Databases And In Silico Tools For Vaccine Design
Novel in silico tools for designing peptide-based subunit vaccines and immunotherapeutics
Sandeep Kumar Dhanda, Bioinformatics Centre, Institute of Microbial Technology, Sector 39A, Chandigarh, India Search for other works by this author on: Bioinformatics Centre, Institute of Microbial Technology, Sector 39A, Chandigarh, India Search for other works by this author on: Bioinformatics Centre, Institute of Microbial Technology, Sector 39A, Chandigarh, India Search for other works by this author on: Bioinformatics Centre, Institute of Microbial Technology, Sector 39A, Chandigarh, India Search for other works by this author on: Bioinformatics Centre, Institute of Microbial Technology, Sector 39A, Chandigarh, India Search for other works by this author on: Bioinformatics Centre, Institute of Microbial Technology, Sector 39A, Chandigarh, India Corresponding author. Gajendra P.S. Raghava, Bioinformatics Centre, Institute of Microbial Technology, Sector 39A, Chandigarh 160036, India. Tel.: +91-172-2690557; Fax: +91-172-2690632; E-mail: raghava@imtech.res.in Search for other works by this author on:
Received:
30 November 2015
Revision received:
09 February 2016
Abstract
The conventional approach for designing vaccine against a particular disease involves stimulation of the immune system using the whole pathogen responsible for the disease. In the post-genomic era, a major challenge is to identify antigenic regions or epitopes that can stimulate different arms of the immune system. In the past two decades, numerous methods and databases have been developed for designing vaccine or immunotherapy against various pathogen-causing diseases. This review describes various computational resources important for designing subunit vaccines or epitope-based immunotherapy. First, different immunological databases are described that maintain epitopes, antigens and vaccine targets. This is followed by in silico tools used for predicting linear and conformational B-cell epitopes required for activating humoral immunity. Finally, information on T-cell epitope prediction methods is provided that includes indirect methods like prediction of Major Histocompatibility Complex and transporter-associated protein binders. Different studies for validating the predicted epitopes are also examined critically. This review enlists novel in silico resources and tools available for predicting humoral and cell-mediated immune potential. These predicted epitopes could be used for designing epitope-based vaccines or immunotherapy as they may activate the adaptive immunity. Authors emphasized the need to develop tools for the prediction of adjuvants to activate innate and adaptive immune system simultaneously. In addition, attention has also been given to novel prediction methods to predict general therapeutic properties of peptides like half-life, cytotoxicity and immune toxicity.
Introduction
The traditional approach to design a vaccine is a long and tedious process that involves the attenuation of the pathogen through sub-culturing followed by its administration. The administration of whole pathogen raises several safety issues and toxicity owing to the unwanted biomaterial. It has been shown in the past that a small peptide or antigenic regions can also activate different arms of the immune system [1]. Thus, peptide can also serve as a vaccine candidate, as well as can be used for designing immunotherapeutic. However, identification of peptides (or epitopes or antigenic regions) that can be used for immunization against a pathogen is one of the major challenges. Numerous tools have been developed in the past for identification of antigenic regions in pathogenic proteins or antigens. These computational tools are playing an important role in the field of immunoinformatics by virtue of their assistance in the screening of potential epitopes from a large number of possible epitopes. Figure 1 demonstrates the application of immunoinformatics in virtual screening of vaccine candidates in a pathogen. The numbers, shown in Figure 1, have been estimated from the whole proteome of Mycobacterium tuberculosis that contain nearly 4000 proteins with average length ∼350 amino acids. It has been shown that all proteins are not vaccine candidates; only around 200 proteins are potential vaccine candidates. These selected vaccine candidates may have some possible peptides, which may be further filtered based on their immunogenic and antigenic potential. Only potential epitopes, which can activate desired arm of the immune system, can be validated using experimental techniques.
Figure 1.
Computational approach for the identification of potential epitopes for designing peptide-based immune therapy or vaccine (e.g. Mycobacterium tuberculosis). A colour version of this figure is available at BIB online: https://academic.oup.com/bib.
Figure 1.
Computational approach for the identification of potential epitopes for designing peptide-based immune therapy or vaccine (e.g. Mycobacterium tuberculosis). A colour version of this figure is available at BIB online: https://academic.oup.com/bib.
This review summarizes the peptide-based novel in silico tools as well as tools developed in the past for the prediction of epitopes that can activate different arms of immune system. For better understanding of the field, these tools are categorized based on their function that includes immunological databases and epitope prediction tools. Finally, the review is concluded with an expert opinion where we have figured out the most commonly used tools in each category.
Repositories of immunological resources
In the past few decades, different research groups have created several databases across the world to serve the scientific community. These databases store different types of information related to the human immune system and pathogenic components responsible for generating the immune responses (Table 1). AntigenDB [7] maintains comprehensive information about experimentally validated antigens that includes structural and functional annotation of antigens. This database will be useful for designing protein- or antigen-based vaccine. PRRDb [12] contains pattern recognition receptors and their ligands; it is important for designing vaccine adjuvants. One of the largest repository of immunological epitopes is Immune Epitope Database (IEDB) [5], which maintains B-cell and T-cell epitopes including non-epitopes. In addition, there are databases that maintain interaction-related information, like antigen–antibody interactions (e.g. AgAbDb [13]) and Major Histocompatibility Complex–peptide (MHC–peptide) interactions (e.g. MPID-T2 [15]). In addition to generalized resources, disease-specific resources have also been developed in the past. These resources provide a comprehensive information required to design subunit vaccine against a specific disease, like MalVac [19], MycobacRV [8] and FungalRV [9]. One of the challenges in the field of immunological databases is to maintain and update information; numerous databases have becomes non-functional and outdated in the past two decades. Thus, it is imperative to use the most updated resources in research to increase the reliability of experiments.
Table 1.
List of databases or repositories developed for maintaining immunological resources
Table 1.
List of databases or repositories developed for maintaining immunological resources
In silico tools for humoral immunity
The humoral immune system recognizes antigenic determinants in pathogenic proteins to activate and generate memory B-cells. These antigenic regions are called B-cell epitopes and can be used to develop immunotherapy or vaccine against deadly pathogens [20–29]. Thus, prediction of these B-cell epitopes with high accuracy is one of the important challenges in the field of epitope-based immunotherapy. Broadly, B-cell epitopes are divided in two categories: continuous (or linear) and discontinuous (or conformational) [21]. Earlier methods were developed on a small data set and were based on various physiochemical properties of amino acids like Bcepred [22, 23]. In 2006, for the first time, a machine learning technique was exploited for developing a computational method to predict continuous B-cell epitopes [24]. Since then, a number of methods were developed using various machine learning techniques like ABCpred [25], BepiPred [26], LBtope [27], LBEEP [28] and APCpred [29]. In 2013, LBtope [27] has been developed to predict linear B-cell epitopes using a large number of experimentally validated B-cell epitopes and non-epitopes. Linear B cell exact epitope predictor (LBEEP), published in 2015, used dipeptide deviation from expected mean as an input feature for the identification of linear B-cell epitopes [28]. APCpred is another support vector machines (SVM)-based method, which was developed using the amino acid anchoring pair composition [29]. In addition to generalized methods, recently an organism-specific method B-cell-hepatitis C virus (HCV) has been developed to predict linear B-cell epitope in HCV [30].
Similarly, methods have also been developed to predict conformational B-cell epitopes in an antigen from its sequence like CBtope [31], or from antigen structure like DiscoTope [32], BEpro [33], etc. Epitopia can use either structure or sequence information for predicting conformation epitopes [34]. Table 2 summarizes different in silico tools developed for predicting linear and conformational B-cell epitopes.
Table 2.
in silico tools for predicting linear and conformational B-cell epitopes
Table 2.
in silico tools for predicting linear and conformational B-cell epitopes
Various research groups have used the B-cell epitope prediction tools for identifying epitopes in the organism for developing vaccine candidates. For example, Menezes-Souza et al. used the BepiPred 1.0 program for predicting the linear B-cell epitopes and found these to be a potential target for diagnosing tegumentary leishmaniasis as well as visceral leishmaniasis [44]. Likewise, Soni et al. used Ellipro and DiscoTope for predicting the linear and conformation B-cell epitopes in the virulence proteins LLO, ILO and SLO, coded by Listeria monocytogenes. They also predicted the antibody-specific epitopes using the server IgPred [45]. In one of the study done by Yang and Zhang group, ABCpred server and BepiPred server were used to predict and screen potential B-cell epitopes on spike glycoprotein of infectious bronchitis virus. They concluded with three potential epitopes on the basis of their antigenicity [46]. CBtope server was used to predict conformational B-cell epitope on collagen VII in the study done by Licarete and colleagues [47]. Experimental studies showed that predicted epitopes were localized within NC1 and NC2 domains and possibly in the hinge region of the antigen [47].
Prediction algorithms for CD8+ T-cell
Over the past three decades, a large number of methods have been developed to predict T-cell epitope. In 1987, based on sequential and structural analysis of T-cell epitopes, direct prediction method was developed [48]. One of the T-cell epitope predicting tools, AMPHI, is based on the hypothesis that T-cell epitopes form stable amphipathic structure [48–50]. Similarly, SOHHA is based on the hypothesis that a helix of 3–5 turn and a strip of hydrophobic residues at one side make T-cell epitopes. Above-mentioned methods are called direct methods, as they directly predict T-cell epitopes instead of MHC binders. In 1990s, structure of MHC–peptide complex was solved by X-ray crystallography, which changed the old concept of structure-based epitope prediction. It is clear now that the peptide bound to MHC has extended conformation [51] and cannot accommodate helix in its groove. This led to development of indirect methods, where MHC binder is predicted instead of T-cell epitope. It includes motif-based methods like EpiMer and Optimer [52]. The EpiMer method's performance was evaluated with the standard method of synthesizing short, overlapping peptides and testing them for their immunogenic reactions. The method was found to be better than AMPHI or other overlapping methods, as it requires synthesis of fewer peptides compared with other methods [53]. The prediction accuracy of both EpiMer and Optimer was found to be in between 60% and 70%. In the past two decades, instead of the T-cell epitope prediction, the scientific community was more curious toward developing methods to predict MHC binders because it provides more specificity. These methods can be classified in two categories: (i) MHC Class I binders (MHC-I) or CD8 T-cell epitopes for endogenous antigen processing and (ii) MHC Class II binders (MHC-II) or helper T-cell epitopes for exogenous antigen processing.
In the endogenous antigen processing, proteasome recognizes the foreign proteins or antigens and cleaves them into small fragments (peptides). Transporter-associated protein (TAP) then transports these fragments or peptides to the endoplasmic reticulum where the association between these peptides and MHC Class I molecules takes place. These antigenic peptides are presented to cytotoxic T lymphocyte (CTL) by MHC Class I molecules (Figure 2). In silico methods have been developed to predict proteasomal cleavage sites in a protein, for example, Pcleavage [54] and NetChop [55]. Pcleavage was developed using SVM technique, and its Matthews correlation coefficient value was found to be 0.54 and 0.43 on in vitro and MHC-ligand data, respectively. The SVM classifier in Pcleavage was able to recognize ∼82% of cleavage sites and ∼45% of non-cleavage sites. This performance was comparable with the NetChop, which was developed using neural network and was able to determine 65% of the cleavage site and 85% of the non-cleavage site correctly. To understand whether proteasomal fragments or peptides will bind to TAP, methods have been developed for predicting TAP binders [56, 57]. Similarly, numerous methods have been developed to predict MHC Class I binders that include ProPred-I, RANKPEP, etc. [58–63]. Propred-I server showed 80% accuracy in predicting MHC allele HLA-A*0201 and H2-Kb binders as well as non-binders. RANKPEP, which was developed using position-specific scoring matrix (PSSM) profile, was able to predict >80% of the epitopes among the top 2% of scoring peptides. NetMHCpan 2.0 server allows users to predict binding peptides for human leukocyte antigen A and B (HLA-A and HLA-B), as well as for chimpanzee, rhesus macaque, gorilla and mouse MHC Class I molecules [64]. NetMHC-4.0 is a sequence alignment-based MHC Class I prediction method, which incorporates the gapped sequence alignment and INDEL events, i.e. insertion and deletion information for predicting peptide-MHC Class I binding affinity [65]. Finally, the MHC-bound peptides are presented to the cell surface where selected peptides are recognized by CD8+ T-cells. To predict whether MHC-bound peptide will be recognized by CD8+ T-cells, direct methods, such as CTLpred, have been developed [66]. Different computational tools developed for predicting CTL epitopes have been listed in Table 3.
Figure 2.
Diagrammatic presentation of endogenous processing of antigens. A colour version of this figure is available at BIB online: https://academic.oup.com/bib.
Figure 2.
Diagrammatic presentation of endogenous processing of antigens. A colour version of this figure is available at BIB online: https://academic.oup.com/bib.
Table 3.
List of significant computational methods available for predicting MHC Class I binders and cytotoxic T-cell epitopes
Table 3.
List of significant computational methods available for predicting MHC Class I binders and cytotoxic T-cell epitopes
Experimental researchers have used the above-mentioned servers in their experiments and found them to be useful in carrying out their research and also validated them. For example, Simoneau et al. used Pcleavage server to predict the proteasomal cleavage site in the SHP-1 domain, which is expressed in the nuclei of intestinal epithelial cells and found that both the Tyr208and Ser591 residues are important for the SHP-1 proteolysis [70]. Similarly, Frahm et al. predicted the proteasomal cleavage site using NetChop method, which has been found to be highly predictive of C termini in HIV-1 epitope [71]. RANKPEP server was used by Mingozzi et al. for predicting T-cell epitopes and was found to be accurate [72]. Nuzzaci et al. used CTLpred for predicting CTL epitopes within a series of HCV-derived antigenic sequences [73]. They predicted three epitopes with high accuracy and found that the predicted epitopes were able to accurately discriminate between T-cell epitopes and non-epitope MHC binders and further validated them experimentally. In a similar study, Geluk et al. identified four peptides from Mycobacterium leprae having HLA binding motifs using ProPred as well as ProPred-I servers [74]. So these studies have shown that bioinformatics servers contribute significantly in developing potential vaccine candidates.
In silico tools for CD4+ T-cells
Exogenous antigen processing pathway has been shown in Figure 3, CD4+ T-cells or helper T-cell recognize antigenic regions or peptide in the form of MHC-II–peptide complex [75]. There is a fundamental difference between MHC-I and MHC-II binding groves; in case of MHC-I, the groove is closed from both sides, whereas it is open in case of MHC-II. Thus, prediction of MHC-II binders is much more difficult than the prediction of MHC-I binders [76]. During the past decade, efforts have been made in developing data-driven methods to predict MHC-II binders. In 1998, Brusic et al. have used an evolutionary algorithm and artificial neural network to predict MHC-II binders [77]. Later, many other algorithms like ant colony [78], hidden Markov models [79], SVM [80], as well as consensus methods integrating the output from two or more methods [81, 82], were proposed for MHC-II binding prediction. In the past, methods have also been developed to predict promiscuous MHC-II binders (which may bind more than one allele) like EpiDOCK, EpiTOP, MHC2Pred, etc. [83–85]. EpiDOCK method is structure based, and models are represented by docking score-based quantitative matrices. EpiTOP is the proteochemometrics-based server for T-cell epitope prediction. Proteochemometrics is a quantitative structure activity relationship (QSAR)-based approach developed by Lapinsh et al. [86]. ProPred is another method developed using quantitative matrix [87]. HLA-DR4Pred is a method for predicting MHC-II binding peptides with accuracy ∼86% and ∼78% in case of SVM and artificial neural network (ANN) models, respectively [88]. Recently, updated NetMHCIIpan 3.1 is a version of a quantitative method that predicts 9 amino acid long binding core of a peptide likely to bind human and mouse MHC Class II molecules [89].
Figure 3.
Diagrammatic presentation of exogenous processing of antigens. A colour version of this figure is available at BIB online: https://academic.oup.com/bib.
Figure 3.
Diagrammatic presentation of exogenous processing of antigens. A colour version of this figure is available at BIB online: https://academic.oup.com/bib.
All the above MHC-II binder prediction methods are indirect methods for predicting T-helper epitopes. Recently, a few direct methods have been developed to predict T-helper (e.g. Th1, Th2) epitopes like IFNepitope, IL4Pred [90, 91], etc. Another recent CD4+ T Cell epitope prediction method is PREDIVAC, which made a significant advancement over the previous methods, as its prediction accounts for 95% of the available MHC Class II protein variants [92]. All these methods are summarized in Table 4.
Table 4.
List of major computational tools commonly used for predicting MHC Class II binders and T-helper epitopes
Table 4.
List of major computational tools commonly used for predicting MHC Class II binders and T-helper epitopes
In human system, immune response initiation and regulation are achieved through recognition of HLA-II presented peptides. These peptides (HLA-II T-cell epitopes) can be targeted for the development of the vaccine. Complete viral proteome and individual antigen have to be studied in multiple spanning patterns to get HLA-II binding peptides. Because of the excessive polymorphism of HLA and pathogenic antigen variation, the time and cost of experimental screening methods would be high. In silico prediction tools have proven to be a complement to experimental studies in epitope mapping process. Mycobacterium. tuberculosis secretes an antigen MPT64 (Rv1980c), which is recognized by Th1 [94], and previous studies have shown that when mycobacterial protein/peptide antigen is associated with HLA-II, it is recognized by human Th1 [95, 96]. In one study, Rv1980 sequence was analyzed for binding to molecules expressed from 51 HLA-DR in ProPred and then predicted regions were experimentally evaluated for HLA-promiscuous Th1 cell reactivity [97]. The study concluded that MPT64 deserves consideration as a candidate vaccine and could have useful diagnostic applications. Similarly, Mustafa used EpiTOP for identifying HLA-promiscous peptide in Mycobacterium bovis BCG-vaccinated healthy subjects [98]. MULTIPRED2 has been used in several experimental studies [99–101] as for the identification of an HPV-16 tumor antigen from cancer biopsy specimen [100] and in the detection of influenza A epitope [101]. Consensus prediction approach, a service provided by IEDB, has been used in various experimental studies [102–106].
Discussion
Considerable progress has been made in the past few decades, as to develop novel in silico tools, which play an important role in designing immunotherapeutic peptide, directly or indirectly. Identification of suitable vaccine candidates, which can induce significant immune response using high-throughput screening, is a time-consuming and labor-intensive approach. The development of in silico tools not only expedites the drug discovery process but also saves the time and labor consumed in channelizing the whole process. One of the challenges in designing peptide-based therapeutics is their toxicity. It could be cytotoxicity or immune toxicity or hemolysis. Peptides are generally considered as good inducers of immune response, but their toxicity limit their approval from clinical trials. A few tools have been developed in the past to predict the toxicity of peptides. In 2014, Gupta et al. have developed a tool, Toxinpred, for predicting the toxic effects of peptides [107]. To address the issue of immune toxicity or allergenicity, researchers developed numerous tools that include Algpred [108], AllerHunter [109] and PREAL [110]. In addition, there are tools or resources that allow users to predict other side effects of peptides like Hemolytik [111] for hemolysis and AHTpin [112, 113] for hypertension. In silico tools like half-life prediction may help researchers in understanding stability of peptide-based vaccine candidates in body fluid [114]. Gosh et al. applied Algpred for assessment of biosafety in antifungal protein to explore its transgenic applications [115]. Similarly, capsid protein from Tobacco mosaic virus has been identified using computational tools and verified in wet laboratory experiments [116].
There is a pressing need to create a pipeline to run multiple algorithms using a single input point. These pipelines may join end to end or in parallel, but the final output should be provided in a single file or folder (Table 5), like Vacceed [117], NERVE [118], EpiToolKit [119]. EpIC is another pipeline that can predict immunogenecity in protein sequences [120]. Vaccine candidates against edwardsiellosis disease and tuberculosis predicted using NERVE pipeline has been validated in experimental studies [121, 122]. The use of Vacceed pipeline has been suggested for rational selection of peptides as candidates [123, 124]. Allard et al. described the immune escape mechanism of HIV-1 Rev epitope using EpiTookit [125].
Table 5.
Miscellaneous in silico tools and resources for designing immunotherapy
Table 5.
Miscellaneous in silico tools and resources for designing immunotherapy
In this review, our main focus is to cover freely available software packages for predicting immunogenic peptides that can stimulate immune system. These methods generally consider static aspect of immune system for designing a vaccine candidate. There is need to understand dynamics behavior of immune system that includes immune-cell populations and their interactions. Over the years, considerable efforts have been made to investigate dynamics of immune system using mathematical modeling, simulation methods and physical concepts [128–132]. Pappalatrado et al. developed an in silico model based on six ordinary differential equation for modeling antigen-specific CTL activation and their differentiation into memory cell on vaccination with dendritic cells [133]. It is able to predict the dimensions of immune response and contemplate the persistence of antigen-specific memory cells. Arianna Palladin et al. showed an integrated in vivo–in silico approach for cancer immunoprevention [134]. They developed an antigen-based mathematical model, SimTriplex, that integrates a genetic algorithm. The model is competitive with human-designed time-taking experiments and gives vaccination protocols against cancer. The whole coverage of immune dynamics is beyond the scope of this review.
Conclusion
The past two decades have seen a tremendous progress in the field of immunoinformatics, which reflects in the form of numerous resources developed in these years. These resources cover nearly all aspects required to design peptide-based immunotherapy. One of the challenges is to identify the best resource for each aspect; for example, user may be interested to know the best method for predicting linear B-cell epitope. To serve the scientific community, particularly experimental researchers, we summarize the best resource for predicting each therapeutic property of peptides (Figure 4). The list of currently functional tools has been compiled based on certain criteria that include prediction accuracy, user-friendliness and data set used for training. Here, we suggest LBtope for predicting linear B-cell epitope because it has been trained on largest data set of experimentally proven B-cell epitopes and non-B-cell epitopes. In case of epitope databases, we suggest IEDB, as it is the largest repository for experimentally verified positive as well as negative epitopes. Similarly, AntigenDB is recommended for designing antigen-based vaccines, as it provides detailed information about an antigen.
Figure 4.
List of in silico tools recommended for researchers working in the field of immunotherapy and vaccine. A colour version of this figure is available at BIB online: https://academic.oup.com/bib.
Figure 4.
List of in silico tools recommended for researchers working in the field of immunotherapy and vaccine. A colour version of this figure is available at BIB online: https://academic.oup.com/bib.
One of the major disadvantages of epitope-based vaccine over conventional approach is its inability to activate the innate immune system. Ideally, a vaccine candidate should have ability to stimulate both adaptive and innate arms of immune system simultaneously. Unfortunately, limited attempts have been made in the past for predicting vaccine targets that may activate innate immune system as well. The rational design of adjuvants largely banks on the search for ligands of innate immunity receptors. Though some molecules like CpG oligonucleotides, lipopeptides, synthetic chemokines, etc. are in clinical trials for their development as adjuvants, the area remains relatively unexplored owing to the limited number of systematic investigations. There is a need to develop peptide-based tools for adjuvant prediction, as we have tool for DNA-based adjuvants [135]. The field of rational vaccine adjuvant design holds a vast potential with respect to host defense peptides. These have been shown to activate the innate immune system and are increasingly being discovered more in numbers.
In case of B-cell epitope prediction, we could not achieve as good accuracy as we have achieved in the case of T-cell epitope prediction. The poor performance could be attributed to the conformational nature of the B-cell epitope. Therefore, there is a need to understand structural aspects of the epitopes before developing tools for the prediction. Alternatively, B-cell epitope algorithm can be improved through the development of species-specific tools like the prediction of B-cell epitopes that can raise antibody response in different animals like mice, rabbit, horse, human, etc. In case of prediction of T-cell epitopes, numerous tools from MHC Class I pathways have been developed, but MHC Class II pathway needs to be explored further. Only a handful of direct T-cell epitope prediction algorithms like PREDIVAC, IFNepitope and IL4pred have been developed so far. There is a great scope for further improvement and also development of new tools for predicting helper T-cell epitopes.
Immunoinformatics tools are required to cope with next-generation sequencing (NGS) data. NGS is based on parallel sequencing technology that generates millions of short reads in a short time at a low cost [136]. In the era of the NGS, size of databases consisting of whole genome of different pathogens is growing at an exponential rate. There is a need to develop computational tools or pipelines that can identify the best vaccine candidates for designing vaccine against a pathogen from its whole-genome sequence. As we are moving in the era of personalized medicine, we could not achieve our dreams as long as we do not have tools where comparison of epitopes from diseased and healthy sample can be achieved. We anticipate that with the advances in NGS technologies and in silico prediction, algorithms will expedite the drug discovery process and novel efficient immunotherapeutic agents will be discovered.
Key Points
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Tremendous progress has been made in the field of immunology over the years that led to development of highly accurate computational tools.
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Immunological databases are playing an important role in the field of immunoinformatics.
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Sequence-based methods are available for predicting linear or conformational B-cell epitopes in antigenic proteins.
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There is a need to develop tools for predicting vaccine adjuvants and peptide toxicity for designing better subunit vaccines.
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In silico tools or pipelines are required for predicting vaccine candidates against a pathogen from its whole genome sequence obtained from NGS.
Sandeep Kumar Dhanda has submitted his PhD thesis in the area of immunoinformatics. He has developed useful tools for the prediction of cytokine response for any peptide.
Salman Saddullah Usmani is a thesis graduate and is interested in understanding the nature of therapeutic peptides for designing better vaccine candidates.
Piyush Agrawal has joined PhD in 2014 and is currently working on the peptide proteins interactions.
Gandharva Nagpal is a PhD scholar interested in designing algorithms to predict the immunostimulation using oligonucleotides.
Ankur Gautam is a postdoc fellow in the laboratory, and he is working on the peptide-based therapeutics.
Gajendra P.S. Raghava is a chief scientist and head of the bioinformatics centre. He has developed several open-source tools to address the diverse problems in biology.
Acknowledgments
The authors are thankful to the Indian Council of Medical Research (ICMR), Department of Biotechnology (DBT), Department of Science and Technology (DST) and Council of Scientific and Industrial Research (CSIR), Government of India for financial support and fellowships.
Funding
Council of scientific and Industrial Research (CSIR) [project: Open Source Drug Discovery and GENESIS BSC0121]; Department of Biotechnology [project: BTISNET], Government of India.
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Databases And In Silico Tools For Vaccine Design
Source: https://academic.oup.com/bib/article/18/3/467/2562752
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