research article identification of secretory proteins in...

Research ArticleIdentification of Secretory Proteins in Mycobacteriumtuberculosis Using Pseudo Amino Acid Composition

Huan Yang,1 Hua Tang,2 Xin-Xin Chen,1 Chang-Jian Zhang,1 Pan-Pan Zhu,1,3

Hui Ding,1 Wei Chen,1,4 and Hao Lin1

1Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China2Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China3Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School,Shenzhen, Guangdong 518055, China4Department of Physics, School of Sciences, Center for Genomics and Computational Biology, North China University ofScience and Technology, Tangshan 063000, China

Correspondence should be addressed to Wei Chen; [email protected] and Hao Lin; [email protected]

Received 23 April 2016; Accepted 18 July 2016

Academic Editor: Xun Lan

Copyright © 2016 Huan Yang et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Tuberculosis is killing millions of lives every year and on the blacklist of the most appalling public health problems. Recent findingssuggest that secretory protein ofMycobacterium tuberculosismay serve the purpose of developing specific vaccines and drugs due totheir antigenicity. Responding to global infectious disease, we focused on the identification of secretory proteins inMycobacteriumtuberculosis. A novel method calledMycoSec was designed by incorporating 𝑔-gap dipeptide compositions into pseudo amino acidcomposition. Analysis of variance-based technique was applied in the process of feature selection and a total of 374 optimal featureswere obtained and used for constructing the final predicting model. In the jackknife test,MycoSec yielded a good performance withthe area under the receiver operating characteristic curve of 0.93, demonstrating that the proposed system is powerful and robust.For user’s convenience, the web server MycoSec was established and an obliging manual on how to use it was provided for gettingaround any trouble unnecessary.

1. Introduction

Mycobacterium tuberculosis (M. tuberculosis or MTB), alsoknown as acid-fast bacilli, is the causative pathogen of thecontagious disease tuberculosis (TB). Over the two decades,despite booming development of molecular technologies anddiagnostic platforms, TB remains a disastrous global healththreat ranking alongside the human immunodeficiency virus(HIV) as the second killer worldwide [1]. There were anestimated 9.6 million new TB cases in 2014, and China isbearing 10 percentage of the global total according to the latestWorld Health Organization (WTO) report [2]. To reversethe severe situation, constant efforts on developing effectivevaccines as well as controlling the spread of MTB are inlong term needed. Since delayed or incorrect diagnosis ofTB markedly aggravates the epidemic ofM. tuberculosis, it is

urgent to develop method for credible early-stage diagnosisof TB and comprehensive understanding of the intrinsicpathogenesis of M. tuberculosis, especially the multidrug-resistant strains ofM. tuberculosis (MDR-TB).

Recent researches suggest that secretory protein antigenscan be used to detect antibodies in infected specimens [3].And it is a well-established fact that effector proteins aremostly secretory proteins that stimulate infection by manip-ulating the host response [4]. In line with nonsecretory pro-teins, M. tuberculosis secretory proteins are also assembledin ribosomes but transported to extracellular milieu throughcertain specific secretion system to attack host cells for sur-vival and reproduction, thus changing the host cell microen-vironment and causing grievous tubercular symptoms ininfected individuals.Hence, distinguishing secretory proteinsfrom nonsecretory proteins is a matter of grave concern for

Hindawi Publishing CorporationBioMed Research InternationalVolume 2016, Article ID 5413903, 7 pageshttp://dx.doi.org/10.1155/2016/5413903

2 BioMed Research International

tracing the real pathogenic factors and developing vaccinesor drugs against TB.

Benefited from the next-generation sequencing technol-ogy, the amount of protein sequences is exploding with anexponential growth, which is far beyond the capacity ofclassical biochemical analysis through advances in experi-mental facilities and molecular biotechnologies. Faced withsuch an embarrassment, appropriate and fast computationalmethods exploit a fresh avenue to investigate the propertiesof M. tuberculosis proteins. However, to the best of ourknowledge, few computational works focused on secretoryproteins in M. tuberculosis. Since a majority of secretoryproteins have a signal peptide by which they were exportedvia the signal peptidase pathway, in 2009, Leversen et al.evaluated the performance of nine signal peptide predictionalgorithms for identification of mycobacterial signal peptidesusing sequence data from proteomic methods [5]. Sequentialand structural characteristics of secretory proteins also helpto address the problem. In 2010, Vizcaı́no et al. identifiedpotential secretory proteins fromMycobacterium tuberculosisH37Rv by screening its genomes using machine-learningtools [6]. The prediction of subcellular locations of M.tuberculosis proteins also provides vital clue for identifyingsecretory proteins. Recently, Zhu et al. used a support vectormachine- (SVM-) based model to predict the subcellularlocalization of mycobacterium proteins [7]. The success onmycobacterium proteins as mentioned above suggested thatbioinformatics approaches are effective strategy for mininguseful information and providing insights into both basicresearch and drug design.

Thus, the current study was devoted to develop a bioin-formatics approach to identify secretory proteins in M.tuberculosis.The work will describe how to construct a validbenchmark dataset, how to develop an effectivemathematicalexpression to formulate the proteins, and what kinds ofmachine-learning method and cross-validation tests wereused in the prediction model. Finally, based on the proposedmethod, a web server calledMycoSec was established.

2. Material and Methods

2.1. Benchmark Datasets. The original datasets used in thisstudy were extracted from the Universal Protein Resource(UniProt) [8]. To guarantee a good quality of data, pro-teins in UniProt were collected confidently according tothe following criteria: (I) only those from M. tuberculosiswere considered; (II) only those reviewed and annotatedby experts were chosen; (III) sequences with ambiguousresidues, such as “𝐵,” “𝑋,” and “𝑍,” were discarded; (IV)sequences that were inferred from homologous proteinswere eliminated; (V) sequences that were fragments ofother proteins were excluded; (VI) sequences that have lessthan 16 amino acids were removed to meet the parameter(𝜆) requirement (see Section 3.1); (VII) sequences with thekeyword “secreted” or “secretory vesicle” in the “subcellularlocation” column were regarded as secretory proteins (posi-tive samples), while sequences without these keywords wereconsidered as nonsecretory proteins (negative samples or

control samples). After going through the above processes, atotal of 420 samples were reserved, including 63 positives and357 negatives. To exclude any homologous bias which maycause overestimation problem of final prediction results, weclustered these protein sequences with the CD-HIT program[9] by setting 30%, a rigorous value of this parameter, as thecutoff threshold of sequence identity to remove the redundantpart. As a result, the benchmark dataset S containing 35secretory proteins and 266 nonsecretory proteins can beexpressed as

S = S+ ∪ S−, (1)

where S+ represented the positive subset, S− represented thenegative subset, and the symbol “∪” represents “union” in theset theory. More detailed information of the datasets can bedownloaded from http://lin.uestc.edu.cn/server/MycoSec/data.html/.

2.2. The Representation of Protein Samples. Translating con-ventional biological problems into computable mathematicalmodels is usually adopted as an accommodation to therequirement of bioinformatics analysis. A protein sequencewith total number of 𝐿 amino acids is usually formulated as

P = R1R2R3⋅ ⋅ ⋅R𝐿, (2)

where R𝑖(𝑖 = 1, 2, 3, . . . , 𝐿) denotes the 𝑖th amino acid

residue in the query protein sample P, of which 𝐿 is thelength of the protein. Almost all the existing machine-learningmethods, regardless of supervised and unsupervisedor semisupervised, such as SVM [10–13], Artificial NeuralNetwork (ANN) [14], 𝐾-Nearest Neighbor (KNN) [15], andensemble classifiers [16–18], can only handle vectors withthe same dimension rather than sequence samples [19].Meanwhile, data discretization is also required by main-stream feature selection strategies [20–22]. Accordingly, theconcept of discrete vector is proposed to realize more generalrepresentations of sequence fragments.

The pseudo amino acid composition (PseAAC) is awidely used method for representing protein sequences forits annexation of long-range sequence-order informationand the correlation of physicochemical properties betweentwo residues, as well as its balance between representativecapability and computational expense. Inspired by PseAAC,we made an improvement by substituting amino acid com-position for 𝑔-gap dipeptide composition.Thus, each proteinsequence in our benchmark dataset can be denoted by a400 + 𝑛𝜆 dimension vector; that is,

P = [𝑥1, 𝑥2, . . . , 𝑥

400, 𝑥400+1

, . . . , 𝑥400+𝑛𝜆

]𝑇

, (3)

where the first 400 elements 𝑥1, 𝑥2, . . . , 𝑥

400are the 𝑔-gap

dipeptide composition and the next 𝑛𝜆 elements 𝑥400+1

,

𝑥400+2

, . . . , 𝑥400+𝑛𝜆

are the first tire to 𝜆th tire correlation fac-tors of protein sequence, which are determined on the basisof physiochemical properties. “𝑇” is a symbol of transpose

BioMed Research International 3

operator.𝑥𝑢

(𝑢 = 1, 2, . . . , 400, . . . , 400+𝑛𝜆) can be calculatedas given in

𝑥𝑢

=

{{{{

{{{{

{

𝑓𝑢

∑400

𝑖=1𝑓𝑖+ 𝜔∑

𝑛𝜆

𝑗=1𝜏𝑗

(1 ≤ 𝑢 ≤ 400)

𝜔𝜏𝑢

∑400

𝑖=1𝑓𝑖+ 𝜔∑

𝑛𝜆

𝑗=1𝜏𝑗

(400 + 1 ≤ 𝑢 ≤ 400 + 𝑛𝜆) ,

(4)

where 𝑓𝑢(𝑢 = 1, 2, . . . , 400) is the occurrence frequency of

the 𝑢th dipeptide in P, as formulated by

𝑓𝑢=

𝑛𝑔

𝑢

∑400

𝑢=1𝑛𝑔

𝑢

=𝑛𝑔

𝑢

𝐿 − 1 − 𝑔. (5)

𝑛𝑔

𝑢(𝑢 = 1, 2, . . . , 400) is how many times the 𝑢th 𝑔-gap

dipeptide appears in P. 𝜔 is the weight coefficient. 𝑛 isthe number of physicochemical properties selected. 𝜆 is thenumber of total counted ranks or tires of the correlationsalong a protein sequence and 𝜏

𝑗(𝑗 = 1, 2, . . . , 𝑛𝜆) is the

𝑗th tire correlation factor that reflects the sequence-ordercorrelation between all the 𝑗th most contiguous dipeptidesalong a protein sequence, as formulated by the followingformula:

𝜏1=

1

𝐿 − 1

𝐿−1

∑

𝑖=1

𝐻1

𝑖,𝑖+1

𝜏2=

1

𝐿 − 1

𝐿−1

∑

𝑖=1

𝐻2

𝑖,𝑖+1

.

.

.

𝜏𝑛=

1

𝐿 − 1

𝐿−1

∑

𝑖=1

𝐻𝑛

𝑖,𝑖+1

𝜏𝑛+1

=1

𝐿 − 2

𝐿−2

∑

𝑖=1

𝐻1

𝑖,𝑖+2

𝜏𝑛+2

=1

𝐿 − 2

𝐿−2

∑

𝑖=1

𝐻2

𝑖,𝑖+2

.

.

.

𝜏2𝑛

=1

𝐿 − 2

𝐿−2

∑

𝑖=1

𝐻𝑛

𝑖,𝑖+2

.

.

.

𝜏𝑛𝜆−1

=1

𝐿 − 𝜆

𝐿−𝜆

∑

𝑖=1

𝐻𝑛−1

𝑖,𝑖+𝜆

𝜏𝑛𝜆

=1

𝐿 − 𝜆

𝐿−𝜆

∑

𝑖=1

𝐻𝑛

𝑖,𝑖+𝜆

(𝜆 < 𝐿) ,

(6)

where the correlation functionH𝑛𝑖,𝑖+𝜆

is calculated by

H𝑛𝑖,𝑖+𝜆

= ℎ𝑛

(R𝑖) ⋅ ℎ𝑛

(R𝑖+𝜆

) , (7)

where ℎ𝑛(𝑅𝑖) denotes the constant value of the 𝑛th kind

physicochemical property forR𝑖and ℎ𝑛(R

𝑖+𝜆) denotes the 𝑛th

physiochemical property value for R𝑖+𝜆

. Due to the differentscales of values among various properties, the followingnormalization process is necessary to attain nondimensionaldata:

ℎ𝑘

(𝑅𝑖) =

ℎ𝑘

0(R𝑖) − ∑20

V=1 ℎ𝑘

0(RV) /20

√∑20

𝑢=1[ℎ𝑘

0(R𝑢) − ∑20

V=1 ℎ𝑘

0(RV) /20]

2

(𝑘 = 1, . . . , 𝑛) ,

(8)

where ℎ𝑘0(R𝑖) is the original value of the 𝑘th kind physico-

chemical property for R𝑖, RV (V = 1, 2, . . . , 20) stands for the

Vth category amino acid residue, and R𝑢

(𝑢 = 1, 2, . . . , 20)

is defined in the same manner as RV. The normalized valuesobtained by (8) will have a zero mean value over the 20 kindsof amino acids and will remain unchanged if going throughthe same conversion procedure again.

It is widely accepted that physicochemical properties ofproteins have impacts upon not only a series of biologicalprocesses [23], such as protein denaturation and renaturation,cell signaling transduction, and change of solution conductiv-ity, but also maintaining tertiary structures, further underly-ing certain molecular functions. Among various properties,hydrophilicity and hydrophobicity were chosen here on thebasis of a priori knowledge that water is the environmentfor the survival of all biological molecules and promotes thetransportation of molecules by an intrinsic liquidity. In ourmodel, the tire correlation of hydrophilic and hydrophobicamino acids was investigated by tuning the parameter 𝜆 inorder to see the global placement in which amino acids werelinked to each other.

2.3. Support VectorMachine. SVM is awelcome andpowerfulmachine-learning algorithm which has been successfullyused in the realm of bioinformatics for pattern recognitionand classification. Established on the theories of Vapnik-Chervonenkis Dimension [24] (VC Dimension) and struc-tural risk minimization, SVM can solve binary nonlinearclassification problems among small samples and achievegood generalization ability by mapping the input data intohigher dimensional feature space (Hibert space) by kernelfunction transformation and then determining an optimalhyperplane to separate a given set of labeled data. Formulticlass classification tasks, “one-versus-one (OVO)” and“one-versus-rest (OVR)” are generally applied to extend thetraditional SVM.Abrief description of formulation of SVM isgiven in [25, 26] and, formore details, please see amonograph[27].

In this work, the toolbox LIBSVM 3.20 was employed toimplement the SVM classifier and perform the prediction,which can be freely downloaded from http://www.csie.ntu.edu.tw/∼cjlin/libsvm/.The feature vectors of protein samples


formulated by (3) were used as inputs of the SVM. Radialbasis function (RBF) was adopted here on account of bettervalidity, less deviation, and faster speed in nonlinear trainingprocess compared with other kernel functions. For pursuitof the optimal model, the penalty constant 𝐶 and the kernelwidth parameter 𝛾 were tuned in an optimization procedureusing a grid search method, of which the search spaces for𝐶 and 𝛾 are [215, 2−5] and [2−5, 2−15] with steps of 2−1 and 2,respectively.

2.4. Performance Evaluation. Here we attempted to evaluatethe performance of a statistical predictor from two parts:(a) evaluating its generalization ability with an impartialcross-validation method; (b) employing appropriate metricsto measure its success rate.

Independent dataset test, subsampling (or k-fold cross-validation) test, and jackknife test are the three widely usedvalidation methods to evaluate the anticipated success rateof a predictor [28]. Among the three methods, jackknife testis deemed the least arbitrary due to its basic idea of leavingout each sample as the testing set iteratively and then findingthe mean value of these calculations. However, compared tothe other two methods, jackknife test is so time-consumingthat it lags the overall computational efficiency. Thus, tobalance between correctness and efficiency, a trade-off wasmade by applying fivefold cross-validation test in the stageof parameter optimization and then switching to jackknifetest for evaluation of final model once the optimal model wasfound.

Here, a set of straightforward methods was provided toassess the prediction quality by using the following threemetrics: sensitivity (Sn) which is also known as recall,specificity (Sp), and average accuracy (AA),which are definedas follows:

Sn = TPTP + FN

(0 ≤ Sn ≤ 1) ,

Sp = TNTN + FP

(0 ≤ Sp ≤ 1) ,

AA = Sn + Sp2

(0 ≤ Ac ≤ 1) .

(9)

In (9), TP, TN, FP, and FN are the number of true positives,true negatives, false positives, and false negatives. Sn indicatesthe ability of correctly judging positive samples, Sp suggeststhe ability of correctly recognizing negative samples, and Acaverages Sn and Sp.

The Receptor Operating Characteristic curves [29] (ROCcurves) were also plotted to describe the performance ofmodels by plotting the Sn (true positive rate (TPR)) againstthe 1-Sp (false positive rate (FPR)) across the entire range ofSVM decision values, under which the area (AUC) can serveas an objective indicator for quality assessments even whenthe classes are of very different sizes. The value 0.5 of AUC isequivalent to random prediction while 1 represents a perfectone. Accordingly, the larger the AUC is, the more credibilitythe predictions have.

2.5. Feature Selection. Feature selection is very important inview of dimension reduction for pinpointing distinguishingfeatures and then improving generalization ability [30]. In thepresent study, we performed feature selection by using theAnalysis of Variance (ANOVA), which is known as a robustand simple method to test the difference in means betweengroups, even when the number of observations is uneven ineach group. Besides, it is easily generalized to more than twogroups without increasing the Type 1 error. According to thebasic idea of ANOVA, features can be ranked by their F-values calculated as follows:

𝐹 (𝑢) =S2B (𝑢)S2W (𝑢)

, (10)

where S2B(𝑢) and S2

W(𝑢) denote the sample variance betweengroups (also called Mean Square Between, MSB) and samplevariance within groups (also called Mean Square Within,MSW) separately; the detailed formulae were given in

S2B (𝑢)

=

∑2

𝑖=1𝑚𝑖((∑𝑚𝑖

𝑗=1𝑥𝑢(𝑖, 𝑗)) /𝑚

𝑖− (∑2

𝑖=1∑𝑚𝑖

𝑗=1𝑥𝑢(𝑖, 𝑗)) /∑

2

𝑖=1𝑚𝑖)

2 − 1,

S2W (𝑢) =∑2

𝑖=1∑𝑚𝑖

𝑗=1𝑥𝑢(𝑖, 𝑗) − (∑

𝑚𝑖

𝑗=1𝑥𝑢(𝑖, 𝑗)) /𝑚

𝑖

∑2

𝑖=1𝑚𝑖− 2

,

(11)

where 𝑥𝑢(𝑖, 𝑗) is the value of the 𝑢th feature of the 𝑗th sample

in the 𝑖th group defined in (4) and𝑚𝑖is the sample size of each

group (here 𝑚1

= 35 and 𝑚2

= 266). For only two groupswere counted in the current study, the degree of freedom(DOF) between and within groups was 1 and (𝑚

1+ 𝑚2− 2),

respectively. Still, it is apparent that a larger value of 𝐹(𝑢)reflects a better discriminative capability of a feature.

Based on the features thus ranked, we used the incre-mental feature selection [31] (IFS) to determine the optimalnumber of features as described below.Thefirst feature subsetwas initialized by a feature with the highest F-value, andthe next subset was composed when the feature with thesecond highest F-value was added. We repeated this processby adding features sequentially from higher to lower F-valuesuntil all candidate features were added. Thus, the 𝑁 featuresets thus formed would be composed of 𝑁 ranked features.The 𝜁th feature set can be formulated as

𝑆𝜁= [𝐹1, 𝐹2, . . . , 𝐹

𝜁] (1 ≤ 𝜁 ≤ 𝑁) . (12)

For each of such𝑁 feature sets, an SVMpredictionmodelwas constructed and examined by the jackknife test on thebenchmark data set. Afterwards, we obtained an IFS curvein a 2D Cartesian coordinate system with index 𝜁 as theabscissa (or X-coordinate) and the AUC as the ordinate (orY-coordinate). The optimal feature set is expressed as

𝑆𝛾= [𝐹1, 𝐹2, . . . , 𝐹

𝛾] (1 ≤ 𝛾 ≤ 𝑁) (13)

with which the IFS curve reaches its peak. In other words,in the 2D coordinate system, the AUC value reaches itsmaximum when 𝑋 = 𝛾.


3. Results and Discussions

3.1. Parameter Optimization. As we can see from (3) to(8), the results of the proposed method depended on threeparameters, that is, 𝜆, 𝑔, and 𝜔. 𝜆 represents the tiers countedfor the global or long-range sequence-order effect and larger𝜆 may contain more global sequence-order information;𝑔 portrayed the local or short sequential tendencies bymeasuring the gap between two amino acid residues; and 𝜔is the weight factor imposed between local and global effectswhich is usually within the limits of 0 to 1. To search for theoptimal values of the three parameters which can achievethe highest accuracy, we performed a series of experimentsaccording to the following standard:

0 ≤ 𝑔 ≤ 9 with step Δ = 1,

1 ≤ 𝜆 ≤ 15 with step Δ = 1,

0 ≤ 𝜔 ≤ 1 with step Δ = 0.1.

(14)

Hence, a total of 10×15×11 = 4950 individual combinations(or points in the 3D parameter space) had to queue up tobe screened for finding the optimal one, which was actuallya routine but tedious process to optimize the model via a3D grid search [32]. In view of computational efficiency,fivefold cross-validation was firstly employed to cope withthe prioritization of parameters, and once the optimal valueswere determined, the rigorous jackknife test was performedto evaluate the success rates of the feature set according to thefour metrics defined in Performance Evaluation section. As aresult, the largest AUC of 0.845 was obtained when 𝑔 = 9,𝜆 = 6, and 𝜔 = 1.

The dimension of the optimal feature set was 400 + 2 ×6 = 412, whichmeans that 400 9-gap dipeptide compositionsand 12 additional hydrophilicity/hydrophobicity componentswould be incorporated in a feature vector fed into thepredictor. Nevertheless, to evade from noise and overfittingproblems, ANOVA was employed to further optimize thefeature set. By calculating the F-value of each candidatefeature and ranking them in descending order, a series offeature sets in various sizes were obtained based on IFSstrategy as illustrated in Feature Selection section. The IFScurve reached its peak when the feature number was 374,indicating that the feature subset was the least redundantand themost discriminative one. Consequently, the AUCwasimproved from 0.85 to 0.93 and the computational time andexpenses were acutely reduced.

3.2. Comparison with Other Classifiers. Subsequent to thedetermination of optimum parameters and feature subset,we further compared the performance between variousclassifiers, namely, SVM, Bayes Net, Radial Basis FunctionNetwork (RBF Network), and Random Forest, by apply-ing the same sequence and feature data under the sameparameters.The softwareWEKA (version 3.8) (http://www.cs.waikato.ac.nz/∼ml/weka/downloading.html) was used toimplement the last three classifiers. Likewise, calculationswere made in line with the four metrics illustrated in

Table 1: Comparing the performance between different classifiers.

Algorithm Sn (%) Sp (%) AA (%) AUCSVM 94.29 80.08 87.18 0.93Random Forest 45.71 84.59 65.15 0.69Bayes Net 82.86 55.64 69.25 0.66RBF Network 85.71 36.09 60.90 0.59

1 − specificity

Sens

itivi

ty

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

1.00.90.80.70.60.50.40.30.20.10.0

SVMBayes Net

Random ForestRBF Network

Figure 1: ROC curves achieved by SVM, Bayes Net, RBF Network,and Random Forest in discriminating secretory proteins fromnonsecretory proteins ofM. tuberculosis.

Performance Evaluation Section. The results are summarizedin Table 1. From Table 1, although relative similar Sns wereproduced by SVM (94.29%), Bayes Net (82.86%), and RBFNetwork (85.71%), the differences in other three metricsamong the four classifiers were conspicuous enough to pro-nounce judgment. Nevertheless, Sn and Sp may lose objec-tivity because of an imbalance in class sizes, and even whenall samples were misjudged as negative, the Sp and overallaccuracy (OA) would still hang in high levels. Therefore, weattachedmore importance toAc instead of Sn or Sp.Aswe cansee from Table 1, SVM also achieved the maximum Ac with87.18%, followed by Bayes Net (69.25%) and Random Forest(65.15%).

In fact, all the metrics mentioned above are staticassessment indicators, except AUC, which emerges as adynamic measure depending on ROC and remains objectiveunder class imbalance conditions. Thus, AUC was chosenas the foremost evaluation criteria to compare performancedisparities among the four algorithms. To visually view thedynamic changing process of Sn (also known as true positiverates or TPR) of a certain algorithm versus its Sp (also knownas true negative rates or TNR), four curves correspondingto four classifiers were plotted in Figure 1 on the basis oftheir confusion matrixes varying with a range of thresholds.As shown in Figure 1, the SVM gave an AUC of 0.93 in thediscrimination between secretory proteins and nonsecretory


Figure 2: Home page of MycoSec web server at http://lin.uestc.edu.cn/server/MycoSec/.

proteins, which is higher than that of the other three classi-fiers. Furthermore, the tendency of SVM curve was steeperand ranmuch closer to the vertical axis than that of the others,which signified a better performance in ROC curve. Thus,compared with the other three state-of-the-art classifiers,SVM is much more powerful and robust for classifyingsecretory and nonsecretory proteins. Two main reasons maylead to the result. SVM is insensitive to the number andprobability distribution of training samples, along with thedimension of input space. Furthermore, SVM can gain theglobal optimal solution of goal function on the basis of convexoptimization theory, while other classifiers based on greedylearning strategies were usually trapped into locally optimalsolutions.

3.3. Web Server Construction. For the convenience of avast majority of experimental scientists, a user-friendly webserver, namely, MycoSec, was established, which is freelyaccessible at http://lin.uestc.edu.cn/server/MycoSec/. Belowis a step-by-step guide on how to use the web server.

First, reasonable and clear as you can see in Figure 2, thehomepage provides several buttons and a textbox lying to thecenter of the whole. A brief introduction of the predictor andthe caveat when using it would be present on clicking on theRead Me button.

Second, either type/paste the query protein sequencesinto the input box or select a FASTA file from a certaindirectory, ensuring that each sequence is longer than 11amino acid residues and without any ambiguous characters.Example sequences in FASTA format can be seen by clickingon the Example button right above the input box.

Third, click on the Submit button to see the predictedresults present in a new webpage. For a query sequence, thefirst column of the results shows its actual label, the secondgives the probability that it belongs to secretory protein class,and the third is the probability that it pertains to nonsecretoryprotein class.

Fourth, the benchmark data used in our analysis can bedownloaded by clicking on the Data button for training andtesting our system or your own model.

Finally, click on the Citation button to find the relevantpapers that document the detailed development and algo-rithm ofMycoSec.

4. Conclusions

Identification of secretory proteins in M. tuberculosis is ofparamount significance for targeting of antigens and vaccinedevelopment, which may contribute to an early diagnose orcure of the terrible disease tuberculosis. In this paper, weproposed a SVM-based algorithm to identify secretory pro-teins inM. tuberculosis. In this method, an improved pseudoamino acid composition which integrates 𝑔-gap dipeptidesalong with physicochemical properties was proposed andused to encode the residue sequences. By parameters searchand feature optimization, we obtained the optimal modelwith an AUC of 0.93 validated by jackknife test. Based onthis model, an online predictor MycoSec was established forthe convenience of researchers in relevant fields.We hope thepredictor will be a smart tool in M. tuberculosis research. Inthe future, we will combine other features such as evolutioninformation to improve classification performance.

Competing Interests

The authors declare no competing financial interests.

Acknowledgments

This work was supported by the National Nature ScientificFoundation of China (no. 61301260), the Applied BasicResearch Program of Sichuan Province (nos. 2015JY0100 andLZ-LY-45), the Scientific Research Foundation of the Educa-tion Department of Sichuan Province (11ZB122), the NatureScientific Foundation of Hebei Province (no. C2013209105),the Fundamental Research Funds for the Central Universitiesof China (no. ZYGX2015J144), the Outstanding Youth Foun-dation of North China University of Science and Technology(no. JP201502), and Program for the Top Young InnovativeTalents of Higher Learning Institutions of Hebei Province(no. BJ2014028).

References

[1] J. Zhang, H. Gou, X. Hu et al., “Status of drug-resistanttuberculosis in China: a systematic review and meta-analysis,”American Journal of InfectionControl, vol. 44, no. 6, pp. 671–676,2016.

[2] World Health Organization, Global Tuberculosis Report 2015,WHO, 2015.

[3] D. Tiwari, S. Haque, R. P. Tiwari, A. Jawed, T. Govender, and H.G. Kruger, “Fast and efficient detection of tuberculosis antigensusing liposome encapsulated secretory proteins of Mycobac-terium tuberculosis,” Journal of Microbiology, Immunology andInfection, 2015.

[4] H. Sonah, R. K. Deshmukh, and R. R. Bélanger, “Computationalprediction of effector proteins in fungi: opportunities andchallenges,” Frontiers in Plant Science, vol. 7, article 126, 2016.

[5] N. A. Leversen, G. A. de Souza, H.Målen, S. Prasad, I. Jonassen,and H. G. Wiker, “Evaluation of signal peptide predictionalgorithms for identification of mycobacterial signal peptidesusing sequence data from proteomic methods,” Microbiology,vol. 155, no. 7, pp. 2375–2383, 2009.


[6] C. Vizcáıno, D. Restrepo-Montoya, D. Rodŕıguez et al.,“Computational prediction and experimental assessment ofsecreted/surface proteins from Mycobacterium tuberculosisH37Rv,” PLoS Computational Biology, vol. 6, no. 6, Article IDe1000824, 2010.

[7] P.-P. Zhu,W.-C. Li, Z.-J. Zhong et al., “Predicting the subcellularlocalization of mycobacterial proteins by incorporating theoptimal tripeptides into the general form of pseudo amino acidcomposition,”Molecular BioSystems, vol. 11, no. 2, pp. 558–563,2015.

[8] A. Bairoch, R. Apweiler, C. H. Wu et al., “The universal proteinresource (UniProt),” Nucleic Acids Research, vol. 33, pp. D154–D159, 2005.

[9] W. Li and A. Godzik, “Cd-hit: a fast program for clusteringand comparing large sets of protein or nucleotide sequences,”Bioinformatics, vol. 22, no. 13, pp. 1658–1659, 2006.

[10] C. Cortes and V. Vapnik, “Support-vector networks,” MachineLearning, vol. 20, no. 3, pp. 273–297, 1995.

[11] D. Li, Y. Ju, and Q. Zou, “Protein folds prediction withhierarchical structured SVM,” Current Proteomics, vol. 13, pp.79–85, 2016.

[12] R. Wang, Y. Xu, and B. Liu, “Recombination spot identificationbased on gapped k-mers,” Scientific Reports, vol. 6, Article ID23934, 2016.

[13] J. Chen, X. Wang, and B. Liu, “IMiRNA-SSF: improving theidentification of MicroRNA precursors by combining negativesets with different distributions,” Scientific Reports, vol. 6, article19062, 2016.

[14] S.-C. Wang, “Artificial neural network,” in InterdisciplinaryComputing in Java Programming, pp. 81–100, Springer, NewYork, NY, USA, 2003.

[15] L. E. Peterson, “K-nearest neighbor,” Scholarpedia, vol. 4, no. 2,article 1883, 2009.

[16] Q. Zou, J. Guo, Y. Ju, M. Wu, X. Zeng, and Z. Hong, “Improv-ing tRNAscan-SE annotation results via ensemble classifiers,”Molecular Informatics, vol. 34, no. 11-12, pp. 761–770, 2015.

[17] C. Lin, W. Chen, C. Qiu, Y. Wu, S. Krishnan, and Q. Zou,“LibD3C: ensemble classifiers with a clustering and dynamicselection strategy,”Neurocomputing, vol. 123, pp. 424–435, 2014.

[18] B. Liu, S. Wang, Q. Dong, S. Li, and X. Liu, “Identificationof DNA-binding proteins by combining auto-cross covariancetransformation and ensemble learning,” IEEE Transactions onNanoBioscience, 2016.

[19] B. Liu, F. Liu, X. Wang, J. Chen, L. Fang, and K. Chou, “Pse-in-One: a web server for generating various modes of pseudocomponents of DNA, RNA, and protein sequences,” NucleicAcids Research, vol. 43, pp. W65–W71, 2015.

[20] I. Jolliffe, Principal Component Analysis, Wiley Online Library,2002.

[21] P. Rivett, “Multidimension scaling for multiobjective policies,”Omega, vol. 5, no. 4, pp. 367–379, 1977.

[22] L. Wei, M. Liao, X. Gao, and Q. Zou, “Enhanced proteinfold prediction method through a novel feature extractiontechnique,” IEEE Transactions on Nanobioscience, vol. 14, no. 6,pp. 649–659, 2015.

[23] J. F. Zayas, “Introduction,” in Functionality of Proteins in Food,pp. 1–5, Springer, New York, NY, USA, 1997.

[24] V. Vapnik, E. Levin, and Y. Le Cun, “Measuring the VC-dimension of a learning machine,” Neural Computation, vol. 6,no. 5, pp. 851–876, 1994.

[25] K.-C. Chou and Y.-D. Cai, “Using functional domain compo-sition and support vector machines for prediction of proteinsubcellular location,” The Journal of Biological Chemistry, vol.277, no. 48, pp. 45765–45769, 2002.

[26] Y.-D. Cai, G.-P. Zhou, and K.-C. Chou, “Support vectormachines for predicting membrane protein types by usingfunctional domain composition,” Biophysical Journal, vol. 84,no. 5, pp. 3257–3263, 2003.

[27] T. S. Furey, N. Cristianini, N. Duffy, D. W. Bednarski, M.Schummer, and D. Haussler, “Support vector machine classifi-cation and validation of cancer tissue samples using microarrayexpression data,” Bioinformatics, vol. 16, no. 10, pp. 906–914,2000.

[28] K.-C. Chou and C.-T. Zhang, “Prediction of protein structuralclasses,” Critical Reviews in Biochemistry andMolecular Biology,vol. 30, no. 4, pp. 275–349, 1995.

[29] A. P. Bradley, “The use of the area under the ROC curvein the evaluation of machine learning algorithms,” PatternRecognition, vol. 30, no. 7, pp. 1145–1159, 1997.

[30] Q. Zou, J. Zeng, L. Cao, and R. Ji, “A novel features rankingmetric with application to scalable visual and bioinformaticsdata classification,”Neurocomputing, vol. 173, pp. 346–354, 2016.

[31] H. Liu and R. Setiono, “Incremental feature selection,” AppliedIntelligence, vol. 9, no. 3, pp. 217–230, 1998.

[32] H. Lin, E.-Z.Deng,H.Ding,W.Chen, andK.-C. Chou, “iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotidecomposition,” Nucleic Acids Research, vol. 42, no. 21, pp. 12961–12972, 2014.

Submit your manuscripts athttp://www.hindawi.com

Hindawi Publishing Corporationhttp://www.hindawi.com Volume 2014

Anatomy Research International

PeptidesInternational Journal of


Hindawi Publishing Corporation http://www.hindawi.com

International Journal of

Volume 2014

Zoology


Molecular Biology International

GenomicsInternational Journal of


The Scientific World JournalHindawi Publishing Corporation http://www.hindawi.com Volume 2014


BioinformaticsAdvances in

Marine BiologyJournal of



Signal TransductionJournal of


BioMed Research International

Evolutionary BiologyInternational Journal of



Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttp://www.hindawi.com Volume 2014


Genetics Research International


Advances in

Virolog y

Hindawi Publishing Corporationhttp://www.hindawi.com

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational



Enzyme Research


International Journal of

Microbiology

research article identification of secretory proteins in...

Documents