Comparison among Feature Encoding Techniques for HIV-1 Protease Cleavage Specificity

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1 Internatonal Journal of Intellgent Systems and Applcatons n Engneerng Advanced Technology and Scence ISSN: Orgnal Research Paper Comparson among Feature Encodng Technques for HIV-1 Protease Cleavage Specfcty Uğur Turhal 1, Murat Gök* 2, Aykut Durgut 3 Accepted 15 th August 2014 DOI: /jsae.21005DOI: /b000000x Abstract: HIV-1 protease whch s responsble for the generaton of nfectous vral partcles by cleavng the vrus polypeptdes, play an ndspensable role n the lfe cycle of HIV-1. Knowledge of the substrate specfcty of HIV-1 protease wll pave the way of development of effcacous HIV-1 protease nhbtors. In the predcton of HIV-1 protease cleavage ste technques, many efforts have been devoted. Last decade, several works have approached the predcton of HIV-1 protease cleavage ste problem by applyng a number of methods from the feld of machne learnng. However, t s stll dffcult for researchers to choose the best method due to the lack of an effectve and up-to-date comparson. Here, we have made an extensve study on feature encodng technques for the problem of HIV-1 protease specfcty on dverse machne learnng algorthms. Also, for the frst tme, we appled OEDICHO technque, whch s a combnaton of orthonormal encodng and the bnary representaton of selected 10 best physcochemcal propertes of amno acds derved from Amno Acd ndex database, to predct HIV-1 protease cleavage stes. Keywords: HIV-1 protease specfcty, Feature extracton, Peptde classfcaton, Machne learnng algorthms, Amno acds 1. Introducton Owng to the fact that HIV-1 protease cleaves vrus polypeptdes at defned susceptble stes, t s responsble for processng polyprotens that contan the structural protens (Gag polyproten) and the enzymes (Gag/Pol polyproten) requred for vrus structure and replcaton (Zachary Q. Beck et al, 2000). The nvestgaton of substrate specfcty of HIV-1 protease s the ultmate goal as well as to dentfy optmal sequences to act as a framework for development of hghly effcent nhbtors whch target the actve ste of the protease. Therefore, the knowledge of the polyproten cleavage stes by HIV-1 protease s vtal. The cleavablty predcton task, gven a sequence of eght amno acds (an octamer), ams at knowng whch peptde sequences are cleaved or non-cleaved by the protease. So far, no perfect task s yet known that determnes where a peptde wll be cleaved or non-cleaved by the protease. A standard feed-forward multlayer perceptron (MLP) was used (You-Dong Ca and Kuo-Chen Chou, 1998, Thomas B. Thompson et al., 1995). Support vector machnes (SVM) was adopted several tmes to predct the cleavablty (Yu-Dong Ca et al., 2002, Thorstenn Rognvaldsson and Lwen You, 2004, Lors Nann, 2006). In (Thorstenn Rognvaldsson and Lwen You, 2004), authors showed that the lnear SVM works well for the problem. But these all works were conducted on an out-ofdate dataset ncludng 362 peptde substrates. More recently, a larger dataset (PR-1625), whch was composed of 1625 peptde sequences, was provded by Kontjevsks et al (Aleksejs Kontjevsks et al., 2007). They proposed a statstcally vald 1,3 Unversty of Balıkesr 10100, Turkey 2 Unversty of Yalova 77100, Turkey * Correspondng Author: Emal: murat.gok@yalova.edu.tr Note :Ths paper has been presented at the Internatonal Conference on Advanced Technology&Scences (ICAT'14) held n Antalya (Turkey), August 12-15, rule-based model for the HIV-1 protease specfcty. In (Bng Nu et al., 2009) k-nearest Neghbors (knn) algorthm was appled wth a feature representaton based on the amno acd propertes constructed by Amno Acd ndex database (AAndex) (Shuch Kawashma and Mnoru Kanehsa, 2000). For the protease specfcty, several studes have been conducted applyng orthonormal encodng (OE) method along wth dfferent machne learnng technques (You-Dong Ca and Kuo-Chen Chou, 1998, Lors Nann, 2006). Ruan et al. (Jshou Ruan et al., 2005) proposed a new encodng, termed composton moment vector (CMV), and based resdue s order n a proten sequence. Another approach s OETMAP encodng (Murat Gök and Ahmet T. Özcert, 2012) whch s n conjuncton of OE and Taylor s Venn-dagram (TVD). OEDICHO feature encodng (Murat Gök and Ahmet T. Özcert, 2012), whch was created for the T-cell eptopes recognton, has been tested for the frst tme for HIV-1 protease specfcty problem n ths study. In ths paper, fve encodng technques wth fve learnng algorthms as a standalone approach have been performed to predct HIV-1 protease cleavage stes on PR-1625 HIV-1 dataset. The computatonal results demonstrate hgher performance of OEDICHO n comparson wth the feature encodng methods on a standalone classfer approaches. 2. Methods 2.1. Feature Encodng Feature encodng, whch s a commonly used technque appled before classfcaton, defnes a mappng from the orgnal representaton space nto a new space where the classes are more easly separable. The goal of feature encodng s to dstll the pattern data nto a more concentrated and manageable form. Ths wll reduce the classfer complexty, ncreasng n most cases classfer accuracy (Alberto J. Perez-Jmenez and Juan C. Ths journal s Advanced Technology & Scence 2015 IJISAE, 2015, 3(2),

2 Perez-Cortes, 2006). The evaluated feature encodng technques are gven n bref terms as follows: 1) OE technque s a common encodng method. Accordng to OE, each amno acd symbol P n a peptde s replaced by an orthonormal vector, d (,,..., ) where j s the Kronecker delta symbol. Then, each P s represented by a 20-bt vector, 19 bts are set to zero and 1 bt s set to one based on alphabetc order of amno acds. Each d vector s orthogonal to the rest of d vectors and P can be any one of the twenty amno acds avalable n human body (Thorstenn Rognvaldsson and Lwen You, 2004). The man drawback of OE technque s that OE bnary feature vectors result n nformaton loss. 2) CMV encodng ncludes nformaton about both composton and poston of amno acds n the sequence. CMV provdes functonal relaton wth the structure content,.e. there must not be two or more prmary amno acd sequences that would have dfferent structure content but the same composton moment vector. The feature vector s obtaned concatenatng the frst, the second and the thrd moment vectors (Jshou Ruan et al., 2005). 3) OETMAP encodng conssts of a conjuncton of OE and Taylor s Venn-dagram methods whch are complementary to each other. OETMAP shows the relatonshp of the 20 naturally occurrng amno acds to a selecton of physcochemcal propertes whch are mportant n the determnaton of proten tertary structure (Murat Gök and Ahmet T. Özcert, 2012) 4) OEDICHO encodng combnes the OE and amno acd physcochemcal propertes knowledge to gve complementary recognton nformaton to OE. It therefore ncreases the effectveness of OE. 10 best physcochemcal propertes were selected wth pc-based encodng and were converted ther ndex s values nto bnary feature vectors. Then OE and ths complementary bnary feature vector were concatenated for each resdue n the peptde sequence (Murat Gök and Ahmet T. Özcert, 2012) Classfcaton A proten sequence s composed of a seres of 20 amno acds represented by characters as A, R, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y and V. A peptde s denoted by P = P4P3P2P1 P1 P2 P3 P4 where P s an amno acd. The scssle bond s located between postons P1 and P1 (Israel Schechter and Areh Berger, 1967). The HIV-1 protease cleavage specfcty can be consdered as a bnary classfcaton problem where an octomer peptde s requred to be assgned to cleavable or non-cleavable class. We used fve types of learnng algorthms such as J48, knn, K-star, lnear SVM and MLP for classfcaton under WEKA data mnng software. The evaluated algorthms are gven n bref terms as follows: 1) J48 classfer s a smple C4.5 algorthm. C4.5 generates classfers expressed as decson trees. In decson tree learnng, the learned functon s represented by a decson tree. Each node n the tree represents a test of some attrbute of the tranng example, and each branch corresponds to one of the possble values for ts source node (attrbute) (Pang-Nng Tan et al., 2005). J48 creates a bnary tree. The confdence factor parameter whch s used for prunng has been set to 0.25.Mnmum number of nstances per leaf has been chosen as 2. 2) knn classfcaton, fnds a group of k objects n the tranng set that are closest to the test object, and bases the assgnment of a label on the predomnance of a partcular class n ths neghborhood. There are three key elements of ths approach: a set of labeled objects, a dstance or smlarty metrc to compute dstance between objects, and the value of k, the number of nearest neghbors. To classfy an unlabeled object, the dstance of ths object to the labeled objects s computed, ts k-nearest neghbors are dentfed, and the class labels of these nearest neghbors are then used to determne the class label of the object (Xndong Wu et al., 2008). 3) KStar s an nstance-based classfer. The class of a test nstance s based upon the class of those nstances n the tranng set smlar to t, as determned by some smlarty measurement. The underlyng assumpton of ths type of classfers s that smlar cases belong to the same class (Du Zhang and Jeffrey J. P. Tsa, 2007). The global blend parameter for KStar has been chosen as 20. 4) SVM s an effectve dscrmnatve classfcaton method of statstc learnng theory and n recent tmes, t s successvely appled by a number of other researchers. SVM ams to fnd the maxmum margn hyperplane to separate two classes of patterns. In lnear SVM classfer, classes of two patterns are lnearly separated by the use of a maxmum margn hyperplane, whch s unquely defned by the support vectors, gves the best separaton between the classes (Chrstopher J.C. Burges, 1998). The parameters of the stand-alone SVM (kernel { lnear }, cost of the constran volaton (C) = 10) have been appled for the dataset. 5) Multple layer perceptron (MLP) are the most common type of feed-forward networks and the neurons are organzed nto layers that have undrectonal connectons between them. Based on ths ratonale, the learnng process can be vewed as the problem of updatng connecton weghts from avalable tranng patterns so that the network can effcently perform a specfc task. Performance s mproved over tme by teratvely updatng the weghts n the network. Ths learnng process can be effcently performed wth the Back-propagaton algorthm, whch s based on gradent descend. Then, the actual output of the network s generated and the (possble) error produced by the dfference between the actual output and the desred output s used to modfy the connectons weghts n order to gradually reduce the overall error (Davd E. Rumelhart et al., 1986). The parameters of MLP were learnng rate = 0.3, momentum = Results 3.1. Expermental Setup We conducted our tests on PR-1625 (Aleksejs Kontjevsks et al., 2007) HIV-1 protease dataset, composed by sequences of eght amno acds that can be cleaved or not by the HIV-1 protease. PR-1625 contans 1625 octamer peptdes, of whch 374 are cleaved and 1251 are non-cleaved. Gven a set of peptde sequences wth known labels of cleavable and non-cleavable, we bult OEDICHO encodng feature vectors. OEDICHO was mplemented as n (Murat Gök and Ahmet T. Özcert, 2012). To create bnary feature vector for HIV-1 protease ste predcton, frstly, 10 best physcochemcal propertes were selected accordng to physcochemcal (pc) encodng technque and were converted ther ndex s values nto bnary feature vectors. Lnear support vector machnes were used as the classfer n case of beng mplementng pc-based encodng. Pc-based encodng was repeated for each 544 physcochemcal propertes ndces n AAndex (Murat Gök and Ahmet T. Özcert, 2012). Accordng Ths journal s Advanced Technology & Scence 2015 IJISAE, 2015, 3(2),

3 Table best physcochemcal propertes selected for PR-1625 Order Physcochemcal Property (PR-1625) 1 Energy transfer from out to n (95%bured) 2 Sde chan nteracton parameter 3 Average relatve fractonal occurrence n AL() 4 Average gan n surroundng hydrophobcty 5 Hydrophobcty-related ndex 6 Membrane preference for cytochrome b: MPH89 7 ALTLS ndex 8 Hydrophobcty scale from natve proten structures 9 Rato of bured and accessble molar fractons 10 Normalzed frequency of turn n alpha+beta class In the second step, for each best property s mean was calculated. If the ndex value s smaller than or equals the mean value, t s accepted as 0. If the ndex value s bgger than the mean value, t s accepted as 1. In ths way, bnary encodng table was obtaned. Consequently, dfferent pc-encodng tables obtaned for each dataset. The bult feature vectors, OE (20-bt) and bnary encodng for the best 10 physcochemcal property of each resdue (10-bt) s concatenated, respectvely. Hence, the feature vector has a dmenson of 240-bt (30-bt x 8 resdue) for each octomer peptde. The performances of the classfers were evaluated by means of accuracy (acc), kappa error (ke), F-score and the Matthews correlaton coeffcent (MCC) value metrcs. True postves (TP), true negatves (TN), false postves (FP) and false negatves (FN) values are obtaned va confuson matrx (Tom Fawcett, 2004, Jn Huang and Charles X. Lng, 2005). Acc s a wdely used measure to determne class dscrmnaton ablty, and t s calculated as: TP TN acc TP FP TN FN (1) In statstcal analyss, quantfyng the performance of classfer to accuracy performance obtaned, 10-best physcochemcal propertes, shown n Table 1, were selected for PR algorthms n terms of just usng the percentage of msses as the sngle meter for accuracy can gve msleadng results. Therefore, the cost of error must also be taken nto account. Kappa error s a good metrc to nspect classfcatons that may be due to chance. Kappa error takes values between ( 1, 1). As the Kappa value approaches to 1, then the performance of the related classfer s assumed to be more realstc rather than by chance. Kappa error s calculated as (Are Ben-Davd, 2008): p0 pc ke (2) 1 pc In Eq. 2, s the total agreement probablty and p c s the agreement probablty due to chance. MCC, whch s used as a measure of the qualty of bnary (twoclass) classfcatons, takes nto account true and false postves and negatves. MCC TP TN FP FN ( TP FP)( TP FN)( TN FP)( TN FN) F-score s a measure of a test's accuracy determnng accuracy accountng for both precson and for recall from confuson matrx. F-score accounted as shown n Eq. 4. precson recall F 2 precson recall 3.2. Performance of Feature Encodng Technques 10-fold cross valdaton (10-fold CV) testng scheme s appled to evaluate the performance of the methods n terms of accuracy, kappa, F-score and averaged over 10 experments on PR In a cross-valdaton run, the 10 folds are randomly created. In 10-fold CV, the encodng scheme methods are traned usng 90 % of the data and the remanng 10 % of the data are used for testng of the methods (Rchard O. Duda et al., 2000). Ths process s repeated 10 tmes so that each peptde n datasets s used once. The 10 folds used n the tranng are dfferent from the 10 folds used n the testng. Havng completed the procedures above, the average accuracy, kappa statstc, F-score and MCC values of the each method over these 10 turns are obtaned, as shown Table 2 and Table 3. (3) (4) Table 3. F-Score and MCC Performances of All Methods on Pr-1625 HIV-1 Dataset Lnear SVM MLP J48 IBK K-Star F-Score MCC F-Score MCC F-Score MCC F-Score MCC F-Score MCC OE 0,94 0,84 0,94 0,84 0,91 0,76 0,90 0,75 0,92 0,79 CMV 0,79 0,47 0,89 0,69 0,90 0,73 0,89 0,69 0,94 0,83 OE+CMV 0,90 0,72 0,87 0,64 0,92 0,76 0,88 0,66 0,89 0,71 OETMAP 0,95 0,85 0,95 0,86 0,91 0,75 0,90 0,74 0,91 0,76 OEDICHO 0,95 0,85 0,96 0,88 0,92 0,77 0,90 0,73 0,90 0,74 The results ponts out that OEDICHO has obtaned the best result for both F-score and MCC values wth the value of 0.96 and 0.88, respectvely. 64 IJISAE, 2015, 1(4), Ths journal s Advanced Technology & Scence 2015

4 Table 2. Accuracy Performance of Amno Acd Encodng Schemes on Pr-1625 HIV-1 Dataset Lnear SVM MLP J48 IBK K-Star Acc (%) Kappa Acc (%) Kappa Acc (%) Kappa Acc (%) Kappa Acc (%) Kappa OE CMV OE+CMV OETMAP OEDICHO The results report that OEDICHO encodng outperforms the competng encodng technques wth the value of %. Also hghest Kappa value of OEDICHO wth MLP (0.88) confrms ts robustness of measurement. Note that IBK algorthm obtaned the worst performance among learnng algorthms wth nearly all feature encodng technques. OEDICHO method yelds hgh accurate emprcal results compared to all feature encodng technques for HIV-1 protease specfcty. We notce that OEDICHO combnes the both effectveness of OE and selected 10-best physcochemcal propertes of AAndex. However, OETMAP uses TVD that s not up-to-date n comparson wth OEDICHO s 10-best physcochemcal propertes. A rule based method, the orthogonal search-based rule extracton (OSRE), s presented by Rognvaldsson et al. (Thorstenn Rögnvaldsson et al., 2009). OSRE obtaned accuracy of 93% on PR OEDICHO shows better performance wth the accuracy of %. 4. Conclusons The problem addressed n ths paper s to recognze, gven a sequence of amno acds, HIV-1 protease cleavage ste. We performed an expermental comparson of fve encodng technque wth fve learnng classfers such as lnear SVM, MLP, J48, IBK and K-Star usng up-to-date PR-1625 HIV-1 dataset. OEDICHO technque, whch jons OE technque and complementary bnary feature vector whch comprses selected 10 best physcochemcal propertes ndex values for each resdue n a peptde sequence, shows the best performance wth MLP algorthm. Besde HIV-1 protease ste predcton, OEDICHO encouraged to be used for other peptde classfcaton problems. Due to the fact that ndependent and accurate classfers can make errors on dfferent regons of the feature space, they can be ensemble to acheve better scores. Based on ths ratonale, future works mght nvolve the ensemble of classfers wth dverse encodng technques especally wth OEDICHO encodng. Acknowledgment Ths work was supported by Yalova Unversty, YL Project (Grant 2014/YL/037). References [1] Zachary Q. Beck, Laurence Hervo, Phlp E. Dawson, John H. Elder and Edwn L. Madson (2000). Identfcaton of Effcently Cleaved Substrates for HIV-1 Protease Usng a Phage Dsplay Lbrary and Use n Inhbtor Development. Vrology. Vol Pages [2] You-Dong Ca and Kuo-Chen Chou (1998). Artfcal Neural Network Model for Predctng HIV Protease Cleavage Stes n Proten. Advances n Engneerng Software. Vol. 29. Pages [3] Thomas B. Thompson, Kuo-Chen Chou and Chong Zheng (1995). Neural Network Predcton of the HIV-1 Protease Cleavage Stes. Journal of Theoretcal Bology. Vol Pages [4] Yu-Dong Ca, Xao-Jun Lu, Xue-Bao Xu and Kuo-Chen Chou (2002). Support Vector Machnes for Predctng HIV Protease Cleavage Stes n Proten. Journal of Computatonal Chemstry. Vol. 23. Pages [5] Thorstenn Rognvaldsson and Lwen You (2004). Why Neural Networks Should Not Be Used For HIV-1 Protease Cleavage Ste Predcton. Bonformatcs. Vol. 20. Pages [6] Lors Nann (2006). Comparson among Feature Extracton Methods for HIV-1 Protease Cleavage Ste Predcton. Pattern Recognton. Vol. 39. Pages [7] Aleksejs Kontjevsks, Jarl E. S. Wkberg and Jan Komorowsk (2007). Computatonal Proteomcs Analyss of HIV-1 Protease Interactome. Protens: Structure, Functon and Bonformatcs. Vol. 68. Pages [8] Bng Nu, Ln Lu, Lang Lu, Tan H. Gu, Ka-Yan Feng, Wen-Cong Lu and Yu-Dong Ca (2009). HIV-1 Protease Cleavage Ste Predcton Based on Amno Acd Property. Journal of Computatonal Chemstry. Vol. 30. Pages [9] Shuch Kawashma and Mnoru Kanehsa (2000). AAndex: Amno Acd Index Database. Nuclec Acds Research. Vol. 28. Page Avalable onlne: [10] Jshou Ruan, Ku Wang, Je Yang, Lukasz A. Kurgan and Krzysztof Cos (2005). Hghly Accurate and Consstent Method for Predcton of Elx and Strand Content from Prmary Proten Sequences. Artfcal Intellgence n Medcne. Vol. 35. Pages [11] Murat Gök and Ahmet T. Özcert (2012). OETMAP: A New Feature Encodng Scheme for MHC Class I Bndng Predcton. Molecular and Cellular Bochemstry. Vol Pages [12] Murat Gök and Ahmet T. Özcert (2012). Predcton of MHC Class I Bndng Peptdes wth a New Feature Encodng Technque. Cellular Immunology. Vol Pages [13] Alberto J. Perez-Jmenez and Juan C. Perez-Cortes (2006). Genetc Algorthms for Lnear Feature Extracton. Pattern Recognton Letters. Vol. 27. Pages [14] Israel Schechter and Areh Berger (1967). On the Sze of the Actve Ste n Proteases. I. Papan. Bochemcal and Bophyscal Research Communcatons. Vol. 27. Pages [15] Pang-Nng Tan, Mchael Stenbach and Vpn Kumar (2005). Introducton to Data Mnng. Addson-Wesley Longman Publshng Co., Inc. Boston, ISBN: [16] Xndong Wu, Vpn Kumar, J. Ross Qunlan, Joydeep Ths journal s Advanced Technology & Scence 2015 IJISAE, 2015, 3(2),

5 Ghosh, Qang Yang, Hrosh Motoda, Geoffrey J. McLachlan, Angus Ng, Bng Lu, Phlp S. Yu, Zh-Hua Zhou, Mchael Stenbach, Davd J. Hand and Dan Stenberg (2008). Top 10 Algorthms n Data Mnng. Knowledge and Informaton Systems. Vol. 14. Pages [17] Du Zhang and Jeffrey J. P. Tsa (2007). Advances n Machne Learnng Applcatons n Software Engneerng. Idea Group Publshng. Page [18] Chrstopher J.C. Burges (1998). A Tutoral on Support Vector Machnes for Pattern Recognton. Data Mnng and Knowledge Dscovery. Vol. 2. Pages [19] Davd E. Rumelhart, Geoffrey E. Hnton and Ronald J. Wllams (1986). Learnng Internal Representatons by Error Propagaton. Parallel Dstrbuted Processng: Exploratons n the Mcrostructure of Cognton. Vol. 1. Pages [20] Tom Fawcett (2004). ROC Graphs: Notes and Practcal Consderatons for Researchers. Techncal Report, HP Laboratores. MS Page Mll Road. Palo Alto CA USA, [21] Jn Huang and Charles X. Lng (2005). Usng AUC and Accuracy n Evaluatng Learnng Algorthms. IEEE Transactons on Knowledge and Data Engneerng. Vol. 17. Pages [22] Are Ben-Davd (2008). Comparson of Classfcaton Accuracy Usng Cohen s Weghted Kappa. Expert Systems wth Applcatons. Vol. 34. Pages [23] Rchard O. Duda, Peter E. Hart and Davd G. Stork (2000). Wley. New York. ISBN: [24] Thorstenn Rögnvaldsson, Terence A. Etchells, Lwen You, Danel Garwcz, Ian Jarman and Paulo J. G. Lsboa (2009). How to Fnd Smple and Accurate Rules for Vral Protease Cleavage Specfctes. BMC Bonformatcs. Vol. 10. Page IJISAE, 2015, 1(4), Ths journal s Advanced Technology & Scence 2015