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Application of Systems Biology and Bioinformatics Methods in Biochemistry and Biomedicine 2014

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Volume 2015 |Article ID 483150 | https://doi.org/10.1155/2015/483150

Gloria P. Monterrubio-López, Jorge A. González-Y-Merchand, Rosa María Ribas-Aparicio, "Identification of Novel Potential Vaccine Candidates against Tuberculosis Based on Reverse Vaccinology", BioMed Research International, vol. 2015, Article ID 483150, 16 pages, 2015. https://doi.org/10.1155/2015/483150

Identification of Novel Potential Vaccine Candidates against Tuberculosis Based on Reverse Vaccinology

Academic Editor: Tao Huang
Received27 Sep 2014
Accepted07 Jan 2015
Published15 Apr 2015

Abstract

Tuberculosis (TB) is a chronic infectious disease, considered as the second leading cause of death worldwide, caused by Mycobacterium tuberculosis. The limited efficacy of the bacillus Calmette-Guérin (BCG) vaccine against pulmonary TB and the emergence of multidrug-resistant TB warrants the need for more efficacious vaccines. Reverse vaccinology uses the entire proteome of a pathogen to select the best vaccine antigens by in silico approaches. M. tuberculosis H37Rv proteome was analyzed with NERVE (New Enhanced Reverse Vaccinology Environment) prediction software to identify potential vaccine targets; these 331 proteins were further analyzed with VaxiJen for the determination of their antigenicity value. Only candidates with values ≥0.5 of antigenicity and 50% of adhesin probability and without homology with human proteins or transmembrane regions were selected, resulting in 73 antigens. These proteins were grouped by families in seven groups and analyzed by amino acid sequence alignments, selecting 16 representative proteins. For each candidate, a search of the literature and protein analysis with different bioinformatics tools, as well as a simulation of the immune response, was conducted. Finally, we selected six novel vaccine candidates, EsxL, PE26, PPE65, PE_PGRS49, PBP1, and Erp, from M. tuberculosis that can be used to improve or design new TB vaccines.

1. Introduction

Tuberculosis (TB) is a chronic infectious disease caused by an acid-fast bacillus, Mycobacterium tuberculosis [1]. TB is the second cause of death caused by an infectious agent throughout the world [2, 3]; in 2012, there were an estimated 8.6 million incident cases of TB globally, which is equivalent to 122 cases per 100,000 people, and the absolute number of cases continues to increase slightly from year to year [4].

The current vaccine against tuberculosis, bacillus Calmette-Guérin (BCG), exerts different levels of protection: from 46 to 100% against the disseminated disease form and from 0 to 80% against pulmonary disease [5, 6]. In addition to this low efficacy, reemergence of the disease caused by the appearance of the acquired immunodeficiency syndrome (AIDS) and multidrug-resistant (MDR) strains has generated requirements for a new and more efficient vaccine against TB [7].

The development of new vaccines starts with the identification of unique components of the microorganism capable of generating a protective immune response [3]. With traditional techniques, this could be a long and arduous process, aside from the difficulty of cultivating the microorganism in the laboratory [810].

Advances in sequencing technology and bioinformatics have resulted in an exponential growth of genome sequence information that has contributed to the development of software that aids genomic analysis in a short period of time and at a low cost. Reverse vaccinology (RV) applied to the genome of a pathogen aims to identify in silico the complete repertoire of immunogenic antigens that an organism is capable of expressing without the need of culturing the microorganism. Additionally, RV can help to discover novel antigens that might be less abundant, not expressed in vitro, or less immunogenic during infection that are likely to be missed by conventional approaches [8, 1114].

The RV process begins with the proteomic information in a database; then, the selection of vaccine candidates is performed by means of different bioinformatics tools that analyze the properties of each protein and the human immune response generated by them [810, 15]. Good vaccine candidates are considered those that do not present homology with human proteins to avoid the generation of a potential autoimmune response [16]; these candidates must also lack transmembrane regions, in order to facilitate their expression. In addition, it is necessary to analyze the lack of cross-reaction among other pathogenic antigens [14]. Another characteristic a good vaccine candidate should have is to possess good antigenic and adhesin properties, which are important for the pathogenesis of the microorganism and for protection against the disease [13, 17]. Extracellular or cell surface localized proteins are good vaccine candidates due to their increased accessibility to the immune system [14, 16]. Currently, software useful for simulation of the immune response has been developed that could help in the search for novel vaccine candidates [15]. In this work, we have applied RV to the M. tuberculosis proteome with the purpose of selecting new antigens that could be used in a novel and more efficient vaccine against TB.

2. Materials and Methods

2.1. Proteome Analysis

New Enhanced Reverse Vaccinology (NERVE) software was downloaded, installed, and utilized to determine vaccine candidates employing the default parameters for Gram-positive bacteria [13]. The proteome sequences of M. tuberculosis H37Rv (NC_000962.2), Mycobacterium bovis AF2122/97 (NC_002945.3), and M. bovis BCG str. Pasteur 1173P2 (NC_008769.1) were downloaded from the Genome Project database of the National Center for Biotechnology Information (NCBI) [18]. Each proteome was analyzed individually by NERVE; conservation values for all proteins were determined comparing the M. bovis and BCG proteome against the M. tuberculosis proteome using the comparative option.

2.2. Antigenicity Determination

The antigenicity value was calculated for each protein using its amino acid sequences and the VaxiJen server, which predicts whether a protein could be a protective antigen. VaxiJen is based on auto cross covariance (ACC) and has a threshold of 0.5 in the antigenicity value [19].

2.3. Selection of Representative Proteins

With the parameters calculated with NERVE and VaxiJen, we selected proteins that presented an antigenicity value ≥0.5, 50% adhesin probability, and without homology with human proteins or transmembrane regions. The proteins selected were grouped according to the family of proteins to which they belong. In this manner, we obtained seven groups: ESX family proteins, PPE family proteins, PE family proteins, PE_PGRS family proteins, lipoproteins, hypothetic proteins, and, the last group, denominated “others,” composed of proteins with different miscellaneous characteristics. The amino acid sequence of each protein were downloaded from the NCBI protein database, and an alignment was made for each group of proteins using Clustal X software [20] in order to select representative proteins from each group.

2.4. Immune Response Simulation

With the amino acid sequences of the proteins selected, a human immune response simulation was performed using the C-ImmSim software to predict whether these proteins could generate a protective immune response against TB [15]. C-ImmSim simulates a portion of a lymph node but is not set up to simulate a realistic concentration of antigen; however, we adjusted the antigen concentration simulation to a high dose, comparable to a vaccination event. Different immunizations were simulated with each protein in the following two different schemes: first, a single immunization with each protein individually at time zero and, second, three immunizations at 0, 2, and 4 weeks with each protein separately. The level of Th1 cells stimulated 80 days after the first injection was identified.

2.5. Protein Analysis

The bioinformatics programs used to study the vaccine candidate’s amino acid sequences included Phobius [21] to calculate and confirm protein subcellular localization more precisely, ANTHEPROT [22], Expasy [23], and IEDB software [24] and their different models for localizing protein regions with greater hydrophilic and greater solvent accessibility related with antigenic regions, and the SYFPEITHI ver. 1.0 program [25], which was used to determine the frequency of presentation of peptides to 35 different alleles of the major histocompatibility complex (MHC). In the case of lipoproteins, we employed only ProPred software [26] to determine the frequency of presentation of 25 amino acid peptides to different alleles of the MHC-II.

2.6. Bibliographic Study

Bibliographic information was sought for each protein using different databases on the website for information regarding its putative function, its use as vaccines, its role in virulence, its corresponding evaluated mutants, its induction of an immune response, and its level of conservation in mycobacterial proteomes.

2.7. Vaccine Candidate Selection

The vaccine candidates were selected using all the results, simulations, and bibliographic information obtained. The candidates possess the best values of the parameters calculated and exert diverse functions that render them useful as different targets in the microorganism (Figure 1).

3. Results and Discussion

3.1. Proteome Analysis

RV offers the advantage of reducing the time and cost of the development process of a new vaccine with the advantage of being safer and more effective. With the purpose of designing a new vaccine against TB with a greater protection level against pulmonary disease, we utilized RV to select vaccine candidates from the M. tuberculosis proteome.

The selection of potential vaccine candidates in this study was based on the analysis of several important properties [13, 19, 21, 27]. (1) Surface proteins or secreted proteins were selected because they are good targets of the immune system effector molecules. (2) Proteins with multiple transmembrane helices were discarded because they are not recommended for vaccine development, especially DNA vaccines, as they are difficult to clone, express, and purify. (3) Adhesin probability was considered an important factor since the first step in bacterial invasion is the contact with host molecules through adhesion structures, making adhesions good vaccine candidates capable of improving the immune response that results in blocking infection. (4) Proteins having similarity to those of the human proteome were avoided. The use of proteins or genes that encode them and having similitude with human proteins or DNA sequences can generate an autoimmune response or recombination and integration events in the host genome, respectively. (5) Proteins with the best values of antigenicity were chosen. Antigenicity is the property of the proteins to be recognized by the immune system; hence, it is desirable to find the highest antigenicity value for the selection of the best potential vaccine candidates. The M. tuberculosis proteome was studied with NERVE software, which identifies in silico vaccine candidates, analyzing the biological characteristics that influence vaccine design.

NERVE selected the M. tuberculosis H37Rv proteome, composed of 3989 proteins; the selection of candidates was performed considering the following characteristics: ≥50% adhesin probability, fewer than two transmembrane regions, and fewer than five proteins similar to the human proteome. In addition to this, the candidates must lack either membrane or cytoplasmic localization. Finally, NERVE selected 331 proteins as vaccine candidates (Additional file 1) (see Supplementary Material available online at http://dx.doi.org/10.1155/2015/483150).

The results were compared with the information deposited in the VIOLIN database [28], and we found several important matches in some antigens. Those coincidences provided support for the results obtained with NERVE. The vaccine candidates selected have diverse putative functions and different conservation values. Moreover, this software tool has the option of comparing the proteomes of two different organisms and of determining a conservation value among all the proteins. In this case, we compared the M. tuberculosis H37Rv proteome against the M. bovis and the BCG proteome [13, 14].

The proteome analysis was finalized by determining the antigenic value of the 331 vaccine candidates using the VaxiJen server to obtain protective antigens prediction.

3.2. Selection of Representative Proteins

Using the calculated characteristics, the number of vaccine candidates was reduced to 73 proteins, with a stricter selection, as mentioned in the Methods section. These proteins were grouped in seven clusters according to their type and the family to which they belong, as follows: the ESX protein family (3 proteins), the PPE protein family (7 proteins), the PE proteins family (8 proteins), PE_PGRS protein family (16 proteins), lipoproteins (5 proteins), hypothetical proteins (21 proteins), and the final group denominated “others” (13 proteins) (Table 1). This process was carried out because it is well known that members within the same protein family possess a close relationship between their sequences and functions.


Protein family groupIDRvVaxiJen antigenicity valueLength (amino acid)Psi BLAST

ESX family gi_15608177_ref_NP_215553_1_Rv1037c0.744494ND
gi_15608338_ref_NP_215714_1_Rv11980.628694ND
gi_15610755_ref_NP_218136_1_Rv3619c0.744494ND

PPE family gi_57116729_ref_YP_177724_1_Rv0388c (PPE9)0.5334180ND
gi_57116916_ref_YP_177840_1_Rv1801 (PPE29)0.5636423ND
gi_57116975_ref_YP_177871_1_Rv2353c (PPE39)0.897354ND
gi_57117024_ref_YP_177677_1_Rv2770c (PPE44)0.5056382ND
gi_57117062_ref_YP_177932_1_Rv3125c (PPE49)0.5581391ND
gi_57117064_ref_YP_177934_1_Rv3135 (PPE50)0.5029132ND
gi_57117135_ref_YP_177998_1_Rv3621c (PPE65)0.5241413ND

PE family gi_57116715_ref_YP_177710_1_Rv0285 (PE5)0.6696102ND
gi_57116910_ref_YP_177834_1_Rv1788 (PE18)0.622899ND
gi_57116913_ref_YP_177837_1_Rv1791 (PE19)0.612594ND
gi_57116998_ref_YP_177888_1_Rv2519 (PE26)0.718492ND
gi_57117110_ref_YP_177975_1_Rv3477 (PE31)0.532598ND
gi_57117136_ref_YP_177999_1_Rv3622c (PE32)0.509999ND
gi_57117151_ref_YP_178010_1_Rv3739c (PE67)0.510177ND
gi_57117167_ref_YP_178025_1_Rv3893c (PE36)0.597177ND

PE_PGRS family gi_57116752_ref_YP_177736_1_Rv0532 (PE_PGRS6)1.789594ND
gi_57116773_ref_YP_177750_1_Rv0746 (PE-PGRS9)1.7153783ND
gi_57116787_ref_YP_177759_1_Rv0832 (PE_PGRS12)0.6034137ND
gi_57116793_ref_YP_177763_1_Rv0872 (PE_PGRS15)2.0866606ND
gi_57116818_ref_YP_177780_1_Rv1067c (PE_PGRS19)2.2481667ND
gi_57116826_ref_YP_177786_1_Rv1091 (PE_PGRS22)2.5016853ND
gi_57116864_ref_YP_177811_1_Rv1441c (PE_PGRS26)2.1299491ND
gi_57116905_ref_YP_177831_1_Rv1795c (wag22)2.0054914ND
gi_57116924_ref_YP_177847_1_Rv1840c (PE_PGRS34)1.5912515ND
gi_57116973_ref_YP_177869_1_Rv2340c (PE_PGRS39)1.0512413ND
gi_57117010_ref_YP_177896_1_Rv2634c (PE_PGRS46)2.146778ND
gi_57117029_ref_YP_177909_1_Rv2853 (PE_PGRS48)2.1046615ND
gi_57117093_ref_YP_177961_1_Rv3344c (PE_PGRS49)3.0927484ND
gi_57117098_ref_YP_177965_1_Rv3367 (PE_PGRS51)2.0713588ND
gi_57117101_ref_YP_177968_1_Rv3388 (PE_PGRS52)2.2486731ND
gi_57117117_ref_YP_177981_1_Rv3512 (PE_PGRS56)3.48811079ND

Lipoproteinsgi_15607723_ref_NP_215097_1_Rv0583c (LpqN)0.6569228ND
gi_15608368_ref_NP_215744_1_Rv1228 (LpqX)0.7609185ND
gi_15608679_ref_NP_216057_1_Rv1541c (LprI)0.5298197ND
gi_15609407_ref_NP_216786_1_Rv2270 (LppN)0.5899175ND
gi_15609921_ref_NP_217300_1_Rv2784c (LppU)0.685171ND

Hypotheticsgi_15607199_ref_NP_214571_1_Rv00570.6907173No matches
gi_57116831_ref_YP_177639_1_Rv1116A0.633791Related with PE family proteins of Mycobacterium
gi_15608277_ref_NP_215653_1_Rv1137c0.76122No matches
gi_15608941_ref_NP_216320_1_Rv1804c0.575108Related with Mycobacterium lipoproteins and gp53 Mycobacterium phage
gi_15609051_ref_NP_216430_1_Rv1914c0.5202135No matches
gi_15609215_ref_NP_216594_1_Rv20780.5425104No matches
gi_15609220_ref_NP_216599_1_Rv20830.8189314No matches
gi_15609401_ref_NP_216780_1_Rv2264c0.5862592Related with proline and threonine rich Mycobacterium proteins, like chaperone molecular and Rhodococcus hypothetical proteins
gi_15609420_ref_NP_216799_1_Rv22830.733664No matches
gi_15609429_ref_NP_216808_1_Rv2292c0.539674No matches
gi_15609439_ref_NP_216818_1_Rv23020.95880Related with transduction signal protein and hypothetical proteins from Nocardia and Frankia, DNA binding protein from Streptomyces
gi_15609797_ref_NP_217176_1_Rv2660c0.907375No matches
gi_15609843_ref_NP_217222_1_Rv2706c0.52785No matches
gi_15610097_ref_NP_217476_1_Rv2960c0.694582No matches
gi_15610135_ref_NP_217514_1_Rv29980.8339153No matches
gi_15610204_ref_NP_217583_1_Rv30670.566136No matches
gi_15610316_ref_NP_217696_1_Rv3180c0.5182144Related with proteins which have pilT domain and with DNA binding proteins from Mycobacterium
gi_15610343_ref_NP_217723_1_Rv3207c0.6281285Related with membrane proteins and lipoproteins from Corynebacterium and Streptomyces
gi_57117091_ref_NP_217854_2_Rv33370.8824128Related with a putative hydrolase from Mycobacterium
gi_15610854_ref_NP_218235_1_Rv3718c0.7971147Related with KanY, polyketide cyclases, and lipid transport proteins from different species
gi_15611034_ref_NP_218415_1_Rv3898c0.7675110no matches

Othersgi_15607173_ref_NP_214545_1_Rv00310.73870ND
gi_57116685_ref_YP_177687_1_Rv0050 (PBP1)0.6113678ND
gi_57116889_ref_NP_216091_2_Rv15750.7415166ND
gi_57117071_ref_YP_177941_1_Rv3198A 0.666984ND
gi_15610488_ref_NP_217869_1_Rv3352c 0.8742123ND
gi_15610946_ref_NP_218327_1_Rv3810 (Erp)0.6734284ND
gi_15609895_ref_NP_217274_1_Rv2758c 0.657488ND
gi_57117060_ref_YP_177930_1_Rv31180.833100ND
gi_15610417_ref_NP_217798_1_Rv32810.7311177ND
gi_15611001_ref_NP_218382_1_Rv38650.604103ND
gi_15609778_ref_NP_217157_1_Rv2641 0.7934152ND
gi_15607954_ref_NP_215329_1_Rv0814c0.833100ND
gi_57116926_ref_YP_177849_1_Rv1860 0.5244325ND

Note: in bold are the highlighted representative proteins selected. ND: not determined.

With the purpose of selecting representative proteins from each group, we used amino acid sequences from their members to perform alignments using the Clustal X program, with the exception of those from the “others” and hypothetical proteins groups.

In the case of the hypothetic proteins, an alignment was carried out using the Psi-BLAST tool from the NCBI website, in order to grant them a putative function; in some cases, a coincidence was not found, but in others we could assume a probable function (Figure 2).

For the selection of representative proteins, we took into account the similarity among the sequences of the group, the best antigenicity values, the high probability to act as an adhesin, and their conservation in the M. bovis and BCG proteome. For this part, we chose 12 representative candidates from the seven protein families (Table 1).

3.3. Immune Response Simulation

The 12 proteins selected were used to conduct simulations of the human immune system response under different conditions with C-ImmSim software. In terms of the results of the simulations, C-ImmSim server showed the following nine graphs for each simulation: B-cell population, B-cell population per state, Th cell population, Th cell population per state, Tc population, Tc population per state, CD population, EP population, and Ab production (Figure 3). We found the same pattern in all the selected proteins with a slight difference among levels. However, we focused mainly on Th1 cells level including all states, because it has been reported that protective immunity against TB is conferred mainly by Th1 cells [14, 29]. We found that the levels of stimulated cells were most similar among the selected proteins when one immunization was performed, but this level improved when the number of immunizations increased, and there was also a remarkable differentiation among the proteins at the final simulation step. PE_PGRS family proteins showed the highest levels of stimulated Th cells, which also generated a good level of B cells stimulation, which is important for complementing the immune response (Figure 4).

3.4. Protein Analysis

The proteins were analyzed individually with several bioinformatics tools to determine whether the protein sequences had a region with important antigenic characteristics, that is, a region where there are matches with hydrophobicity, solvent accessibility, presentation to MHC, and antigenicity.

We did not find a protein that clearly possesses an antigenic region that could be used as an epitope or as a fusion in a vaccine. Conversely, we determined that all the proteins had high values of antigenicity in different parts of the sequence; thus, we recommend the use of complete proteins in a vaccine formulation because using only a fragment could eliminate some epitopes necessary for a complete and protective human immune response against the whole microorganism.

We also found that all of the proteins selected as vaccine candidates could be presented to several MHC with a high probability value, resulting in good probability of immune response induction against these components of M. tuberculosis.

In this analysis process, we identified protein subcellular localizations using Phobius tools to confirm the results emitted by NERVE, because Phobius software is more accurate than the program (HMMTOP) utilized by NERVE [21]. This characteristic is important in a vaccine candidate because proteins with cytoplasmic or membrane localization are less antigenic than extracytoplasmic proteins.

3.5. Bibliographic Study

We wanted to know whether the proteins would be safe if we used them on a vaccine formulation prior to the preclinical trials; thus, we studied the information published about different characteristics related with their impact on virulence.

We were able to observe that some proteins have not been studied, but we found information about other members of their family groups, such as PE, PPE, and PE_PGRS proteins, suggesting an influence on immune system evasion and antigenic variation, an important feature in considering a protein that will be included in a vaccine [39, 47, 59]; besides, PE_PGRS family proteins are restricted to pathogenic mycobacteria and, in particular, PE_PGRS11 and PE_PGRS 17 have been reported to induce maturation and activation of human dendritic cells, enhancing the ability of the latter to induce Th cells stimulation [26, 60].

In case of the antigen LppN we did not find specific information, besides, almost all the proteins in M. tuberculosis genome lack conserved regions, which means that they are unique proteins with different characteristics. Some lipoproteins are major antigens in the Mycobacterium genus that can generate also a cellular and humoral immune response but without immune memory response [50, 6164].

Erp protein is a virulence factor present only in the Mycobacterium genus [5153], it is an immunodominant antigen related to pathogenicity and is strongly induced in nutrient starvation related to the latency phase [53]. On the other hand, PBP1 protein is important in the replication phase because it catalyzes the final steps of bacterial cell wall peptidoglycan synthesis [54, 65, 66].

The EsxL candidate is an ESX-like protein with very similar characteristics to Esat-6, which is an immunodominant secreted protein used in research associated with the diagnosis of TB and new TB vaccines [30]. Esat-6 is a strong T-cell antigen, and its family members are involved in virulence and in host-pathogen interplay via either antigenic variation or antigenic drift [31, 32, 67].

We did not find any information related with hypothetical proteins Rv3207c and Rv3718c, and, up to date, it is unknown if these proteins could be of any risk within a vaccine formulation; thus further studies are needed to elucidate the role of these proteins in this kind of biological products. About the remainder candidates, we found no information that related them with high levels of bacterial virulence; conversely, we identified highlighted features that render these proteins as good vaccine candidates.

3.6. Vaccine Candidate Selection

All parameters studied were instrumental to determine the different and important characteristics to be taken into account for an adequate vaccine candidate. At the end of this study, we selected the following six proteins as the best vaccine candidates: EsxL (NCBI gene locus tag Rv1198), PE26 (NCBI gene locus tag Rv2519), PPE65 (NCBI gene locus tag Rv3621c), PE_PGRS49 (NCBI gene locus tag Rv3344c), PBP1 (NCBI gene locus tag Rv0050), and Erp (NCBI gene locus tag Rv3810). These proteins are representative of their family groups and have the best characteristics of antigenic value, adhesin probability, and stimulation of Th cells (Table 2). In addition, the information contained in the publications on these proteins indicates that they could be safe.


CharacteristicESXLPE_PGRS49PE26PPE65ErpPBP1

Adhesin probability (%)807889828673

Antigenicity value (VaxiJen)0.62863.09270.7180.52410.67340.6113

Th1 cell number stimulated
(1/3 injections)
13200/1740017400/2720016000/2260012600/2200010000/1800013000/19800

M. bovis conservation1.78603.3966.5760.2711.046

BCG conservation1.78603.2986.6560.2711.046

Immunologic relationIt is suggested to be ESXA similar.Their protein family members are important antigens recognized by vaccinated and TB patients serum. They are strongly related with antigenic drift and with evasion of immune response.Their protein family members were used as strain differentiation markers, related with antigenic drift and with evasion of immune response and virulence.Specific antibodies on cavitary TB patients have been detected.Not reported

Characteristic or functionIt is suggested to be ESXA similar.The function of their members are varied, some of them are related with granuloma specific expression. They are also related with infectivity influence, necrosis, and apoptosis induction. They are restricted to Mycobacterium pathogenic species.They are located on 3 conserved PAIs and 10% of the Mycobacterium genome is related with this family proteins.Erp membrane protein precursor, phagosome produced. Their expression is related with nutrient reductions and in vivo after infection but before clinical tuberculosis in a rabbit model.Penicillin binding protein of high molecular weight and membrane bound related with peptidoglycan synthesis. It is expressed only in vivo.

Vaccine trialsNot reportedA DNA vaccine with a protein related member Rv1818c has been reported, which generates protection only with the PE domain and better response with the complete protein.Not reportedNot reportedNot reported

References[3038][29, 3946] [29, 32, 34, 4749] [26, 39, 5052][5358]

Note: ESXL (ESXL family); PE_PGRS49 (PE_PGRS family); PE26 (PE family); PPE65 (PPE family); Erp and PBP1 (Others).

These proteins play different roles in mycobacterial pathogenicity and possess featured values of antigenicity and immune response induction; in addition, the PE_PGRS49 candidate is not present in M. bovis or in the BCG proteome, an important characteristic that could be useful in the improvement of a specific immune response against pulmonary disease in the current BCG vaccine.

4. Conclusions

The application of bioinformatics programs for the identification of proteins that could be used as vaccine candidates is a very useful, easier, and shorter process compared with traditional vaccinology, which is important for the research concerning public health.

Using RV, we selected six novel vaccine candidates from the M. tuberculosis H37Rv proteome, employing mainly in silico studies. The six proteins selected: EsxL, PE26, PPE65, PE_PGRS49, PBP1, and Erp correspond to several family proteins and possess different characteristics that are useful and important in vaccine design. The bibliographic information also indicated that they might be safe.

The potential vaccine candidates selected in this work could be used in different vaccine designs to conduct experiments in order to validate them as DNA vaccines, rBCG, or as recombinant proteins, to improve protection against TB, rendering the new vaccines more effective against the pulmonary disease.

We did not propose a specific and unique antigenic region within these protein structures; however, the bioinformatics and bibliographic analyses showed characteristics that make them valuable putative vaccine candidates that could be used further to experimentally investigate whether they are suitable for the development of a new vaccine against tuberculosis.

Conflict of Interests

The authors declare that they have no competing interests.

Authors’ Contribution

Gloria P. Monterrubio-López, Jorge A. González-Y-Merchand, and Rosa María Ribas-Aparicio contributed equally to the original concept and design described in the paper. Gloria P. Monterrubio-López performed experiments and contributed in the areas of data collection, analysis, and interpretation. Rosa María Ribas-Aparicio supervised the overall project, providing essential guidance. Gloria P. Monterrubio-López and Rosa María Ribas-Aparicio drafted the paper. All authors revised the paper critically and read and approved the final version.

Acknowledgments

The authors thank Margaret Bruner and Ingrid Mascher for English editing. Rosa María Ribas-Aparicio and Jorge A. González-Y-Merchand are grateful for support of this work from Instituto Politécnico Nacional Grants nos. SIP-20144063 and SIP-20140970. Gloria P. Monterrubio-López thanks CONACyT-México and PIFI-IPN-México for graduate studies fellowships. The authors are also grateful to Dr. Sandro Vivona at Stem CentRx and Dr. Alfonso Méndez-Tenorio, ENCB-IPN professor, for their kind help in installing NERVE and to Dr. Filippo Castiglione at the Istituto per le Applicazioni del Calcolo (IAC) for his valuable advice on the use of the C-ImmSim server.

Supplementary Materials

Results emitted by NERVE after the M. tuberculosis H37Rv proteome analysis. NERVE selected 331 proteins out of 3989 proteins of M. tuberculosis H37Rv proteome. The selection of vaccine candidates was based on the analysis of several important properties: ≥50% of adhesion probability, fewer than two transmembrane regions, no protein similarity to human proteome, not membrane or cytoplasmic localization. Conserved values are also shown, comparing each candidate against M. bovis and BCG proteomes. Finally, NERVE search and obtain a putative function for each protein.

  1. Supplementary Material

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