• Users Online: 776
  • Home
  • Print this page
  • Email this page
Home About us Editorial board Ahead of print Current issue Search Archives Submit article Instructions Subscribe Contacts Login 

Table of Contents
Year : 2022  |  Volume : 59  |  Issue : 2  |  Page : 154-162

In silico study to predict promiscuous T cell and B cell-epitopes derived from the vaccine candidate antigens of Plasmodium vivax binding to MHC class-II alleles

1 Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Shaqra University, Shaqra, Kingdom of Saudi Arabia
2 Universal Group of Institutions, Lalru, Punjab, India
3 Department of Basic Oral Medicine and Allied Dentistry, College of Dentistry, Taif University, Taif, Kingdom of Saudi Arabia
4 Postgraduate Institute of Medical Education and Research, Chandigarh, India
5 Department of Basic Oral Medicine and Allied Dentistry, College of Dentistry, Taif University, Taif, Kingdom of Saudi Arabia; Faculty of Biotechnology and Applied Sciences, Shoolini University, Himachal Pradesh, India

Date of Submission15-Feb-2021
Date of Acceptance29-Nov-2021
Date of Web Publication08-Sep-2022

Correspondence Address:
Umar Farooq
Department of Basic Oral Medicine and Allied Dentistry, College of Dentistry, Taif University, Taif, KSA; Faculty of Biotechnology and Applied Sciences, Shoolini University, Solan, HP, India

Login to access the Email id

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/0972-9062.335726

Rights and Permissions

Malaria is one of the major causes of health and disability globally, even after tremendous efforts to eradicate it. Till date no highly effective vaccine is available for its control. The primary reason for the low efficacy of vaccines is extensive polymorphism in potential vaccine candidate antigen genes and HLA polymorphisms in the human population. This problem can be resolved by developing a vaccine using promiscuous peptides to combine the number of HLA alleles. This study predicted T and B cell epitopes (promiscuous peptides) by targeting PPPK-DHPS and DHFR-TS proteins of Plasmodium vivax, using different in silico tools. Selected peptides were characterized as promiscuous peptides on the basis of their immunogenicity, antigenicity and hydrophobicity. Furthermore, to confirm their immunogenicity, these peptides were utilized for molecular modelling and docking analysis. For determining the requisite affinity with distinct HLA Class-I, and HLA Class-II alleles, only five peptides for DHFR-TS and 3 peptides for PPPK-DHPS were chosen as promiscuous peptides. The D1 peptide has the maximum binding energy with HLA alleles, according to HLA-peptide complex modelling and binding interaction analyses. These findings could lead to the development of epitope-based vaccinations with improved safety and efficacy. These epitopes could be major vaccine targets in P. vivax as they possess a higher number of promiscuous peptides. Also, the B cell epitopes possess maximum affinity towards different alleles as analyzed by docking scores. However, further investigation is warranted in vitro and in vivo.

Keywords: Epitopes; Malaria; HLA; Immunodiagnostic; Promiscuous peptides; Homology modelling; Subunit vaccine; PPPK-DHPS; DHFR-TS

How to cite this article:
Khan N, bin-Mwena MN, Alruways MW, Allehyani NM, Alanzi MO, Shahzad, Khan A, Sehgal R, Tripathi P K, Farooq U. In silico study to predict promiscuous T cell and B cell-epitopes derived from the vaccine candidate antigens of Plasmodium vivax binding to MHC class-II alleles. J Vector Borne Dis 2022;59:154-62

How to cite this URL:
Khan N, bin-Mwena MN, Alruways MW, Allehyani NM, Alanzi MO, Shahzad, Khan A, Sehgal R, Tripathi P K, Farooq U. In silico study to predict promiscuous T cell and B cell-epitopes derived from the vaccine candidate antigens of Plasmodium vivax binding to MHC class-II alleles. J Vector Borne Dis [serial online] 2022 [cited 2022 Nov 28];59:154-62. Available from: https://www.jvbd.org/text.asp?2022/59/2/154/335726

  Introduction Top

Malaria is a major tropical illness, with an anticipated global incidence of 1.5 billion cases, and 7.6 million deaths expected between 2000 to 2019[1], with roughly 94 percent of cases, and deaths occurring in Africa, where malaria mortality is higher than elsewhere in the world[1]. One of the five species of Plasmodium is Plasmodium vivax (Pv) that causes human infection. Vivax malaria is increasingly recognized as a leading cause of severe morbidity and mortality, with a considerable negative impact on the global health[2],[3]. P. vivax is frequently seen in low frequency but transmissible parasite quantities in the peripheral blood circulation of the host. It can also transform into undetectable dormant liver stages (hypnozoites), to cause recurrent infection and sickness (relapse). It is possibly due to this fact that antimalarial medications are ineffective in completely controlling the condition. Additionally, drug resistance to chloroquine and other anti-malarial medicines[4] is a significant challenge.

Vaccine development can be a successful strategy in controlling malaria due to the parasite’s strong survival in poor environmental circumstances and efficient transmission at lower parasite levels. The selection of a suitable antigen to produce an efficient immune response to control malaria is a primary emphasis in developing a vaccine against the disease[5].

Different proteins involved at specific stages of Plasmodium infection are the primary targets for developing an effective vaccine[6]. Several antigenic proteins have been identified, and their genes have been characterized from different stages of the life cycle of Plasmodium[4],[5],[6],[7]. The presence of extensive antigenic polymorphism in these vaccine candidate proteins is a major hurdle in developing a subunit vaccine. Genetic polymorphism in several vaccine candidate antigen genes i.e., msp-1[4]ama-1[7],[8] and dbp[9],[10], has already been reported from many parts of the world for P. vivax, including India[4],[7]. Extensive polymorphism exhibited by antigens of P. vivax and human leukocyte antigen (HLA) is one of the main reasons for the limited efficiency of the malaria vaccine[4].

Considering all these factors, promiscuous peptides that can bind with different HLA alleles could be utilized to develop peptide-based vaccines[11]. Rather than using a wet lab approach to identify a promiscuous peptide, we could use a computational approach to predict promiscuous peptides by screening and analyzing the target gene sequences; this would then demonstrate optimal binding to HLA alleles prevalent in the inhabitants to be vaccinated.

Checking binding efficiency of the predicted HLA alleles with promiscuous peptides by using in silico tools offers more practical and economic methods[12]. Moreover, computational methods also limit the use of animal models with wet lab experiments which also reduces the cost and further supports the designing of novel vaccine candidates. Furthermore, structure-based research (SAR) utilizing molecular docking is a key method for confirming the binding effectiveness of promiscuous peptides with HLA[13],[14]. Docking can be used to screen many promiscuous peptides to see how they interact with one another and if they can invigorate the immune system. This could be quite useful in determining the peptide immunogenicity.

Keeping these points in view, we planned to discover the T cell and B cell antigenic determinants targeting the potential vaccine candidates DHFR-TS and PPPK-DHPS of P. vivax. The identified promiscuous T cell epitopes were further docked with HLA alleles to know the interaction of these predicted epitopes with HLA alleles.

  Material & Methods Top

Retrieval of the target sequences

The target genes of P. vivax were chosen based on the literature review. These target genes could potentially be used for the development of a vaccine against P. vivax. The amino acid sequence of these vaccine candidates i.e., DHFR-TS, and PPPK-DHPS were fetched from the NCBI (www.ncbi.nlm.nih.gov) using the accession no. DQ517900.1, and EU478871.1, and were compared with PlasmoDB database (www.plasmodb.org/plasmo), and UniProt (www.uniprot.org).

HLA allele selection

The major and frequently occurring HLA class-I alleles i.e., HLA-A*11:01, HLA-A*02:01, HLA-A*02:06, HLA-A*02:11, HLA-A*33:03, HLA-B*01:01, HLA-B*35:01 and HLA-B*40:06 and HLA class-II alleles i.e., DRB1*10:01,DRB1*140:4,DRB1*15:02,DRB1*03:01, DRB1*07:01, DRB1*11:01 and DRB1*15:01[15],[16],[17],[18] among Indian populations were selected for the study. These HLA DR alleles are common in the Indian communities, and our findings are being applied to P. vivax strains, which are common in India. They infect individuals particularly with one of the HLA DR alleles.

Prediction of promiscuous peptides

The T-cell specific epitopes were predicted using the Immune Epitope Database (IEDB) (www.iedb.org)[19] and the NetMHCPan web server (www.cbs.dtu.dk/services/NetMHCpan-3.0)[20]. Artificial neural networks were utilized to predict the binding of peptides to HLA class-I MHC molecules using the NetMHC3.0 server (www.cbs.dtu.dk/services/NetMHC-3.0)[21], NetMHCIIPan 2.4,[22] and Immune Epitope Database and Analysis Resource (IEDB) MHC class-II binding prediction tools NN-align,[23] (C) SMM-align,[24] (D) ARB (average relative binding)[25], and (E) NetMHCII2.2[26] were used to envisage peptides specific for HLA class-II alleles. For each tool, all settings and values were kept as default. Strong binders were defined as epitopes with IC50 values of less than 500 nm, weak binders were defined as peptides with IC50 values between 500–1000 nm, and non-binders were defined as epitopes with IC50 values > 500 nm[27]. Promiscuous peptides were selected using a 50% binding affinity criterion which defined a promiscuous peptide as one with an IC50 value less than 500 nm and 50% of the alleles. Additionally, A peptide with an IC50 value less than 500 nM with all the specified alleles was also classed as a 100% promiscuous peptide binder to find the best binding promiscuous peptides. The promiscuous peptides chosen were then assessed for affinity with various HLA alleles.

Identification of B-cell epitopes

B-cell epitopes were divided into two main categories:linear (continuous) epitopes and conformational (discontinuous) epitopes. However, different studies indicate that about 90% of the residues are conformational, while only 10% are linear. Two different web-based servers BCEpred and ABCpred, were used to predict linear B cell antigenic determinants. We may be able to find the most promising promiscuous peptides suitable for creating a peptides-based subunit vaccination by employing various servers[27]. BCEpred (www.imtech.res.in/raghava/ bcepred/) program analyzes physicochemical parameters such as hydrophilicity, flexibility/mobility, accessibility polarity, and exposed surface to forecast B cell epitopes[28]. The ABCpred ( www.imtech.res.in/raghava/abcpred/ )[29] server also predicted linear B cell epitope regions within an antigen sequence, using an artificial neural network.

Hydrophobicity attribute

Hydrophobicity analysis of selected promiscuous peptides was performed by using the peptide property calculator program[31]. Promiscuous peptides with <50% hydrophobicity are perfectly soluble in aqueous solution and the peptides with hydrophobicity >50% are moderately or insoluble in the aqueous solution. The promiscuous peptides with hydrophobicity >50% were selected further for protein homology modelling analysis.

Antigenicity and allergenicity of promiscuous peptides

The antigenicity of the predicted epitopes was determined by ANTIGENpro (www.scratch.proteomics. ics.uci.edu/) and VaxiJen (www.jenner.ac.uk/VaxiJen)[32]. ANTIGENpro is the first program that uses microarray reactivity data to predict full-length protein antigenicity for five pathogens. It’s an in silico predictor of protein antigenicity that is alignment-free and pathogen-agnostic. It basically gives an idea about the likelihood of a protein to be antigenic. Furthermore, the VaxiJen server automates protein classification based on physicochemical parameters rather than sequence alignment, it can work alone or in conjunction with other servers.

Modelling of promiscuous peptides (epitopes)

The 3D models of promiscuous peptides were generated using the in silico tool PEPFOLD, de novo peptide structure prediction[33],[34]. It’s a de novo peptide structure prediction server used to characterize peptides and protein fragments structurally[35],[36]. PEP-FOLD (www.bioserv.rpbs.univ-paris-diderot.fr/PEP-FOLD) allows for the treatment of 9-36 amino acid cyclic peptides which are linear and with disulfide bond. The best PEPFOLD models differ from the complete NMR structures by an average RMSD of 2.75 Å.

Modelling of HLA-alleles

The 3D models of promiscuous peptides (epitopes) were required to determine their prominent interaction with HLA alleles. All the promiscuous peptides were modeled using UCSF Chimera[36], a flexible tool for interactive visualization and study of molecular structures and related data, including density maps, supramolecular assemblies, sequence alignments, docking findings, trajectories, and conformational ensembles. Usually, the primary protein sequence is utilized for the Peptide Modelling. The 3D models of primary sequence of peptides were generated, and all parameters like Φ and Ψ angles were used with default values. Finally, all the peptide models were minimized by using an Amber field with 100 gradient steps.

Docking interaction of modelled structure of HLA-alleles with epitopes

AutoDock 1.4.6 was used for the 3D Model docking interaction of promiscuous peptides. AutoDock file was prepared by adding polar hydrogens and partial charges, with defining rotatable bonds. Adding polar hydrogens and merging nonpolar hydrogens were also used to make the modeled protein. In addition, the Kollman charge and atom type parameters were included. A grid map was created around the active site as well as the necessary surrounding surface. The Lamarckian genetic search algorithm was used, and the docking run was set at 250 runs. The maximum number of energy evaluations was 25,0000 / run and the maximum number of generations in the genetic algorithm was set at 27,000[36].

Ethical statement: Not applicable

  Results Top

T-cell epitope prediction

Many peptides were predicted from the two vaccine candidate antigens DHFR-TS and PPPK-DHPS, which possess different binding affinities with different classes of HLA-I and HLA-II alleles. Interestingly, a total of 609 peptides from DHFR-TS and 701 from PPPK-DHPS for HLA-I were predicted, whereas 603 and 695 peptides for HLA class-II allele were obtained from DHFR-TS and PPPK-DHPS, respectively. Promiscuous peptides were categorized on the basis of 100% binding affinity with different HLA class-II alleles. The promiscuous peptides of the DHFR-TS were 5/603 and for PPPK-DHPS 3 of 695. All these peptides have shown 100% binding affinity with all HLA class-II alleles [Table 1]. We have studied the hydrophobicity attribute of promiscuous peptides since it is an important physicochemical property that is used to characterize secondary structures in the proteins. [Table 1] shows that the peptides with hydrophobicity >50% are more soluble in water than those with hydrophobicity <50%. We have observed that D1, D2, and P1 are antigenic against both the bacterial as well as viral pathogens [Table 1]. However, no promiscuous peptides for class HLA-I alleles were found from both the genes DHFR-TS and PPPK-DHPS.
Table 1: Comparative analysis of antigenic properties of promiscuous peptides with bacteria and viruses predicted form DHFR-TS, PPPK-DHPS gene of Plasmodium vivax

Click here to view

Linear B-cell epitope prediction


Antigenic propensity, flexibility, hydrophilicity, accessibility, exposed surface, turns, polarity factors were used to identified B-cell linear epitopes within DHFR-TS and PPPK-DHPS gene sequences. The best-predicted region for B-cell epitope was observed in between 131–135 base pair. This region was present prominently in almost all parameters analyzed to identify B-cell epitopes [Table 2] & [Table 3].
Table 2: Identified B-cell epitopes for DHFR-TS gene using BCEpred server

Click here to view
Table 3: Identified B-cell epitopes for PPPK-DHPS gene using BCEpred server

Click here to view

(ii) DHFR-TS, PPPK-DHPS analysis ABCpred

The B-cell epitopes were further identified using AB-Cpred online prediction tool. The threshold was adjusted to 0.8, and the window length was adjusted to 16. The remaining parameters were unchanged. A total of thirty-one B-cell epitopes were found from DHFR-TS region, and the minimum and maximum threshold scores of epitopes ranging from 0.80 to 0.93 [Table 4]. Similarly, a total of twenty-seven B-Cell epitopes were identified from PPPK-DHPS with minimum and maximum threshold score of B cell epitopes ranging from 0.80 to 0.93 [Table 5].
Table 4: Amino acid sequence Starting point Score

Click here to view
Table 5: Amino acid sequence Starting point Score

Click here to view

Hydrophobicity of promiscuous peptides

The hydrophobicity analysis revealed that the promiscuous peptides D1, D2, D4, D5, and P1, P2 had confirmed hydrophobicity <50% [Table 1]. Hence, all these peptides D1, D2, D4, D5, and P1, P2 were selected for further structural verification.

Antigenicity and allergenicity

The antigenicity of the promiscuous peptides was predicted using two servers. The VaxiJen 2.0 server (Threshold value for bacterial model: minimum 0.1004 and maximum 0.7127, and threshold value for parasitic model: minimum -0.2915, and maximum 0.6167). The ANTIGENpro server with the minimum and maximum threshold values observed were 0.089440, and 0.190487, respectively. The antigenicity, and allergenicity analysis revealed that all the predicted promiscuous peptides were antigenic in nature with or without adjuvant.

Homology modelling of HLA-alleles

(i) Molecular docking

The molecular docking of the promiscuous peptides with HLA class-II alleles with AutoDock web-based program revealed that the highest affinities were obtained for DHFR-TS promiscuous peptides with docking energies ranging from -8.1 to -9.6 [Table 6]. The promiscuous peptide D1 shows the highest binding energy, i.e., -8.1 with DRB1*01:01 [Figure 2] which reveals that this peptide is best fit in the peptide-binding cleft of HLA, and based on which that peptide could be further utilized for the vaccine development. The docking analysis of other peptides P2, D2, and P1 have shown low binding energy -9.7, -9.6, and -8.6, respectively [Figure 1],[Figure 2],[Figure 3],[Figure 4],[Figure 5]; hence these peptides were not suitable for further study.
Figure 1: Molecular Docking of peptide D1 (LQPAQFIHILGNAHV) with HLA-DRBI*01:01 (3QXA).

Click here to view
Figure 2: Molecular Docking of P1 (RLHFLVLNGVPRYRV) peptide with HLA-DRB1*01:01 (3QXA).

Click here to view
Figure 3: Molecular Docking of DHFR-TS peptide D2 (QPAQFLIHILGNAHVY) with HLA-DRBI*15:01 (1BX2).

Click here to view
Figure 4: Molecular docking of P2 (LHFLVLNGVPRYRVL) peptide with HLA-DRB1*15:01 (1BX2).

Click here to view
Figure 5: Pictorial representation of docked structure of P2 peptide (LHFLVLNGVPRYRVL) with HLA-DRB1*01:01 A in surface. and mesh B. form. The peptide is binding in inner groove of HLA molecule.

Click here to view
Table 6: Sequence Docking Energies (Kcal/mol)

Click here to view

  Discussion Top

Malaria is a serious public health problem in the tropical and sub-tropical regions of developing countries due to its high morbidity and fatality rates in people. Several efforts are currently being made for the development of a malaria vaccine. Several vaccines have been developed and have entered phase 1 and 2 clinical trials. During clinical trials, these vaccines have shown low efficacy, the reason of low efficacy may be due to the presence of antigenic polymorphism in vaccine candidate antigens and HLA DR alleles. Hence predicting promiscuous peptides derived from these vaccine candidate genes that could bind with different HLA alleles that triggers effective immune response against parasites is a challenge.

In silico tools, for identifying vaccine candidate antigens could be an advantage in searching for a novel and potential vaccine candidate that can elicit a better immune response. The vaccine development based on synthetic peptides is already proved to a be successful strategy[39],[40]. Previously, we used in silico tools to predict promiscuous peptides predicted from DBL-3γ, CIDR-1 region of Pfemp-1 antigen, and EBA-175 antigen of P. falciparum. All the predicted peptides have shown immunogenicity in vitro and successfully proliferated T-cells in PBMCs culture[14],[41]. The Flow cytometric analysis of sensitized cultured PBMCs supernatant revealed the presence of anti-inflammatory cytokines in high concentrations[14]. Following this, we also predicted immunogenic promiscuous peptides from the promoter region of the Pfs-25 antigen of P. falciparum by computational analysis[11]. This study aimed to identify B and T cell epitopes from two vaccine candidate antigen genes DHFR-TS and PPPK-DHPS of P. vivax. Considering some of the factors like antigenicity, hydrophobicity, and immunogenicity, only two peptides for each gene were found to be promiscuous in both DHFR-TS and PPPK-DHPS genes. Similarly, promiscuous T-cell epitopes were identified from MSP-1 of P. vivax[37]. Furthermore, these four peptides were selected for structure-based analysis by using docking analysis. The B-cell epitopes were also predicted for the same genes, and the best-predicted region for the B-cell epitope was 131–135, because these regions are present in all parameters. Yoelis et al.[38] also identified T-cell, and B-cell epitopes by using in silico tools. The present study suggests that the PPPK-DHPS and DHFR-TS targeting epitopes could be the potential vaccine targets to develop a subunit vaccine against P. vivax. The study also determined that these two antigens PPPK-DHPS and DHFR-TS possess many promiscuous peptides (T-cell epitopes and B-cell epitopes), and have the highest affinity towards different alleles as analyzed by docking scores. These findings supported the selection of promiscuous peptides as vaccine candidate antigens. However, further investigation using in vitro and in vivo models is required.

  Conclusion Top

There are many vaccines that have been developed for malaria that entered phase I clinical trials, however, only few have reached the phase II clinical trial like Pvs25, and PvCSP based on the sexual stage, and pre-erythrocytic stages, respectively. Recently, another recombinant vaccine VMP001/AS01B based on CSP entered in Phase 1/2a clinical trials[42]. In the prevention of uncomplicated malaria, RTS,S/AS01E was found to be non-inferior to chemoprevention. In comparison to any intervention alone, the combination of these interventions resulted in a significantly lower frequency of fatal malaria[43]. The RTS,S/AS01 malaria vaccine was recently recommended by World Health Organization (WHO) for the prevention of P. falciparum malaria in children residing in WHO-defined moderate to high transmission areas. Only selective vaccines elicit an effective immune response against the challenge. The theory of a peptide-based vaccination for P. vivax, on the other hand, could be investigated. It is based on the prediction of antigenic epitopes that can effectively activate the immune system and protect the host from malarial infection. This computer-aided vaccine design approach is a time-saving and cost-effective technique. In this study, promiscuous peptides were identified from two vaccine candidate antigen genes PPPK-DHPS and DHFR-TS of P. vivax, by using a combined approach of sequence-based and molecular simulation-based. It is an efficient protocol for the rationale design of the vaccine against various infection-causing organisms. Conclusively, more in vitro analysis and in vivo could aid in the development of a peptide-based vaccine.

Conflict of interest: None

  Acknowledgements Top

We acknowledge the help of Shoolini University, Solan, HP, India for providing the facility and financial support to complete this work.

  References Top

World Health Organization., World Malaria Report, Geneva. W. H. O. 2020.  Back to cited text no. 1
Battle KE, Lucas, Michele Nguyen, Rosalind E Howes, Anita K Nandi, Katherine A Twohig et al. Mapping the global endemicity and clinical burden of Plasmodium vivax, 2000–17: a spatial and temporal modelling study. Lancet 2019; 394: 332–43.  Back to cited text no. 2
Prince RN, Commons RJ, Battle KE, Thrieme K, Mendis K. Plasmodium vivax in the Era of the shrinking P. falciparum Map. Trends in Parasitology 2020; 36(6): 560–570.  Back to cited text no. 3
Farooq U, Malla N, Dubey ML. Polymorphism in merozoite surface protein-1 gene in north and northwest Indian field isolates of Plasmodium vivax. Indian J Med Res 2009; 130: 736–741.  Back to cited text no. 4
Garzón-Ospina D, Buitrago SP, Ramos AE, Patarroyo MA. Identifying Potential Plasmodium vivax Sporozoite Stage Vaccine Candidates: An Analysis of Genetic Diversity and Natural Selection. Frontiers in genetics 2018; 9: 10.  Back to cited text no. 5
Barry AE, Arnott A. Strategies for designing and monitoring malaria vaccines targeting diverse antigens. Front Immunol 2014; 5: 359.  Back to cited text no. 6
Farooq U, Mahajan RC. Drug resistance in malaria. Journal of Vector Borne Diseases 2004; 41(pt 3/4): 45.  Back to cited text no. 7
Moon SU, Na BK, Kang JM, Kim JY, Cho SH, Park YK et al. Genetic polymorphism and effect of natural selection at domain I of apical membrane antigen-1 (AMA-1) in Plasmodium vivax isolates from Myanmar. Acta Tropica 2010; 114(2): 71–75.  Back to cited text no. 8
Sousa TN, Ceravolo IP, FernandesFontes CJ, Couto A, Carvalho LH & Brito CF. The pattern of major polymorphisms in the Duffy binding protein ligand domain among Plasmodium vivax isolates from the Brazilian Amazon area. Molecular and Biochemical parasitology 2006; 146(2): 251–254.  Back to cited text no. 9
Gosi P, Khusmith S, Khalambaheti T, Lanar D, Schaecher K, Fukuda M and Miller S. Polymorphism patterns in Duffy-binding protein among Thai Plasmodium vivax isolates. Malar J 2008; 7(1): 112.  Back to cited text no. 10
Khan N, Farooq U, Chauhan S, Khan A, Khan A. Protein Modelling and Binding Analysis of HLA-DR Promiscuous T cell Epitopes from Pfs-25 Ookinete Surface Antigen. JSMC Bioinform, Genomics, Proteomics 2019; 2: 11.  Back to cited text no. 11
Prakash N, Patel S, Faldu NJ, Ranjan R, Sudheer DVN. Molecular Docking Studies of Antimalarial Drugs for Malaria. J Computational Sci Syst Biol 2010; 3: 070–073.  Back to cited text no. 12
Kashyap M, Farooq U, Jaiswal V. Homology modelling of frequent HLA class-II alleles: A perspective to improve prediction of HLA binding peptide and understand the HLA associated disease susceptibility. Infect Genet Evol 2016; 44: 234–244.  Back to cited text no. 13
Khan, N, Kumar, R, Chauhan, S, Farooq, U. An immunoinformatics approach to promiscuous peptide design for the Plasmodium falciparum erythrocyte membrane protein-1. Mol Biosyst 2017; 13: 2160–2167.  Back to cited text no. 14
Agrawal S, Batnagar S, Bhardwaj U, Khan F. Int. J. Hum. Genet 2001; 1(4): 283–291.  Back to cited text no. 15
Middleton, D Menchaca L, H. Rood & R. Komerofsky. Tissue Antigens 2003; 61(5): 403–407.  Back to cited text no. 16
Kankonkar SR, Shankarkumar U. HLA DRB1 Gene Study in Different Population Groups From Mumbai, Maharashtra, India. Int J Hum Genet 2005; 5: 267.  Back to cited text no. 17
Agrawal S, Srivastava SK, Borkar M, Chaudhuri TK. Genetic affinities of north and northeastern populations of India: inference from HLA-based study. Tissue Antigens 2008; 72: 120–130.  Back to cited text no. 18
Vita R, Overton JA, Greenbaum JA, Ponomarenko J, Clark JD, Cantrell JR, et al. The immune epitope database (IEDB) 3.0. Nucleic acids research, 43(Database issue), 2015; D405–D412.  Back to cited text no. 19
Nielsen M and Andreatta M, NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets. Genome Med 2016; 8: 33.  Back to cited text no. 20
Nielsen M, Lundegaard C, Worning P, et al. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci 2003; 12(5):1007–1017.  Back to cited text no. 21
Nielsen M, Lundegaard C, Blicher T, Peters B. Quantitative Predictions of Peptide Binding to Any HLA-DR Molecule of Known Sequence: NetMHCIIpan. PLoS Comput Biol 2008; 4: 1000–1007.  Back to cited text no. 22
Nielsen M, Lund O. NN-align An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction. BMC Bioinf 2009; 10: 296:  Back to cited text no. 23
Nielsen M, Lundegaard C, Lund O. Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method. BMC Bioinf 2007; 8: 238.  Back to cited text no. 24
Bui HH, Sidney J, Peters B, Sathiamurthy M. Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications. Immunogenetics 2005; 57: 304–314:  Back to cited text no. 25
Nielsen M, Lundegaard C, Worning P, Hvid CS, Lamberth K. Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach. Bioinformatics 2004; 20(9): 1388–1397.  Back to cited text no. 26
Topalian SL, Gonzales MI, Parkhurst M, Southwood YF. Melanoma-specific CD4+ T cells recognize nonmutated HLA-DR-restricted tyrosinase epitopes. J Exp Med 1996; 183: 1965–1971.  Back to cited text no. 27
Saha S, Raghava GPS, Nicosia G, Cutello V, Bentley PJ, Timis J Bce Pred. Prediction of Continuous B-Cell Epitopes in Anti-genic Sequences Using Physico-chemical Properties ICARIS. LNCS 2004; 3239: 197–204.  Back to cited text no. 28
Saha, S. and Raghava GPS. Prediction of Continuous B-cell Epitopes in an Antigen Using Recurrent Neural Network. Proteins 2006; 65(1): 40–48.  Back to cited text no. 29
Dhanda SK, Vir P, G. P. S. Raghava. Designing of interferongamma inducing MHC class-II binders. Biol Direct 2013; 8(30): 1–15.  Back to cited text no. 30
Kyte and Doolittle RFA. A simple method for displaying the hydropathic character of a protein. J Mol Biol 1982; 157: 105–132.  Back to cited text no. 31
Shey RA, Ghogomu SM, Esoh KK. In-silico design of a multiepitope vaccine candidate against onchocerciasis and related filarial diseases. Sci Rep 2019; 9: 4409 .  Back to cited text no. 32
Duvignaud JB, Leclerc D, Gagne SM. Structure and dynamics changes induced by 2,2,2-trifluoro-ethanol (TFE) on the N-terminal half of hepatitis C virus core protein. Biochem Cell Biol 2010; 88: 315–323.  Back to cited text no. 33
Ragunathan P, Ponnuraj K. Expression, purification and structural analysis of a fibrinogen receptor FbsA from Streptococcus agalactiae. Protein J 2011; 30: 159–166.  Back to cited text no. 34
Gupta SK, Singh A, Srivastava M, Gupta SK, Akhoon BA. In silico DNA vaccine designing against human papillomavirus (HPV) causing cervical cancer. Vaccine 2009; 28: 120–131.  Back to cited text no. 35
Kumar R, Kumar S, Sangwan S, Yadav IS, Yadav R. Protein modeling and active site binding mode interactions of myrosinase–sinigrin in Brassica juncea-An in silico approach. J Mol Graph Model 2011; 29: 740–746.  Back to cited text no. 36
Ivette Caro-Aguilar, Alexendra Rodriguez, J. Mauricio Calvo-Calle, Fanny Guzman, Patricia De la Vega, Manuel Elkin Patarroyo, et al. Plasmodium vivax Promiscuous T-Helper Epitopes Defined and Evaluated as Linear Peptide Chimera Immunogens. Infection and Immunity 2002; 70(7): 3479–3492.  Back to cited text no. 37
Yoelis Yepes-Pérez Y, López C, Suárez CF, Patarroyo MA. Plasmodium vivax Pv12 B-cell epitopes and HLA-DRβ1*-dependent T-cell epitopes in vitro antigenicity. PLoS ONE 2018; 13(9): e0203715.  Back to cited text no. 38
Cespedes N, Arevalo-Herrera M, Felger I, Reed S, Kajava AV, Corradin G, et al. Antigenicity and immunogenicity of a novel chimeric peptide antigen based on the P. vivax circumsporozoite protein. Vaccine 2013; 31(42): 4923–30.  Back to cited text no. 39
Silva-Flannery LM, Cabrera-Mora M, Jiang J, Moreno A. Recombinant peptide replicates immunogenicity of synthetic linear peptide chimera for use as pre-erythrocytic stage malaria vaccine. Microbes and infection / Institut Pasteur 2009; 11(1): 83–91.  Back to cited text no. 40
Chauhan S, Kumar R, Khan N, Verma S, Sehgal R, Tripathi PK, et al. Designing peptide-based vaccine candidates for Plasmodium falciparum erythrocyte binding antigen 175. Biologicals 2020; 67: 42–48.  Back to cited text no. 41
Jason W. Bennett, Anjali Yadava, Donna Tosh, Jetsumon Sattabongkot, Jack Komisar, Lisa A. Ware, et al. Phase 1/2a Trial of Plasmodium vivax Malaria Vaccine Candidate VMP001/AS01B in Malaria-Naive Adults: Safety, Immunogenicity, and Efficacy. PLoS Negl Trop Dis 2016; 10(2): e0004423.  Back to cited text no. 42
Daniel Chandramohan, Issaka Zongo, Issaka Sagara, Matthew Cairns, Rakiswendé-Serge Yerbanga, Modibo Diarra, et al. Seasonal Malaria Vaccination with or without Seasonal Malaria Chemoprevention. N Eng J Med 2021; 385: 1005–1017.  Back to cited text no. 43


  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]


    Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
    Access Statistics
    Email Alert *
    Add to My List *
* Registration required (free)  

  Material & M...
  In this article
Article Figures
Article Tables

 Article Access Statistics
    PDF Downloaded61    
    Comments [Add]    

Recommend this journal