Abstract
Purpose
Mucorales has been described to be widely distributed during the most recent COVID-19 pandemic, with a greater frequency of disease in India, particularly among those with immune deficiencies. This study aims to use computational tools to develop a vaccine.
Design/methodology/approach
The authors investigated at Mucorales proteins that had previously been associated to virulence factors. Recent research suggests that a vaccine based on high-level cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL) and B-cell lymphocyte (BCL) epitopes from diverse proteins might be developed. Furthermore, the vaccine assembly contains the targeted epitopes as well as PADRE peptides to induce an immune response. Computational approaches were used to analyze the immunological parameters used to build the suggested vaccine and validate its TLR-3 binding.
Findings
These studies show that the vaccination is capable of triggering a particular immune response. The authors offer a technique for developing and evaluating candidate vaccines using computational tools. To the best of their knowledge, this is the first immunoinformatic research of a prospective mucormycosis vaccine.
Originality/value
During this audit, a successful attempt was made to create a subunit MEV against black fungus. In the current study, MEV has been proposed as a suitable neutralizer candidate since it is immunogenic, secure, stable and interacts with human receptors. A stream study, on the other hand, is produced via a mixed vaccinosis approach. Following that, vaccinologists may perform more exploratory testing to evaluate whether the vaccine is effective.
Keywords
Citation
Venkateswarulu, T.C., Shaik, A.T., Meduri, D.S., Vajiha, V., Dhusia, K. and Peele, A. (2024), "
Publisher
:Emerald Publishing Limited
Copyright © 2023, T.C. Venkateswarulu, Asra Tasneem Shaik, Druthi Sri Meduri, Vajiha, Kalyani Dhusia and Abraham Peele
License
Published in Arab Gulf Journal of Scientific Research. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
A fungus is a multicellular eukaryotic organism that includes yeasts, mold and more familiar mushrooms. Fungi are heterotrophic. Fungi reproduce by releasing spores into the environment, spreading through inhalation or direct contact. It is widespread, showing up in soil, air and even healthy people's noses and mucous. Fungi can take two forms: mold and yeast (Aguilera & González-Toril, 2019). Mold lives in a long, multiple-threaded cell 32 structure, whereas yeast lives in a single cell. The clinical term for black fungus is mucormycosis. Molds found in the environment, such as Rhizopus arrhizus, cause the resulting infection (belongs to the Mucoraceae family). According to the Centers for Disease Control and Prevention, mucormycosis is caused by a group of molds known as Mucormycetes (CDC). It most commonly affects the eyes, bones, nerves, brain, sinuses or lungs, and symptoms include fever, cough and shortness of breath if it gets to the lungs. And, if left untreated, proves fatal (Hadiyanto, Wilda, Cahyadi, & Adisuhanto, 2021). Mucormycosis emerged as another concerning infectious disease amid the life-threatening COVID-19 pandemic. There have been reports of it, mainly in India. This infection may primarily infect those with weakened immune systems who cannot fight off the infection (Fayemiwo & Adegboro, 2021). This infection is a significant concern for COVID-19 recovered patients because they took a lot of antibiotics and steroids to treat COVID-19, making them vulnerable to fungal infections due to their immunocompromised state (Roudbary et al., 2021). More than 4,300 people are thought to have died in India due to this lethal “black fungus.” According to some sources, approximately 45,374 cases of this deadly infection known as mucormycosis have been reported (Sahoo et al., 2022). It typically strikes 12-18 days after recovery from Covid and attacks the eyes, nose and sometimes the brain. Antifungal medication is used to diagnose black fungus. Medication such as isavuconazole, posaconazole and amphotericin B can prevent the fungus from growing. Surgery is required in severe cases to remove the infected tissue. Medical Professionals are moving on to the principle of developing an effective vaccine that could limit the spread of the infection and lower the overall mortality rate. Vaccines are designed to stimulate to fight specific pathogens. They contain components such as weakened or inactivated viruses, bacterial proteins or genetic material that mimics the pathogen, and upon subsequent exposure to the same pathogen, the body is better able to respond quickly and strongly due to the earlier immunization. It provides a safe and effective way to protect the body from disease-causing pathogens, before exposure to them. Vaccines are a powerful tool for eliciting the production of specific antibodies, which are proteins produced by immune cells called B-cells. These antibodies are capable of recognizing and neutralizing the pathogens targeted by the vaccine, thus helping to prevent their entry into cells or enhancing their clearance from the body. This is an important mechanism for protecting against infection from the targeted pathogen and is one of the main reasons why vaccines are such an effective tool for disease prevention. By stimulating the production of antibodies, the vaccine helps to train the body's immune system to recognize and respond to the particular pathogen, providing a form of immunity even in the event of a later encounter. Vaccines are not just effective for producing antibodies, but can also activate cell-mediated immunity. This type of immunity is especially important for combatting intracellular pathogens, such as viruses, as it involves the activation of T-cells. These immune cells directly attack and destroy infected cells, or help coordinate the immune response in other ways. Cell-mediated immunity is thus an important part of a comprehensive immune system, and vaccines can be incredibly useful in activating it. Vaccine design can be built using either traditional or modern technologies. Multi-epitope vaccines (MEVs) are more advantageous than conventional vaccines because they save time, are less expensive, are considered safe and effective, are specific and stable and do not require microbial culturing. Currently, immunoinformatics has paved the way for in silico vaccination computational vaccinology, in which scientific questions about vaccination are solved using computer-driven algorithms and various computational tools that aid in data analysis. The results have been promising in the majority of cases. Recent advancement in the field of vaccination is an in silico approach, which could combat various pathogens and take over the ability to elicit cellular and humoral responses in human hosts. To make a multi-epitope vaccination, we used an in silico technique (Kadam, Sasidharan, & Saudagar, 2020). We focused on identifying the target protein sequence derived from NCBI (National Center for Biotechnology Information), a leading source for databases, genomic data and computational biology research. Using various Internet applications, T-cell (MHC-Class I, MHC-Class II) and B-cell epitopes were later predicted. Its epitopes were chosen, and then allergenicity, immunogenicity, conservancy and antigenicity were tested. The vaccine was created using the obtained proteins and appropriate linkers cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL) and B-cell lymphocyte (Rakib et al., 2020). The vaccine sequence is then inserted into a vector, converted into a DNA sequence, followed by molecular docking and immune stimulation. The current study aims to create a vaccine against black fungus using various web-based tools.
2. Methods
2.1 Protein sequence retrieval and analysis
The black fungus [Rhizopus arrhizus] proteome sequence was downloaded in FASTA format from the NCBI (Accession id = BAH03542.1) database (Abe, Asano, & Sone, 2009) and predicted for B- and T-cell epitopes, T-cell epitopes, located on the surface of APCs and delivered by MHC molecules to T-cell receptors. To anticipate epitopes from MHC classes I and II, the Net CTL 1.2 server and Net MHC 4.0 server were used to trace epitopes inside the specified sequence (Larsen et al., 2007; Fadilah, Erlina, Paramita, & Istiadi, 2021) (Table 1, Table 2). As a result, T-cell-mediated immunity would be induced by these epitopes (Mahapatra et al., 2022). There are two types of B-cell epitopes in the adaptive humoral immune system: linear (continuous) and conformational (discontinuous). The linear epitopes were predicted using the ABCPred server (Saha & Raghava, 2006) (Table 3, Table 4).
2.2 The development of a multiepitope vaccine
The derived epitopes were chosen for construction of a polytope vaccine. Adjuvants can be used to boost the immune response of the epitopes. The L7/L12 adjuvant is a ribosomal protein that was linked with the EAAAK linker and other linkers due to its capacity for inducing DC maturation; GGGGS was used to link CTL epitopes of MHC Class-Ⅰ; GPGPG was used to link HTL epitopes of MHC Class-Ⅱ; while KK linker was used to link B-Cell epitopes to preserve the immunogenic activities (Sami et al., 2021). HTLs have a role in activating CTLs and other immune cells by releasing cytokines such as IFN-γ, interleukin-4 and interleukin-10. This shows that cytokine-inducing HTL epitopes are essential for vaccinations to work (Figure 1).
2.3 The succeeding sequence is the constructed vaccine
EAAAKMSITKDQIIEAVAAMSVMDVVELISAMEEKFGVSAAAAVAVAAGPVEAAEEKTEFDVILKAAGANKVAVIKAVRGATGLGLKEAKDLVESAPAALKEGVSKDDAEALKKALEEAGAEVEVKEAAAKAKFVAAWTLKAAAGGGGSSTAPDMDLLGGGGSATTMANHLGPGPGVLRKLFELCLQTTLGPGPGSQALFSRWSLWWPVKKLLGDSRTTTRGIKLFSWIKKAKFVAAWTLKAAAGGGGS.
2.4 Determining the attributes of the polytope construct
The top-scoring epitopes from the predicted T and B-cell epitopes were chosen to evaluate their antigenic, allergic, toxicity and immunogenic aspects. AlgPred 2.0 is a web server for predicting allergenic regions in proteins, with a prediction threshold of 0.5 (Sharma et al., 2021) (Table 5), (Table 6), (Table 3). Toxinpred, an InSilco web server, was used to aid in the prediction of least toxic epitopes (Table 7), (Table 8) and (Table 9) (Kalita, Padhi, Zhang, & Tripathi, 2020). Defined by calculating its fraction, the conservancy of the protein's epitopes was done utilizing the IEDB server (Azim et al., 2019). Vaxijen 2.0 was used to anticipate the protective antigenicity of the protein's epitopes (Table 10), (Table 11), (Table 12) (Foroutan, Ghaffarifar, Sharifi, & Dalimi, 2020). Furthermore, immunogenicity was predicted using the IFN epitope web server (Table 13), (Table 14) and (Table 15).
2.5 Prediction of tertiary structure
The vaccine construct was built up of multiple epitopes, and the tertiary structure of a MEV was generated using Alphafold 2.0, an AI-powered platform with an algorithm for computationally predicting the 3-D structure of a protein. Chimera software (Figure 2) can be used to see this 3-D structure. The Z-score, QMEANDisCO and overall quality factor were calculated using ProSA and ERRAT2. To gain knowledge about the protein, qualitative analysis was performed using the Ramachandran plot with the help of the PDBsum web server (Figure 3). (Narang et al., 2022).
2.6 Cloning
JCAT, a codon-optimized web server, was utilized to convert vaccine construct protein sequence to DNA sequence. The converted sequence was uploaded to SnapGene, a molecular design and cloning tool (Susithra Priyadarshni, Isaac Kirubakaran, & Harish, 2021; Gustiananda, Sulistyo, Agustriawan, & Andarini, 2021). PET 28a plasmid was used as a vector, and the resultant circular DNA was cloned using ECoR1 and BamH1 restriction enzymes (Figure 4).
2.7 Molecular docking
Cluspro, a web-based program, was utilized to conduct docking tests on the vaccine design and the human immune receptor for a vaccination based on numerous epitopes. The interaction of the vaccine design with the target immune cells of an organism can result in the production of an optimum immune response. Because of its well-known function in the development of antiviral immune responses, TLR3 was chosen as a target (Sudeshna Panda et al., 2022). Chimera software can be used to visualize TLR-3 (Figure 5). For the intended immune response to be generated, ideal contacts between the receptor and the antigen are necessary. The docking findings indicated acceptable contacts between these two molecules with a weighted score of −1104.8 (Figure 6).
2.8 Analysis of vaccine construct and receptor complex using molecular dynamics simulation
The stability of the docked complex of vaccine construct and TLR3 was evaluated using a 50ns DESMOND simulation. Trajectories were saved every 2fs, and the root mean square deviation (RMSD), and root mean square fluctuation (RMSF) were computed using DESMOND (RMSF).
3. Results and discussion
The amino acid sequences of Mucorales proteins previously associated with virulence factors were used in the current study to derive the epitopes in the IEDB software tool, which gave several numbers of combinatorial peptide sequences, of which the low adjusted rank peptides are reported to be good binders, and B-cell epitope conservation data indicated that 5 of the 8 epitopes were entirely conserved. Furthermore, in MHC-I, 5 of the 8 expected findings were completely retained, but in MHC-II, 6 of the 9 predicted results were intact. A total of IFN-generating epitopes were discovered in target proteins. All of the T- and B-cell epitopes chosen were projected to be non-toxic.
The final vaccine design was made up of four subunits (CTL-1, CTL-2, HTL-1 and BCL-1) joined by an EAAAK linker and GGGGS, and including NotI and BamHI restriction enzyme recognition sites in their coding DNA sequences. Because of its features in the formation of junctional epitopes, the GPGPG linker was included. These linkers help epitopes unite, and all other epitopes were added for their qualities in boosting immunization and epitope presentation. GPGPG, KK and linkers were utilized for CTL, HTL and BCL epitopes, respectively, to help in polytope conformation, and a polytope including 63 amino acids was created.
It was then tested for antigenic properties utilizing the Vaxijen server. The proteins were discovered to be highly antigenic. Both AllerTop V2 and ToxinPred tools yielded the outcome as non-allergic and non-toxin, respectively. After studying the data, Alphafold v2.0 was used to predict 3D models of the given epitopes by adding the appropriate linkers. The expected model was employed for further investigation.
The Z-score measures the total energy deviation of the predicted model with respect to an energy distribution derived from random conformations. The analysis from ProSA indicates a Z-score with overall model quality of −3.21 (Figure 7). The average per residue score and the provided error estimate based on global QMEANDisCo was given as 0.56, showing the good quality of the model. Analysis of non-bonded interactions between different atom types and overall quality factor using ERRAT2 shows score of 86.91, with most of the residues less than 95% error rate (Figure 8).
The Ramachandran plot reveals that the predicted model has 80% amino acids in the core area of the Ramachandran plot. It is widely assumed that if 90% of the residues fall within the permitted range, the model is deemed accurate and reliable. Figure 3 depicts the Ramachandran plot of the simulated structure. Secondary structure prediction is used to establish where beta strands and alpha helix are located within a protein family. PSIPRED and Alphafold were utilized to construct the secondary structure of the final vaccine. Figure 9 visually illustrates the secondary structural features. The proposed secondary structure is composed of 54.6% alpha-helix, 6.8% beta-strand and 38.5% coil.
To manufacture the vaccine candidate, the up-stream protocol requires a design of gene construct with a cloning vehicle. In Figure 4, the translated DNA of the polytope construct is in silico ligated in pET28(a) plasmid will enrich the quantity of polytope which upon down-streaming steps, formulating with TLR3 agonist, stabilizers and preservatives, vaccine for black fungus could be formulated for pre-clinical trials.
The docking results demonstrated appropriate contacts between these two molecules (Figure 6), with a weighted score of −1104.8.DESMOND used to perform MD analysis on the docked complex of TLR3 and vaccine construct. As seen in the graph, the average RMSD and RMSF values obtained were 7A and 1A, respectively. The complex's C-alpha atoms were quite unstable at the start of the simulation, but they immediately regained stability at 22 n sec and remained stable throughout the 50-n sec simulation. During the simulation, the complex had an average RMSD of 7. To gain structural information about the complex, C-alpha atoms were aligned with the reference set. As a result, Figure 10 displays a well-equilibrated system experiencing substantial conformational changes over the simulation.
An RMSF study of a system provides the variations in a particular collection of atoms from their mean locations. The analysis was carried out on the system's C-alpha atoms using the DESMOND program (Narang et al., 2021). The peaks in the graph indicate atom fluctuations; substantial variations in the graph were seen at the early residues with indices ranging from 200 to 400. The residues with indices 600 to 850 exhibited the lowest variations in the system of 919 residues, with an average RMSF of 1 (Figure 11). These findings support the docking interaction investigation by revealing that MEV can bind to immune receptors robustly enough to elicit an immune response against black fungus.
4. Conclusion
The recent black fungus outbreak resulted in the deaths of many people in India and caused the country's economy to collapse. There is no recognized treatment or immunization that is effective against black fungus. During this audit, a successful attempt was made to create a subunit MEV against black fungus. In silico immunoinformatic techniques were employed to produce a possibly safe MEV capable of eliciting three types of safe responses: humoral, natural and cell. In the current study, MEV has been proposed as a suitable neutralizer candidate since it is immunogenic, secure, stable and interacts with human receptors. A stream study, on the other hand, is produced via a mixed vaccination approach. The aforementioned attributes authenticate that the constructed polytope would be a vaccine candidate, which, upon formulation, will be used as a therapeutic candidate among the endemic group.
Figures
Figure 1
Visual representation of a sequence of epitopes and linkers used for polytope construct
Figure 2
3D Predicted structure of constructed polytope of black fungus (Rhizopus arrhizus) using AlphaFold Google Colab notebook
Figure 3
The Ramachandran plot of the modeled structure of black fungus generated using PDBSum web server indicates a predominant helix structure
Figure 4
Polytope construct ligated at MCS cloned in pET28a (+) vector at Sac I and Bam HI restriction sites using SnapGene tool (polytope construct highlighted in red)
Figure 5
TLR3 structure of Homo Sapiens derived from the PDB as a target for molecular docking
Figure 6
The docked conformation of human TLR3 with the predicted AlphaFold structure of a black fungus (Rhizopus arrhizus) with a weighted score of −1104.8
Figure 7
The ProSA Z-score of overall model quality for predicted protein given as −3.21 indicates a good model
Figure 8
ERRAT predicted overall quality factor for structure analysis of the constructed model with a score of 86.911. The plot represents quality score along with error rates for the residues
Figure 9
Secondary structure of multiepitope vaccine (MEV). The alpha helix residues are pink, the coil residues are gray and the beta-strand is yellow
Figure 10
RMSD analysis of TLR3 bound to vaccine construct (selected protein) indicating stability from 22ns to the end of the simulation, that is 50ns
Figure 11
RMSF analysis of TLR3 bound to a vaccine construct (selected protein) and stabilizing at the C-terminus, representing the fluctuations in the initial residues
List of T-cell epitopes chosen based on C-terminal cleavage and TAP score generated from IEDB web server
| Residue No | Peptide sequence | MHC binding affinity | Rescale binding affinity | c-terminal cleavage affinity | Transport affinity | Prediction score |
|---|---|---|---|---|---|---|
| 348 | WTDDLQALF | 0.6653 | 2.8249 | 0.8918 | 2.205 | 3.0689 |
| 540 | CTAQVVPIY | 0.6273 | 2.6636 | 0.9708 | 2.823 | 2.9504 |
| 691 | LSPFLYSIY | 0.4345 | 1.845 | 0.9068 | 2.963 | 2.1291 |
| 1153 | LTSTVDSLF | 0.4378 | 1.859 | 0.7615 | 2.567 | 2.1016 |
| 455 | GSSLPSTRY | 0.4094 | 1.7381 | 0.9783 | 2.836 | 2.0267 |
| 122 | STLLVHCVY | 0.3729 | 1.5834 | 0.976 | 3.021 | 1.8809 |
| 72 | TSWQQYHTY | 0.3349 | 1.422 | 0.9789 | 3.152 | 1.7264 |
| 1136 | SIDTSGSLF | 0.3496 | 1.4846 | 0.6219 | 2.594 | 1.7075 |
| 986 | HVKQSVLAY | 0.308 | 1.3077 | 0.9656 | 3.124 | 1.6087 |
| 412 | LSDTTATIK | 0.3385 | 1.4372 | 0.7634 | 0.188 | 1.5611 |
| 688 | GSILSPFLY | 0.2955 | 1.2546 | 0.9313 | 2.806 | 1.5346 |
| 135 | LSDLEAMDI | 0.2772 | 1.1769 | 0.504 | 0.457 | 1.2753 |
| 1154 | TSTVDSLFF | 0.2298 | 0.9757 | 0.8372 | 2.566 | 1.2296 |
| 199 | QLAYGIPTY | 0.1844 | 0.7831 | 0.9758 | 2.984 | 1.0787 |
| 69 | RLMTSWQQY | 0.1753 | 0.7443 | 0.9546 | 3.301 | 1.0526 |
| 279 | LTEPDCLYV | 0.1988 | 0.8439 | 0.8911 | 0.285 | 0.9918 |
| 278 | RLTEPDCLY | 0.1567 | 0.6654 | 0.8945 | 3.206 | 0.9599 |
| 904 | LTSTPSMHV | 0.1838 | 0.7806 | 0.9159 | 0.27 | 0.9314 |
| 304 | YTSPTNSTA | 0.2102 | 0.8924 | 0.3864 | −0.571 | 0.9219 |
| 586 | SLDPVQGGF | 0.1515 | 0.6431 | 0.9667 | 2.286 | 0.9024 |
| 310 | STAPDMDLL | 0.1666 | 0.7072 | 0.7322 | 1.144 | 0.8742 |
| 43 | TVDQTTNSL | 0.1614 | 0.6855 | 0.9705 | 0.844 | 0.8733 |
| 719 | YSSNPQREF | 0.1509 | 0.6405 | 0.6302 | 2.499 | 0.86 |
| 322 | LTSIIHSCL | 0.146 | 0.62 | 0.9543 | 0.766 | 0.8014 |
| 442 | ATTMANHLK | 0.1468 | 0.6231 | 0.8251 | 0.705 | 0.7821 |
| 117 | LSCQVSTLL | 0.137 | 0.5816 | 0.9079 | 1.055 | 0.7705 |
| 96 | LLVNPHCPY | 0.1067 | 0.4531 | 0.9758 | 3.08 | 0.7535 |
| 34 | WNANGCNRY | 0.1109 | 0.4708 | 0.9414 | 2.81 | 0.7526 |
Source(s): Table by authors
Helper T-cell (HTL) epitopes for Rhizopus arrhizus polyprotein using MHC II module (NetMHC −4.0-server)
| Allele | Peptide | VaxiJen probability (threshold 0.5) |
|---|---|---|
| HLA-A0201 | VLRKLFELCLQTTL | Antigen |
| HLA-A0201 | SQALFSRWSLWWPV | Antigen |
Source(s): Table by authors
List of selected B-CELL epitopes by using ABCpred server
| Rank | Sequence | Start position | Score |
|---|---|---|---|
| 1 | RWSLWWPVVCQVLLEIEQ | 1107 | 0.93 |
| 2 | TIFIRPRFEYGLCICTFL | 854 | 0.92 |
| 3 | VDRAIIWRALETYISPAL | 637 | 0.9 |
| 4 | TGGYTSVDDRLAHLSTDA | 968 | 0.89 |
| 5 | DRLAHLSTDAHVKQSVLA | 976 | 0.88 |
| 5 | SDTTATIKRIRRNRTMSP | 413 | 0.88 |
| 6 | ALYCRRRATWKQFCETLS | 391 | 0.87 |
| 6 | SIIHSCLDDSVGQKKPRG | 324 | 0.87 |
| 7 | LLFRLCWIWSAVPAAWCT | 524 | 0.86 |
| 7 | GPTVAATTMANHLKQVFS | 437 | 0.86 |
| 7 | CWNTQLAYGIPTYCAQGR | 195 | 0.86 |
| 7 | LLGDSRTTTRGIKLFSWI | 171 | 0.86 |
| 8 | NAWFWTDDLQALFDRREQ | 344 | 0.85 |
| 8 | TRGIKLFSWILENGMTCW | 179 | 0.85 |
| 8 | GPIHASRAHLLSCLRVAE | 1055 | 0.85 |
Source(s): Table by authors
List of predicted B-CELL epitopes after allergenicity analysis
| B-CELL epitopes | ML score | MERCI score | BLAST score | Hybrid score | Prediction |
|---|---|---|---|---|---|
| B-CELL 1 | 0.22 | 0 | 0 | 0.22 | Non-allergen |
| B-CELL 2 | 0.28 | 0 | 0 | 0.28 | Non-allergen |
| B-CELL 3 | 0.26 | 0 | 0 | 0.26 | Non-allergen |
| B-CELL 4 | 0.3 | 0 | 0 | 0.3 | Non-allergen |
| B-CELL 5 | 0.28 | 0 | 0 | 0.28 | Non-allergen |
| B-CELL 6 | 0.29 | 0 | 0 | 0.29 | Non-allergen |
| B-CELL 7 | 0.26 | 0 | 0 | 0.26 | Non-allergen |
| B-CELL 8 | 0.25 | 0 | 0 | 0.25 | Non-allergen |
Source(s): Table by authors
List of predicted CTL epitopes after allergenicity analysis using AllerTOP web server
| MHC Class I | Peptide sequence | ML score | MERCI sore | BLAST score | Hybrid Score | Prediction |
|---|---|---|---|---|---|---|
| CTL 1 | STLLVHCVY | 0.3 | 0 | 0 | 0.3 | Non-allergen |
| CTL 2 | GSILSPFLY | 0.29 | 0 | 0 | 0.29 | Non-allergen |
| CTL 3 | LSDLEAMDI | 0.28 | 0 | 0 | 0.28 | Non-allergen |
| CTL 4 | RLMTSWQQY | 0.27 | 0 | 0 | 0.27 | Non-allergen |
| CTL 5 | LTSTPSMHV | 0.29 | 0 | 0 | 0.29 | Non-allergen |
| CTL 6 | STAPDMDLL | 0.3 | 0 | 0 | 0.3 | Non-allergen |
| CTL 7 | LTSIIHSCL | 0.28 | 0 | 0 | 0.28 | Non-allergen |
| CTL 8 | ATTMANHL | 0.3 | 0 | 0 | 0.3 | Non-allergen |
Source(s): Table by authors
List of predicted HTL epitopes after allergenicity analysis
| MHC Class II | Peptide sequence | ML score | MERCI score | BLAST score | Hybrid score | Prediction |
|---|---|---|---|---|---|---|
| HTL 1 | RGIKLFSWILENGM | 0.2 | 0 | 0 | 0.2 | Non-allergen |
| HTL 2 | VLTLLFRLCWIWSA | 0.21 | 0 | 0 | 0.21 | Non-allergen |
| HTL 3 | LTLLFRLCWIWSAV | 0.21 | 0 | 0 | 0.21 | Non-allergen |
| HTL 4 | LLFRLCWIWSAVPA | 0.29 | 0 | 0 | 0.29 | Non-allergen |
| HTL 5 | VLRKLFELCLQTTL | 0.3 | 0 | 0 | 0.3 | Non-allergen |
| HTL 6 | ILSPFLYSIYINSL | 0.29 | 0 | 0 | 0.29 | Non-allergen |
| HTL 7 | SPFLYSIYINSLPA | 0.3 | 0 | 0 | 0.3 | Non-allergen |
| HTL 8 | FLYSIYINSLPALL | 0.3 | 0 | 0 | 0.3 | Non-allergen |
| HTL 9 | SQALFSRWSLWWPV | 0.3 | 0 | 0 | 0.3 | Non-allergen |
| HTL 10 | ALFSRWSLWWPVVC | 0.29 | 0.29 | 0 | 0 | Non-allergen |
| HTL 11 | FSRWSLWWPVVCQV | 0.29 | 0.29 | 0 | 0 | Non-allergen |
| HTL 12 | FLDKIRPMSLTSTV | 0.2 | 0.2 | 0 | 0 | Non-allergen |
Source(s): Table by authors
List of predicted CTL epitopes after toxicity analysis using ToxinPred web server
| MHC Class I | Peptide sequence | SVM score | Prediction | Hydrophobicity | Hydropathicity | Hydrophilicity | Charge | Mol wt |
|---|---|---|---|---|---|---|---|---|
| CTL 1 | STLLVHCVY | −0.84 | Non-toxin | 0.15 | 1.39 | −1.17 | 0.5 | 1034.37 |
| CTL 2 | GSILSPFLY | −1.02 | Non-toxin | 0.22 | 1.11 | −1.07 | 0 | 996.3 |
| CTL 3 | LSDLEAMDI | −1.28 | Non-toxin | 0 | 0.5 | 0.23 | −3 | 1006.26 |
| CTL 4 | RLMTSWQQY | −1.44 | Non-toxin | −0.27 | −1.06 | −0.61 | 1 | 1212.52 |
| CTL 5 | LTSTPSMHV | −1.04 | Non-toxin | 0 | 0.23 | −0.59 | 0.5 | 972.25 |
| CTL 6 | STAPDMDLL | −1.16 | Non-toxin | −0.04 | 0.13 | 0.06 | −2 | 962.2 |
| CTL 7 | LTSIIHSCL | −0.68 | Non-toxin | 0.16 | 1.51 | −0.94 | 0.5 | 986.33 |
| CTL 8 | ATTMANHL | −0.1 | Non-toxin | −0.01 | 0.15 | −0.65 | 0.5 | 858.09 |
Source(s): Table by authors
List of predicted HTL epitopes after toxicity analysis
| MHC Class II | Peptide sequence | SVM score | Prediction | Hydrophobicity | Hydropathicity | Hydrophilicity | Charge | Mol wt |
|---|---|---|---|---|---|---|---|---|
| HTL 1 | RGIKLFSWILENGM | −0.85 | Non-toxin | −0.02 | 0.24 | −0.35 | 1 | 1664.22 |
| HTL 2 | VLTLLFRLCWIWSA | −0.81 | Non-toxin | 0.2 | 1.66 | −1.31 | 1 | 1721.35 |
| HTL 3 | LTLLFRLCWIWSAV | −0.78 | Non-toxin | 0.2 | 1.66 | −1.31 | 1 | 1721.35 |
| HTL 4 | LLFRLCWIWSAVPA | −0.76 | Non-toxin | 0.19 | 1.45 | −1.19 | 1 | 1675.27 |
| HTL 5 | VLRKLFELCLQTTL | −0.75 | Non-toxin | −0.05 | 0.84 | −0.4 | 1 | 1677.3 |
| HTL 6 | ILSPFLYSIYINSL | −1.19 | Non-toxin | 0.21 | 1.26 | −1.2 | 0 | 1643.17 |
| HTL 7 | SPFLYSIYINSLPA | −1.28 | Non-toxin | 0.13 | 0.68 | −0.98 | 0 | 1585.03 |
| HTL 8 | FLYSIYINSLPA | −1.25 | Non-toxin | 0.23 | 1.39 | −1.26 | 0 | 1627.17 |
| HTL 9 | SQALFSRWSLWWPV | −1.13 | Non-toxin | 0.02 | 0.12 | −1.01 | 1 | 1763.23 |
| HTL 10 | ALFSRWSLWWPVVC | −0.92 | Non-toxin | 0.13 | 0.91 | −1.23 | 1 | 1750.29 |
| HTL 11 | FSRWSLWWPVVCQV | −0.81 | Non-toxin | 0.06 | 0.56 | −1.16 | 1 | 1793.32 |
| HTL 12 | FLDKIRPMSLTSTV | −0.95 | Non-toxin | −0.09 | 0.32 | −0.14 | 1 | 1608.13 |
Source(s): Table by authors
List of predicted B-CELL epitopes after toxicity analysis
| B-CELL epitopes | Peptide sequence | SVM score | Prediction | Hydrophobicity | Hydropathicity | Hydrophilicity | Charge | Mol wt |
|---|---|---|---|---|---|---|---|---|
| B-CELL 1 | VDRAIIWRALETYISPAL | −0.53 | Non-toxin | −0.02 | 0.52 | −0.32 | 0 | 2087.71 |
| B-CELL 2 | DRLAHLSTDAHVKQSVLA | −1.05 | Non-toxin | −0.17 | −0.13 | 0.08 | 1 | 1961.47 |
| B-CELL 3 | SDTTATIKRIRRNRTMSP | −0.32 | Non-toxin | −0.49 | −1.23 | 0.66 | 4 | 2104.66 |
| B-CELL 4 | ALYCRRRATWKQFCETLS | −0.1 | Non-toxin | −0.31 | −0.54 | −0.01 | 3 | 2232.85 |
| B-CELL 5 | LLFRLCWIWSAVPAAWCT | −0.27 | Non-toxin | 0.17 | 1.28 | −1.22 | 1 | 2136.86 |
| B-CELL 6 | LLGDSRTTTRGIKLFSWI | −0.53 | Non-toxin | −0.11 | 0.08 | −0.19 | 2 | 2064.69 |
| B-CELL 7 | NAWFWTDDLQALFDRREQ | −0.83 | Non-toxin | −0.28 | −1.07 | 0.1 | −2 | 2311.75 |
| B-CELL 8 | TRGIKLFSWILENGMTCW | −0.58 | Non-toxin | −0.01 | 0.2 | −0.56 | 1 | 2155.84 |
Source(s): Table by authors
List of predicted CTL epitopes after antigenicity analysis
| MHC Class I | Epitope | Antigenicity |
|---|---|---|
| CTL 6 | STAPDMDLL | Antigen |
| CTL 8 | ATTMANHL | Antigen |
Source(s): Table by authors
List of predicted HTL epitopes after antigenicity analysis
| MHC Class II | Epitope | Antigen |
|---|---|---|
| HTL 5 | VLRKLFELCLQTTL | Antigen |
| HTL 9 | SQALFSRWSLWWPV | Antigen |
Source(s): Table by authors
List of predicted B-CELL epitopes after antigenicity analysis
| B-CELL epitopes | Epitope | Antigenicity |
|---|---|---|
| B-CELL 6 | LLGDSRTTTRGIKLFSWI | Antigen |
Source(s): Table by authors
Final selection of CTL epitopes after IFN epitope analysis
| MHC Class I | Peptides | Length | Score |
|---|---|---|---|
| CTL 6 | STAPDMDLL | 9 | 0.0971443 |
| CTL 8 | ATTMANHL | 8 | 0.1735979 |
Source(s): Table by authors
Final selection of HTL epitopes after IFN-epitope analysis
| MHC Class II | Sequence | Method | Result | Score |
|---|---|---|---|---|
| HTL 5 | VLRKLFELCLQTTL | SVM | NEGATIVE | −0.079391402 |
| HTL 9 | SQALFSRWSLWWPV | SVM | NEGATIVE | −0.066486387 |
Source(s): Table by authors
List of predicted B-cell epitopes after IFN-epitope analysis
| B-CELL epitopes | Epitope | Method | Result | Score |
|---|---|---|---|---|
| B-CELL 6 | LLGDSRTTTRGIKLFSWI | SVM | POSITIVE | 0.9737392 |
Source(s): Table by authors
Author’s contribution: T.C. Venkateswarulu, Kalyani Dhusia and Abraham Peele Karlapudi and Vajiha designed the study and reviewed the manuscript. T.C.Venkateswarulu, Druthi Sri Meduri , Asra Tasneem Shaik, and Kalyani Dhusia analyzed the data and wrote the manuscript. Vajiha contributed by collecting the samples and performing the experiments. Druthi Sri Meduri , Asra Tasneem Shaik analyzed the results. All authors have read and approved the manuscript.
Conflict of interest: The authors declare that they have no conflict of interest in the publication.
Research involving human participants and/or animals: Not applicable.
Informed consent: Not applicable.
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Acknowledgements
The authors acknowledge the facilities supported by the Department of Science and Technology, the Government of India (FIST Project No: LSI-576/2013), and the Centre of Excellence, Department of Biotechnology, Vignan’s Foundation for Science, Technology and Research.