An immunoinformatics study: designing multivalent T-cell epitope vaccine against canine circovirus
Journal of Genetic Engineering and Biotechnology volume 19, Article number: 121 (2021)
Canine circovirus is a deadly pathogen of dogs and causes vasculitis and hemorrhagic enteritis. It causes lethal gastroenteritis in pigs, fox, and dogs. Canine circovirus genome contains two main (and opposite) transcription units which encode two open reading frames (ORFs), a replicase-associated protein (Rep) and the capsid (Cap) protein. The replicase protein and capsid protein consist of 303 amino acids and 270 amino acids respectively. Several immuno-informatics methods such as epitope screening, molecular docking, and molecular-dynamics simulations were used to craft peptide-based vaccine construct against canine circovirus.
The vaccine construct was designed by joining the selected epitopes with adjuvants by suitable linker. The cloning and expression of the vaccine construct was also performed using in silico methods. Screening of epitopes was conducted by NetMHC server that uses ANN (Artificial neural networking) algorithm. These methods are fast and cost-effective for screening epitopes that can interact with dog leukocyte antigens (DLA) and initiate an immune response. Overall, 5 epitopes, YQHLPPFRF, YIRAKWINW, ALYRRLTLI, HLQGFVNLK, and GTMNFVARR, were selected and used to design a vaccine construct. The molecular docking and molecular dynamics simulation studies show that these epitopes can bind with DLA molecules with stability. The codon adaptation and in silico cloning studies show that the vaccine can be expressed by Escherichia coli K12 strain.
The results suggest that the vaccine construct can be useful in preventing the dogs from canine circovirus infections. However, the results need further validation by performing other in vitro and in vivo experiments.
Circoviruses are unenveloped, spherical viruses with a diameter of about 20 nm and a circular, single-stranded DNA genome of about 2 kb belonging to the Circoviridae family . Canine circovirus (CanineCV)  or dog circovirus (Dog CV) belong to the genus Circovirus. They are the smallest known autonomously replicating, capsid encoding animal pathogens. Their genome contains two main (and opposite) transcription units which encode two open reading frames (ORFs), a replicase-associated protein (Rep) and the capsid (Cap) protein . This virus replicates its genomes using a circular, ds replicative form (RF) DNA intermediate which is produced using host cell DNA polymerases during the S phase of cell division. The RF serves as a template for the generation of viral ssDNA, probably using the rolling circle replication (RCR) mechanism. Their genome consists of two coding and two noncoding parts. For unique viral replicase and capsid proteins, there are only two open reading frames (ORFs). Viral replicase protein and capsid protein consist of 303 amino acids and 270 amino acids respectively. A series of 30 arginine amino acids from the amino terminus is used in the gene coding for the capsid. For DNA binding, this particular stretch is hypothesized to be important [4, 5]. Canine circovirus has been described in cases of dogs with vasculitis and/or hemorrhagic enteritis in the USA and Italy [6, 7]. Regardless of the prevalence of circovirus infection, the pathogenic role of canine circovirus in single or polymicrobial infections is undetermined as well as the prevalence of this virus in other wild carnivores . A closely related circovirus was detected in foxes with meningoencephalitis in the UK. The pathogenesis of canine circovirus infection is incompletely understood . Canine circovirus has only been detected in the USA [7, 10], Italy , Germany (GenBank accession number: KF887949), and China (GenBank accession number: KT946839) . Song et al. isolated Porcine circovirus type 2 (PCV2) in raccoon dogs (Mangut) in China which caused failure in reproduction of raccoon dogs and concluded the route of transmission of PCV2 from pigs to raccoon dogs . Kotsias et al. isolated canine circovirus along with canine parvovirus (CPV) in samples from an outbreak of fatal gastroenteritis in dogs in Argentina (South America) and showed after phylogenetic analysis that UBA-Baires strain is closely related to European strains than to viruses detected in North America or Asia . As canine circovirus can be fatal to the dog population, it is imperative to look for novel ways to combat their infections. One such strategy can be developing vaccine candidates that can prevent dogs from canine circovirus infections by generating a robust immune response. Hence, in this study, a multi epitope vaccine construct has been designed against canine circovirus by targeting their replicase and capsid proteins via in silico approach. As, these proteins are highly conserved, are involved in viral pathogenesis, and can help to generate immune response, they can be a good target for identifying epitopes that could be used to design a vaccine construct which can help protect the dog population from potentially lethal canine circovirus infections [13,14,15]. Such in silico studies have been previously performed to design vaccine construct against dengue, Candida auris, human cytomegalovirus, SARS-CoV-2, Lassa virus, human papillomavirus, cervical cancer, and hepatitis C virus [16,17,18,19,20,21,22,23,24,25,26]. In our study, various immunoinformatic tools were used to design a potential vaccine construct against canine circovirus. First highly antigenic epitopes were selected and their interaction with dog leukocyte antigen (DLA) molecules was analyzed by molecular docking and molecular dynamics simulation studies. Then antigenic epitopes were linked with RS09 and flagellin adjuvants along with PADRE sequence by GGS linkers to construct a vaccine candidate. The physiochemical properties, antigenicity, allergenic potential, and secondary and tertiary structure of the designed vaccine construct were also predicted using different webservers. Finally, the ability of the vaccine construct to be cloned and expressed was also analyzed by in silico cloning method. This vaccine construct could be beneficial in protecting the dogs from canine circovirus infections.
Proteomic data retrieval
ViPR database was used as it is based on IEDB for database assessment that assisted us in gathering information regarding viral proteome. The viral replicase and capsid proteins structural sequences of canine circovirus were retrieved from the NCBI-Proteomic database with accession ID’s: QFU80922.1 replicase [Canine circovirus] and QBQ20241.1 capsid Protein [Canine circovirus] (Table 1).
Epitopes selection and structure prediction
The NetMHCpan-4.1 server predicts peptides/epitopes from viral proteomic determinants by using artificial neural networks to varied MHC molecule with a familiar sequence (ANNs) . To predict the epitopes, dog MHC molecules DLA-8803401, DLA-8850101, and DLA-8850801 were selected. The predicted epitopes were selected on the basis of bind level, i.e., if they are strong binder or weak binder. The weak and strong binders were determined by using the default parameters of the webserver. By default, a peptide will be predicted as strong binder if its percentage rank is below 0.5% and a peptide will be predicted as weak binder if its percentage rank is above 0.5% but below 2% rank . VaxiJen was also used in this study to determine the antigenic potential of the selected epitope . Then, selected epitopes structural prediction was conducted by using PEP-FOLD, a de novo approach aimed at predicting peptide structures from amino acid sequences. This approach couples the deep learning algorithm and a coarse-grained force field based on amino acids to determine the conformations of consecutive amino acid residues as per their physiochemical relationships in secondary and tertiary folding .
Molecular docking and simulation at epitope-DLA level
For molecular docking between DLA proteins and epitopes, PatchDock webserver  and Autodock Vina  were used. The structures for DLA allelic sets were retrieved from the RCSB-PDB database. DLA alleles DLA-8803401 and DLA-8850801 were considered for performing the molecular docking and simulation analysis. The PDB ID of DLA-8803401 and DLA-8850801 are “7CJQ” and “5F1I” respectively. For molecular docking and simulation studies, the PDB file of the epitopes and DLA were used. PatchDock assists the user to determine atomic contact energies, and Autodock vina assists the user to determine binding energies for perfectly docked complexes. Furthermore, docking of the epitopes with DLA molecules was also performed using ClusPro 2.0 webserver . ClusPro is a protein-protein docking server which results in 10 models of the docking complex by defining the centers of highly populated clusters of low energy docked structures , while for stability analysis of docked complexes molecular simulation was performed by deploying Gromacs .
Full-fledged in silico vaccine construction
The final vaccine construct was designed by linking the selected antigenic epitopes by GGS linkers to RS09 and Salmonella dublin flagellin protein as adjuvants. Pan HLA DR-binding epitope (PADRE) sequence was also added to vaccine construct for providing stability to the vaccine construct. The final vaccine construct was then subjected to stability analysis and physio-chemical characteristics determination by deploying the “Expasy ProtParam” . The solubility of the vaccine was evaluated by the Solpro web server . The Solpro web server predicts the solubility of proteins with 74% accuracy after expression in Escherichia coli .
Vaccine construct prediction and affirmation
The secondary structure of vaccine was determined by using PSIPRED webserver . This web server helps in the prediction of beta-sheets, alpha helices, and coils in proteins by using feed-forward neural networks . The tertiary structure of the vaccine was determined by using the I-TASSER server which uses iterative template-based fragments assembly simulations and multiple threading approaches to predict the 3D model of proteins  and uses iterative template-based fragments assembly simulations and multiple threading approaches to predict the 3D model of proteins. Finally, the tertiary structure of the modeled vaccine was validated by the Procheck web server .
Codon adaptation and vaccine in silico cloning
For efficient cloning and expression of the vaccine in expression vectors, codon optimization is imperative. JCAT web server was used for codon optimization of vaccine for expression in E. coli K12 strain . The “SnapGene” restriction cloning module was used for in silico cloning of vaccine.
Proteome structure and sequence
We retrieved FASTA sequences of protein from the NCBI database as summarized in Table 1.
Predicted epitopes and DLA structure
NetMHCpan 4.1 server was deployed to find out interacting epitopes; lowest values were preferred while selecting epitopes based on prediction scores. For viral replicase 294 epitopes interacting with each DLA, the allele was identified, while for viral capsid protein 261 epitopes interacting with each DLA allele were identified. Out of these 555 epitopes, the best 20 were considered based on epitope prediction scores. VaxiJen server was also used for determining antigenicity with the threshold value of 0.7. Five epitopes were selected out of 20 epitopes based on their antigenicity (Table 2). Structures were constructed by deploying the PEP-FOLD server, which follows de novo criteria for predicting perfect conformation for epitopes. The structures for DLA allelic sets were retrieved from the RCSB-PDB database; two major DLA alleles were considered during investigation DLA-8803401 and DLA-8850801 with PDB IDs “7CJQ” and “5F1I” respectively. Both the structures of DLA alleles were crystal structure and can be easily accessed by their unique PDB IDs.
Molecular docking and simulation
Docking studies were conducted to determine the efficiency of binding or interaction between DLA alleles and respective epitopes. Most negative ACE (atomic contact energy) values and binding energies indicate perfect interaction between the selected epitopes and DLA alleles (Table 3). The 5 best possible docked complexes were showed perfect hydrogen bond interactions during visualization analysis by deploying PyMol visualizing tool. All the H-bond interactions are shown in Fig. 1. The bonding criteria for 5 selected complexes were found, as follows: 5F1I-GTMNFVARR (2.8Å Hydrogen bond Arg192 to Val6), 5F1I-HLQGFVNLK (3.2Å Hydrogen bond Ala79 to Val6), 7CJQ-ALYRRLTLI (3.1Å Hydrogen bond Ser57 to Thr7), 7CJQ-YIRAKWINW (2.2Å Hydrogen bond Glu129 to Trp9), and 7CJQ-YQHLPPFRF (2.7Å Hydrogen bond Lys48 to Phe7).
With the help of Gromacs tool, molecular dynamics (MD) simulations were performed using OPLS-AA field. OPLS-AA counts all atoms of docked complexes. The complex was stabilized towards the edges in a cubical box having a distance of 1.5 nm applying clear TIP4P water model. Charges were neutralized by adding Na+ ions in this biomolecular simulation. Steepest descent technique of 200,000 steps with 0.001 nm initial step-size was employed for energy minimization. For MD simulations, leap-frog algorithm to assimilate Newton’s equations was used. Using LINCS (LINear Constraint Solver) algorithm and periodic boundary conditions (PBC) in all directions, the bond lengths were maintained. Cut-off of 0.9 nm distance and Particle-Mesh Ewald (PME) methods were used for the short-range interactions and for long-range interactions respectively. The temperature (300 K) and pressure (1 bar) were regulated by V-rescale thermostat and Parrinello-Rahman barostat respectively. Molecular dynamics used in the isobaric-isothermal ensemble calculations without position restraint during 100 ns. Root mean square deviation (RMSD) plots indicate the selectable range of 0 to 8 Å for all considered epitopes in Fig. 2.
Full-fledged vaccine construction
The epitopes that were predicted as highly antigenic and could bind with the DLA molecules were selected for the vaccine design. RS09 and N and C terminal of Salmonella Dublin flagellin protein (UNIPROT ID: Q06971) were used as adjuvants. PADRE sequence was incorporated in the vaccine to develop stability and linked to each other by the GGS linker sequence. The final vaccine sequence is summarized in Table 4. Then, the physiochemical properties like stability, number of amino acids, molecular weight, isoelectric point, aliphatic index, number of positively and negatively charged amino acids, and various other properties and also the solubility of the vaccine were evaluated by the Solpro as summarized in Table 5. The vaccine construct was predicted as antigenic, non-allergic, stable, and soluble.
Predicted secondary and tertiary structure
The PSIPRED computed secondary structure of the vaccine contains beta-sheets, alpha helices, and coils are visualized in Fig. 3. The I-TASSER predicted best tertiary structure of the vaccine model had a C-score of − 0.60 and RMSD is 7.7 ± 4.3A. C-score should be in the range of − 5 to 2. The 3D structure of the vaccine construct is shown in Fig. 4. The MolProbity and Procheck validated the quality of the tertiary structures of vaccine; Ramachandran plot is shown in Fig. 5. Overall, 98.9% of all the residues were in allowed or favored region. Out of 320 residues, 281 residues were non-glycine and non-proline.
Codon adaptation and vaccine in silico cloning
For efficient cloning and expression of the vaccine in expression vectors, codon optimization is imperative. The codon-optimized JCAT vaccine construct had a CAI value of 0.98 and a GC content of 52.81. In Fig. 6, CAI and genetic inspectional set for a full-fledged vaccine is provided.
During cloning, the codon-optimized vaccine sequence was inserted between XhoI (158) and EcoRI (192) restriction sites of pET28a (+) vector, as represented in Fig. 7.
There are two key inversely organized ORFs for Circoviruses that encode the replicase protein and capsid protein respectively [7, 10]. The replication-associated protein (Rep), which has sequence motif characteristic of proteins involved in rolling circle replication (RCR) , is the most conserved circovirus protein and is involved in the replication of the virus. In SARS-CoV-2, it has been reported that the replicase protein plays an important role in viral pathogenesis apart from its importance in viral replication process . As replicase protein is highly conserved and plays an important role in pathogenesis, it can be a good candidate to predict epitopes for designing a vaccine construct. The capsid-associated protein (Cap) is the structural component of the viral capsid and is the main antigenic protein of canine circoviruses because it uses repeated subunits to compose the entire capsid structure of the virus; Cap is significantly divergent and is characterized by an N-terminal region rich in basic amino acids that may provide DNA binding activity . Previously, three epitopes, NKPWH, QSLFFF, and KHSRYFT, were predicted from the capsid protein of Porcine circovirus type 3 (PCV-3) . These epitopes were highly conserved B-cell epitopes and could be used to develop vaccine against PCV-3 . In another study the capsid protein has been targeted to develop vaccine against dengue-2 virus . This capsid-based vaccine developed cell-mediated immunity in monkeys . In this study, these two proteins of canine circovirus, replicase and capsid proteins, were targeted to design a multivalent epitope-based vaccine by immunoinformatic approach. The immunoinformatic approach has been previously used to design vaccine candidates against other animal, fish, and poultry pathogens such as Avian avulavirus, foot and mouth disease virus, seven banded grouper nervous necrosis virus, and Mycoplasma gallisepticum [42,43,44,45]. However, there has been no report of development of vaccine candidate against canine circovirus by targeting its capsid and replicase protein by immunoinformatic approach so far. The in silico immunoinformatic approach has become the first step in developing vaccine candidates because of its cost-effective and time-saving manner [16, 46, 47]. This approach can also help to overcome limitations such as genetic variations, antigenic shifts, and antigenic drifts during the vaccine development process .
ViPR database was used for gathering information regarding viral proteome. Specific viral replicase and capsid proteins FASTA sequences retrieved from the NCBI database. The retrieved protein sequence data was analyzed by the NetMHCpan-4.1 webserver to predict epitopes that could bind with DLA molecules. The database also includes information about antigens of dog leukocytes (dog leukocyte antigen (DLA)) . To predict the epitopes, dog leukocyte antigens DLA-8803401, DLA-8850101, and DLA-8850801 were selected because they are part of the major histocompatibility complex in dogs and are associated with the regulation of antigens in the immune system. Then, the epitopes which could bind strongly with DLA molecules were evaluated for their antigenic potential by VaxiJen webserver. The epitopes which had VaxiJen score above 0.7 were selected for further analysis as they are considered to be highly antigenic . Altogether 5 epitopes were identified as highly antigenic: YQHLPPFRF, YIRAKWINW, ALYRRLTLI, HLQGFVNLK, and GTMNFVARR. Then, the structure of these antigenic epitopes was predicted. The interaction of these epitopes with the DLA alleles was evaluated by molecular docking study. The negative binding energy between the interactions of epitopes with the DLA molecules implies stable interactions and good binding affinity . Furthermore, the molecular dynamics simulation analysis also corroborated that the interactions among the epitopes and the DLA alleles were stable. After the selection of the epitopes, they were linked by GGS linkers to adjuvants such as RS09 (APPHALS) and N and C terminal sequence of Salmonella dublin flagellin protein along with PADRE sequence to design the final vaccine construct. These linker and adjuvants have been previously used in multivalent epitope-based vaccine design of herpes simplex virus and human papillomavirus, Candida auris, human cytomegalovirus, and dengue virus [16, 17, 22, 23, 26, 50]. PADRE (AKFVAAWTLKAAA) sequence was added to enhance the stability and potency of the vaccine construct [50, 51]. The physiochemical properties like stability, number of amino acids, molecular weight, isoelectric point, aliphatic index, and number of positively and negatively charged amino acids and various other properties of the vaccine were also determined by the ExPASy ProtParam web server . The final vaccine construct has 320 amino acids and it was predicted to be stable, soluble, antigenic, and non-allergic by different webservers. To determine the secondary structure of the vaccine, PSIPRED web server was used . This web server predicted the presence of coils, strands, and helix in the final canine circovirus vaccine construct. The tertiary structure of the vaccine was determined by using the I-TASSER server. The analysis of the tertiary structure by Ramachandran plot showed that that quality of the 3D model of vaccine construct was good. Most of the residues (90.9%) were in allowed or favored region. The quality of the vaccine construct was also analyzed by C-score. Usually the C-score should be between the range of −5 and 2 . Higher C-score implies good quality of the tertiary structure of the protein . Here, the C-score of the vaccine construct was predicted as −0.60 which implies that the protein tertiary structure was of good quality. Finally, the in silico cloning analysis showed that the vaccine can be cloned and expressed in Escherichia coli for commercial large production. The results obtained in this study show that the vaccine construct could be a good candidate in protecting dogs from canine circovirus by generating robust immune response. However, these claims need further validation by performing different in vitro and in vivo experiments.
In this research, we designed a multi epitope-based vaccine against canine circovirus by deploying immunoinformatic approach. We determined 5 epitopes YQHLPPFRF, YIRAKWINW, ALYRRLTLI, HLQGFVNLK, and GTMNFVARR from the capsid and replicase proteins of canine circovirus that were successfully used to craft a full-fledged vaccine construct using various in silico tools. This help in reducing time taking trial and error peptide screening for identifying vaccine candidates. This is very cost-effective method and easy to conduct before wet-lab trials. Such methods open new dimensions in veterinary research.
Availability of data and materials
All data is provided in the manuscript.
Root mean square deviation
Root mean square fluctuation
- ACE value:
Atomic contact energy value
Severe acute respiratory syndrome-coronavirus 2
Todd D, McNulty MS, Adair BM, Allan GM (2001) Animal circoviruses. Adv Virus Res 57:1–70. https://doi.org/10.1016/s0065-3527(01)57000-1
Anderson A, Hartmann K, Leutenegger CM, Proksch AL, Mueller RS, Unterer S (2017) Role of canine circovirus in dogs with acute haemorrhagic diarrhoea. Vet Rec 180(22):542. https://doi.org/10.1136/vr.103926
Kotsias F, Bucafusco D, Nuñez DA, Lago Borisovsky LA, Rodriguez M, Bratanich AC (2019) Genomic characterization of canine circovirus associated with fatal disease in dogs in South America. PLoS One 14(6):e0218735. https://doi.org/10.1371/journal.pone.0218735
Cruz TF, Batista TN, Vieira EM, Portela LMF, Baccarin AM, Gradiz JJ, Araújo Junior JP (2020) Genomic characterization of Canine circovirus detected in a dog with intermittent hemorrhagic gastroenteritis in Brazil. Ciênc Rural 50(5):e20190909. https://doi.org/10.1590/0103-8478cr20190909
Sun W, Wang W, Cao L, Zheng M, Zhuang X, Zhang H, Yu N, Tian M, Lu H, Jin N (2021) Genetic characterization of three porcine circovirus-like viruses in pigs with diarrhoea in China. Transbound Emerg Dis 68(2):289–295. https://doi.org/10.1111/tbed.13731
Decaro N, Martella V, Desario C, Lanave G, Circella E, Cavalli A, Elia G, Camero M, Buonavoglia C (2014) Genomic characterization of a circovirus associated with fatal hemorrhagic enteritis in dog, Italy. PLoS One 9(8):e105909. https://doi.org/10.1371/journal.pone.0105909
Li L, McGraw S, Zhu K, Leutenegger CM, Marks SL, Kubiski S, Gaffney P, dela Cruz Jr FN, Wang C, Delwart E, Pesavento PA (2013) Circovirus in tissues of dogs with vasculitis and hemorrhage. Emerg Infect Dis 19(4):534–541. https://doi.org/10.3201/eid1904.121390
Zaccaria G, Malatesta D, Scipioni G, Di Felice E, Campolo M, Casaccia C et al (2016) Circovirus in domestic and wild carnivores: an important opportunistic agent? Virology 490:69–74. https://doi.org/10.1016/j.virol.2016.01.007
Plog S, Kubiski S, Enders J, Lübke-Becker A, Pesavento PA, Gruber AD (2017) Detection of dog circovirus in a dog without typical lesions – what do we know about subclinical infection? J Comp Pathol 156(1):75. https://doi.org/10.1016/j.jcpa.2016.11.058
Kapoor A, Dubovi EJ, Henriquez-Rivera JA, Lipkin WI (2012) Complete genome sequence of the first canine circovirus. J Virol 86(12):7018. https://doi.org/10.1128/JVI.00791-12
Hsu H-S, Lin T-H, Wu H-Y, Lin L-S, Chung C-S, Chiou M-T, Lin CN (2016) High detection rate of dog circovirus in diarrheal dogs. BMC Vet Res 12(1):116. https://doi.org/10.1186/s12917-016-0722-8
Song T, Hao J, Zhang R, Tang M, Li W, Hui W, Fu Q, Wang C, Xin S, Zhang S, Rui P, Ren H, Ma Z (2019) First detection and phylogenetic analysis of porcine circovirus type 2 in raccoon dogs. BMC Vet Res 15(1):107. https://doi.org/10.1186/s12917-019-1856-2
Gil L, Izquierdo A, Lazo L, Valdés I, Ambala P, Ochola L, Marcos E, Suzarte E, Kariuki T, Guzmán G, Guillén G, Hermida L (2014) Capsid protein: evidences about the partial protective role of neutralizing antibody-independent immunity against dengue in monkeys. Virology 456–457:70–76. https://doi.org/10.1016/j.virol.2014.03.011
Graham RL, Sparks JS, Eckerle LD, Sims AC, Denison MR (2008) SARS coronavirus replicase proteins in pathogenesis. Virus Res 133(1):88–100. https://doi.org/10.1016/j.virusres.2007.02.017
Ilyina TV, Koonin EV (1992) Conserved sequence motifs in the initiator proteins for rolling circle DNA replication encoded by diverse replicons from eubacteria, eucaryotes and archaebacteria. Nucleic Acids Res 20(13):3279–3285. https://doi.org/10.1093/nar/20.13.3279
Akhtar N, Joshi A, Kaushik V, Kumar M, Mannan MA (2021) In-silico design of a multivalent epitope-based vaccine against Candida auris. Microb Pathog 155:104879. https://doi.org/10.1016/j.micpath.2021.104879
Akhtar N, Joshi A, Singh J, Kaushik V (2021) Design of a novel and potent multivalent epitope based human cytomegalovirus peptide vaccine: an immunoinformatics approach. J Mol Liq 335:116586. https://doi.org/10.1016/j.molliq.2021.116586
Akhtar N, Joshi A, Singh B, Kaushik V (2020) Immuno-informatics quest against COVID-19/SARS-COV-2: determining putative T-cell epitopes for vaccine prediction. Infect Disord Drug Targets 20. https://doi.org/10.2174/1871526520666200921154149
Joshi A, Joshi BC, Mannan MA, Kaushik V (2020) Epitope based vaccine prediction for SARS-COV-2 by deploying immuno-informatics approach. Inform Med Unlocked 19:100338. https://doi.org/10.1016/j.imu.2020.100338
Kaushik V, Singh B, Singh J (2018) Bioinformatics techniques used in hepatitis C virus research. J Pure Appl Microbiol 11:921–932
Kaushik V, Chauhan G, Singh J (2014) In Silico peptide-based vaccine design against non-structural Protein 5 of Hepatitis C Virus. Int J Pharm Pharm Sci 6:80–82
Krishnan GS, Joshi A, Akhtar N, Kaushik V (2021) Immunoinformatics designed T cell multi epitope dengue peptide vaccine derived from non structural proteome. Microb Pathog 150:104728. https://doi.org/10.1016/j.micpath.2020.104728
Negahdaripour M, Eslami M, Nezafat N, Hajighahramani N, Ghoshoon MB, Shoolian E, Dehshahri A, Erfani N, Morowvat MH, Ghasemi Y (2017) A novel HPV prophylactic peptide vaccine, designed by immunoinformatics and structural vaccinology approaches. Infect Genet Evol 54:402–416. https://doi.org/10.1016/j.meegid.2017.08.002
Sanami S, Azadegan-Dehkordi F, Rafieian-Kopaei M, Salehi M, Ghasemi-Dehnoo M, Mahooti M, Alizadeh M, Bagheri N (2021) Design of a multi-epitope vaccine against cervical cancer using immunoinformatics approaches. Sci Rep 11(1):12397. https://doi.org/10.1038/s41598-021-91997-4
Sayed SB, Nain Z, Khan MSA, Abdulla F, Tasmin R, Adhikari UK (2020) Exploring Lassa virus proteome to design a multi-epitope vaccine through immunoinformatics and immune simulation analyses. Int J Pept Res Ther 26(4):2089–2107. https://doi.org/10.1007/s10989-019-10003-8
Yazdani Z, Rafiei A, Valadan R, Ashrafi H, Pasandi MS, Kardan M (2020) Designing a potent L1 protein-based HPV peptide vaccine: a bioinformatics approach. Comput Biol Chem 85:107209. https://doi.org/10.1016/j.compbiolchem.2020.107209
Reynisson B, Alvarez B, Paul S, Peters B, Nielsen M (2020) NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res 48(W1):W449–W454. https://doi.org/10.1093/nar/gkaa379
Doytchinova IA, Flower DR (2007) VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics 8(1):4. https://doi.org/10.1186/1471-2105-8-4
Lamiable A, Thévenet P, Rey J, Vavrusa M, Derreumaux P, Tufféry P (2016) PEP-FOLD3: faster de novo structure prediction for linear peptides in solution and in complex. Nucleic Acids Res 44(W1):W449–W454. https://doi.org/10.1093/nar/gkw329
Schneidman-Duhovny D, Inbar Y, Nussinov R, Wolfson HJ (2005) PatchDock and SymmDock: servers for rigid and symmetric docking. Nucleic Acids Res 33(Web Server):W363–W367. https://doi.org/10.1093/nar/gki481
Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. J Comput Chem 31:455–461. https://doi.org/10.1002/jcc.21334
Kozakov D, Hall DR, Xia B, Porter KA, Padhorny D, Yueh C, Beglov D, Vajda S (2017) The ClusPro web server for protein-protein docking. Nat Protoc 12(2):255–278. https://doi.org/10.1038/nprot.2016.169
Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, Lindahl E (2015) GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2:19–25. https://doi.org/10.1016/j.softx.2015.06.001
Gasteiger E, Hoogland C, Gattiker A, Duvaud S, Wilkins MR, Appel RD et al (2005) Protein identification and analysis tools on the ExPASy server. In: Walker JM (ed) Proteomics Protoc. Handb. Humana Press, Totowa, pp 571–607. https://doi.org/10.1385/1-59259-890-0:571
Magnan CN, Randall A, Baldi P (2009) SOLpro: accurate sequence-based prediction of protein solubility. Bioinformatics 25(17):2200–2207. https://doi.org/10.1093/bioinformatics/btp386
Buchan DWA, Jones DT (2019) The PSIPRED Protein Analysis Workbench: 20 years on. Nucleic Acids Res 47(W1):W402–W407. https://doi.org/10.1093/nar/gkz297
Yang J, Yan R, Roy A, Xu D, Poisson J, Zhang Y (2014) The I-TASSER suite: protein structure and function prediction. Nat Methods 12(1):7–8. https://doi.org/10.1038/nmeth.3213
Laskowski RA, Rullmannn JA, MacArthur MW, Kaptein R, Thornton JM (1996) AQUA and PROCHECK-NMR: programs for checking the quality of protein structures solved by NMR. J Biomol NMR 8(4):477–486. https://doi.org/10.1007/BF00228148
Grote A, Hiller K, Scheer M, Münch R, Nörtemann B, Hempel DC et al (2005) (2005) JCat: a novel tool to adapt codon usage of a target gene to its potential expression host. Nucleic Acids Res 33(Web Server):W526–W531. https://doi.org/10.1093/nar/gki376
Niagro FD, Forsthoefel AN, Lawther RP, Kamalanathan L, Ritchie BW, Latimer KS, Lukert PD (1998) Beak and feather disease virus and porcine circovirus genomes: intermediates between the geminiviruses and plant circoviruses. Arch Virol 143(9):1723–1744. https://doi.org/10.1007/s007050050412
Jiang M, Guo J, Zhang G, Jin Q, Liu Y, Jia R, Wang A (2020) Fine mapping of linear B cell epitopes on capsid protein of porcine circovirus 3. Appl Microbiol Biotechnol 104(14):6223–6234. https://doi.org/10.1007/s00253-020-10664-2
Joshi A, Pathak DC, Mannan MA, Kaushik V (2021) In-silico designing of epitope-based vaccine against the seven banded grouper nervous necrosis virus affecting fish species. Netw Model Anal Health Inform Bioinforma 10(1):37. https://doi.org/10.1007/s13721-021-00315-5
Liu YF, Lin CY, Hong HM (2017) In silico design, synthesis and potency of an epitope-based vaccine against foot-and-mouth disease virus. Int J Pharmacol 13(2):122–133. https://doi.org/10.3923/ijp.2017.122.133
Mayahi V, Esmaelizad M, Ganjalikhany MR (2020) Development of Avian avulavirus 1 epitope-based vaccine pattern based on epitope prediction and molecular docking analysis: an immunoinformatic approach. Int J Pept Res Ther 26(3):1513–1522. https://doi.org/10.1007/s10989-019-09952-x
Mugunthan SP, Harish MC (2021) Multi-epitope-based vaccine designed by targeting cytoadherence proteins of Mycoplasma gallisepticum. ACS Omega 6(21):13742–13755. https://doi.org/10.1021/acsomega.1c01032
Ben-Yedidia T, Arnon R (2007) Epitope-based vaccine against influenza. Expert Rev Vaccines 6(6):939–948. https://doi.org/10.1586/147605126.96.36.1999
Qamar MTU, Saleem S, Ashfaq UA, Bari A, Anwar F, Alqahtani S (2019) Epitope-based peptide vaccine design and target site depiction against Middle East respiratory syndrome coronavirus: an immune-informatics study. J Transl Med 17(1):362. https://doi.org/10.1186/s12967-019-2116-8
Oyarzún P, Kobe B (2016) Recombinant and epitope-based vaccines on the road to the market and implications for vaccine design and production. Hum Vaccines Immunother 12(3):763–767. https://doi.org/10.1080/21645515.2015.1094595
Adhikari UK, Tayebi M, Rahman MM (2018) Immunoinformatics approach for epitope-based peptide vaccine design and active site prediction against polyprotein of emerging Oropouche virus. J Immunol Res 2018:6718083–6718022. https://doi.org/10.1155/2018/6718083
Hasan M, Islam S, Chakraborty S, Mustafa AH, Azim KF, Joy ZF, Hossain MN, Foysal SH, Hasan MN (2019) Contriving a chimeric polyvalent vaccine to prevent infections caused by herpes simplex virus (type-1 and type-2): an exploratory immunoinformatic approach. J Biomol Struct Dyn 38(10):2898–2915. https://doi.org/10.1080/07391102.2019.1647286
Ghaffari-Nazari H, Tavakkol-Afshari J, Jaafari MR, Tahaghoghi-Hajghorbani S, Masoumi E, Jalali SA (2015) Improving multi-epitope long peptide vaccine potency by using a strategy that enhances CD4+ T help in BALB/c Mice. PLoS One 10(11):e0142563. https://doi.org/10.1371/journal.pone.0142563
All the authors are thankful towards the School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, Punjab (India).
Ethics approval and consent to participate
Not applicable. No impact on ethical standards in this study, and there is no human or animal involvement.
Consent for publication
All authors have no conflict of (competing) interests
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Jain, P., Joshi, A., Akhtar, N. et al. An immunoinformatics study: designing multivalent T-cell epitope vaccine against canine circovirus. J Genet Eng Biotechnol 19, 121 (2021). https://doi.org/10.1186/s43141-021-00220-4