Hierarchical kernel mixture models for the prediction of AIDS disease progression using HIV structural gp120 profiles

Transcription

1 PROCEEDINGS Open Access Herarchcal kernel mxture models for the predcton of AIDS dsease progresson usng HIV structural gp20 profles Paul D Yoo,2*, Yung Shwen Ho 3, Jason Ng 4, Mchael Charleston 5, Ntn K Saksena 3, Pengy Yang 6, Albert Y Zomaya,5 From Asa Pacfc Bonformatcs Network (APBoNet) Nnth Internatonal Conference on Bonformatcs (InCoB200) Tokyo, Japan September 200 Abstract Changes to the glycosylaton profle on HIV gp20 can nfluence vral pathogeness and alter AIDS dsease progresson. The characterzaton of glycosylaton dfferences at the sequence level s nadequate as the placement of carbohydrates s structurally complex. However, no structural framework s avalable to date for the study of HIV dsease progresson. In ths study, we propose a novel machne-learnng based framework for the predcton of AIDS dsease progresson n three stages (RP, SP, and LTNP) usng the HIV structural gp20 profle. Ths new ntellgent framework proves to be accurate and provdes an mportant benchmark for predctng AIDS dsease progresson computatonally. The model s traned usng a novel HIV gp20 glycosylaton structural profle to detect possble stages of AIDS dsease progresson for the target sequences of HIV + ndvduals. The performance of the proposed model was compared to seven exstng dfferent machne-learnng models on newly proposed gp20- Benchmark_ dataset n terms of error-rate (MSE), accuracy (CCI), stablty (STD), and complexty (TBM). The novel framework showed better predctve performance wth 67.82% CCI, 30.2 MSE, 0.8 STD, and 2.62 TBM on the three stages of AIDS dsease progresson of 50 HIV+ ndvduals. Ths framework s an nvaluable bonformatcs tool that wll be useful to the clncal assessment of vral pathogeness. Background The human mmunodefcency vrus (HIV) s responsble for the acqured mmunodefcency syndrome (AIDS) dsease and 33 mllon people are nfected globally. Infected ndvduals can lve a normal lfe wth drug treatment, but most wll eventually progress to AIDS. The duraton of dsease vares between ndvduals. Some HIV + patents can progress towards AIDS wthn two years of prmary nfecton (rapd progressors RP). RP show rapd rse n plasma vrus and rapd declne n CD + T cell counts. On the other hand, another group of HIV + patents show steady but gradual ncrease n vrema and decrease n T cell counts over 0-5 years (slow progressors SP). Only about % of HIV + therapy naïve ndvduals can mantan vrus level below detecton level, robust T cell counts and experence sustaned mmune response for more than 20 years (long term non-progressors LTNP). Wth such a great dfference n AIDS dsease progresson among HIV + patents, much can be learned at the level of dfferences n vral archtecture that exsts n HIV varants evolvng at dfferent stages of HIV dsease and under dfferent mmunologc constrants n a gven host. Glycans on the HIV glycoproten 20 (gp20) surface mask mportant vral eptopes that host antbodes recognze [,2], preventng the eradcaton of the vrus. The rapd mutaton n gp20 durng vral evoluton further creates an ever changng landscape of glycosylaton patterns of HIV surface glycoproten gp20 (also known as the carbohydrate landscape ) thatfavours host mmune evason. Ths observaton has been termed the glycan sheld of HIV [3] and s drectly responsble for the persstence of vral nfecton even after therapy. Thus, any modfcaton to the glycosylaton profle of 200 Yoo et al; lcensee BoMed Central Ltd. Ths s an open access artcle dstrbuted under the terms of the Creatve Commons Attrbuton Lcense ( whch permts unrestrcted use, dstrbuton, and reproducton n any medum, provded the orgnal work s properly cted.

2 Page 2 of 0 gp20 s lkely to affect vral susceptblty to host mmune response [4], transmsson effcency [5], nfectvty [6] and AIDS dsease progresson [7]. Whle the glycosylaton of HIV s the man barrer to vral control and eradcaton, t s possble to harness the protectve glycosylaton profles on gp20 aganst the vrus [8] and develop a glycan based approach to vaccne desgn. We have prevously reported on our fndngs on glycosylaton ste nteracton wthn the envelope gp20 [9], whch are consstent wth the fndngs by Poon et al [0]. The assocaton of multple glycans wthn env gp20 could be due to the structural placement of the glycosylaton stes after proten foldng. Glycosylaton stes that are far away at the sequence level mght actually be close together n three-dmensonal (3D) structure of a proten. Thus, the understandng of gp20 glycosylaton structural (3D) profle modfcaton can explan the determnants of HIV dsease progresson. Studes to date have manly focused on the changes to sngle glycosylaton stes at the sequence level, whle the analyss of complete gp20 structural glycan modfcaton s new. Ths could be due to the lack of an analyss framework for multple glycan comparson across the entre gp20 sequence. In ths paper, we ntroduce a novel statstcal kernel model, whch s desgned to learn the complex glycan nteractons and predct the dfferences n AIDS dsease progresson usng the structural 3D glycan profle. It nvolves the desgn of sem-parameterzed, and supportvector asssted herarchcal mxture model, whch s able to effectvely capture the nformaton of non-local nteractons wth strong resstance to vanshng gradent and hgh-dmensonalty problems. The proposed framework successfully classfed the changes to glycosylaton profles and segregated HIV dsease groups. These results show the utlty of new bonformatcs and machnelearnng tools n provdng useful bologcal understandng of glycosylaton patterns durng AIDS dsease progresson. Methods Our approach to the predcton of AIDS dsease progresson conssts of three consecutve steps: () comprehensve HIV dataset constructon for the purpose of benchmarkng 3D structure-based HIV progress classfcaton methods. (2) novel gp20 structural profle desgn and (3) the constructon of sem-parameterzed, and support vector asssted herarchcal mxture model for the explotaton of non-local nteracton nformaton from the profle. gp20 benchmark dataset gp20-benchmark_ s a newly developed comprehensve dataset for benchmarkng 3D structure-based AIDS dsease progress classfcaton methods. Based on AIDS progresson rates, HIV + ndvduals have been dvded nto three categores such as rapd progressors (RP), slow progressors (SP) and long-term non-progressors (LTNP) []. RP patents progress towards AIDS wthn two years of prmary nfecton. RP show rapd rse n plasma vrus and rapd declne n CD + Tcellcounts.SP group of HIV + patents show steady but gradual ncrease n vrema and decrease n T cell counts over 0-5 years. Only about % of HIV + therapy naïve ndvduals (LTNP) can mantan vrus level below detecton level, robust T cell counts and experence sustaned mmune response for more than 20 years []. A total of 50 env gp20 samples were manually extracted from 50 HIV + ndvduals, of whch 0 samples were LTNPs, were RPs and 29 were SPs. The lst of samples wth the Gen- Bank accesson number, year of sample collecton and dsease type are shown n Table. gp20-benchmark_ s avalable at edu.au/~yangpy/software/aids-progresson.html Structural gp20 proflng The glycosylaton of HIV occurs on the asparagne resdue of a NX[ST] motf where X can be any amno acd resdue except prolne [35,36]. To create a whole envelope glycan profle, the 3D coordnates of the asparagne resdue from every glycosylaton ste were extracted from the gp20 structure models. The extracted glycans from the query model were matched to an approprate glycosylaton ste from the template model (Fgure, left-hand). Mutatons, nsertons and deletons (ndels) are common wthn the gp20 gene and can thus complcate the matchng of target and template glycosylaton stes. Further, t s also dffcult to dfferentate whether a glycosylaton ste has moved f the neghbourng glycans are clustered close together. To overcome these problems, we developed a protocol to loosely encapsulate the movement, nserton and deleton of glycosylaton stes n the HIV envelope gp20 models, to facltate the glycan matchng process. Frst, the glycosylaton stes were separated nto ther respectve regons (V,V2,C2,V3,C3,V4,C4,V5,C5).Theglycanfrom the query model can only be matched wth the template glycan of the same regon. Second, wthn each regon, a dstance matrx was created by calculatng the 3D dstance between every glycan of the query and template models. Fnally, every glycan n the query model was matched to the closest glycan from the target model and ther dstance noted. The addton of glycosylaton stes as compared to the template model s expressed as an addtonal dstance value n the glycosylaton profle. The movement of glycosylaton stes s reflected as a sgnfcant change n dstance value n the profle, whle retanng ts assocaton

3 Page 3 of 0 Table Patent dataset used for structural glycan proflng Cohort/ Country Sample GenBank Accesson No Year of sample collecton Dsease type USA A AY RP USA A2 AY RP USA A3 AY RP USA B4 AY RP USA B6 AY RP USA C7 AY RP USA C8 AY RP USA D9 AY RP USA D0 AY RP USA D AY RP USA E2 AY RP USA F AY SP USA F2 AY SP USA F3 AY SP USA G4 AY SP USA G5 AY SP USA G6 AY SP USA H7 AY SP USA H8 AY SP USA J AY SP USA K4 AY SP USA K5 AY SP USA K6 AY SP USA L7 AY SP USA M AY LTNP USA M2 AY LTNP USA M3 AY LTNP USA M4 AY LTNP USA M5 AY LTNP USA N8 AY LTNP Canada CAN_A_ AY LTNP Canada CAN_A_3 AY LTNP Canada CAN_A_5 AY LTNP Canada CAN_A_6 AY LTNP Canada CAN_B_3 AY SP Canada CAN_B_4 AY SP Canada CAN_B_5 AY SP Canada CAN_B_6 AY SP Canada CAN_C_2 AY SP Canada CAN_C_3 AY SP Canada CAN_C_5 AY SP Canada CAN_C_6 AY SP Canada CAN_C_8 AY SP Canada CAN_C_0 AY SP Australa 8 GQ SP Australa 82 GQ SP Australa BB_76 GQ SP Australa BB_24 GQ SP Australa BB_42 GQ SP Australa BB_92 GQ SP wth the same template glycan. Fnally, the deleton of glycans was coded as an absent dstance value n the profle as compared to other models. The glycan profle reflects the varaton n the global glycosylaton pattern n relaton to the gven template model. The glycan profles for all the sequences n our dataset are compled n an N? M matrx, where N s the maxmum number of glycans n our dataset and M s the number of sequences n the dataset. Sequences wth fewer glycosylaton stes were padded wth a dstance value of zero on ether sde of the matrx. Nonlocal nteractons n sequence and vanshng gradent problem A machne learnng framework wth kernel method [2] was used to learn and characterze the changes to glycosylaton profles for the predcton of HIV dsease progresson. Exstng large machne-learnng algorthms lke neural networks have performed well n the analyss of sequence-based problems, where the nformaton s processed n the order that they are gven. However, these were not desgned to analyse the long-range dependences between glycosylaton stes at the sequence level. Ths s due to the lack of an effcent algorthm for numercal optmzaton, commonly known as the vanshng-gradent problem [3]. For example, when the glycosylaton stes are analysed n the order they appear n the sequence, the dependency between a glycan X and a prevously processed glycan Y mght not be learnt. If glycan X and glycan Y are processed mmedately after one another (short range), ther dependency can be learnt. However, f there are multple glycans between X and Y (long range), the neural network can only learn the dependences between glycan X and the sum of all glycan nformaton before X (ncludng glycan Y). Whle the dependences between X and Y are somewhat establshed because of the partal ncluson of glycan Y n the combned nformaton, the long range dependency learned between glycans X and Y are not suffcent to classfy HIV dsease progresson. Furthermore, error mnmzaton s known to fal n the presence of dependences that are far apart n the sequence space [4,5]. Support vector asssted herarchcal mxture model (SV-HMM) One possble soluton to ths problem s to amelorate the vanshng gradent problem by provdng the kernel functon wth nformaton of dstantly located glycan. Ths gves the machne-learnng framework a far chance of learnng any long-range nteracton between glycosylaton stes. We developed a herarchcal kernel mxture model that combnes a modular approach wth local lnear support vector classfers, whch can effectvely analyse dstantly related nformaton.

4 Page 4 of 0 Fgure Annotaton of the glycans on the template env gp20 crystal structure PB4C (left-hand), and varatons n structural locatons of glycans (rght-hand). At the top level of our framework, we have adopted a well-known Herarchcal Mxture of Experts (HME) model for regresson and classfcaton [6]. The HME model s a tree-lke structure (Fgure 2) that uses the dvde and conquer prncple to learn the nteractons between HIV glycosylaton stes. At the bottom of the tree (leaf nodes) are multple localty effectve support vector, that wll analyse the smlartes between the gven glycosylaton stes. HME descrbes a condtonal probablty dstrbuton over a vector t of target varables, condtoned on a vector x of nputs. Consder the case of functonal mappng learnng of the type y = f()based x on tranng data set T=(x (t),y (t) ),t=0,, nwth x = { x, x2,, x n } and a correspondng desred response y = { y, y2,, y n }.All of the networks, both experts and gatng, receve the same nput vector at the t th tme nstant, x (t). However, whle the gatng networks use ths nput to compute confdence level values for the outputs of the connected expert networks, the expert networks themselves use the nput to generate an estmate of the desred output value. The outputs of the gatng networks are scalar values and are a partton of unty at each pont n the nput space,.e. a probablty set. Thus, consder a twolayered bnary branchng HME: Each of the expert local models (, j) produces outputs y j from the nput vector x (t) t accordng to the relatonshp: yj = f x ( ) (, wj),, where f s a neural-network mappng usng nput x (t) and ts correspondng weght matrx w j. The outputs of the gatng network g at the top level are computed accordng to: g e = wth = v x k e k T () t where V s the weght vector assocated wth gatng network g. Due to the specal form of the softmax beng non-lnear, the g s are postve and sum up to one for each nput vector x (t). The lower level gatng networks compute ther output actvatons smlar to the top level gatng network accordng to the followng expresson: g j j e = wth = v x k e k j T () t The output actvatons of the expert networks are weghted by the gatng networks output actvatons as they proceed up the tree to form the overall output vector. Specfcally, the output of the th nternal node n the second layer of the tree s: y = g y j j j whle the output at the top level node s: y ( t ) = gy Snce both the g s and the y s depend on the nput x (t), the overall output of the archtecture s a non-lnear functon of the nput.

5 Page 5 of 0 Fgure 2 Modular herarchcal kernel experts as a global model. HME has a tree-lke structure that uses the dvde and conquer prncple to learn the nteractons between HIV glycosylaton stes. At the bottom of the tree (leaf nodes) are multple localty effectve support vector, that wll analyse the smlartes between the gven glycosylaton stes. Local kernel machne as a collaboratng flter to SV-HMM As a key local collaborator, weuseasupport-vector classfer [7] to assst the global HME model. SVMs are known as maxmum margn classfers snce they classfy ther objects by mnmzng the emprcal generalzaton error and maxmzng the geometrc margn smultaneously. Where the two classes are not separable, they map the nput space nto a hgh-dmensonal feature space (where the classes are lnearly separable) by usng a non-lnear kernel functon. The kernel functon calculates the scalar product of the mages of two examples n the feature space. Gven a n-dmensonal nput vector, x =(x,x 2,,x n ) wth class labels, y Î {+, }, (=,2,...,N), the hyperplane decson functon of bnary SVM wth kernel method s wrtten as: f( x) = sgn ya ( x), ( x) + b sgn yak( x, x) b Φ Φ = + = = and the followng quadratc program: maxmze Wa () = a aayykx j j (, xj) 2 = j, = subject to a 0, =,...,, and ay = 0. = where s the number of tranng patters; a are the parameters of the SVM; k (, ) s a sutable kernel functon, and b s the bas term. Sem-parametrc modellng to the local kernels The use of x examples, especally n hgh-dmensonal space causes several key problems. Frst, the good data fttng capacty of the flexble model-free approach often tends to ft the tranng data very well and thus, have a low bas. However, the potental rsk s overfttng that causes hgh varance n generalsaton. In general, the varance s shown to be a more mportant factor than the learnng bas n poor predcton performance [8]. Second, wth the hgh-dmensonal data such as protens, as the number of hdden nodes of the network s severely ncreased, t eventually leads to an exponental rse n computatonal complexty. A hgh complexty model generally shows a low bas but a hgh varance [9]. On the other hand, a model wth low complexty shows a hgh bas but a low varance. Hence, a good model should balance well between model bas and model varance. Ths problem s generally regarded as the term bas-varance tradeoff. One of the solutons to the above problem s the socalled sem-parametrc modelng. Sem-parametrc models take assumptons that are stronger than those of nonparametrc models, but are less restrctve than those of parametrc model. In partcular, they avod many serous practcal dsadvantages of non-parametrc methods at the prce of an ncreased rsk of specfcaton errors. To sem-parameterze SV-HMM, we substtute the centrod vectors from vorono regon [20] for each

6 Page 6 of 0 tranng sample x used n the SVM decson functon (Fgure 3). Consder a tranng sequence consstng of M source vectors, T={x,x 2,,x m }. M s assumed to be suffcently large and so that all the statstcal propertes of the source are captured by the tranng sequence. We assume that the source vectors are k-dmensonal, X m = (x m,,x m,2,, x m,k ), m=,2,,m. These vectors are compressed by choosng the nearest matchng vectors and form a codebook consstng the set of all the code-vectors. N s the number of code-vectors, C={c,c 2,,c n } and each code-vector s k-dmensonal, c n =(c n,,c n,2,,c n, k),n=,2,,n. The representatve codevector s determned to be the closest n Eucldean dstance from the source vector. The Eucldean dstance s defned by: k 2 j j j= dxc (, ) = ( x c ) where x j s the j th component of the source vector, and c j s the j th s components of the code-vector c. S n s the nearest-neghbor regon assocated wth code-vector c n, and the parttons of the whole regon are denoted by P= {S,S 2,,S N }. If the source vector X m s n the regon S n, ts approxmaton can be denoted by Q(X m )=c n, f X m Î S n. The Vorono regon s defned by: V = { x R : x c x c, for all j } k To fnd the most optmal C and P, vector quantzaton uses a square-error dstorton measure specfyng exactly how close the approxmaton s. The dstorton measure can be gven as: M 2 Dave Xm Q Xm Mk m= If C and P are soluton to the above mnmzaton problem, then t must satsfy two condtons namely nearest neghbor and centrod condtons. The nearest neghbor condton ndcates the sub-regon S n should consst of all vectors that are closer to c n than any of the other code-vectors. It s wrtten as: = ( ) { } 2 2 n n n S = x : x c x c, n = 2,,..., N The centrod condton requres the code-vector c n should be average of all those tranng vectors that are n ts Vorono Regon S n. c n X m Xm Sn =, n = 2,,..., N Xm Sn j The centrod vector of each vorono regon whch can be expressed as: xm Xm S Q( Xm) = c =, = 2,,..., N N The new local SVM s approxmaton can be wrtten as: f() x = sgn ( w () x + b) = sgn yak(, x c) + b = and the followng quadratc program: maxmze Wa () = a aayykq j j ( (), x Qj()) x 2 = j, = subject to a 0, =,...,, and ay = 0. = SVM s consdered as a purely non-parametrc model, whereassv-hmmsconsderedassem-parametrc model as t adopts the method of groupng the assocated nput vectors n each class. Hence, the performance of the proposed model has some advantages n comparson to both pure parametrc models and pure non-parametrc models n terms of learnng bas and generalzaton varance especally on hgh-dmensonal proten datasets. Predcton of AIDS dsease progresson Our herarchcal mxture modelng contans a number of consecutve steps. Frst, structural glycosylaton profles from our HIV + RP, SP and LTNP sequences were used as the nput data n our computatonal kernel analyss. By learnng the dfferences n glycosylaton changes between patent samples, the SV-HMM was used to classfy a prevously unseen glycosylaton profle nto RPs, SPs or LTNPs. Second, the predcted classfcatons were compared wth the true dsease group for the glycosylaton profle. These experments were performed n a seres of steps. The SV-HMM and seven other wellregarded machne learnng models, transductve support vector machne (SVM LIB ) [2], SVM based decorate model (Decorate SVM ) [22], mult-layered perceptron (MLP) [23], radal bass functon network (RBFN) [24], logstcs [25], and decson trees (J48) [26] were used to analyse the glycosylaton profles. For the analyss of each kernel model, we performed a tenfold cross-valdaton evaluaton on the dataset most wdely adopted method for far model evaluaton of general computatonal sequence-based classfcaton [37-42]. Durng the cross-valdaton phase, one fold data were randomly chosen and excluded from the tranng

7 Page 7 of 0 Fgure 3 The basc archtecture of sem-parameterzed SV-based local models. The centrod vectors from vorono regon for each tranng sample x used n the SVM decson functon. SVM s consdered as a purely non-parametrc model, whereas SV-HMM s consdered as semparametrc model as t adopts the method of groupng the assocated nput vectors n each class. RBF kernel has been used for the SV-based local models. set before model learnng began, and were later used to test the performance of the learnt model. Ths process was repeated ten tmes. The cross valdaton protocol was chosen to solve the potental problems caused by resdual evaluatons. Ths s because, f the entre dataset were used for tranng, the model could not provde adequate ndcaton of ts effectveness n predctng any unseen data. Thus, t does not matter how the data s dvded as every data pont gets to be n the test set exactly once and, n the tranng sets, nne out of ten tmes. Snce every data pont s tested exactly once, t has been a wdely used evaluaton method for real expermental sequence data, (just as the case n gp20 benchmark experment) [37-42]. Fnally, each model has been measured by the predctve accuracy (CCI: correctly classfed nstances) of the kernel model. The fnal CCI value was calculated based on the average of all the predcton accuraces observed durng the tenfold valdaton process. The mean squared error (MSE: a quantty used to measure how close forecasts or predctons are to the eventual outcomes), tme (TBM: tme to buld model), and model stablty (STD: ST Devaton obtaned from ten sub-samples) were also measured n the experments. The stepwse procedure has been provded n Fgure 4. Results In our comparson between the eght machne-learnng models, SVM-asssted herarchcal mxture models (SV- HMM: sem-parametrc, HME SVM : non-parametrc) were shown to be most sutable (CCI: 67.82%, 69.38%) for the classfcaton of HIV dsease types usng the glycosylaton profles (Table 2). In general, SVM-based models such as SV-HMM, HME SVM,SVM LIB and Decorate SVM performed better than the other models n terms of CCI. Ths suggests that the lnear support vector approaches wth RBF kernel used n the standard SVMmodels accurately learns the characterstcs from structural gp20 profles for the predcton of AIDS dsease progresson. Mean testng data ±STD obtaned by ANOVA test usng optmal settngs for each model. The ANOVA CCI and STD of each model are nsgnfcant, whch suggest that all the models are performng consstently n the experment. The performance of our sem-parameterzed SV-MHH model was the most stable (STD: 0.8) n the classfcaton of HIV dsease stages than the other machne-learnng models, wth the excepton of the Logstc model, whch also gave good predcton stablty due to ts statstcal nature. These results showed that the herarchcal mxture model approach used n the proposed SV-HMM model can consstently capture crtcal changes to HIV glycosylaton profles and can be used to predct AIDS dsease progresson. Assocatve dfferences n the glycosylaton profle that determne dsease progresson mght be located far apart n the sequence, but close together n the tertary structure. The results also suggest that the modular approaches used n SV-HMM and HME SVM s able to capture these non-local nteractons. Thus, our proposed models are resstant to model over-fttng and weak sgnal to nose rato durng the learnng phase and can avod the vanshng gradent problem n ths scenaro. Our structural glycosylaton profles of our gp20 models were created usng the template crystal structure. The dstance between the matchng glycosylaton stes from our model to the template structure (Fgure, left-hand) was calculated to create the glycan profle. Most glycosylaton stes had an equal contrbuton of structural

8 Page 8 of 0 Fgure 4 The flowchart of SV-HMM showng the stepwse procedure. The above fgure shows the stepwse procedure we have performed. () data collecton, buldng gp20 benchmark dataset and pre-processng datasets; (2) structural gp20 profle constructon ncludng matchng up and calculatng the 3D dstance between every glycan of the query and template models. (3) the nformaton obtaned n (2) and (3) were combned and normalsed to fall n the nterval [, ] to be fed nto networks; (4) target levels were assgned to each profle (postve, +, for RPs, 0 for SP. for LTNP); (5) a hold-out method, to dvde the combned dataset nto ten subsets (tranng and testng sets); (6) model tranng on each set, to create a model; (7) smulaton of each model on the test set, to obtan predcted outputs; and (8) post-processng to fnd predcted HIV progressor groups. The procedure from (6) to (8) was performed teratvely untl we obtaned the most sutable kernel and the optmal hyperparameters for SV-HMM for gp20 benchmark dataset. varatons by the three dsease groups, wth the excepton of glycan N23 n the C3 regon, where there were more structural glycan dfferences n the LTNP models (Fgure, rght-hand). When we compared the average structural varaton observed, glycan stes 5, 29, 243, 254 were more structurally varable (>50%). The gp20 proten exsts as a trmer and the nner doman of gp20 (coloured grey) faces the trmer axs (left-hand). The outer doman (coloured blue) faces away from the trmer axs and s exposed on the envelope surface. Coloured red spheres represent the glycosylaton stes on our template structure. The V3, V4 and V5 loop regons are annotated. The CD4 molecule (yellow) that bnds to gp20 s ncluded to gve a better perspectve of the proten. Each pe chart (rght-hand) represents the percentage of glycan structural varatons by the LTNP, RP or SP samples at each glycosylaton ste. The area of the pe charts s proportonal to average structural varaton observed. Connectng lnes ndcate Table 2 Model comparson on gp20_benchmark_ dataset Models MSE RP MSE SP MSE LTNP MSE Overall CCI STD TBM SV-HMM HME SVM SVM LIB Decorate SVM MLP RBFN Logstc J The fnal CCI value was calculated based on the average of all the predcton accuraces ob-served durng the tenfold valdaton process. MSE, TBM and STD (ST Devaton obtaned from ten sub-samples) ndcate the accuracy, complexty and stablty of the model respectvely. the prmary (sequence) orderng of the glycosylaton stes. Dscusson A predcton accuracy of 67 to 69% was acheved n the classfcaton of HIV envelope sequences from RP, SP and LTNP patents. The structural glycan proflng technque was desgned to encapsulate the frequent and vared structural changes to glycosylaton stes observed n HIV envelope gp20. Structural glycan modfcatons from the entre envelope gp20 were characterzed at the same tme to provde a macroscopc vew of the carbohydrate landscape. Interactons between glycosylaton stes were analysed by the dvson of the glycan nput space nto smaller and more manageable problems, usng the modular kernel desgn adopted n herarchcal kernel mxture archtecture. These partal solutons were then ntegrated to yeld an overall soluton to the whole problem. The modular approach enforces constant error flow through the nternal states of neural-network unts. By dong so, we were able to study nteractons between glycosylaton stes that are located far apart n the sequence, whch were not possble usng tradtonal machne-learnng methods. Whle the framework was successful n dentfyng crtcal changes to glycan profles for dfferent dsease progresson rates, we were lmted to the number of avalable patent samples that were well characterzed clncally. Havng more SP sequences n our dataset also meant that we had to address the potental bas n our learnng model. Another reason for the reducton of predctve power for HIV dsease progresson could be due to the absence of structural nformaton from the V-V2 loops of gp20. Durng the crystallzaton process, the V-V2 loops were normally deleted n order to promote better

9 Page 9 of 0 crystallzaton. Thus, no V-V2 structural nformaton s avalable to date. Unfortunately, the V and V2 glycans are mportant to dsease progresson. Structurally, the V and V2 glycans protect the brdgng sheet between the nner and outer doman of HIV envelope gp20 [27], and nfluence AIDS progresson through hostreceptor modulaton durng an nfecton. Any changes to the V2 glycan can potentally restrct the capacty of HIV- to replcate [28]. Wthout the correct glycosylaton profle from the V V2 loops, we can only, at best, develop a partal mappng of the envelope carbohydrate landscape to determne AIDS progresson. Durng the glycan proflng, the V3 regon of our sequence was not ncluded n the analyses, as the template crystal structure (PDB: 2B4C) was not glycosylated n the V3 regon. Ths mght also have partally dsadvantaged the model s predcton power, as the V3 regon s known to ndrectly nfluence dsease progresson rates. When HIV nfects a host cell, the gp20 proten frst attaches to a CD4 molecule, followed by the bndng to ether the CCR5 or CXCR4 co receptor [29,30]. Vral strans that preferentally bnd to CCR5 are less pathogenc, whle vruses that prefer CXCR4 are more vrulent and are assocated wth faster dsease progresson [3]. The ablty to use the CXCR4 co-receptor s nfluenced by the absence of the glycan n the V3 regon only [32], whereas the use of CCR5 co-receptor s collectvely nfluenced by the amno acd makeup and glycan profles n the V, V2 and V3 regons all together [32,33]. Thus, the occluson of V3 glycans from our study mght have partally weakened our predcton results. On the contrary, the stable predcton rate wthout the nfluence of V, V2 and V3 glycans showed that the other glycans outsde these regons are mportant to dsease progresson. Ths observaton s consstent wth several prevously reported fndngs. For example, glycan modfcaton wthn the C3 regon can affect vral fusogencty and entry knetcs [6], whle both V4 [34] and C4 [7] glycans can affect the CD4 bndng. Concluson Ths paper addressed two mportant ssues n the predcton of AIDS dsease progresson. Frst, t provdes a formal framework for researchers to understand the effect of whole envelope structural glycan modfcaton aganst phenotypc changes to vral pathogeness, lke receptor bndng ablty, replcaton effcency and nfectvty predctons. It s the frst study n the lterature that uses the structural glycan nformaton n the analyss of HIV whch was made possble by the avalablty of the HIV envelope crystal structures and sophstcated homology modellng protocols. Second, our novel framework uses sem-parameterzed, support-vector asssted herarchcal mxture model, whch s able to effectvely explot the nformaton of non-local nteractons wth strong resstance to vanshng-gradent and hgh-dmensonalty problems. Ths also provded a way of fne-tunng the model by the adjustment of hyper-parameters as well as provdng effcent sem-parametrc approxmaton. Wth the newly bult gp20-benchmark_ dataset, our novel framework whch uses SV-HMM and HIV structural gp20 profle has set an mportant benchmark for the computatonal predcton of AIDS dsease progresson. Lst of abbrevaton used AIDS: Acqured Immune Defcency Syndrome; ANOVA: The Analyss Of Varance; CCI: Correctly Classfed Instances); HIV: Human Immunodefcency Vrus; HKMM: Herarchcal Kernel Mxture Model; HME: Herarchcal Mxture of Experts; J48: Decson Trees; MLP: Mult-Layered Perceptron; PDB: Proten Data Bank; RBFN: Radal Bass Functon Network; RP: Rapd Progressors; SP: Slow Progressors; STD: ST Devaton; STNP: Long Term Non-Progressors; SVM: Support Vector Machne; TBM: Tme to Buld Model. Competng nterests The authors declare that there s no competng nterests. Authors contrbutons PDY developed and mplemented the new kernel model (HKMM). YSH and PDY developed glycol-profle and gp20 dataset, and drafted the manuscrpt. YSH, JN, PY nterpreted the results wth PDY. NKS, MC and AYZ edted the manuscrpt and offered much advces and nsght durng the project. Acknowledgement Ths work s supported by KUSTAR, and Centres for Dstrbuted and Hgh Performance Computng, Mathematcal Bology & Sydney Bonformatcs, Unversty of Sydney, Australa. Ths artcle has been publshed as part of BMC Genomcs Volume Supplement 4, 200: Nnth Internatonal Conference on Bonformatcs (InCoB200): Computatonal Bology. The full contents of the supplement are avalable onlne at Author detals Centre for Dstrbuted and Hgh Performance Computng, Unversty of Sydney, NSW 2006, Australa. 2 Department of Computer Engneerng, Khalfa Unversty of Scence, Technology and Research (KUSTAR), Abu Dhab, U.A.E. 3 Retrovral Genetc Laboratory, Centre for Vrus Research, Westmead Mllennum Insttute, Westmead, NSW 245, Australa. 4 Etsalat Brtsh Telecom Innovaton Centre (EBTIC), P.O. 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