuracies of the four fused models are higher than those of the three anterior models. Among those four fused models, the accuracy of the model combining PSSM, AAC and AC is lower than that of PSSM and AC based model that ob- tains the best performance with an accuracy of 76.45%. So the final SVM model was built based on PSSM and AC. The kernel function of SVM is radio basis function (rbf), and the parameters of C and γ are listed in the table by downloading at http://chemlab.scu.edu.cn/predict_ SubNL/index.htm. In order to further examine the prediction power of the current classifier, the performance of this method was also compared with those of the existing methods on the same training dataset. The results obtained by several algorithms with different substitution models were sum- marized in Table 3. From Table 3, we can see that the accuracy obtained by Nuc-PLoc [44] is much higher than those of ProtLoc [43], AAC based SVM and OET-KNN [42]. When compared to Nuc-PLoc, our method obtains a better performance with the accuracy of 76.5%. It means our method is successful in predicting protein subnuclear localization only using primary sequences of proteins Table 2. Overall accuracies by jackknife tests with different substitution models on the benchmark dataset of Table 1 Substitution Model AACa ACb PSSMc AAC+ACd PSSM+AAC PSSM+ACd PSSM+AAC+ACd Accuracy 73.82% 64.13% 73.85%74.05% 75.97% 76.45% 75.99% a: Amino acid composition b: Auto covariance c: Position specific scoring matrix d: While fused models were constructed, a weight factor added on AC is 0.05. ![]() 54 R. Q. Xiao et al. / J. Biomedical Science and Engineering 2 (2009) 51-56 SciRes Copyright © 2009 JBiSE Table 3. Overall accuracy by jackknife tests with different algorithms on the benchmark dataset of Table 1 Algorithm Protein sample descriptor Overall accuracy ProtLoca,d Amino acid composition 261/714=36.6% SVMd Amino acid composition 349/714=48.9% OET-KNNb,d PseAA Composition 397/714=55.6% Nuc-PLocc,d Fusion of PsePSSM and PseAA Composition 481/714=67.4% Our method Combination of PSSM and AC 546/714=76.5% a: See Cedano et al. (1997)[86] b: See Shen and Chou (2005)[42] c: See Shen and Chou (2007)[44] d: The results were from Shen and Chou (2007)[44], and the original data could been seen in that article. Table 4. The MCC values obtained by the jackknife tests with Nuc-PLoc and our method on the benchmark dataset of Table 1 Matthew’s correlation coefficient Subnuclear localization Nuc-PLoca Our methodb Chromatin S1 0.60 0.55 Heterochromatin S2 0.52 0.58 Nuclear envelope S3 0.53 0.65 Nuclear matrix S4 0.52 0.61 Nuclear pore complex S5 0.70 0.72 Nuclear speckle S6 0.43 0.57 Nucleolus S7 0.57 0.57 Nucleoplasm S8 0.31 0.54 Nuclear PML body S9 0.32 0.51 a: The results were from Shen and Chou (2007)[44], and the original data could be seen in that article. b: The classifier fused PSSM and AC. In addition, to evaluate the stability of our method, the values of the MCC for the nine subsets were compared based on Nuc-PLoc and our current predictor, respec- tively, as seen in Table 4. For nine subsets, our method yields a higher MCC than Nuc-PLoc, except the subset S1. So, compared to the existing methods, our classifier combined with PSSM and AC has further improved the prediction accuracy of protein subnuclear localization. 4. CONCLUSION In this paper, a new classifier was developed by fusing PSSM and AC for predicting protein subnuclear local- ization only using the primary sequences of nuclear pro- teins. The SVM predictor was constructed based on PSSM and AC. AC variables represent the interactions between amino acids in protein sequences; PSSM de- scribes the evolutionary information. So the method in- corporated not only the evolution information, but also the sequence-order information. Compared with the cur- rent methods, this method successfully raises the predic- tion accuracy. Hence, it may be a good supplementary tool for protein function studies. ACKNOWLEDGEMENT The authors gratefully thank Shen and Chou for sharing the benchmark dataset. The work was funded by the National Natural Science Founda- tion of China (No. 20775052). 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