key: cord-0900902-aplgf3qy
authors: Bukhari, Syed Nisar Hussain; Jain, Amit; Haq, Ehtishamul; Mehbodniya, Abolfazl; Webber, Julian
title: Machine Learning Techniques for the Prediction of B-Cell and T-Cell Epitopes as Potential Vaccine Targets with a Specific Focus on SARS-CoV-2 Pathogen: A Review
date: 2022-01-24
journal: Pathogens
DOI: 10.3390/pathogens11020146
sha: d79d3a8ffa4efa4c87bcdda67e970578109309d2
doc_id: 900902
cord_uid: aplgf3qy

The only part of an antigen (a protein molecule found on the surface of a pathogen) that is composed of epitopes specific to T and B cells is recognized by the human immune system (HIS). Identification of epitopes is considered critical for designing an epitope-based peptide vaccine (EBPV). Although there are a number of vaccine types, EBPVs have received less attention thus far. It is important to mention that EBPVs have a great deal of untapped potential for boosting vaccination safety—they are less expensive and take a short time to produce. Thus, in order to quickly contain global pandemics such as the ongoing outbreak of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), as well as epidemics and endemics, EBPVs are considered promising vaccine types. The high mutation rate of SARS-CoV-2 has posed a great challenge to public health worldwide because either the composition of existing vaccines has to be changed or a new vaccine has to be developed to protect against its different variants. In such scenarios, time being the critical factor, EBPVs can be a promising alternative. To design an effective and viable EBPV against different strains of a pathogen, it is important to identify the putative T- and B-cell epitopes. Using the wet-lab experimental approach to identify these epitopes is time-consuming and costly because the experimental screening of a vast number of potential epitope candidates is required. Fortunately, various available machine learning (ML)-based prediction methods have reduced the burden related to the epitope mapping process by decreasing the potential epitope candidate list for experimental trials. Moreover, these methods are also cost-effective, scalable, and fast. This paper presents a systematic review of various state-of-the-art and relevant ML-based methods and tools for predicting T- and B-cell epitopes. Special emphasis is placed on highlighting and analyzing various models for predicting epitopes of SARS-CoV-2, the causative agent of COVID-19. Based on the various methods and tools discussed, future research directions for epitope prediction are presented.

An antigenic determinant (AD) is a portion of an antigen molecule known as an epitope that is recognized by the human immune system, specifically by antibodies or T and B cells [1] . Recognition of epitopes is considered important in EBPV design to contain pandemics, epidemics, and endemics due to the outbreak of infectious diseases. The ongoing COVID-19 pandemic due to the SARS-CoV-2 outbreak is the latest among the

An antigen is any substance that causes the immune system to produce antibodies against it. Its molecules are large biological polymers and introduce various molecular attributes that act as interaction sites between antibodies, T H cells and B cells, and antigen molecules. These interaction sites are called epitopes [10] [11] [12] . Epitopes are of two types: Bcell epitopes (BCEs) and T-cell epitopes (TCEs). The fragment of an antigen that is attached to an antibody is called the B-cell epitope [13] . The BCEs are recognized by B cells and comprise a solvent region that is exposed to an antigen. On the other hand, T cells have a receptor on their surface, known as the T-cell receptor (TCR) [13] . When presented on the surfaces of APCs that are linked to MHC molecules, the TCR aids in antigen recognition. TCEs identified by CD8 and CD4 T cells are represented by MHC class I (MHC I) and class II (MHC II) molecules, respectively [13] . Figure 1 shows an antibody containing two paratopes, indicating that these two paratopes can bind to two pathogens [14, 15] . Chemical interactions between epitopes and paratopes that promote antigen-antibody binding are non-covalent [16] [17] [18] .

The identification of epitopes is of great importance for many reasons, including EBPV design, antibody production, and immunodiagnostic tests. They also play a crucial role in activating the human immune system. Among the reasons listed, EBPV design is important for researchers, biologists, and scientists because there are numerous drawbacks to using whole-organism vaccines, particularly in immunocompromised patients [19, 20] . EBPVs can be utilized to overcome the issues associated with heterogeneous and multicomponent vaccines and are seen as an alternative to traditional vaccines. They can act as powerful alternatives to conventional vaccines due to their low production cost, having less reactogenic and allergenic responses. A well-trained ML model of experimentally determined epitopes and non-epitopes can identify potential epitopes as vaccine candidates quickly and efficiently and can reduce the burden related to the epitope mapping process by decreasing the potential epitope candidate list for experimental trials. Using the wet-lab experimental approach to identify these epitopes is time-consuming and costly because the experimental screening of a vast number of potential epitope candidates is required. However, epitope prediction methods based on ML can prove to be cost-effective, scalable, and fast. The most recent vaccine technology is based on RNA vaccines, which have the distinct advantage of being simple to design and manufacture. Epitopes are critical, but often overlooked, for boosting the effectiveness of RNA vaccines. Although RNA vaccines can encode any gene of interest, even the most recent designs commonly encode sequences of original genes from the natural virus. Epitope prediction can be useful in assisting RNA vaccine design by guiding the sequence design and vaccine structure. RNA (mRNA) vaccines, on the other hand, can benefit from epitope-based design approaches, in which both B-cell and T-cell epitopes can be used for vaccine design. The epitope properties determine whether or not the RNA vaccine will elicit an immune response and which types of responses will be elicited. The subsequent sections will provide a systematic review of various state-of-the-art and relevant ML-based methods and tools developed for predicting TCEs and BCEs in general, with an emphasis on predicting epitopes for SARS-CoV-2. Based on the various state-of-the-art machine learning methods and tools discussed, future research directions for the prediction of epitopes are presented.

The main motivations behind this review are as follows:

1.

To highlight the work done in T-and B-cell epitope prediction using ML, along with the strengths and limitations of the existing ML methods and tools, with the aim of promoting the EBPV design approach as this approach has received less attention so far. This will also stimulate continuing research efforts for designing an EBPV.

With the increase in data related to antigenic determinants (TCEs and BCEs) and advances in immunoinformatics, the scientific community is overwhelmed.

To provide future directions in terms of taking advantage of ensemble ML and exploring additional physicochemical properties of amino acids, and to use other confusion matrix-based performance metrics apart from accuracy and area under the curve (AUC) for designing an effective EBPV.

ML is concerned with the automated learning of machines that is not explicitly programmed. It focuses on making data-driven predictions and has several applications in bioinformatics [21] . Bioinformatics deals with applying computational techniques to derive knowledge from biological data. It covers the collection, retrieval, storage, manipulation, and data modeling for analysis or prediction using various algorithms and software [21] . Earlier, one had to explicitly program bioinformatics algorithms, which was an extremely laborious task for predicting protein structures [21] . However, with the advent of ML algorithms, such problems have become much easier to solve. In recent years, the exponential growth of T-and B-cell epitope data has become the primary motivation for researchers to develop ML-based methods for the prediction of ADs or IRDs, i.e., B-and T-cell epitopes. ML applied to experimentally determined peptide sequence data of pathogens (virus, bacteria, etc.) opens up new frontiers for areas such as EBPV design, antibody production, and immunodiagnostic tests. The ML-based in silico approach has emerged as a promising field for epitope prediction [22] . Accordingly, various ML-based studies and methods exist that utilize the physicochemical properties of amino acids as features or descriptors for the prediction of epitopes. Table 1 summarizes these studies, along with our opinions in terms of their strengths and limitations. In [24] , potential epitope-based vaccine candidates were explored. After retrieving 600 genome sequences of SARS-CoV-2 from the ViPR repository, CD8+ and CD4+ epitopes and B-cell (linear) epitopes were generated and screened for immunogenicity, antigenicity, and non-allergenicity.

The results of [25] reported 19 candidate T-cell epitopes (CD8+), which were found to overlap strongly with 8 B-cell epitopes. The results provide the basis for an experimental design for a suitable peptide vaccine against SARS-CoV-2.

R. Moody et al. [26] Authors used IEDB prediction tools for predicting B-cell epitopes and those with high scores in terms of prediction were selected as candidate epitopes. The epitopes were then matched to human proteins using NCBI Blast technology.

The findings showed eleven (11) novel B-cell epitopes in the host that were capable of explaining key elements of COVID-19 extrapulmonary disease that previous research had not been able to explain.

Jespersen MC et al. [27] The authors employed feedforward neural networks (FFNN) with two hidden layers, each with 25 neurons, an activation function (sigmoid) at all neurons, and an ADAM as an optimizing function to predict antibody-specific epitopes (B cell) or epitope targets of provided cognate antibodies. The dataset was obtained from the IEDB database. PCA was used for dimensionality reduction before the model was trained.

It was shown that a simple set of attributes retrieved from the cognate antibody boosted the rate of accuracy in predicting individual epitopes. Furthermore, sophisticated features such as Zernike Moments can improve the model's predictive potential. When compared to DiscoTope 2.0, this model performs better in finding patches overlapping with an actual patch of an epitope in cross-validation and on an independent dataset.

Ling-yun Liu et al. [28] The authors used PCA and RNN networks. They converted the physicochemical properties into digital vectors, intending to have high-dimensional feature space, and later PCA was applied to process them. The output from PCA was used as an input to the RNN for predicting epitopes.

Prediction results obtained by this process demonstrated that PCA reduced dimensions, but at the same time, original features of the main component were retained, and the rate of prediction was also improved. 

Bin Cheng et al. [29] Authors introduced a novel scale to measure feature importance, called the relevance of amino acid pair (RAAP). RAAP was calculated by decomposing the sequences of amino acids based on their physicochemical properties.

The successful prediction rate was drastically improved here by using LSTM. It does not suffer from gradient instability and is good enough for textual classification sequences. Fivefold cross-validation was used to test and validate the models.

Balachandran Manavalan et al. [30] Here, a non-redundant dataset was constructed containing 5500 BCEs experimentally validated, and 6893 non-B-cell epitopes were retrieved from IEDB. Then, an ensemble model to predict B-cell epitopes based on ERT (extremely randomized tree) and a classifier called GB (gradient boosting) was developed. The model works based on the physicochemical properties, AA composition, and combination of dipeptides and PCP as the input features.

After performing cross-validation on a benchmark dataset, it was shown that this model performed far better than the individual classifiers such as ERT and GB, with an MCC (Matthews correlation coefficient) of 0.454.

A cost-sensitive strategy based on bagging MDT was suggested, which integrates two ensemble-based learning algorithms. Without employing the prediction of a pre-trained single predictor, it makes it independent of multiple prediction tools. It can also learn a meta-classification architecture with varied data, without being constrained by a particular hierarchy.

It was demonstrated that the performance of prediction is superior as compared to a single epitope predictor. However, epitope prediction based on meta-learning is purely dependent upon the predictive strength of various other pre-trained linear and conformational epitope prediction tools, which cannot be retained directly by users. Hence, this limits the flexibility and applicability of these meta-classifiers.

Jing Ren et al. [32] The authors proposed a novel staged heterogeneity-based learning model. The model learns both heterogeneity and characteristics of data in a phased manner to identify residue of antigens of conformational B-cell type epitopes that are heterogeneous, purely based on sequences of antigens. In the first stage, the model is made to learn the generic epitope pattern with propensities, and in the second stage, the same model is made to learn the complementarity of the propensities used in the first stage, which is heterogeneous but this time on a small dataset of experimentally verified epitopes.

It was demonstrated that if heterogeneity was learned well, the transferability of the model improved remarkably in handling new data.It was tested and validated on two different datasets: one with epitopes determined experimentally and another with computationally defined. It showed outstanding performance that was around twice that of existing predictors, including CBTOPE.

Georgios A. et al. [33] A novel method, "SEPIa", has been proposed here to predict B-cell epitopes from protein sequences and is sufficiently faster, and it can also be applied to large-scale datasets. The model is the combination of two classifiers, random forest and naïve Bayes algorithm.

The average prediction accuracy of SEPIa is limited. The AUC score is 0.65 in both 10-fold cross-validation and on the independent test dataset, which is higher than other approaches tested on the same test dataset.

Gene Sher et al. [25] Authors proposed a novel, analytically trained DREEP (Deep Ridge Regressed Epitope Predictor) based on string kernels using a deep neural network tailored to predict continuous epitopes.

The model was tested with input as long sequences of proteins from datasets such as AntiJen, Pellequer, and HIV. The results were compared with epitope predictors such as DMNLBE, LBtope, etc. Using the area under the curve (AUC) metric, the model achieved performance improvements over SARS by 13.7%, HIV by 8.9%, and Pellequer by 1.5%. 

Wen Zhang et al. [34] Authors attempted to differentiate immunogenic epitopes from non-immunogenic epitopes based purely on their primary structure. To effectively utilize various features, an ensemble method based on a genetic algorithm was proposed.

The model was tested on two benchmark datasets: IMMA2, PAAQD. The model was compared with methods such as POPI, PAAQD, and POPISK, which are considered state-of-the-art in nature. The model performed better, with an AUC score on IMMA2 of 0.846 and 0.829 on PAAQD.

Wei Zheng et al. [35] The authors used ensemble learning to improve the prediction of BCEs. Their ensemble method combined twelve SVMs. To handle imbalanced datasets, resampling and AdaBoost methods were used.

The proposed ensemble model achieved an AUC score of 0.642-0.672 on the training dataset with five-fold cross-validation and an AUC score of 0.579-0.604 on the test dataset.

Jian Zhang et al. [36] To predict antigenic determinants, the authors devised a cost-sensitive ensemble approach, and a spatial clustering-based algorithm was used to identify probable epitopes.

The model performed admirably in terms of prediction. AUC scores of 0.721 and 0.703 were obtained using leave-one-out cross-validation (LOOCV) on two benchmark datasets: bound and unbound.

Kavitha K V et al. [37] PCA was used to reduce dimensions and to filter out the essential features; for prediction purposes, a random forest algorithm was used.

Experimental results showed that the random forest-based classifier had an improved prediction accuracy rate as compared to BCPred, AAP, etc.

Wen Zhang et al. [38] The authors used sequence-derived features and developed an ensemble model based on random forest to predict epitopes accurately.

The model was evaluated using the leave-one-out cross-validation procedure, and an AUC score of 0.687 and 0.651 on bound and unbound datasets was obtained.

Ping Chen et al. [39] Authors reviewed various prediction models for epitopes, such as models based on SVM, neural network, random forest, etc., to defend computational approaches in the prediction of epitopes as in silico methods require a lot of effort and time.

Apart from defending the computational approaches, it was also concluded that there is a limitation to current models as it is impossible to devise an exact model without having complete knowledge of the immune system, and current models are simply best at approximation.

Claus Lundegaard et al. [40] Here, an artificial neural network was used. The standard feedforward neural network with backpropagation was employed to predict epitopes.

The dataset was retrieved from the SYFPEITHI database.

The model efficiently and accurately predicts MHC class I type peptides and outperforms the existing methods.

The specific regions of proteins responsible for triggering an immune response mediated by B or T cells are known as epitopes. As epitopes are central to the EBPV design process, the use of computational techniques to predict them is urgently needed. In the following sub-sections, we discuss the tools being used for the prediction of T-and Bcell epitopes.

The primary basis for T-cell epitope prediction is peptide-MHC binding prediction. A number of tools and methodologies for predicting T-cell epitopes have been developed and are freely available online. We hereby provide a categorized review of these tools based on the methods they use for prediction. The methods used are structure-based (SB), motif matrix (MM), sequence motif (SM), quantitative affinity matrix (QAM), artificial neural network (ANN), support vector machine (SVM), the quantitative structure-activity relationship model (QSAR), and combined (using QAM and ANN). All these tools have been illustrated in Table 2 . For each tool, we have mentioned the URL and which class of MHC binding prediction is supported (class I or II or both). As shown in Table 2 , these tools only assess a peptide's binding capability. It is still difficult for these methods to estimate deterministically whether a given peptide is an epitope or not. CTLpred [41] , one of the servers, works in this category; however, it is limited to peptides with a length of up to 9 mers only. However, the benefit of using ML algorithms for epitope prediction for the methods illustrated in Table 2 is that they address two distinct problems: the differentiation of MHC binders from non-binders and the prediction of the binding affinity of a peptide to MHC molecules. The first issue has been addressed by using classifiers such as ANNs, SVMs, decision trees (DT), and Hidden Markov models (HMMs). All of these classifiers have been trained on data containing peptides that have or do not have binding affinity to the MHC molecule. ML classifiers were developed on a dataset of peptides with an affinity to the MHC molecule to solve the second problem, i.e., binding affinity prediction. Here, SVMs and ANNs have been used to first predict affinity for MHC I and then for MHC II molecules. However, when using the MHC binding model to predict T-cell epitopes, difficulty arises due to MHC polymorphism [42] . To address this, pan MHC-specific models were created by training ANNs on data containing MHC residues [43] . Furthermore, it has been established that combining different approaches and providing a consensus prediction improves peptide-MHC prediction [44] . 

MotifScan [53] www.hiv.lanl.gov/content/immunology/ motif_scan/motif_scan, accessed on 10 December 2021 Both X ---

EpiJen [54] ddg-pharmfac.net/epijen/EpiJen/EpiJen.htm, accessed on 10 December 2021 I -X X X Propred [55] imtech.res.in/raghava/propred/, accessed on 10 December 2021 II X X -- It is also illustrated in Table 2 whether the tools provide a prediction of supertypes-S, quantitative binding affinity-A, proteasomal cleavage-P, and TAP binding-T. These are denoted by a cross (X) in an affirmative case.

The goal of predicting BCEs is to make it easier to identify a BCE for antigen replacement in an antibody production process. BCEs are classified into two types: conformational and linear. As shown in Figure 2 , linear BCEs are composed of consecutive peptides and residues. Conformational ones, on the other hand, are formed of patches of solvent-exposed atoms from non-sequential residues. As a result, conformational and linear BCEs are also known as discontinuous and continuous BCEs. Only a few native antigens have linear BCEs, while approximately 90% of BCEs are conformational [73] . There are a number of tools and methods developed to predict B-cell epitopes and many are available online and free to use. In this review, we have categorized these tools based on the type of epitope they predict (linear or conformational), as illustrated in Table 3 . Regarding Linear BCEs, although being in the minority, their prediction has received more attention. A few existing bioinformatics-based tools, such as PEOPLE [75] and PRED-ITOP [89] for BCE prediction, make use of propensity scales. The tool PREDITOP [89] is based on a multi-parametric method using the accessibility, hydrophilicity, and flexibility properties of amino acids. On the other hand, PEOPLE [75] is also based on these parameters but includes the assessment of β-turns. However, in [90] , by Blythe and Flower, it has been shown that the amino acid propensity scale is unreliable for predicting epitope location.

The unreliability issue in predicting BCEs due to amino acid scales has been mitigated using ML algorithms. To differentiate BCEs from non-epitopes, ML algorithms have been trained on feature vectors extracted from BCEs. A few methods, as illustrated in Table 3 , based on ML include ABCpred [79] , BCPREDS [78] , LBtope [76] , SVMtrip [77] , and BepiPred [74] . It has been reported that methods based on ML techniques outperform the techniques based on amino acid scales [91] . Conformational BCEs constitute the majority portion; however, their prediction is lagging behind that of linear types due to two main reasons. Firstly, their prediction necessitates knowledge of the 3D protein structure. Only a limited percentage of proteins have 3D information [92] . Secondly, extracting conformational epitopes for specific antibody synthesis from a protein context is a difficult process that requires the use of appropriate scaffolds for epitope grafting. Therefore, their prediction thus far is of less relevance for EBPV design. The methods and tools listed in Table 3 for the prediction of conformational BCEs identify only generic antigenic areas, ignoring antibodies, which are typically overlooked [93] . As previously stated, these approaches require knowledge of an antigen's 3D structure. Ansari and Raghava [94] proposed a model termed "CBTOPE" to predict these epitopes using an antigen's primary sequences. The model has been developed using SVM, utilizing sequence-derived and physicochemical properties of epitopes. Using cross-validation techniques, the CBTOPE model achieved an accuracy rate of 86.6%.

Coronaviruses belong to the family Coronaviridae, the enveloped viruses having a large single-stranded RNA genome whose length ranges from 26 to 32 kilobases [95] . In [96] , by Lineburg and colleagues, it has been found that, among 26 viral proteins of SARS-CoV-2, a few proteins on its surface, such as the spike protein (S), are more variable, while others are more conserved and internal, such as the nucleocapsid protein (N). It has been found that the spike protein (S) is responsible for activating cytotoxic CD8+ T cells and hence is considered an ideal vaccine target.

The infection caused by SARS-CoV-2 elicits both adaptive and innate arms of immunity [97] . In general, antigen-presenting cells recognize viruses. Once T-cell activation happens, CD4+ T cells mainly differentiate into effector cells, which produce cytokines and chemokines; cytotoxic CD8+ T cells, on the other hand, are key players in the immune response to viral infection, as they participate directly in viral clearance [98] . It has been demonstrated that T cells, apart from targeting the structural proteins of coronaviruses, are also responsible for lung immunopathological damage due to SARS-CoV and MERS-CoV [99, 100] . Thus, in the case of SARS-CoV-2, the major focus has been on identifying viral T-cell epitopes presented on human leukocyte antigens (HLA) [101, 102] . Therefore, the focus of this review in the case of SARS-CoV-2 is the prediction of TCEs.

According to the literature review, authors started using ML methods reasonably quickly, as soon as the initial genome sequences of SARS-CoV-2 became public in early 2020, to recommend T-cell epitopes as potential vaccine candidates for SARS-CoV-2 [103] . The existing methods based on ML that have been utilized can predict either CD8+ or CD4+ T-cell epitopes and are listed in Table 4 . A few techniques listed in Table 4 have "pan" as a suffix, which indicates an ability to predict the binding of HLA peptides for a huge collection of the alleles inside a particular HLA type, including those not present in the training dataset [111] . A few studies have also used algorithms specific to HLA-I, namely Net_Chop [113] and NetCTL1.2 [114] , where extra-and intracellular variables responsible for the presentation of HLA antigens were integrated to improve the prediction accuracy of the binding of peptide HLA. The methods NetCTL-1.2 [114] and NetChop [113] have also been utilized in a few studies, where extraand intracellular variables have been integrated, which are responsible for presenting HLA antigens. It is essential to mention here that almost all modern T-cell epitope prediction systems use ANNs. A few early ones (such as RANKPEP [115] and CTLPred [41] ) used a different ML approach, support vector machines (SVM). The spike proteins in the original virus bind to the ACE2 receptor on human cells. It has been reported in [116] that the D614G mutation alters the genetic code of the spike protein of SARS-CoV-2, where a change in a single amino acid takes place, and most of the COVID-19 vaccines are based on this spike protein. Due to this mutation, the virus spreads faster and the spikes become more stable than those in the original virus. As a result, more functional spikes are available to bind to ACE2 receptors, making the virus more infectious. Crooke et al. [117] developed a computational model using various open-source algorithms and web-based tools to analyze the SARS-CoV-2 proteome so as to identify antigenic and putative T-cell and B-cell epitopes as potential vaccine targets. After using a set of stringent selection criteria to filter out the peptide epitopes, the study discovered 41 T-cell epitopes (5 HLA class I, 36 HLA class II) and six B-cell epitopes that have the potential to serve as primary targets for epitope-based peptide vaccine development against SARS-CoV-2.

By now, it is clear that the key to designing an EBPV is the identification of BCEs and TCEs [118, 119] . Several studies have been performed to predict BCEs and TCEs, as illustrated in Table 1 . For each study, we have mentioned our opinions in terms of their strengths and limitations. Apart from these studies, several tools and methods are available online for free to predict B-and T-cell epitopes, as illustrated in Tables 2 and 3 . The methods used to predict SARS-CoV-2 epitopes are listed in Table 4 ; again, these predict only the peptide-binding capacity. This is a limitation with these methods; instead of predicting the binding capability of a peptide, predicting epitopes deterministically is desired. Because viruses continue to mutate, as with SARS-CoV-2, existing vaccines may prove to be somewhat less effective against new variants. Either the vaccine's composition has to be changed or a new vaccine needs to be developed to protect against these variants [120] . Time being the critical factor, EBPVs can be a great solution. Based on the research conducted, EBPVs are highly recommended vaccines and should be considered in the quest for the rapid development of protective vaccines. Below, we mention the future research directions for epitope prediction as predicting epitopes is a sensitive task and needs due attention in order to improve it.

The majority of current state-of-the-art approaches estimate a peptide's binding capability. These approaches struggle to predict deterministically whether a given peptide is an epitope or not. CTLpred [41] , one of the servers, operates in this category; however, it is limited to peptides that are up to 9 mers in length. To circumvent the limitations of the previous approaches, a direct method of predicting epitopes is sought. Furthermore, the technique should be capable of predicting variable-length peptides with a length greater than 9 mers. 2.

Current state-of-the-art ML epitope prediction approaches rely heavily on just a few classifiers, including ANNs, SVMs, and Hidden Markov models (HMM) [121] .

There are other robust classifiers available that can be utilized to achieve even more promising results, including decision trees (DT), random forest (RF), convolutional neural networks (CNNs), and AdaBoost [122] . In the literature surveyed, ANN-based models constitute the majority of the epitope prediction methods. However, relying on ANNs only is not safe. ANNs suffer from a hardware dependency as they require processors with parallel processing power in accordance with their structure [123] . Because epitope prediction is such a delicate task, the ANN's behavior is occasionally unexplainable. When an ANN generates a probing solution, it does not explain why or how it was generated, which reduces the trust in the network [123] . However, to have high-performing models and robust models for applications such as the healthcare domain, explainable ML can be explored, which is in its initial stage and remains an open issue [124] . Gagniuc et al. have proposed a spectral-based forecast model as an alternative to the classical ANN. In their experiment, the ANN categorized the collection of data fairly but failed to reveal any useful information about the evolution of a subject over time. In this regard, forecasts based on Markov chains or traditional statistical methodologies have produced more trustworthy outcomes in the biology and medicine domains. The proposed novel method of analysis based on spectral forecasts outperformed the classical ANNs [125] .

Moreover, instead of relying on predictions by a single model, we can combine several robust classifiers, called an ensemble model. Ensemble learning (EL) is a powerful technique for boosting the model accuracy by combining a number of base classifiers [126] . Such a technique has considerably better generalization capability than its individual counterparts. Indeed, EL is appealing because it can elevate weak learners (also known as base classifiers), which are marginally better than random guesses, to strong learners, which can make accurate forecasts [127] . The base classifiers vote for a new data instance, and, based on the majority of votes, a class label is returned. An ensemble model can be created by training homogeneous base models on different subsets of the training set or heterogeneous base models using the same training dataset. The main three types of ensembling techniques are bagging, boosting, and stacking. Multiple base learners (homogenous) can be integrated in bagging using different sub-samples from the same dataset [128] . The final prediction is obtained by taking the average prediction from multiple base learners. In boosting, base learners are added sequentially, and the predictions reported by previous learners are corrected. The final output is decided by taking the weighted average of all the predictions [128] . On the other hand, stacking involves fitting heterogeneous base learners on the same dataset [128] and then using another learner to learn how to best combine all the predictions. Moreover, while dealing with complex data, such as high-dimensional, imbalanced, noisy data, etc., traditional ML algorithms may fail to produce satisfactory results. The reason for this is that, for these methods, it is difficult to capture various attributes and the underlying layout of the data. Ensemble learning aims to combine data modeling, data fusion, and data mining into a cohesive framework [129] To conclude, the main reasons for employing ensemble learning in epitope prediction are as follows: 

In the literature surveyed, not all physicochemical properties of amino acids have been utilized to extract features from peptide sequences. To have a robust epitope prediction system in place, additional physicochemical properties need to be explored [131, 132] . 5.

The existing ML-based methods for epitope prediction have been assessed using metrics such as accuracy and area under the curve (AUC). However, other confusion matrix-based performance metrics such as Gini, specificity, sensitivity, F-score, kappa, Matthews correlation coefficient (MCC), and precision, etc., can be utilized to analyze the performance of the model in a better way.

Prediction of T-and B-cell epitopes can play a game-changing role in the EBPV design process, as well as in disease diagnosis. In this study, a review of various existing studies for epitope prediction has been provided. Moreover, a review has been provided for the state of-the-art ML-based tools that are available online and free to use for researchers working in vaccine design. The COVID-19 pandemic, caused by the SARS-VoV-2 virus, has resulted in a dramatic loss of human life worldwide and poses an unprecedented challenge to public health, food systems, and the workplace [133] . Accordingly, a special emphasis has been placed on highlighting and analyzing various ML-based methods that have been proposed and used for predicting epitopes of SARS-CoV-2 for EPBV design in order to contain the COVID-19 pandemic. However, it is important to mention here that the application of epitope prediction tools/methods to SARS-CoV-2 presented in this review is not satisfactorily developed, and only a few them have been applied for SARS-CoV-2 epitope prediction. Another reason to place special emphasis on SARS-CoV-2 is that the EPBV design approach seems to be a promising alternative in order to quickly design new vaccines against different variants of the virus as it continues to mutate [134] . Based on the various state-of-the-art ML methods discussed, future research directions for epitope prediction have been presented. From the literature reviewed, it has been observed that focus has been given to peptide-binding capability prediction instead of deterministically predicting whether a peptide is an epitope or not. In addition, the majority of the MLbased prediction models are based on a single classifier. However, instead of relying on a single model, several robust classifiers can be combined into an ensemble model in order to enhance the epitope prediction accuracy. To conclude, it is important to mention that the prediction of T-cell epitopes is much more reliable and advanced as compared to the prediction of B-cell epitopes. Moreover, if epitopes are predicted efficiently using computational approaches (ML-based methods), they can be used as futuristic vaccine candidates with fewer side effects compared to conventional vaccine designs subjected to in vitro and in vivo scientific assessments. The technology developed would help the broad scientific community working in vaccine development to save time in screening the active epitope candidates against the inactive ones. In conclusion, it is relevant to provide a review of the existing ML-based state-of-the-art methods for TCE and BCE prediction because EBPVs have significant potential and should be considered in the quest for the rapid development of a protective vaccine against a pathogen, specifically for SARS-CoV-2, as there is a strong likelihood that the virus will mutate further. This will also stimulate continuing research efforts for the EBPV design process. 

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Antibody-protein binding and conformational changes: Identifying allosteric signalling pathways to engineer a better effector response

An Introduction to Antibodies: Antibody-Antigen Interaction

SARS vaccines: Where are we?

The outbreak of SARS-CoV-2 pneumonia calls for viral vaccines

A New Ensemble Learning Framework for Improved Linear B-Cell Epitope Prediction

An Approach for a Synthetic CTL Vaccine Design against Zika Flavivirus Using Class I and Class II Epitopes Identified by Computer Modeling. Front

Deep learning methods improve linear B-cell epitope prediction

Immunoinformatics prediction of overlapping CD8 + T-cell, IFN-γ and IL-4 inducer CD4 + T-cell and linear B-cell epitopes based vaccines against COVID-19 (SARS-CoV-2)

Predicted B Cell Epitopes Highlight the Potential for COVID-19 to Drive Self-Reactive Immunity

Prediction of Linear B-cell Epitopes Based on PCA and RNN Network

Prediction of Continuous B-cell Epitopes Using Long Short Term Memory Networks

Computational Ensemble Approach for Immune System Study: Conformational B-cell Epitope Prediction

Staged heterogeneity learning to identify conformational B-cell epitopes from antigen sequences

SEPIa, a knowledge-driven algorithm for predicting conformational B-cell epitopes from the amino acid sequence

Deep ridge regressed epitope predictor

Accurate Prediction of Immunogenic T-Cell Epitopes from Epitope Sequences Using the Genetic Algorithm-Based Ensemble Learning

An ensemble method for prediction of con-formational B-cell epitopes from antigen sequences

Conformational B-Cell Epitopes Prediction from Sequences Using Cost-Sensitive Ensemble Classifiers and Spatial Clustering

An integrative approach to CTL epitope prediction: A combined algorithm integrating MHC class I binding, TAP transport efficiency, and proteasomal cleavage predictions

Sequence Variability Analysis of Human Class I and Class II MHC Molecules: Functional and Structural Correlates of Amino Acid Polymorphisms

Toward more accurate pan-specific MHC-peptide binding prediction: A review of current methods and tools

A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach

EpiDOCK: A molecular docking-based tool for MHC class II binding prediction

The first web-based vaccine design program for reverse vaccinology and applications for vaccine development

PEPVAC: A web server for multi-epitope vaccine development based on the prediction of supertypic MHC ligands

Selection of Conserved Epitopes from Hepatitis C Virus for Pan-Populational Stimulation of T-Cell Responses

MHC class I antigenic peptide processing prediction

PREDIVAC: CD4 + T-cell epitope prediction for vaccine design that covers 95% of HLA class II DR protein diversity

Database for MHC ligands and peptide motifs

Enhancement to the RANKPEP resource for the prediction of pep-tide binding to MHC molecules using profiles

A server for multistep T cell epitope prediction

ProPred: Prediction of HLA-DR binding sites

Generation of tissue specific and promiscuous HLA ligand databases using DNA mi-croarrays and virtual HLA class II matrices

ProPred1: Prediction of promiscuous MHC Class-I binding sites

Scheme for ranking potential HLA-A2 binding peptides based on independent binding of individual peptide side-chains

EpiTOP-A proteochemometric tool for MHC class II binding prediction

MHCPred: A server for quantitative prediction of peptide-MHC binding

MULTIPRED2: A computational system for large-scale identification of peptides predicted to bind to HLA supertypes and alleles

Reliable prediction of T-cell epitopes using neural networks with novel sequence representations

Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence

Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method

Quantitative Predictions of Peptide Binding to Any HLA-DR Molecule of Known Sequence: NetMHCIIpan

In Silico Identification of Supertypes for Class II MHCs

Prediction of IL4 Inducing Peptides

Integrated modeling of the major events in the MHC class I antigen processing pathway

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Prediction of MHC class I binding peptides, using SVMHC

SVM based method for predicting HLA-DRB1*0401 binding peptides in an antigen sequence

Immune epitope database analysis resource (IEDB-AR)

CEP: A conformational epitope prediction server

BepiPred-2.0: Improving sequence-based B-cell epitope prediction using conformational epitopes

Predictive estimation of protein linear epitopes by using the program PEOPLE. Vaccine

Improved Method for Linear B-Cell Epitope Prediction Using Antigen's Primary Sequence

SVMTriP: A Method to Predict Antigenic Epitopes Using Support Vector Machine to Integrate Tri-Peptide Similarity and Propensity

Predicting linear B-cell epitopes using string kernels

Prediction of continuous B-cell epitopes in an antigen using recurrent neural network

Prediction of residues in discontinuous B-cell epitopes using protein 3D structures

Improved discontinuous B-cell epitope prediction using multiple distance thresholds and half sphere exposure

ElliPro: A new structure-based tool for the prediction of antibody epitopes

Epitopia: A web-server for predicting B-cell epitopes

Improving B-cell epitope prediction and its application to global antibodyantigen docking

Prediction of antigenic epitopes using support vector regression and multiple server results

Epitope mapping from affinity-selected peptides

CBTOPE-Prediction of Conformational B-cell Epitopes

Automated Detection of Conformational Epitopes Using Phage Display Peptide Sequences

PREDITOP: A program for antigenicity prediction

Benchmarking B cell epitope prediction: Underperformance of existing methods

Towards a consensus on datasets and evaluation metrics for developing B-cell epitope prediction tools

Nature of the protein universe

Antibody specific epitope prediction-Emergence of a new paradigm

Identification of conformational B-cell Epitopes in an antigen from its primary sequence

Genetic Recombination, and Pathogenesis of Coronaviruses

CD8+ T cells specific for an immunodominant SARS-CoV-2 nucleocapsid epitope cross-react with selective seasonal coronaviruses

Viral and host factors related to the clinical outcome of COVID-19

The CD8 T Cell Response to Respiratory Virus Infections

Memory T cell responses targeting the SARS coronavirus persist up to 11 years post-infection

Pathogenic human coronavirus infections: Causes and consequences of cytokine storm and immunopathology

Cell Responses to Viral Infections-Opportunities for Peptide Vaccination

T-cell quality in memory and protection: Implications for vaccine design

In silico T cell epitope identification for SARS-CoV-2: Progress and perspectives

NetCTLpan: Pan-specific MHC class I pathway epitope predictions

Benchmarking predictions of MHC class I restricted T cell epitopes in a comprehensively studied model system

Mass Spectrometry Profiling of HLA-Associated Peptidomes in Mono-allelic Cells Enables More Accurate Epitope Prediction

Open-Source Class I MHC Binding Affinity Prediction

Improved methods for predicting peptide binding affinity to MHC class II molecules

NetMHCIIpan-3.0, a common pan-specific MHC class II prediction method including all three human MHC class II isotypes

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

Defining HLA-II Ligand Processing and Binding Rules with Mass Spectrometry Enhances Cancer Epitope Prediction

Predicting HLA class II antigen presentation through integrated deep learning

The role of the proteasome in generating cytotoxic T-cell epitopes: Insights obtained from improved predictions of proteasomal cleavage

Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction

Prediction of MHC class I binding peptides using profile motifs

Structure of SARS-CoV-2 spike protein

Immunoinformatic identification of B cell and T cell epitopes in the SARS-CoV-2 proteome

A Novel Ensemble Machine Learning Model for Prediction of Zika Virus T-Cell Epitopes

A pneumonia outbreak associated with a new coronavirus of probable bat origin

The Effects of Virus Variants on COVID-19 Vaccines

SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction

Using Random Forest to Predict T -Cell Epitopes of Dengue Virus

Artificial Neural Network Disadvantages. Retrieved 4

Spectral forecast: A general purpose prediction model as an alternative to classical neural networks

Machine Learning-Based Ensemble Model for Zika Virus T-Cell Epitope Prediction

Research on Ensemble Learning

A Gentle Introduction to Ensemble Learning Algorithms

Peptides: A Package for Data Mining of Antimicrobial Peptides

GGobi Foundation. Peptider: Evaluation of Diversity in Nucleotide Libraries

A Novel Smart Healthcare System Design for Internet of Health Things

Ensemble Machine Learning Model to Predict SARS-CoV-2 T-Cell Epitopes as Potential Vaccine Targets

Institutional Review Board Statement: Not applicable.

Data Availability Statement: Not applicable.

The authors declare no conflict of interest.