international journal of scientific ......from b cell epitopes which showed 97.39% population...

INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 9, ISSUE 03, MARCH 2020 ISSN 2277-8616

5657

IJSTR©2020

www.ijstr.org

Computational approach used for designing of

potential B cell epitope based vaccine construct

against Plasmodium falciparum Manisha Pritam, Garima Singh, Suchit Swaroop, Rajnish Kumar and Satarudra Prakash Singh

Abstract— Plasmodium falciparum is a parasite that is responsible for causing human malaria. According to WHO latest report, 228

million cases and 405000 deaths were reported from all over the World in 2018. Several B cell epitopes against Plasmodium falciparum

were identified and reported in databases but most of them were not reached to vaccine level. After performance of conventional method

sometimes designed vaccines were failed at different level of clinical phase trial. So, in order to reduce the risk of failure and minimizing the

cost, time and efforts we used computational approach to design vaccine construct. In present study we used experimentally reported B

cell epitopes for designing of vaccine construct. IEDB server facilitated to retrieve 1077 experimentally reported B cell epitopes which were

further screened to obtain 8 B cell epitopes that potentially induce the cytokine response along with humoral response. By using finally

screened B cell epitopes and three different types of linkers we have designed three vaccine constructs (VC) and performance of 3 VC was

evaluated on basis of their structural and immune induction analysis through several bioinformatics tools. Further we have studied the

molecular interaction of designed VC (BVC1, BVC2 and BVC3) with malaria specific antibodies. Immunoglobulin (IgG1 and IgG3) were

used for molecular docking study as they were found to be associated with pathogen clearance. Further, we also predicted T cell epitopes

from B cell epitopes which showed 97.39% population coverage for World population. The designed VC BVC3 emerged to have

significantly high potential to be utilized as potential vaccine candidate for activation of immune response. Although the study is performed

through benchmarked bioinformatics tools but needs to be validated through experimental validation.

Index Terms— B cell epitope, malaria, computational approach, vaccine construct, antibody.

—————————— ——————————

1 INTRODUCTION

n 2018, malaria cases and deaths were reported from all over the world especially from tropical regions which provides data of 228 million cases and 405000 deaths (WHO

report, 2019). Malaria is mainly caused by infection of parasite named Plasmodium falciparum. Drugs are available for malaria treatment but excessive use of drugs against parasite, leads to emergence of drug resistance and caused major menace for malaria protection [1]. As a substitute towards malaria protection there is urgent need for development of efficient vaccine. Decades of research reveals that antibodies can provide protection against malaria during infection, disease and transmission. Antibody are able to provide protection at different stage of parasitic life cycle such as asexual (pre-erythrocytic stage and erythrocytic stages) and the sexual stage (erythrocytic stage). Pre-erythrocytic stage, asexual pre-erythrocytic and sexual erythrocytic stage of parasite comprises sporozoites (blood and hepatocytes), merosomes/merozoites (hepatocytes and blood) and gametocytes (blood), respectively [2]. It was previously reported that antibodies provide protection against malaria

parasite at blood-stage infection by preventing the invasion of parasite to red blood cells and also help to destroy infected red blood cells. The antibodies IgG1 and IgG3 can liaise with NK cell to destroy infected red blood cells [3, 4]. Antibody is associated with humoral immune system which play important role in parasite clearance [5]. When antigen interacts with antibody it induces the humoral immune response. B cell epitopes are sequence/ set of amino acids present in antigen that can interact with antibody or B cell receptor [6]. The amino acids present in antigen that interacts with antibody at spatial proximity formed by proper folding of protein recognized as conformational B cell epitope. Most of the B cell epitopes found as conformational B cell epitope [7]. The continuous sequence of amino acids that interacts with antibody is known as linear B cell epitopes [8]. B cell epitopes are associated with adaptive immune system because they induce B-cell immune response and enhance the durability of protection [9].

In last few decades development of immunoinformatics and computational approaches play a major role in development cost effective and fast development of vaccine candidates against various pathogenic or microbial diseases. Immunoinformatics provides platform for prediction of epitopes, prediction of immune response (cytokine inducer), development of vaccine, in silico validation and comparative analysis of vaccine [10, 11, 12, 13]. In recent time multi-epitope based vaccine designing is more trending due to its higher stability, non-allergic, good immune inducer, safer and convenient approach. Multi-epitope vaccine consist several epitopes of different antigens which significantly enhances the immune response [14, 11].

I

————————————————

Manisha Pritam is currently pursuing Ph.D. in Biotechnology in Amity University, Lucknow campus, India. E-mail: [email protected]

Garima Singh is currently pursuing Ph.D. in Biotechnology in Amity University, Lucknow campus, India. E-mail: [email protected]

Suchit Swaroop is assistant professor at Lucknow University, India. E-mail: [email protected]

Rajnish Kumar is assistant professor at Amity University, Lucknow campus, India. E-mail: [email protected]

Satarudra Prakash Singh is associate professor at Mahatma Gandhi Central University, Bihar, India. E-mail: [email protected]


5658

IJSTR©2020

www.ijstr.org

Monoclonal antibody technique adequately contributed in identification of B cell epitopes that can induce humoral immune response. In last few decades several B cell epitopes were identified from different antigens of pre-erythrocytic stage, asexual pre-erythrocytic and sexual erythrocytic stage of parasite. Some of the identified epitopes were transformed into vaccine and pursuing under clinical phase trials. RTS,S/AS01 (PfCSP) and Plasmodium falciparum reticulocyte- binding protein homologue 5 (PfRH5) vaccine are potential antibody inducing vaccines that contains pre- erythrocytic and asexual erythrocytic antigen, respectively [15]. Pfs25 vaccine induces transmission blocking antibodies which avert the transmission of parasite. Pfs25 vaccine is also in clinical trial phase I [16] (Chichester et al., 2018). Till date RTS,S/AS01 is the only malaria vaccine that passed the clinical phase trial III and under pilot implementation. Although RTS,S/AS02A is under pilot implementation but were limited to selected African region (Malawi, Ghana and Kenya). Other limitations of RTS,S/AS02A are low efficacy for clinical malaria(39.0%) and severe malaria case (20.5%), rapid waning of vaccine and yet not available for worldwide population [17, 18]. So, there is great need to develop a vaccine for worldwide population with significantly high efficacy and durable protection. Our present study dedicated to develop a promising multi-epitope vaccine by using immunoinformatics approach which can provide long lasting protection against malaria. In order to design a potential vaccine construct with durable protection we utilized the experimentally reported B cell epitopes. Furthermore, we screened the B cell epitopes on the basis of potential inducer of Th1 and Th2 response by prediction of cytokines inducer. All the finally screened B cell epitopes are potential inducer of IL-10, IFN-γ and IL-4 cytokines. Analysis of molecular interaction between designed VC and antibody (IgG1 and IgG3) was performed. Finally, we also executed a comparative analysis of designed VC with other antigens that were bind with antibody (IgG1 and IgG3) in their co-crystal structure.

2 RESEARCH METHOD

2.1 DATA RETRIEVAL

By using IEDB (https://www.iedb.org/) database we retrieved 1077 non redundant experimentally reported linear B cell epitopes.

2. 2 SCREENING OF HIGHLY POTENTIAL IMMUNOGENIC B

CELL EPITOPE

The retrieved linear B cell epitopes were further lead to screening of antigenic sequences. VaxiJen 2.0 (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html) tool was used for the prediction of antigenicity of linear B cell epitope at threshold 1.0. The obtained antigenic linear B cell epitope were further screened on the basis of cytokine inducers. We used IL10Pred

(http://crdd.osdd.net/raghava/il10pred/predict.php), IFNepitope (crdd.osdd.net/raghava/ifnepitope/) and IL4Pred (webs.iiitd.edu.in/raghava/il4pred/scan.php) tool for prediction IL-10, IFN-γ and IL-4 cytokines, correspondingly.

2.3 DESIGNING OF VC AND PHYSICOCHEMICAL TEST

By using finally screened B cell epitopes and different linkers we have designed three vaccine constructs. To prevent the interaction of N-terminal epitopes with other domain of vaccine construct we have used EAAAK linker [19]. In order to analyse the best linker for B cell epitope we used three different linkers. We used GGGS and GPGPG and KK linkers to link B cell epitopes and designed three vaccine construct BVC1, BVC2 and BVC3, respectively [20]. Prediction of physicochemical properties of designed VCs was done by using ExpasyProtParam (web.expasy.org/protparam/).

2.4 PREDICTION OF SECONDARY AND TERTIARY

STRUCTURE OF DESIGNED VACCINE CONSTRUCTS

For prediction of secondary and tertiary structures of vaccine constructs (BVC1, BVC2 and BVC3) we used PSIPRED (bioinf.cs.ucl.ac.uk/psipred/) and RaptorX (raptorx.uchicago.edu/) tools, correspondingly. Furthermore, ModRefiner (https://zhanglab.ccmb.med.umich.edu/ModRefiner/) and PROCHECK (servicesn.mbi.ucla.edu/PROCHECK) tools were used for refinement and validation of tertiary structure.

2.5 MOLECULAR DOCKING OF VC AND ANTIBODY

Molecular docking of VC and antibody was performed by using ClusPro 2.0 (cluspro.bu.edu/home.php). For analysis of molecular interaction between antibody and VC we retrieved crystal structure of IgG1 (PDB ID: 6B5L) and IgG3 (PDB ID: 5BK0) antibody from RCSB PDB. In our study we used IgG1 and IgG3 cytophilic antibodies because they show high level of titration during infection and associated with protection against plasmodium parasite [21, 22, 23]. IgG1 and IgG3 antibody were separated from co-crystal structure of PfCSP peptide 20 with human protective antibody CIS43 (PDB ID: 6B5L) and crystal structure of 663 Fab bound to circumsporozoite protein NANP 5-mer (PDB ID: 5BK0), respectively [24, 25]. For further validation we used PatchDock tool (https://bioinfo3d.cs.tau.ac.il/PatchDock/) for molecular docking of VC and antibody. For detail analysis of molecular interaction between VC and antibody we used UCSF chimera software.

2.6 PREDICTION OF T CELL EPITOPES AND PPC VALUE

T cell epitope were predicted from sequences of B cell epitopes through MHC I and II prediction tool of IEDB server. The MHC class I and II epitopes were predicted at threshold IC50 ≤500. Further, predicted population coverage (PPC) value of T cell epitopes were predicted by using population coverage tool of IEDB (http://tools.iedb.org/population/) server.


5659

IJSTR©2020

www.ijstr.org

Combined PPC was performed for World and endemic region of malaria. North Africa, India, East Africa, South America, Southeast Asia, Central Africa, West Africa and South Africa are major endemic regions of malaria.

3 RESULT AND DISCUSSION

3.1 SCREENING OF HIGHLY POTENTIAL IMMUNOGENIC B

CELL EPITOPE

Out of 1077 experimentally reported linear B cell epitopes we have screened 264 antigenic linear B cell epitope at threshold 1.0. Out of 264 antigenic linear B cell epitope only 40 antigenic linear B cell epitopes were predicted as IL-10 cytokine inducer. Out of 40 linear B cell epitopes only 11 antigenic linear B cell epitopes were predicted as IFN-γ inducer. Finally, we have screened 8 antigenic linear B cell epitopes that were predicted as potential inducer of IFN-γ, IL-10 and IL-4 cytokines. In case of malaria IFN-γ, IL-10 and IL-4 cytokines were accompanying with malaria protection [26, 27]. These 8 highly immunogenic linear B cell epitopes was further used to design vaccine construct. The antigen of respective B cell epitopes was given in Table 1. Most of the B cell epitopes was found in liver stage antigen 1. The antigens involved were merozoite surface protein, cytoadherence linked asexual protein (CLAG),

reticulocyte-binding protein 3, thrombospondin-related anonymous protein (TRAP), Liver stage antigen 1 and conserved Plasmodium membrane protein, unknown function. The group of antigens used in this study covers all stages of Plasmodium falciparum life cycle.

3.2 DESIGNING OF VC AND PHYSICOCHEMICAL TEST

By using finally screened experimentally reported B cell epitope and different linkers we designed three different VC

(BVC1, BVC2 and BVC3) (Table 2). All designed B cell epitope VC (BVC1:-1.032, BVC2:-1.093 and BVC3:-1.438) shows hydrophilic nature due to negative GRAVY value [28]. Similar pI value is obtained for BVC1 and BVC2 was 9.18 and for BVC3 obtained pI was 9.92.

3.3 PREDICTION OF SECONDARY AND TERTIARY

STRUCTURE OF DESIGNED VACCINE CONSTRUCTS

Physicochemical properties (hydrophilicity), secondary structure (beta-turns) and tertiary structure of a protein are associated with presence of B cell epitopes (linear and conformational). Several in silico tools were developed for prediction of B cell epitopes by using protein structural and physicochemical properties [29]. In our study all designed VC was obtained as hydrophilic in nature, presence of beta-turns and persistent tertiary structures. The secondary structure analysis of a protein is associated with stability and function of a protein [30, 31, 32, 33, 34]. The predicted secondary structure of designed VCs showed 27.48%, 25.69% and 60.64% alpha helices in BVC1, BVC2 and BVC3, respectively. In case of β-strands, 12.28%, 11.17% and 03.87% was obtained for BVC1, BVC2 and BVC3, respectively. The predicted coil was 60.23%, 63.12% and 35.48% in BVC1, BVC2 and BVC3, respectively (Fig 1). According to secondary structure analysis it is observed that the VC designed by using KK linker was containing 60.64% alpha helices, which is highly proficient for VC designing. Validation of tertiary structure was performed by analysis of Ramachandran plot of designed VCs. The predicted tertiary structure of BVC1 and BVC3 showed >95% of amino acids in favored region while BVC2 showed 72.8% (Fig 2). According to result obtained by analysis of tertiary

TABLE 1 DETAILS OF PREDICTED CYTOKINE (IFN-GAMMA, IL-10 AND IL-4)

INDUCING SCORES OF B CELL EPITOPES.

TABLE 2 DETAILS OF DESIGNED VC OF EXPERIMENTALLY REPORTED B CELL

EPITOPE


5660

IJSTR©2020

www.ijstr.org

structure of designed VC we found that BVC1 and BVC3 tertiary structure was highly reliable [35].

3.4 MOLECULAR DOCKING OF VC AND ANTIBODY

After analysis of result obtained by ClusPro 2.0 we have finally obtained eight docked model (B1-B8) (Table 4). The obtained score of ClusPro 2.0 molecular docking for all

designed VC was higher than that of both controls. In case of PatchDock molecular docking analysis it was found that all the designed BVC show significantly very high than that of both controls (Table 5). In case of IgG1, the score obtained for control was -449.4 but the designed VC showed ≤ -699.0 score which indicates the better interaction of designed VC with antibody. Similarly, in case of IgG3, the score obtained for control was -630.9 but the designed VC showed ≤ -644 score which indicates the better interaction of designed VC with antibody. NMR and X-ray crystallography techniques are best method for B cell epitope identification but they are expensive, time consuming and arduous process [36, 37]. To minimize the cost and time consumption we used immunoinformatics approach for estimation of conformational B cell epitopes. Antigen and antibody can interact together with low energy intermolecular

TABLE 3 DETAILS OF PREDICTED SECONDARY AND TERTIARY STRUCTURE OF

DESIGNED VC

TABLE 5 DETAILS OF OBTAINED SCORE OF MOLECULAR DOCKING ANALYSIS

OF ANTIBODY AND DESIGNED VC/CONTROL.

TABLE 4 DETAILS OF MOLECULAR DOCKING ANALYSIS OF ANTIBODY AND

DESIGNED VC/CONTROL

Fig.1. Descriptive view of secondary structure of designed VC obtained from PsiPred.

Fig.2. Ramachandran plot of tertiary structure of designed VC.


5661

IJSTR©2020

www.ijstr.org

forces like hydrogen bond and van der Waals force [7, 38]. After analysis of molecular interaction of VC and antibody we obtained some conformational B cell epitopes within VC (BVC1, BVC2 and BVC3). Lys-61, Tyr-69, Thr-70 and Asn-96 are conformational B cell epitopes obtained within BVC1. Tyr-37 and Gly-28 are conformational B cell epitopes obtained

within BVC2. BVC3 contains a greater number of conformational B cell epitopes. Lys-43, His-44, Lys-48, Cys-84, Lys-87 Arg-90 and Lys-93 was obtained conformational B cell epitopes in BVC3 (Table 6). Out of eight B cell epitopes, KKMSQFIDKKFIPLVFYTLK (CLAG) and NSRDSKEISIIENTN (Liver stage antigen 1) of BVC1 and BVC3 was found to bind with both IgG molecules (IgG1 and IgG3). Besides theses KKMSQFIDKKFIPLVFYTLK and NSRDSKEISIIENTN B cell epitopes also consists some conformational B cell epitopes which significantly increases the efficacy of vaccine construct. After overall analysis of vaccine and antibody interaction we obtained that BVC3 was best interacting VC with both IgG molecules (IgG1 and IgG3). In case of IgG1, BVC3 was interacts with antibody by Lys-43, His-44, Lys-48, Arg-90 and Lys-93 residues of BVC3. In case of IgG3, BVC3 was interacts with antibody by Cys-84, Lys-87 and Lys-93 residues of BVC3. The generated models of molecular docking (B1-B8) were further visualized and color determination of chains was done by Pymol and UCSF Chimera software. For detail analysis and clear visualization of docked model we have generated cartoon and surface representation pictures for all models (Fig 3). The obtained models clearly showed that all designed VCs were able to interact with antibody.

TABLE 6 DETAILS OF ANTIBODY INTERACTED B CELL EPITOPES OBTAINED BY

ANALYSIS OF MOLECULAR INTERACTION OF VC AND ANTIBODY.

Fig.3. Crystal structure of IgG3 (663 Fab) and IgG1 (CIS43 Fab) complexed with Control/BVC. (A): Cartoon illustration of 663 Fab complexed with Circumsporozoite protein NANP 5-mer (control). Circumsporozoite protein NANP 5-mer, 663 antibody heavy chain and 663 antibody light chain are showed in red, green and yellow, respectively. (B): Surface representation of 663 Fab complexed with Circumsporozoite protein NANP 5-mer (control). (C): Cartoon illustration of 663 Fab complexed with BVC1. (D): Surface representation of 663 Fab complexed with BVC1. (E): Cartoon illustration of 663 Fab complexed with BV21. (F): Surface representation of 663 Fab complexed with BVC2. (G): Cartoon illustration of 663 Fab complexed with BVC3. (H): Surface representation of 663 Fab complexed with BVC3. Designed VC (BVC1, BVC2 and BVC3), 663 antibody heavy chain and 663 antibody light chain are showed in red, green and yellow, respectively.

(I): Cartoon illustration of CIS43 Fab complexed with PfCSP peptide 20(control). PfCSP peptide 20, CIS43 antibody heavy chain and CIS43 antibody light chain are showed in red, cyan and orange, respectively. (J): Surface representation of CIS43 Fab complexed with PfCSP peptide 20(control). (K): Cartoon illustration of CIS43 Fab complexed with BVC1. (L): Surface representation of CIS43 Fab complexed with BVC1. (M): Cartoon illustration of CIS43 Fab complexed with BVC2. (N): Surface representation of CIS43 Fab complexed with BVC2. (O): Cartoon illustration of CIS43 Fab complexed with BVC3. (P): Surface representation of CIS43 Fab complexed with BVC3. Designed VC (BVC1, BVC2 and BVC3), CIS43 antibody heavy chain and CIS43 antibody light chain are showed in red, cyan and orange, respectively.


5662

IJSTR©2020

www.ijstr.org

3.5 PREDICTION OF T CELL EPITOPES AND PPC VALUE

Total 87 T cell epitopes were predicted by 8 B cell epitopes which includes 38 MHC class I and 49 MHC class II epitopes, correspondingly. The predicted T cell epitope showed 97.39% of PPC value for World population. The minimum and maximum combined PPC obtained for predicted T cell epitope was 78.90% and 93.33% for South Africa and North Africa, correspondingly (Fig 4).

4 CONCLUSION

After decades of research, the vaccine designing for malaria vaccine is still challenging. There is no licensed malaria vaccine available in the market for worldwide population. Thousands of B cell epitopes were also reported in databases but they were unable to achieve vaccine transformation. In present study, we screened eight highly immunogenic B cell epitopes to design VC. We also performed the analysis for best linker for design B cell epitope vaccine constructs and found linker KK as best linker in order to design B cell epitope vaccine constructs. The vaccine constructs BVC3 contains linker KK and showed best structural and physicochemical properties along with best interaction with both IgG molecules (IgG1 and IgG3). After different types of analysis we found that BVC3 construct is best promising VC for immune induction against malaria. BVC3 is able to induce humoral and innate immunity. Further experimental validation is required for designed VC. Beside these our research findings may fascinates the researchers to utilize the experimentally reported epitopes of databases. The concept of designing of VC from B cell epitope of database can significantly useful and cost effective for development of vaccine against other

pathogenic or life threatening diseases.

ACKNOWLEDGMENT

Authors would like to acknowledge Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow for providing laboratory workspace.

REFERENCES

[1] H.A. Antony, N.S. Topno, S.N. Gummadi, D.S. Sankar, R. Krishna and S.C.

Parija, "In silico modeling of Plasmodium falciparum chloroquine resistance

transporter protein and biochemical studies suggest its key contribution to

chloroquine resistance," Acta Trop, 189, pp. 84-93, Jan 2019.

[2] J.P. Julien and H. Wardemann, "Antibodies against Plasmodium

falciparum malaria at the molecular level," Nat Rev Immunol., Vol.

19, pp. 761-775, Dec 2019, doi: 10.1038/s41577-019-0209-5.

[3] D.H. Lee, K.B. Chu, H.J. Kang, S.H. Lee, M. Chopra, H.J. Choi, E.K.

Moon, K.S. Inn and F.S. Quan, "Protection induced by malaria virus-

like particles containing codon-optimized AMA-1 of Plasmodium

berghei", Malar J., Vol. 18, pp. 1-12, Dec 2019, doi: 10.1186/s12936-

019-3017-2.

[4] K.S. Burrack, G.T. Hart, and S.E. Hamilton, "Contributions of natural

killer cells to the immune response against Plasmodium," Malaria

journal, Vol. 18, pp. 1-9, Sep 2019, doi:10.1186/s12936-019-2953-1.

[5] G.S. Kirimanjeswara, S. Olmos, C.S. Bakshi and D.W. Metzger,

"Humoral and cellmediated immunity to the intracellular Pathogen

Francisella tularensis," Immunol Rev., Vol. 225, pp. 244–55, Oct 2008,

doi: 10.1111/j.1600-065X.2008.00689.x.

[6] J. Ponomarenko, H.H. Bui, W. Li, N. Fusseder, P.E. Bourne, A. Sette,

and B. Peters, "ElliPro: a new structure-based tool for the prediction

of antibody epitopes," BMC bioinformatics, Vol. 9, pp. 1-8, Dec 2008,

doi:10.1186/1471-2105-9-514.

[7] P. Sun, H. Ju, Z. Liu, Q. Ning, J. Zhang, X. Zhao and Y. Li,

"Bioinformatics resources and tools for conformational B-cell epitope

prediction," Computational and mathematical methods in medicine,

Vol. 2013, pp. 1-1, Jul 2013, doi:10.1155/2013/943636.

[8] L. Potocnakova, M. Bhide and L.B. Pulzova, "An Introduction to B-

Cell Epitope Mapping and In Silico Epitope Prediction," Journal of

immunology research, Vol. 2016, pp. 1-11, 2016,

doi:10.1155/2016/6760830.

[9] M.C. Jespersen, S. Mahajan, B. Peters, M. Nielsen and P. Marcatili,

"Antibody Specific B-Cell Epitope Predictions: Leveraging

Information From Antibody-Antigen Protein Complexes," Frontiers

in immunology, Vol. 10, pp. 1-10, Feb 2019,

doi:10.3389/fimmu.2019.00298.

[10] N. Khatoon, R.K. Pandey and V.K. Prajapati, "Exploring Leishmania

secretory proteins to design B and T cell multi-epitope subunit

vaccine using immunoinformatics approach," Sci Rep., Vol. 7, pp. 1-

12, Aug 2017, doi: 10.1038/s41598-017-08842-w.

[11] V. Chauhan, T. Rungta, K. Goyal and M.P. Singh, "Designing a multi-

epitope based vaccine to combat Kaposi Sarcoma utilizing

immunoinformatics approach," Sci Rep., Vol. 9, pp. 1-15. Feb 2019,

doi.org/10.1038/s41598-019-39299-8.

[12] M. Dalsass, A. Brozzi, D. Medini, and R. Rappuoli, "Comparison of

Open-Source Reverse Vaccinology Programs for Bacterial Vaccine

Antigen Discovery," Frontiers in immunology, Vol. 10, pp. 1-12, Feb

2019, doi:10.3389/fimmu.2019.00113.

[13] K. Naz, A. Naz, S.T. Ashraf, M. Rizwan, J. Ahmad, J. Baumbach and

Fig.4. Predicted population coverage of combined T cell epitopes.


5663

IJSTR©2020

www.ijstr.org

A. Ali, "PanRV: Pangenome-reverse vaccinology approach for

identifications of potential vaccine candidates in microbial

pangenome" BMC Bioinformatics, Vol. 20, pp. 1-10, 2019, doi:

10.1186/s12859-019-2713-9.

[14] M. Negahdaripour, N. Nezafat, M. Eslami, M.B. Ghoshoon, E.

Shoolian, S. Najafipour and Y. Ghasemi, "Structural vaccinology

considerations for in silico designing of a multi-epitope vaccine"

Infection, genetics and evolution : journal of molecular epidemiology

and evolutionary genetics in infectious diseases, Vol. 58, pp. 96–109,

2018, doi:10.1016/j.meegid.2017.12.008.

[15] K.E. Wright, K. A. Hjerrild, J. Bartlett, A.D. Douglas, J. Jin, R.E.

Brown, R. E.,J.J. Illingworth, R. Ashfield, S.B. Clemmensen, W.A.D.

Jongh, S.J. Draper and M.K. Higgins, "Structure of malaria invasion

protein RH5 with erythrocyte basigin and blocking antibodies,"

Nature, Vol. 515, pp. 427–430, Nov 2014, doi:10.1038/nature13715.

[16] J.A. Chichester, B.J. Green, R.M. Jones, Y. Shoji, K. Miura, C.A. Long,

C.K. Lee, C.F. Ockenhouse, M.J. Morin, S.J. Streatfield and V.

Yusibov, "Safety and immunogenicity of a plant-produced Pfs25

virus-like particle as a transmission blocking vaccine against malaria:

A Phase 1 dose-escalation study in healthy adults," Vaccine, Vol. 36,

pp. 5865–5871, Sep 2018, doi:10.1016/j.vaccine.2018.08.033.

[17] World malaria report 2018, Geneva: World Health Organization.

https://www.who.int/malaria/publications/world-malaria-report-

2018/en/. 2018.

[18] World malaria report 2019, Geneva: World Health Organization.

https://www.who.int/malaria/publications/world-malaria-report-

2019. 2019.

[19] M. Saadi, A. Karkhah, and H.R. Nouri, "Development of a multi-

epitope peptide vaccine inducing robust T cell responses against

brucellosis using immunoinformatics based approaches," Infection,

genetics and evolution : journal of molecular epidemiology and

evolutionary genetics in infectious diseases, Vol. 51, pp. 227–234, July

2017, doi:10.1016/j.meegid.2017.04.009.

[20] M. Hasan, S. Islam, S. Chakraborty, A.H. Mustafa, K.F. Azim, Z. F.

Joy, M.N. Hossain, S.H. Foysal and M.N. Hasan, "Contriving a

chimeric polyvalent vaccine to prevent infections caused by herpes

simplex virus (type-1 and type-2): an exploratory immunoinformatic

approach," Journal of biomolecular structure & dynamics, pp. 1–18,

Aug 2019, doi:10.1080/07391102.2019.1647286.

[21] C. Dobaño, R. Santano, M. Vidal, A. Jiménez, C. Jairoce, I. Ubillos

and G. Moncunill, "Differential Patterns of IgG Subclass Responses to

Plasmodium falciparum Antigens in Relation to Malaria Protection

and RTS,S Vaccination," Frontiers in immunology, Vol. 10, pp. 1-19,

March 2019, doi:10.3389/fimmu.2019.00439.

[22] T.E. Kwenti, T.A. Kukwah, T. Kwenti, B.R. Nyassa,M.H. Dilonga, G.

Enow-Orock and T. Nkuo-Akenji, "Comparative analysis of IgG and

IgG subclasses against Plasmodium falciparum MSP-119 in children

from five contrasting bioecological zones of Cameroon," Malaria

journal, Vol. 18, pp. 1-13, Jan 2019, doi:10.1186/s12936-019-2654-9.

[23] T.A. Rawlinson, N.M. Barber, F. Mohring, J.S. Cho, V. Kosaisavee,

S.F. Gérard and S.J. Draper, "Structural basis for inhibition of

Plasmodium vivax invasion by a broadly neutralizing vaccine-

induced human antibody," Nature microbiology, Vol. 4, pp. 1497–

1507. 2019, doi:10.1038/s41564-019-0462-1.

[24] G. Triller, S.W. Scally, G. Costa, M. Pissarev,C. Kreschel, A. Bosch

and H. Wardemann, "Natural Parasite Exposure Induces Protective

Human Anti-Malarial Antibodies," Immunity, Vol. 47, pp. 1197–1209,

Dec 2017, doi:10.1016/j.immuni.2017.11.007.

[25] N.K. Kisalu, A.H. Idris, C. Weidle, Y. Flores-Garcia, B.J. Flynn, B.K.

Sack and R.A. Seder, "A human monoclonal antibody prevents

malaria infection by targeting a new site of vulnerability on the

parasite," Nature medicine, Vol. 24, pp. 408–416. May 2018,

doi:10.1038/nm.4512.

[26] D. Prakash, C. Fesel, R. Jain, P.A. Cazenave, G.C. Mishra and S. Pied,

"Clusters of cytokines determine malaria severity in Plasmodium

falciparum-infected patients from endemic areas of Central India,"

The Journal of infectious diseases, Vol. 194, pp. 198–207, Jul 2006,

doi:10.1086/504720.

[27] D.G.W Alanine, D. Quinkert, R. Kumarasingha, S. Mehmood, F.R

Donnellan, N.K. Minkah, B. Dadonaite, A. Diouf, F. Galaway, S.E.

Silk, A. Jamwal, J.M. Marshall, K. Miura, L. Foquet, S.C. Elias, G.M.

Labbé, A.D. Douglas, J. Jin, R.O. Payne, J.J Illingworth, D.J. Pattinson,

D. Pulido, B.G. Williams, W.A. de Jongh, G.J. Wright, S.H.I Kappe,

C.V. Robinson, C.A. Long, B.S. Crabb, P.R. Gilson, M.K. Higgins and

S.J. Draper, "Human Antibodies that Slow Erythrocyte Invasion

Potentiate Malaria-Neutralizing Antibodies," Cell, Vol. 178, pp. 216-

228. Jun 2019, doi: 10.1016/j.cell.2019.05.025.

[28] G. Singh, M. Pritam, M. Banerjee, A.K. Singh and S.P. Singh,

"Genome based screening of epitope ensemble vaccine candidates

against dreadful visceral leishmaniasis using immunoinformatics

approach," Microbial pathogenesis, Vol. 136, Nov 2019,

doi:10.1016/j.micpath.2019.103704.

[29] W. Fleri, S. Paul, S.K. Dhanda, S. Mahajan, X. Xu, B. Peters and A.

Sette, "The Immune Epitope Database and Analysis Resource in

Epitope Discovery and Synthetic Vaccine Design," Frontiers in

immunology, Vol. 8, pp. 1-16, March 2017,

doi:10.3389/fimmu.2017.00278.

[30] B. Rost, "Review: Protein Secondary Structure Prediction Continues

to Rise," Journal of Structural Biology, Vol. 134, pp. 204–218, May

20001, doi:10.1006/jsbi.2001.4336.

[31] S.C. Lovell, I.W. Davis, W.B. Arendall, P.I. de Bakker, J.M. Word,

M.G. Prisant, J.S. Richardson and D.C. Richardson, "Structure

validation by Calpha geometry: phi,psi and Cbeta deviation,"

Proteins. Vol. 50, pp. 437-50. Feb 2003, doi: 10.1002/prot.10286.

[32] S. Ding, Y. Li, Z. Shi and S. Yan, "A protein structural classes

prediction method based on predicted secondary structure and PSI-

BLAST profile," Biochimie., Vol. 97, pp. 60-65, Feb 2014, doi:

10.1016/j.biochi.2013.09.013.

[33] T.A. Adekiya, R.T. Aruleba, S. Khanyile, P. Masamba, B.E. Oyinloye

and A.P. Kappo, "Structural Analysis and Epitope Prediction of

MHC Class-1-Chain Related Protein-A for Cancer Vaccine

Development," Vaccines (Basel), Vol. 6, pp. 1-12, Dec 2017,

doi:10.3390/vaccines6010001.

[34] D.W. Buchan and D.T. Jones, "The PSIPRED Protein Analysis

Workbench: 20 years on," Nucleic Acids Research, Vol. 47, pp. W402–

W407. Jul 2019, doi: 10.1093/nar/gkz297.

[35] R.A. Laskowski, M.W. MacArthur, D.S. Moss and J.M. Thornton,

"PROCHECK: a program to check the stereochemical quality of

protein structures," J. Appl. Cryst., Vol. 26, pp. 283-291, Dec 1996,

doi.org/10.1107/S0021889892009944.

[36] J.J. Rux and R.M. Burnett, "Type-specific epitope locations revealed

by X-ray crystallographic study of adenovirus type 5 hexon,"

Molecular therapy : the journal of the American Society of Gene

Therapy, Vol. 1, pp. 18–30. Jan 2000, doi:10.1006/mthe.1999.0001.

[37] M. Mayer and B. Meyer, ―Group epitope mapping by saturation

transfer difference NMR to identify segments of a ligand in direct


5664

IJSTR©2020

www.ijstr.org

contact with a protein receptor,‖ Journal of the American Chemical

Society, vol. 123, pp. 6108–6117, Jun 2001, DOI: 10.1021/ja0100120.

[38] M. Pritam, G. Singh, S. Swaroop, A.K. Singh, S.P. Singh, "Exploitation

of reverse vaccinology and immunoinformatics as promising

platform for genome-wide screening of new effective vaccine

candidates against Plasmodium falciparum," BMC Bioinformatics.

Vol. 19, pp. 219-230, Feb 2019, doi: 10.1186/s12859-018-2482-x.

international journal of scientific ......from b cell epitopes which showed 97.39% population...

Documents