# REVERSE VACCINOLOGY

EDITED BY : Pedro A. Reche, Richard Moxon and Rino Rappuoli PUBLISHED IN : Frontiers in Immunology

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ISSN 1664-8714 ISBN 978-2-88963-385-2 DOI 10.3389/978-2-88963-385-2

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# REVERSE VACCINOLOGY

Topic Editors: Pedro A Reche, Complutense University of Madrid, Spain Richard Moxon, University of Oxford, United Kingdom Rino Rappuoli, GlaxoSmithKline (Italy), Italy

Citation: Reche, P. A., Moxon, R., Rappuoli, R., eds. (2020). Reverse Vaccinology. Lausanne: Frontiers Media SA. doi: 10.3389/978-2-88963-385-2

# Table of Contents

# *05 Editorial: Reverse Vaccinology* Richard Moxon, Pedro A. Reche and Rino Rappuoli

Gandharva Nagpal, Salman Sadullah Usmani and Gajendra P. S. Raghava

*38 Bacterial Vaccine Antigen Discovery in the Reverse Vaccinology 2.0 Era: Progress and Challenges*

Fadil A. Bidmos, Sara Siris, Camilla A. Gladstone and Paul R. Langford

*45 A Review on T Cell Epitopes Identified Using Prediction and Cell-Mediated Immune Models for* Mycobacterium tuberculosis *and*  Bordetella pertussis

Yuan Tian, Ricardo da Silva Antunes, John Sidney, Cecilia S. Lindestam Arlehamn, Alba Grifoni, Sandeep Kumar Dhanda, Sinu Paul, Bjoern Peters, Daniela Weiskopf and Alessandro Sette

Martin Christopher James Maiden

*91 Comparison of Open-Source Reverse Vaccinology Programs for Bacterial Vaccine Antigen Discovery*

Mattia Dalsass, Alessandro Brozzi, Duccio Medini and Rino Rappuoli

*103 The Development of a Vaccine Against Meningococcus B Using Reverse Vaccinology*

Vega Masignani, Mariagrazia Pizza and E. Richard Moxon

*117 Comprehensive Evaluation of the Expressed CD8+ T Cell Epitope Space Using High-Throughput Epitope Mapping* Paul V. Lehmann, Maneewan Suwansaard, Ting Zhang, Diana R. Roen, Greg A. Kirchenbaum, Alexey Y. Karulin, Alexander Lehmann and Pedro A. Reche

#### *130 Application of Modeling Approaches to Explore Vaccine Adjuvant Mode-of-Action*

Paul R. Buckley, Kieran Alden, Margherita Coccia, Aurélie Chalon, Catherine Collignon, Stéphane T. Temmerman, Arnaud M. Didierlaurent, Robbert van der Most, Jon Timmis, Claus A. Andersen and Mark C. Coles

# Editorial: Reverse Vaccinology

Richard Moxon1 \* † , Pedro A. Reche2 \* † and Rino Rappuoli 3,4 \* †

*1 Department of Paediatrics, University of Oxford, Oxford, United Kingdom, 2 Department of Immunology & O2, Complutense University of Madrid, Madrid, Spain, 3 GSK, Siena, Italy, 4 Faculty of Medicine, Imperial College, London, United Kingdom*

Keywords: infectious diseases, vaccines, reverse vaccinology, microbiology, vaccinology

**Editorial on the Research Topic**

#### **Reverse Vaccinology**

For many, the semantics of the term "reverse vaccinology" may be puzzling. Literally, it implies a complete change of direction or action in the study of vaccines. The non-obvious point is that this volte face first came about through whole genome sequencing (WGS). WGS revolutionized biology, including microbiology. Specifically, it introduced a top-down, computer data-based approach to the discovery of candidate vaccine antigens; highly sensitive, but not specific and, crucially, not hypothesis driven. This contrasted with the classical laboratory based, hypothesis driven analysis of microbes to identify components that could elicit protective immunity. Reverse vaccinology relies on the use of computational methods and tools to identify vaccine candidates for further experimentation, refinement of which is crucial for their optimal use as argued and detailed by Dalsass et al. These computational tools serve to anticipate antigens that are likely to induce protective responses as well as the precise antigen regions, epitopes, recognized by the immune system (1).

#### Edited and reviewed by:

*Denise Doolan, James Cook University, Australia*

#### \*Correspondence:

*Richard Moxon<EMAIL_ADDRESS>Pedro A. Reche<EMAIL_ADDRESS>Rino Rappuoli<EMAIL_ADDRESS>
*†These authors have contributed equally to this work*

#### Specialty section:

*This article was submitted to Vaccines and Molecular Therapeutics, a section of the journal Frontiers in Immunology*

> Received: *03 November 2019* Accepted: *13 November 2019* Published: *03 December 2019*

#### Citation:

*Moxon R, Reche PA and Rappuoli R (2019) Editorial: Reverse Vaccinology. Front. Immunol. 10:2776. doi: 10.3389/fimmu.2019.02776*

Reverse vaccinology was first used to predict potential antigens for a vaccine against the B strains of Neisseria meningitidis (meningococci) in the 1990's, reviewed by Masignani et al. It is worth emphasizing that the formulation of this complex vaccine could not have been achieved without the systematic, WGS-based approach to population biology succinctly captured in the review by Maiden, a pioneer in laying the foundations of the molecular epidemiological tools so crucial to the design of vaccines both for infectious and non-infectious diseases. More recently, Bianconi et al. have been successful in applying the classical reverse vaccinology approach for Pseudomonas aeruginosa. Starting from the 5,570 open reading frames of the genome, they selected 52 vaccine candidates by applying a number of filters to exclude the proteins predicted not to be present on the bacterial surface, to be variable in different strains, to have homology to human proteins, or to be homologous to E. coli proteins. Of the 52 predicted vaccine antigens, 30 were successfully expressed and several of those gave a quite remarkable protection in the mouse challenge model. However, one of the main aims of this current series on reverse vaccinology is to highlight how many new concepts and technologies have been recruited to facilitate vaccine design including contributions from proteomics, immunology, structural biology, systems biology, and mathematical modeling. Thus today, the change of direction and action in vaccine research, captured in the term reverse vaccinology, embodies much more than innovation in antigen discovery.

Bidmos et al. describe the isolation and recombinant expression of the variable regions of heavy (VH) and light (VL = κ or λ) chain genes of immunoglobulin (IgG) using a variety of molecular tools. Referred to as reverse vaccinology 2.0, this permits the high-throughput screening of large numbers of antibody-secreting cells and was employed to identify functional anti-Staphylococcus aureus monoclonal antibodies induced during bacteremia (2) and anti-MTB surface antigen antibodies cloned from patient-derived plasmablasts of reactivated memory B-cell origins (3). In a further study, Bidmos et al. describe successful efforts to utilize the reverse vaccinology 2.0 approach to identify novel functional meningococcal antigens with the potential to expand the coverage of currently licensed meningococcal B vaccines.

**5**

The synergism of immune-information and systems immunebiology with WGS provides crucial tools that consider not just the challenges of the identification and molecular diversity of target antigens, but the importance of expression levels and how these variables, along with host genetic variation, impact on B-cell immune responsiveness. Immunologists do their best to identify the optimal epitopes of antigens for candidate vaccines, as exemplified by the work of Nagpal et al. These authors applied an immunoinformatic pipeline that led to identifying epitopebased vaccine candidates against 14 pathogenic bacteria and made them available through a web-resource named VacTarBac. Bacteria are complex pathogens encompassing numerous protein antigens that when targeted for epitope prediction will result in a huge number of candidates. But, this plethora of information and the challenge of what can be reasonably subjected to further rigorous investigation is a daunting challenge. Thereby, to simplify further experimental advances, the authors implemented a system to identify and prioritize virulence factors or other essential genes required for pathogenicity while also discarding epitopes cross-reactive with self-proteins. The application of stringent prioritization criteria to the selected 14 pathogenic bacteria led to the identification of just 252 unique B-cell and T-cell epitopes.

T-cell epitopes can be predicted starting from WGS. Tian et al. show how they made a full map of the T-cell epitopes starting from the 4,000 open reading frames of Mycobacterium tuberculosis. A metric (immunogenicity score) was devised based on predictions of their immunodominance, promiscuity, HLA restriction and conservation. In a second example, they describe how the prediction of the T-cell epitopes of Bordetella pertussis antigens, not just those included in currently licensed acellular vaccines, may help to design novel formulations based on Th1 and Th17 immunity to overcome the limitations of the existing vaccines which induce mostly a Th2 based immunity. Degoot et al. describe a new method to predict peptide binding to major histocompatibility complex class two (MHC-II) molecules, which is the main basis to anticipate CD4 T cell epitopes. The method is based on structural analyses of peptide-MHC II interactions and can predict peptide binding for all three human MHC-II loci (HLA-DR, HLA-DP, and HLA-DQ). The authors report that the performance of the method is in general

#### REFERENCES

comparable to neural network methods and is superior in predicting peptide binding to HLA-DP molecules. The main advantage of this approach reported over other machine learning models is that of being rooted on actual physicochemical peptide-MHC-II binding interactions. A main handicap is however that the authors have not made available the method for rigorous independent comparisons.

Sánchez-Ramón et al. makes a well-argued case for trainedimmunity based vaccines (TIbV). These are vaccines that induce an innate, non-specific immunity for long periods of time. A typical example of a TIbV Vaccine is BCG which induces two types of immunity, one based on adaptive immunity specific for Mycobacterium tuberculosis, and the other based on innate immunity which is non-specific but so effective that it is also recommended to cure prostate cancer. This immunity induces activation of dendritic cells, activation of non-specific effector responses of innate immune cells such as monocytes and macrophages and is maintained overtime by epigenetic changes. Vaccine adjuvants and non-toxic derivatives of toxins (4) inducing non-specific protection against bacteria or viruses can be considered a proxy of TIbV.

Buckley et al. describe modeling approaches that provide exciting insights into AS01, one of the most successful adjuvants licensed for human use, and how such an adjuvant may work. According to Sánchez-Ramón et al., in addition to adaptive immune responses, it is also likely to induce trained immunity. AS01 has been licensed for the RTS-S vaccine against malaria and the Shingrix vaccine against Shingles and is part of the first successful clinical trial showing protection from disease in people infected by Mycobacterium tuberculosis.

A quarter of a century after WGS revolutionized biology, this series is a timely and exciting opportunity to reflect on what has been achieved by reverse vaccinology and how best to galvanize future efforts to improve global public health through rigorous and imaginative exploitation of the explosion in technologies that can be used to develop a broad range of novel vaccines.

#### AUTHOR CONTRIBUTIONS

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

disease. J Immunol. (2004) 173:7435–43. doi: 10.4049/jimmunol.173. 12.7435

**Conflict of Interest:** RR was employed by GSK and RM holds a consultancy agreement as a scientific adviser to GSK.

The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Moxon, Reche and Rappuoli. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# **Trans-Allelic Model for Prediction of Peptide:MHC-II Interactions**

*Abdoelnaser M. Degoot 1,2,3 \*, Faraimunashe Chirove2 and Wilfred Ndifon1 \**

*1African Institute of Mathematical Sciences (AIMS), Muizenberg, South Africa, 2School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa, 3DST-NRF Centre of Excellence in Mathematical and Statistical Sciences (CoE-MaSS), Gauteng, South Africa*

Major histocompatibility complex class two (MHC-II) molecules are trans-membrane proteins and key components of the cellular immune system. Upon recognition of foreign peptides expressed on the MHC-II binding groove, CD4+ T cells mount an immune response against invading pathogens. Therefore, mechanistic identification and knowledge of physicochemical features that govern interactions between peptides and MHC-II molecules is useful for the design of effective epitope-based vaccines, as well as for understanding of immune responses. In this article, we present a comprehensive transallelic prediction model, a generalized version of our previous biophysical model, that can predict peptide interactions for all three human MHC-II loci (HLA-DR, HLA-DP, and HLA-DQ), using both peptide sequence data and structural information of MHC-II molecules. The advantage of this approach over other machine learning models is that it offers a simple and plausible physical explanation for peptide–MHC-II interactions. We train the model using a benchmark experimental dataset and measure its predictive performance using novel data. Despite its relative simplicity, we find that the model has comparable performance to the state-of-the-art method, the NetMHCIIpan method. Focusing on the physical basis of peptide–MHC binding, we find support for previous theoretical predictions about the contributions of certain binding pockets to the binding energy. In addition, we find that binding pocket *P*5 of HLA-DP, which was not previously considered as a primary anchor, does make strong contribution to the binding energy. Together, the results indicate that our model can serve as a useful complement to alternative approaches to predicting peptide–MHC interactions.

**Keywords: major histocompatibility complex (MHC), modeling peptide–MHC-II interactions, antigen presentation, machine learning, inverse statistical mechanics**

# **1. INTRODUCTION**

Major histocompatibility complex class two (MHC-II) molecules are surface proteins that exist on the membrane of antigen presenting cells (APCs) such as macrophages, dendritic cells, and B cells. They bind short peptide fragments derived from exogenous proteins and present them to *CD*4 + helper-T cells. Upon the recognition of foreign peptides presented by MHC-II molecules, the helper-T cells (precisely speaking, *CD*4 + effector T cells) will initiate proper adaptive immune responses, including enabling sufficient maturation of B cells and cytotoxic *CD*8 + T cells (1). Therefore, the binding of peptide to MHC-II molecules is considered to be a fundamental and pre-requisite step in the initiation of adaptive immunity (2, 3). As such, mechanistic identification of the basic determinants of peptide–MHC-II interactions presents potential for understanding the immune system's mechanisms and improving the process of designing peptide- and protein-based vaccines.

#### *Edited by:*

*Pedro A. Reche, Complutense University of Madrid, Spain*

#### *Reviewed by:*

*Anne Searls De Groot, EpiVax, United States Morten Nielsen, Technical University of Denmark, Denmark*

> *\*Correspondence: Abdoelnaser M. Degoot<EMAIL_ADDRESS>Wilfred Ndifon<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Vaccines and Molecular Therapeutics, a section of the journal Frontiers in Immunology*

> *Received: 06 February 2018 Accepted: 06 June 2018 Published: 20 June 2018*

#### *Citation:*

*Degoot AM, Chirove F and Ndifon W (2018) Trans-Allelic Model for Prediction of Peptide:MHC-II Interactions. Front. Immunol. 9:1410. doi: 10.3389/fimmu.2018.01410*

**7**

MHC genes for humans, referred to as human leukocyte antigen (HLA), are among the most polymorphic genetic elements found within a long continuous stretch of DNA on chromosome 6 (4). Such high polymorphism reflects the immense contribution of MHC molecules to the adaptive immune system and underpins their capacity to recognize a wide range of pathogens. Nonetheless, some viruses, such as hepatitis C, avian/swine influenza, and human immunodeficiency virus (HIV), undergo extensive mutations that allow them to partially escape recognition by the MHC molecules (5). MHC genes can be divided into HLA class I, II, and III. Loci corresponding to HLA class I are A, B, and C; HLA class II loci are DP, DQ, and DR; HLA class III genes encode for several other immune-related proteins and provide support for the former two classes (1, 4).

MHC-II molecules account for the likelihood of success of organ transplantation, and there are well-established associations between many disorders and particular classes of MHC-II molecules. These include the contribution of HLA-DQ genes to insulin-dependent diabetes (6); HLA-DR genes to multiple sclerosis; and narcolepsy (7) along with other autoimmune diseases resulting from degeneracy and misregulation in the process of peptide presentation (8). Moreover, genetic and epidemiological data have implicated MHC-II molecules in susceptibility to many infectious diseases such as HIV/AIDS, malaria (9), and cancer (10).

Experimental assays for prediction of peptide–MHC-II interactions are often faced with important obstacles, including substantial resources needed for laboratory work, high time, and labor demands. This is the case in particular, for experimental work aimed at finding out which promiscuous epitopes bind to specific MHC molecules, a necessary step in the design of peptide-based vaccines which protect against a broad range of pathogen variants. Computational methods, which are more efficient and less costly than biological assays, have been employed to complement these assays. Due to advances in sequencing technologies, immunological data have grown at an unprecedented pace and continue to accrue. This has been exploited in systematic computational analyses of genomes of multiple pathogens to determine which subunits might induce a potent immune response. The results have been the design and development of new vaccine candidates against HIV, influenza, and other hyper-variable viruses (11). Use of computational methods has significantly reduced experimental effort and costs by up to 85% (12).

Many immunoinformatics methods for prediction of peptide–MHC interactions, for both class I and II, have been developed based on machine learning approaches such as simple pattern motif (13), support vector machine (SVM) (14), hidden Markov model (HMM) (15), neural network (NN) models (16–18), quantitative structure–activity relationship (QSAR) analysis (19), structure-based methods, and biophysical methods (2, 20, 21; Degoot et al., unpublished). These methods can be divided into two categories, namely, intra-allele (allele-specific) and trans-allele (pan-specific) methods. Intra-allelic methods are trained for a specific MHC molecule on a limited set of experimental peptide-binding data and applied for prediction of peptides binding to that molecule. Because of the extreme polymorphism of MHC molecules, the existence of thousands of allele variants, combined with the lack of sufficient experimental binding data, it is impossible to build a prediction model for each allele. Thus, trans-allele and general purpose (22) methods such as *MULTIRTA* (2), *NetMHCIIpan* (18), and *TEPITOPEpan* (23) have been developed using richer peptide-binding data expanding over many alleles or across species (18). Similar methods for MHC-I are also available such as NetMHCpan (24, 25) and KISS (26).

The trans-allelic models are often designed to extrapolate either structural similarities or shared physicochemical binding determinants among HLA genes, to predict affinities for alleles that are not part of the training dataset. These models generally have better predictive performance for new alleles and a wide range of potential applications compared with the intra-allelic models.

Most of the existing trans-allelic models for MHC-II are extended versions of their earlier intra-allelic counterparts: TEPI-TOPEpan (23) was extended from *TEPITOPE* (27); *MULTIRTA* (2) evolved from *RTA* (20); and the series of *NetMHCIIpans* (1.0, 2.0, 3.0, and 3.1) (17, 18, 28, 29) were generalized from the NN align (30) method. In the same vein, in this article, we present a trans-allele method, an extension of our previous method (Degoot et al., unpublished), for prediction of peptide-HLA class II interactions based on biophysical ideas.

The remarkable strength of the method presented here over other existing advanced data-driven approaches is its physical basis. We formulate the process of binding affinity between peptide and MHC-II molecule as an inverse problem of statistical physics. From the observable macroscopic (bound and unbound) states of experimental data, we compute the microscopic parameters (Hamiltonians for amino acid residues involved in the interaction) that govern the system. In fact, many problems in computational biology can be solved in such a way (31, 32), taking advantage of the availability of vast amount of genomic data and high resolution structural information. Solutions obtained using this approach are more plausible and physically interpretable than those obtained using mere sequence-based methods (2; Degoot et al., unpublished). In addition, because sparsity is a hallmark feature of biological processes, we adjust the model's parameters via incorporating an L1 regularization term into the model. The L1 constraint, commonly named *Lasso*, encourages sparsity and improves the predictive performance of the model on novel data.

The rest of this article is organized as follows: in Section 2.1, we describe the idea of MHC-II polymorphic residue groups, which is employed to capture structure similarity among MHC-II alleles. In Section 2.2, we define our methodology and formulate the learning function. After that we briefly describe the benchmark dataset used to test the predictive performance of the model in Section 2.3 and present the results in Section 3. Finally, in Section 3.3, we summarize and discuss our results and compare our method with the state-of-the art method.

#### **2. MATERIALS AND METHODS**

## **2.1. MHC-II Polymorphic Residue Groups**

Crystal structures revealed that an MHC molecule is a combination of two domains, an *α* helix and a *β* sheet, linked together to form a Y-shaped groove which is used to locate peptides, and both domains equally contribute to the binding affinity. For HLA-I molecules, the *β* domain is largely conserved, and variation occurs mostly in the *α* domain. On the other hand, polymorphism occurs in both domains of HLA-II molecules; except for HLA-DR alleles, where the variation takes place in the *β* domain. In addition, the peptide-binding groove of the HLA-II is open at both ends, which allows binding peptides of variable lengths (33), ranging from 9 to 30 amino acid residues, or even an entire protein (29, 34). This is in contrast to the peptide-binding groove of the HLA-I alleles, which accommodate only short peptides of lengths ranging from 8 to 11 amino acids. This flexible constraint on peptide lengths together with its immense polymorphism, contribute to a lower predictive performance of computational methods for peptide–MHC-II interactions compared with MHC-I methods (2, 22).

The notion of MHC polymorphic residue groups, introduced by Bordner and Mittelmann (2), is based on a simple observation of an intrinsic (independent of peptide) feature of the MHC-II binding groove. Although a peptide could bind to an MHC-II molecule in various registers, due to the open-ended nature of the MHC-II binding groove, the strength of the binding affinity is primarily determined by 9 residues occupying the binding groove pockets. Interestingly, most of polymorphism in MHC-II genes occurs at these binding pockets (see the discussion in Section 3.3).

From the limited available crystallographic structural data of peptide–MHC-II complexes for a few MHC-II molecules from the Protein Data Bank (PDB) (35) (summarized in Table S1 in Supplementary Material), sets of important positions for the polymorphic residues in the binding groove that contact one or more peptide-binding cores and are within a distance of not more than 4 Å (2, 18, 36) in one or more of the MHC-II complex structures can be extracted. Then, by extrapolating the similarities among MHC molecules, their corresponding residues in different genes are determined using multiple sequence analysis (MSA) (37). Exploiting the fact that HLA-DR alleles are polymorphic only in the *β* domain and have the same *α* domain, the polymorphic residue groups for HLA-DR are extracted from its *β* domain sequences. Similarly, assuming sufficiency of the *β* domains for predicting MHC–peptide binding preferences (2) and for the sake of simplicity of the model, residue groups for HLA-DP and HLA-DQ were also extracted from the *β* domain.

Next, the set of polymorphic residues that always co-occur at the specified positions are clustered into the same group. The rationale of clustering polymorphic residue groups, rather than individual residues, is to avoid over-parametrization of the model. Table S2 in Supplementary Material shows such polymorphic residue groups for HLA-DRB, HLA-DP, and HLA-DQ alleles, assembled by the procedures described earlier.

#### **2.2. Trans-Allele Model**

In our previous intra-allele model (Degoot et al., unpublished) the probability of peptide P (k) to bind an MHC molecule M(T(k)) was computed as follows:

$$\pi\left(\mathbb{P}^{(k)}, \mathbb{M}^{(\mathsf{T}(k))}\right) = \frac{1}{1 + e^{\delta \mathsf{E}^{(k)}}},\tag{1}$$

where *δ*E (k) is the change in binding energy in terms of the sum of the differences of first- and second-order Hamiltonians between the bound and unbound states. Specifically, *δ*E (k) is given by the following equation:

$$\delta \mathbf{E}^{(k)} = \underbrace{\sum\_{i=1}^{|\mathbf{P}^{(k)}|} \delta H^{(1)}\left(\mathbf{a}\_{i}\right) + \sum\_{i=1}^{9} \delta H^{(1)}\left(\mathbf{b}\_{i}\right) + \delta S}\_{\text{first-order Hamiltonians}}$$

$$+ \underbrace{\sum\_{i=1}^{|\mathbf{P}^{(k)}|} \sum\_{j=1}^{9} \sum\_{r=1}^{\mathbf{R}} \delta H^{(2)}\left(\mathbf{a}\_{ir}^{(k)}, \mathbf{b}\_{j}\right)}\_{\text{second-order Hamiltonians}},\tag{2}$$

in which |P (k) | is the length of peptide k, R is the number of all possible configurations (registers) in which the peptide binds to the particular MHC molecule, and *δ*S is the difference in entropy between the bound and unbound states.

For the trans-allele model, two changes were introduced into the second term of equation (2). First, instead of residue-residue interaction, *δ*H (2) (a (k) ir *,* bj), with a (k) ir on the peptide sequence and bj on the MHC binding pocket, we rather focus on residuepolymorphic group interaction, *δ*H (2) (a (k) ir *,* gjn), where gjn is residue group number *n* of position *j* as defined in Section 2.1. Next, we introduce a binary operator T(k, j, n) that equals 1 if the MHC molecule type, M(T(k)), corresponding to peptide P (k) contains polymorphic residue group *n* at the set of pre-determined positions of pocket *j*, and equals 0 otherwise. Hence, *δ*E (k) is given by the following equation:

$$\delta \mathbf{E}^{(\mathbf{k})} = \underbrace{\sum\_{i=1}^{|\mathbf{P}^{(\mathbf{k})}|} + \delta H^{(1)}\left(\mathbf{a}\_{i}\right) + \sum\_{i=1}^{9} \delta H^{(1)}\left(\mathbf{b}\_{i}\right) + \delta s}\_{\text{first-order Hamiltonians}}$$

$$+ \underbrace{\sum\_{i=1}^{|\mathbf{P}^{(\mathbf{k})}|} \sum\_{j=1}^{9} \sum\_{r=1}^{\mathbf{G}\left(\mathbf{j}\right)} \sum\_{n=1}^{\mathbf{G}\left(\mathbf{j}\right)} \delta H^{(2)}\left(\mathbf{a}\_{ir}^{(k)}, \mathbf{g}\_{jn}\right) \mathsf{T}\left(\mathbf{k}, \mathbf{j}, n\right)}\_{\text{second-order Hamiltonians}}, \quad \text{(3)}$$

where G(j) is the number of polymorphic residue groups for binding pocket *j*. Column two of Table S2 in Supplementary Material shows G(j), *j* = 1, 2, *. . .*, 9, for HLA-DR, HLA-DP, and HLA-DQ alleles.

Let ∆ denote the model's parameters. Using equations (1) and (3), we formulate, through the maximum likelihood approach, the following cost function:

$$\mathcal{L}(\mathsf{P}, \mathsf{M}|\Delta) = \underset{\{\Delta\}}{\operatorname{argmin}} \left( \sum\_{k=1}^{\mathsf{K}} \mathsf{G}^{k} \left( \Delta^{k} \right) + \lambda \mathcal{P} \left( \Delta \right) \right), \tag{4}$$

where G k (∆) is the empirical loss function given by the following equation:

$$\mathbf{G}^{\mathbf{k}}\left(\Delta\right) = \mathbf{y}^{\mathbf{k}}\log(\pi^{\mathbf{k}}\left(\Delta\right)) + \left(1 - \mathbf{y}^{\mathbf{k}}\right)\log(1 - \pi^{\mathbf{k}}\left(\Delta\right)), \quad \text{(5)}$$

and y k *∈*{0, 1} is the experimental value; *y* = 1 for binding peptides and *y* = 0 for non-binding ones. *λP*(∆) is a regularization term with the following form:

$$
\lambda \mathcal{P} \left( \Delta \right) = \lambda ||\Delta||\_1 = \lambda \sum\_{i=1}^d \left| \Delta \right|, \tag{6}
$$

where *λ* > 0 is a hyper-parameter and *d* is the dimension of parameter vector ∆, which varies depending on the type of MHC-II molecule. The *L*1 constraint penalty term *P*(∆), also known as Lasso (38), has an important role in the model. As the model is defined on a large number of parameters (*d* = 2,321, 561, and 401 for HLA-DR, HLA-DP, and DQ molecules, respectively) a few parameters are expected to contribute to the binding affinity while the rest are expected to be noisy. Lasso has the capability to filter out the noisy parameters by inducing sparsity in the model, as it shrinks most of the parameter values to 0, and avoids data overfitting. The hyper-parameter *λ* controls the degree of sparsity of the model; the larger the value of *λ* the more sparse the model. Equation (4) is a non-linear and non-smooth function; due to the *L*1 constraint. But it is a convex function and we solved it, after quadratic approximation, by means of an iterative, cyclic coordinate descent approach using a soft-thresholding operator. This learning function takes the form of a generalized linear model and the algorithm we used to solve it is both fast and efficient. Details of this optimization method are found in Friedman et al. (39) and are summarized in the supplementary material.

## **2.3. Binding Affinity Dataset**

The model has been developed by using both quantitative peptidebinding data and MHC-II molecule sequences. We obtained a total of 51,023 peptide-binding data for 24 HLA-DR, 5 HLA-DP, and 6 HLA-DQ from the IEDB database (40). This is a wellcurated dataset and was used to develop NetMHCIIpan (18), the state-of-the-art method. The binding affinities data were given in the form of log-transformed measurements of the IC50 (half maximum inhibition concentration) according to the formula 1 *−* log(*IC*50)/log(50,000) (16). We dichotomized these data using a moderate threshold of IC50 500 nM (*≡*0.426 of log-transformed data). Peptides with *IC*50 less than or equal 500 nM (*≥*0.426 of logtransformed value) were considered as binders, and non-binders otherwise. This moderate threshold, which has been used in other previous methods including the state-of-the art method (20, 29, 30, 41), allows us to make direct comparisons.

Amino acid sequences for the MHC-II alleles used in this study were obtained from the EMBL-EBI online-database (42). **Table 1** gives a summary of the peptide-binding dataset used to develop the method.

#### **3. RESULTS**

This section presents prediction results of the model obtained from the dataset of three MHC-II allotypes as described in Section 2.3. We applied a fivefold cross validation analysis to the model and compared it against its intra-allelic version (Table S3 in Supplementary Material). We also examine its predictive performance on data which were previously unseen by the model.

#### **3.1. Performance of the Trans-Allele Model**

We tested the predictive performance of the model by using fivefold cross validation. The partitioning of the data used in fivefold cross validation was previously done by Andreatta et al. (29), by clustering together peptides in a way that minimizes over-estimation of predictive performance, using the technique described by Nielsen et al. (30). **Figure 1** shows results of the test done using alleles belonging to the three MHC-II loci considered in this study. The performance was measured in terms of area under the curve (AUC) (43) values, which range between 0 and 1. The higher the AUC value the better the predictive performance of model. Values below 0.5 reflect a worse performance than a random test. The model has an excellent performance for HLA-DP molecules (average AUC value = 0.930), and a good predictive power for both HLA-DQ and HLA-DR molecules (average AUC values = 0.830 and 0.802, respectively). The surprisingly excellent performance for HLA-DP could be the result of both a higher structural similarity (see Section 3.3) and a higher number of peptides per allele for HLA-DP. Indeed, for all HLA-DP alleles, the number of available peptides exceeds the empirically required number of peptide-binding measurements (*≈*200 peptides (22)), but this is not the case for all HLA-DR alleles. HLA-DQ alleles have sufficient number of peptide measurements but these have a lower structural similarity compared with the corresponding peptides for HLA-DP alleles (see Section 3.3).

## **3.2. Comparing the Intra-Allele vs Trans-Allele Methods**

Table S3 in Supplementary Material shows AUC values obtained with the intra-allele and trans-allele versions of the model. For the intra-alleles version, the model was evaluated on peptide-binding data corresponding to an individual allele only. On average, the performance of the trans-allele model is comparable to that of the intra-allele model for HLA-DP (0.930 vs 0.928), it is worse for HLA-DQ (0.830 vs 0.857) and it is better for HLA-DR (0.780 vs 0.771) (**Figure 2**).

These results demonstrate two important observations. First, there is a common binding preference among MHC-II loci, which is the basis of all trans-allelic models, and that has been successfully captured by the definition of MHC-II polymorphic groups for HLA-DP loci, and to a lesser extent for HLA-DQ and HLA-DR. Second, the trans-allelic model is able to extrapolate similarities among the MHC-II allotypes and achieve good predictive performance. As a result, the overall performance of the trans-allelic model is comparable to that of intra-allele model, even though the former model is applied on a much diverse set of MHC-II sequences.

A decreased performance of the trans-allelic model when compared with the intra-allelic method for HLA-DQ molecules is consistent with results reported in NetMHCIIpan (18). Here we suggest that this is probably because of the limited structural information available for HLA-DQ alleles. In fact, because of this limited structural information there are only 17 polymorphic residue groups for all the 9 binding pockets defined for HLA-DQ alleles. By contrast, there are 25 and 115 polymorphic residue groups defined for HLA-DP and HLA-DR molecules, respectively. **TABLE 1** | Overview of the MHC-II peptide-binding data utilized in this study.

*The first column gives the names of the 34 genes used to develop the method, distributed as 24, 5, and 6 for HLA-DR, HLA-DP, and HLA-DQ genes, respectively. The second column represents the index for each allele in the EMBL-EBI database (42). The third and fourth columns give the total number of peptide and the number of binder peptides, receptively, per allele. The last column shows the percentage of binder peptides. Binder peptides were identified using an IC50 binding cutoff of 500 nM, as in previous studies (2, 17, 18, 30). The last row presents the overall statistics for the last three columns.*

Another reason for the reduction of the trans-allelic model's performance for HLA-DQ alleles is that there is a large sequence diversity of MHC-II molecules belonging to this locus. We will examine the empirical support for this assertion in Section 3.3.

# **3.3. Prediction on a Novel Dataset**

We examined the predictive power of the model on a blind dataset- i.e., a dataset which was not used in the training phase. More precisely, to make peptide-binding predictions for a particular allele, we train the model on an entirely different allele. The allele used for training was chosen based on its similarity to the focal allele as quantified using three different metrics: nearest-neighbor, Hamming distance, and Leave-One-Out (LOO) approach.

In the nearest-neighbor approach the distance between two MHC molecules is defined (17) as follows:

$$d\left(A,B\right) = 1 - \frac{\mathcal{S}\left(A,B\right)}{\sqrt{\mathcal{S}\left(A,A\right)\mathcal{S}\left(B,B\right)}}\tag{7}$$

in which *S*(*A*, *B*) is the score of the BLOSUM50 (44) metric between amino acid sequences of*A*and *B*. The BLOSUM50 metric measures genetic distance between two sequences by quantifying the likelihood that one amino acid will be substituted by another amino acid on evolutionary time scales. Hamming distance simply counts the different occurrences of corresponding amino acid residues between two sequences. In both nearest-neighbor and Hamming metrics, we train the model on peptide data belonging to the corresponding nearest allele to parameterize the model, and then we assess its accuracy in terms of AUC values calculated

HLA-DR with AUC = 0.802.

based on peptide data belonging to the focal allele using those parameters.

However, unlike the TEPITOPE and the series of NetMHCI-Ipan methods which defined nearest neighbor at pocket level, we derive both the nearest-neighbor metric and the Hamming distance at residue level. Our choice is based on the fact that accounting for the entire MHC-II sequence provides a broader allele coverage (2) and hence extend the model's applicability. Computing sequence similarity at residue level is an intuitive and natural approach to perform comparative analysis of sequences rather than other artificial ways that may be more computationally efficient. We found that 71% (for HLA-DR), 60% (HLA-DP), and 67% (HLA-DQ) of alleles used for training were consistent between the residue-level and pocket-level approaches. These statistics indicate that, as mentioned before, most of MHC-II polymorphisms occur at the binding pockets.

The LOO approach involved partitioning data into two parts; the peptide-binding data not belonging to the allele under consideration are used to learn the model's parameters and the remaining data, the peptide-binding data belonging to the focal allele, are used as test data. **Figure 3** shows a comparison of results from these three approaches (details are in Table S4 in Supplementary Material). The results show that, regardless of the metric we used, the trans-allele method has a high predictive power for HLA-DP allele and a moderate predictive power for the other alleles.

The much higher predictive power for HLA-DP compared with the other alleles is likely due to the comparatively lower sequence diversity of HLA-DP alleles. To make this assertion more precise we carried out a regression analysis by defining the AUC values from LOO approach as functions of both NN and Hamming metric distances. **Figure 4** gives results of our analysis. As seen in **Figure 4**, all HLA-DQ alleles fall below the least squares lines for both metrics (blue points). We also found that model performance for HLA-DP allele (red points) increases as the distance between alleles decreases. The authors of NetMHCIIpan also arrived at the same conclusion (18), but only for the NN metric.

### **3.4. Analysis of the Model's Parameters**

To determine the key factors that contribute to the binding affinities for the three MHC-II alleles considered in this study, we calculated the Hamiltonians corresponding to each amino acid residue and the 9 binding pockets of the MHC-II binding groove. These values were then averaged over only the polymorphic residue groups defined for each pocket containing the particular amino acid.

difference in the HLA-DP loci is limited.

**FIGURE 3** | Average performance results of the model in terms of AUC values for the three metrics: NN approach (gray bars), Hamming metric (blue bars), and the LOO method (red bars). Except for HLA-DQ loci, the LOO approach significantly out performs the other two metrics. Such results indicate that this method performs better than a random test even for un-characterized MHC-II molecules.

Analysis of HLA-DR parameters revealed that pocket *P*1 has moderate attractive interactions with peptide (negative energies indicated by blue color in **Figure 5**), via hydrophobic **(I**, **L**, **W**, **Y)** side chains and, to lesser extent, via the aromatic **(F**, **W)** amino acids and a single hydrophilic residue **(K)**. Remarkably, previous studies (2, 46) arrived at a similar conclusion of a large tendency of position *P*1 toward interactions involving the hydrophobic side chains. The repulsive interactions (positive energies indicated by red color in **Figure 5**) of pocket *P*1 mostly occur with the hydrophilic side chains **(D**, **E**, **N**, **S**, **T)** and the aliphatic residue **(A)**. Generally, most of the primary anchor pockets (*P*1, *P*4, *P*6, *P*7, *P*9) confer attractive interactions, but the pocket *P*1 makes the largest contribution. This is consistent with results obtained using the MULTIRTA method (2). Among the secondary anchors,

**FIGURE 4** | Regression analysis of AUC values from the LOO approach as function of: **(A)** nearest-neighbor and **(B)** Hamming distances. Negative slope lines in both graphs obtained by the least square fit method, with p-values 0.185 and 0.0.033 for both metrics, respectively. These lines and p-values associated with were produced using glm2 package in R (45).

we found that pocket *P*2 has attractive interactions with aromatic **(F**, **Y)** and the hydrophobic **(I**, **M**, **Y)** side chains. The most repulsive interactions come from the pocket *P*8, which has a strong unfavorable interactions involving the side chains of residues**C**, **D**, **E**, **F**, **G**, **I**, **L**, **W**, and **Y** (see **Figure 5A**).

For HLA-DP, we found that pocket *P*9 has significantly attractive interactions involving the hydrophobic residue (**L**). This is consistent with the previous results of Ref. (47) (see **Figure 5B**). Also, we found that pockets *P*4 and *P*5 have important attractive interactions with peptide via hydrophobic **(Y)** and aromatic **(F)** side chains, respectively. The contribution of the pocket *P*4 is concordant with other studies such as (41), but the contribution of the pocket *P*5 was not reported in the study of Andreatta and Nielsen (47), which was specifically dedicated to HLA-DQ and HLA-DP alleles. Furthermore, we found that the other two pockets *P*1 and *P*6, which were reported as primary anchors in that study, have a moderate contribution to calculated bind energies (see **Figure 5B**).

The pattern of energetic contributions for HLA-DQ alleles is less ordered. There is no common pattern except the observation of significant attractive interaction of pocket *P*1 via the hydrophobic residue **(W)** and the repulsive interaction of pocket *P*4 via the side chains **C**, **E**, and **D** (see **Figure 5C**). This finding is in line with the observations of Morten et al. (47).

# **3.5. Discussion**

Interactions between peptides and MHC-II molecules are central to the adaptive immune system. Precise prediction and knowledge of the physicochemical determinants that govern such interaction is useful in designing effective and affordable epitope-based vaccines, and in providing insights about the immune system's mechanism as well as in understanding the pathogenesis of diseases. In this study, we have developed a trans-allelic model that can predict peptide interactions to the three human MHC-II loci. It can be readily applied to MHC-II molecules of other species provided that relative structural information are available. This method is based on biophysical ideas, an alternative to the dominant machine learning approaches.

The model presented here is, in addition to NetMHCIIpan, only the second trans-allelic method that allows comprehensive prediction analysis of peptide binding to all three human MHC-II loci. Most trans-allelic models for MHC-II peptides are restricted to HLA-DR and HLA-DP alleles. The TEPITOPEpan method (23), which is popular among immunologists and is the successor of a pioneer method in this field, is limited to HLA-DR alleles.

In this work we employed the definition of MHC polymorphic residue groups of the MULTIRTA method (2), which is more intuitive and inclusive than the MHC pseudo sequences of NetMHCIIpan (18), in developing our trans-allelic model. Utilizing new structural data for MHC-II complexes, which were not present when MULTIRTA was being developed, we extended that idea to cover all three human MHC-II loci. There exist similar exercises for capturing structural similarity among MHC molecules. The earlier works of Murthy and Stern (48)

sensitivity and specificity) by calculating an AUC value. The higher the AUC value the better the predictive performance. The plot shows the average difference between the AUC values for alleles belonging to the same locus obtained using our model vs. the corresponding values obtained using NetMHCIIpan, when similarity is defined based on either **(A)** the NN or **(B)** the LOO metric. Error bars denote SDs. Strikingly, our model performs better than NetMHCIIpan when predicting peptide binding to HLA-DQ using the NN metric (p-value = 0.015). For all other cases, both models have equivalent performance.

and Sinigaglia and Hammer (49) were mostly limited to HLA-DR molecules. But in a previous study (2), the "polymorphic residue groups" were shown to be useful for inferring the interaction energy. This physical way of capturing structural similarity among MHC molecules works well in our biophysical approach.

We compared how well our model predicts the MHC-II allele binding preferences of a novel peptide dataset vs. how well the state-of-the-art NetMHCIIpan method performs the same task. In this comparison we applied both our model and NetMHCIIpan to predict binding preferences for peptides known to either bind or not bind a reference allele after training both models using peptide-binding data for a second allele. For a given MHC-II locus, the second allele was the one that was most similar to the reference allele. Similarity was quantified based on either a leave-one-out approach or a nearest-neighbor approach (see Section 3.3). When using the nearest-neighbor approach, we found that our model performs significantly better than NetMHCIIpan in predicting peptide-binding preferences for HLA-DQ alleles (P-value = 0.015; **Figure 6A**). Furthermore, at the 95% confidence level, for all other cases, we found no significant difference between the performances of the two models (**Figure 6**).

These results are reassuring and indicate that our inversephysics approach constitutes a promising complement to the widely used pattern-based approach to peptide–MHC-II binding predictions. The outstanding predictive accuracy of the NetMHCIIpan is not the result of its theoretical basis. Rather it derives from the use of sophisticated ensembles of neural networks, which are very powerful. However, our method has a distinguishing advantage over all the advanced machine learning models in that it is more physically meaningful. It is worth noting that our prediction results of peptide–MHC-II interaction were based on *in silico* analysis of real data. Additional, *in vivo* and *in vitro* investigations are needed to further validate the reported predictive performance.

# **REFERENCES**

# **DATA AVAILABILITY STATEMENT**

The peptide dataset used to evaluate this method can be found in the [IEDB] (http://tools.iedb.org/main/datasets/), and the MHC-II sequences data also can be found in the [EMBL-EBI] (ftp://ftp.ebi.ac.uk/pub/databases/ipd/imgt/hla/fasta/DRB \_prot.fasta).

# **AUTHOR CONTRIBUTIONS**

All authors contributed equally to this work.

## **ACKNOWLEDGMENTS**

The support of the DST-NRF Centre of Excellence in Mathematics and Statistical Sciences (COE-MaSS) toward this research is hereby acknowledged. Opinions expressed and conclusions arrived at, are those of the authors and are not necessarily to be attributed to the CoE-MaSS. We also gratefully acknowledge the support of the Centre for High Performance Computing (CHPC) at Cape Town, South Africa, for providing us access to their computational facilities.

# **FUNDING**

This work was supported by the following grants: AD is funded by DST-NRF Centre of Excellence in Mathematical and Statistical Sciences (CoE-MaSS, award number BA2017/050) and the African Institute for Mathematical Sciences (AIMS) South Africa; WN is funded by the AIMS Global Secretariat.

# **SUPPLEMENTARY MATERIAL**

The Supplementary Material for this article can be found online at https://www.frontiersin.org/articles/10.3389/fimmu.2018.01410/ full#supplementary-material.

class II molecules. *J Biosci Bioeng* (2002) 94(3):264–70. doi:10.1016/S1389- 1723(02)80160-8

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2018 Degoot, Chirove and Ndifon. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Cross-Reactive Bactericidal Antimeningococcal Antibodies Can Be Isolated From Convalescing Invasive Meningococcal Disease Patients Using Reverse Vaccinology 2.0

*Fadil A. Bidmos1 \*, Simon Nadel1,2, Gavin R. Screaton1 , J. Simon Kroll 1 and Paul R. Langford1*

*1Section of Paediatrics, Department of Medicine, Imperial College London, London, United Kingdom, 2St. Mary's Hospital, Paddington, London, United Kingdom*

#### *Edited by:*

*Rino Rappuoli, GlaxoSmithKline, Italy*

#### *Reviewed by:*

*Sanjay Ram, University of Massachusetts Medical School, United States Mariagrazia Pizza, GlaxoSmithKline, Italy*

> *\*Correspondence: Fadil A. Bidmos<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Vaccines and Molecular Therapeutics, a section of the journal Frontiers in Immunology*

> *Received: 17 April 2018 Accepted: 29 June 2018 Published: 16 July 2018*

#### *Citation:*

*Bidmos FA, Nadel S, Screaton GR, Kroll JS and Langford PR (2018) Cross-Reactive Bactericidal Antimeningococcal Antibodies Can Be Isolated From Convalescing Invasive Meningococcal Disease Patients Using Reverse Vaccinology 2.0. Front. Immunol. 9:1621. doi: 10.3389/fimmu.2018.01621*

The threat from invasive meningococcal disease (IMD) remains a serious source of concern despite the licensure and availability of vaccines. A limitation of current serogroup B vaccines is the breadth of coverage afforded, resulting from the capacity for extensive variation of the meningococcus and its huge potential for the generation of further diversity. Thus, the continuous search for candidate antigens that will compose supplementary or replacement vaccines is mandated. Here, we describe successful efforts to utilize the reverse vaccinology 2.0 approach to identify novel functional meningococcal antigens. In this study, eight broadly cross-reactive sequence-specific antimeningococcal human monoclonal antibodies (hmAbs) were cloned from 4 ml of blood taken from a 7-monthold sufferer of IMD. Three of these hmAbs possessed human complement-dependent bactericidal activity against meningococcal serogroup B strains of disparate PorA and 4CMenB antigen sequence types, strongly suggesting that the target(s) of these bactericidal hmAbs are not PorA (the immunodominant meningococcal antigen), factor-H binding protein, or other components of current meningococcal vaccines. Reactivity of the bactericidal hmAbs was confirmed to a single ca. 35 kDa protein in western blots. Unequivocal identification of this antigen is currently ongoing. Collectively, our results provide proof-of-principle for the use of reverse vaccinology 2.0 as a powerful tool in the search for alternative meningococcal vaccine candidate antigens.

Keywords: *Neisseria meningitidis*, invasive meningococcal disease, reverse vaccinology 2.0, human monoclonal antibodies, vaccines

# INTRODUCTION

*Neisseria meningitidis* is a major obligate human pathogen that frequently colonizes the nasopharynx asymptomatically, in a state known as carriage (1). Occasionally, invasive meningococcal disease (IMD) occurs, through invasion of pharyngeal tissues, proliferation in blood (meningococcal septicemia), and crossing of the blood–brain barrier leading to meningitis (2, 3). More than 70,000

**18**

cases of IMD are reported annually worldwide with case fatality ratios between 5 and 15%, even with therapeutic intervention (4, 5). Debilitating neurological sequelae are common among survivors of IMD (6–8).

The use of currently available polysaccharide conjugate meningococcal vaccines has been effective against targeted serogroups (mainly serogroups A, C, W, and Y) within vaccinated populations (9, 10). For serogroup B strains, which account for more than 60% of IMD in the UK and Europe (11, 12), vaccine development has focused heavily on sub-capsular vaccine components owing to the unsuitability of the serogroup B capsule (13). One of these vaccines, 4CMenB (or Bexsero®), is a protein-based vaccine whose major components are the factor-H binding protein (fHbp) (variant 1.1), Neisserial heparin-binding antigen (NHBA variant 2), Neisserial adhesin A (NadA variant 3), and the detergent-extracted outer membrane vesicle component of the New Zealand epidemic strain (with PorA variant P1.4) (14). Like the polysaccharide conjugate vaccines, accruing data shows high effectiveness of 4CMenB (15). However, we are seeing a gradual recrudescence of carriage and disease to pre-vaccine levels through vaccine-driven strain replacement (16–19). In addition, there are concerns that the changing epidemiology of IMD (20–22) may lead to a significant reduction in the efficacy of the vaccines in the long term. These limitations, coupled with the huge potential of the meningococcus to generate extensive antigenic diversity (leading to vaccine/immune escape) (23) justify the search for novel vaccine candidate antigens.

Preclinical vaccine development methods are enriched by detailed analysis of the human immune response to etiological agents of infectious diseases. For example, with the development of high-throughput technologies, deep sequencing of the gene segments encoding the variable regions of antibody heavy (VH) and light (VL = κ or λ) chains in a given B cell repertoire is providing valuable information useful in understanding adaptive immunity to infections, autoimmunity, and malignancies (24, 25). Identifying the targets of antibodies of interest by cloning and *in vitro* expression of VH and VL chains of B-cell antibodies is a powerful approach, which can be utilized to inform on the functional immunogenicity of both known and novel antigens. The use of this approach, termed reverse vaccinology 2.0 (26), in the cloning of neutralizing human recombinant monoclonal antibodies [human monoclonal antibodies (hmAbs)] from patients convalescing from viral infectious diseases is well documented; the first studies in the use of reverse vaccinology 2.0 focused on the isolation and functional characterization of antibodies targeting the dengue, HIV, and influenza viruses (27–29). The power of the approach lies in the expression of paired VH and VL regions from individual plasmablasts or memory B cells; the output being the expression of hmAbs mimicking natural VH + VL combinations induced in the host.

Because of the transience of peak plasmablast circulation [reviewed in Ref. (30)] and the higher incidence of IMD among infants and toddlers (placing a limitation on blood sample volume), we aimed to assess whether reverse vaccinology 2.0 could be employed in the discovery of novel meningococcal antigens of vaccine potential. In this brief research report, we will outline findings relating to the following aims: (i) whether cross-reactive antimeningococcal hmAbs targeting surface proteins could be cloned from patient samples; and (ii) if these hmAbs possessed bactericidal activity against a wide panel of strains, specifically those not covered by the protein-based meningococcal vaccines.

# MATERIALS AND METHODS

#### Ethics Statement and Study Participants

Studies with human blood samples were approved by the London—Fulham Research Ethics Committee (Ref.: 11/LO/1982). Informed written consent was obtained from patients or their representatives in accordance with the Declaration of Helsinki. Patients were recruited following admission to the Imperial Healthcare (St. Mary's Hospital) Paediatric Intensive Care Unit (PICU), London, UK.

Patient SM-P02 was a 7-month-old baby who presented with fever, irritability, and reduced oral intake. Rapidly spreading petechial rash was detected a few hours after arrival at the hospital. Blood cultures confirmed meningococcal septicemia. Molecular typing of the isolate, M14-240312, revealed that it was a serogroup B strain (MenB). A 4 ml blood sample was collected 7 days post-admission to St. Mary's Hospital PICU, following confirmation of negative blood cultures.

#### Bacterial Strains

Assays performed with whole bacteria or cell lysates involved patient isolates M14-240312 and a 16-strain meningococcal panel reflecting the current genetic epidemiological prevalence in the UK (obtained from the UK Meningococcal Reference Unit, Public Health England, Manchester) (**Table 1**). This 16-strain panel includes 10 strains genotypically mismatched for all the major 4CMenB antigens. Typing information was obtained from the

Table 1 | MenB strains composing the 17-strain screening panel.

*a Dash (–) denotes absence of NadA in isolate.*

*bND denotes no data, i.e. no typing information available for this antigen.*

*Neisseria* MLST database hosted on https://pubmlst.org/neisseria/. Meningococci were routinely grown on 5% horse blood agar or in brain heart infusion broth supplemented with 5% Levinthal's.

# Cell Sorting, cDNA Synthesis, and VH/VL Cloning

Peripheral blood mononuclear cells (PBMCs) were extracted from a 4 ml blood sample using a density gradient centrifugation method (Leucosep) and sorted singly into individual wells of 96-well plates containing catch buffer (10 mM Tris pH 8.0, 10 U RNAsin) as previously described (31). PBMCs were incubated with a cocktail of anti-human monoclonal antibodies targeting CD3 (PerCP/Cy5.5), CD14 (PerCP/Cy5.5), CD19 (RPE), CD20 (PerCP/Cy5.5), CD27 (FITC), CD38 (APC), and CD56 (PerCP/Cy5.5) for 30 min on ice, in the dark. Stained PBMCs were analyzed using a BD FACSAria III cell sorter. Plasmablasts were gated as follows: CD3−, CD14−, CD19+, CD20−, CD56−, CD27high, and CD38high. A single freeze-thaw cycle was used to lyse cells and release RNA. cDNA was synthesized from released RNA using the QIAGEN OneStep RT-PCR kit, as per manufacturer's protocols. A nested PCR was used to amplify the VH and VL regions; primers contained restriction endonuclease sites, which facilitated cloning into respective AbVec-IgH (*AgeI* + *SalI*), AbVec-Igκ (*AgeI* + *BsiWI*), or AbVec-Igλ (*AgeI* + *XhoI*) expression vectors.

# Expression of hmAbs in Human Embryonic Kidney (HEK-293) Cells

HEK-293 cells were transiently transfected with cognate plasmid pairs using polyethyleneimine as transfection agent, as previously described (31). Culture supernatants were harvested after 72 h. HmAbs were purified from culture supernatants with single-use Protein G spin columns (Ab SpinTrap, GE Healthcare), as per manufacturer's instructions.

# Assessment of hmAb Specificity

HmAb reactivity to surface-bound meningococcal antigens was assessed using indirect cell-based ELISAs, as previously described (32). Briefly, inactivated bacterial whole cell suspensions were normalized to an OD600 of ~0.5. Whole cells (100 µl of cell suspensions) or 1 µg of 4CMenB in Carbonate-Bicarbonate buffer (Sigma-Aldrich, cat. no. C3041) were transferred into designated wells of flat-bottom polystyrene 96-well plates and incubated at 4°C overnight. Wells were subsequently incubated with 200 µl of blocking buffer (PBS, 0.05% Tween-20, 1% BSA) for 1 h at room temperature prior to addition of 100 µl of an appropriate dilution of hmAbs or plasma IgG. Following a 1-h incubation, wells were washed thrice to remove unbound antibodies. A 1:2,000 dilution of an anti-IgG alkaline phosphatase conjugate (Sigma-Aldrich, cat. no. A9544) was used to probe wells for 1 h at room temperature. Wells were washed five times before addition of a phosphatase substrate (Bio-Rad, cat. no. 172-1063) as per the manufacturer's instructions. Signal detection was performed at OD405 using a microplate reader.

Further investigations into hmAb reactivity were performed using western blotting, as described elsewhere (33).

# Serum Bactericidal Activity (SBA) Assay

Functional activity of antimeningococcal IgG was determined using the standardized SBA assay (34). Briefly, 103 CFU meningococci were incubated with IgG (hmAb or purified total plasma IgG) (final assay concentration of 25%) and exogenous human complement (25%) for 90 min in a humidified CO2 incubator at 37°C, with gentle shaking. IgG that effected a reduction in meningococcal CFU by >50% after 90 min compared to negative controls (complement only; IgG only) was considered to possess SBA activity.

# RESULTS

# Induction of a Functional Immune Response in Patient SM-P02

Flow cytometric analysis showed that the circulating plasmablast population in patient blood was low, measured at 0.3% (**Figure 1**). To assess whether these plasmablasts represented a functional response to the meningococcal infection, total IgG purified from

patient plasma was assayed for bactericidal activity. Purified total IgG, rather than plasma, was assayed because of the presence of administered antibiotics in patient plasma. Reactivity of purified SM-P02 IgG to the patient isolate (M14-240312) and two other strains (MC58, which expresses the fHbp variant 1.1 in 4CMenB; and M08-240276, which is mismatched for all 4CMenB antigens) was assessed in ELISAs prior to assessment of SBA activity against the three strains. SM-P02 IgG bound to the infecting isolate, M14-240312, in cell-based ELISAs with corresponding SBA activity against the isolate. Despite significant binding of SM-P02 IgG to MenB strains MC58 and M08-240276 in ELISAs, no bactericidal activity was discerned against these strains. Thus, the induction of a specific bactericidal response in the patient to M14-240312 was confirmed (**Figure 2A**). Furthermore, the antimeningococcal immune response in patient SM-P02 was exclusive of the antigenic components of 4CMenB as no reactivity of SM-P02 plasma IgG to the vaccine antigens was discerned in ELISAs, under the experimental conditions employed in this study (**Figure 2B**).

## *In Vitro* Cloning and Expression of Antimeningococcal Antibodies From Patient Plasmablasts

Amplification of the VH and VL (κ or λ) gene segments from 336 plasmablasts occurred at a PCR efficiency of 80%. Sequencing of these variable region gene segments showed biased usage of the IGHV3-7:IGλV2-8 gene pair, accounting for 16.7% of V gene usage. Of 139 recombinant hmAbs that were successfully expressed in HEK-293 cells, eight (**Figure 3A**) were reactive with the infecting MenB strain (M14-240312) and, in varying degrees, with the other members of the MenB strain panel (**Figure 3B**). None of these eight hmAbs were reactive with *Actinobacillus pleuropneumoniae* cells (a porcine respiratory pathogen). All antimeningococcal hmAbs possessed the IGHV3 class gene, albeit with different subclasses and light chain gene pairs; the exception being P02-4F2 (IGHV4-59:IGκV1/1D-39). Two of these hmAbs (P02-5E10 and P02-6E9), however, possessed identical V gene pairs (IGHV3-30:IGκV4-1).

Specificity of the antimeningococcal hmAbs for the serogroup B capsule was ruled-out using the periodate assay, described in Ref. (35) (data not shown). Denaturing western blot data showed highly specific reactivity of three hmAbs (P02-1A1, P02-5E10, and P02-6E9) with a ~35 kDa meningococcal protein present in six members of the 17-strain panel (~35%). The target epitope of hmAb P02-1A1 was present in more strains (*n* = 6) than those of hmAbs P02-5E10 (*n* = 3) and P02-6E9 (*n* = 2) (**Figure 4**). The other five hmAbs, which were broadly cross-reactive with the strain panel (collectively recognizing 12 out of 17 strains), were non-reactive in denaturing western blots.

## Cloned Antimeningococcal hmAbs Possess SBA Activity Against Patient Isolate M14-240312

To assess the functional activity of cloned recombinant hmAbs, SBAs were performed using purified hmAbs at a final assay concentration of 80 µg/ml. Three hmAbs (P02-1A1, P02-5E10, and P02-6E9), in synergy with exogenous human complement, possessed SBA activity against strain M14-240312. All other hmAbs were non-bactericidal (**Figure 5**).

# SBA Activity of hmAbs Is Strongly Linked to Surface Expression of Target Epitopes

To assess the breadth of SBA exhibited by hmAbs P02-1A1, P02-5E10, and P02-6E9, each hmAb was assayed for bactericidal activity against the 17-strain panel. HmAb P02-1A1-mediated killing of the patient isolate, M14-240312, and four other strains to which it bound in immunoassays (M07-240646, M07-240657, M08-240014, and M10-240474). Only one strain, M07-240669,

12. M08-240164; 13. M08-240276; 14. M10-240474; 15. M10-240476; 16. M10-240480; 17. M11-240016; 18. M11-240123; 19. *A. pleuropneumoniae*.

which showed positive reactivity with hmAb P02-1A1 in immunoassays was resistant to SBA activity of P02-1A1.

Consistent with results obtained with P02-1A1, hmAb P02-5E10 possessed SBA activity against strain M10-240474 (to which it bound in immunoassays) but not strain M07-240669, suggesting resistance of M07-240669 to complement-dependent killing. Interestingly, all three bactericidal hmAbs could mediate killing of other strains to which no discernible reactivity was found in immunoassays; hmAb P02-1A1 reproducibly mediated killing of strain M08-240276 while both P02-5E10 and P02-6E9 mediated killing of strain M10-240474 (**Figure 6**; **Table 2**).

#### DISCUSSION

Despite vaccination, the ongoing threat from IMD is unquestionable, especially in the African meningitis belt, justifying the search for novel approaches to vaccine candidate discovery. Reverse vaccinology 2.0 has been useful in understanding the human adaptive immune response to disseminated viral (36, 37) and bacterial infections (38, 39). Hence, it presents as a potentially powerful tool that may identify novel meningococcal vaccine candidate antigens or reinforce the candidacy of some known antigens.

In the present study, we succeeded in isolating functional hmAbs from a 7-month-old patient convalescing from IMD despite challenging practical issues. The most affected age group for IMD is 6- to 24-months, and patients presenting at the St. Mary's PICU were mostly in this age group—children from whom the availability of sufficient quantities of blood sample for the isolation of PBMCs was very limited (4 ml). The isolation of specific antimeningococcal plasmablasts was further complicated by a paucity of knowledge on the magnitude and timing of peak plasmablast responses following primary meningococcal infection in infants. The plasmablast population measured in the single sample obtained at 7 days post-admission analyzed in this study was 0.5%. Plasmablast population following acute infection with dengue virus (37) occurred at 6–7 days postinfection. While the induction of peak plasmablast response following infection with nosocomially acquired bacteria such as *Acinetobacter baumanii* occurred in most patients at 8–16 days with 40–80% of the total B-cell population being plasmablasts, they composed >2% of B-cell population in patient samples obtained at 0–7 days postinfection (38). The

Figure 4 | Specificity of patient-derived antimeningococcal (hmAbs) for linear epitopes in denaturing western blots. Lysates from normalized suspensions of selected MenB strains were used as template in western blot experiments. A 1:1,000 dilution of each hmAb was employed.

low plasmablast induction seen in IMD patient SM-P02 may, therefore, be patient-specific and the timing of the peak level of cycling plasmablasts was likely missed (less than or more than 7 days post-admission). It is also plausible that low plasmablast induction is characteristic of a population at particular risk of IMD. Larger patient cohorts will be required to investigate this theory. Optimization of the IgG-cloning technique to increase the number of antimeningococcal plasmablasts using a recently published Ig capture-based assay (40) should improve productivity of the approach.

Reactivity of the antimeningococcal hmAbs isolated in this study with heterologous MenB strains possessing disparate PorA types strongly suggests that the antigen target of the hmAbs is not PorA, the immunodominant meningococcal antigen. It is likely that the three bactericidal hmAbs are reactive with a similar linear epitope contained in a yet-to-be identified ca. 35 kDa antigen. This 35 kDa antigen is surface-expressed in ~35% of the MenB strains, with heterologous 4CMenB antigen types, employed in this study. It is pertinent to note, however, that the ~35% presence of the linear epitope is specific to hmAbs generated in this study, and the antigen which it composes may possess more immunogenic surface-exposed epitopes and exhibit a wider presence among MenB and other meningococcal strains. One of these bactericidal hmAbs, P02-1A1, is reactive with a more diverse strain panel and possesses a higher bactericidal titer than hmAbs P02-5E10 and P02-6E9. This could be a result of somatic hypermutation of the P02-5E10 and P02-6E9 antibodies leading to the production of hmAb P02-1A1 with enhanced binding efficiency (affinity and/or avidity). Determination of the unequivocal identities of these hmAbs and their reactivities/ SBA with other serogroups is currently ongoing. Taken together, data generated so far on these bactericidal hmAbs (P02-1A1, P02-5E10, and P02-6E9) strongly suggest that their target is not PorA, fHbp, or any of the 4CMenB recombinant antigens signifying its novelty and most importantly, candidacy for inclusion in future vaccine preparations. It is acknowledged, however,

hmAbs normalized to final assay concentration of 80 µg/ml, in three biological replicates. Percentage survival after 60-min incubation (T60) in the presence of 25% human complement versus inoculum (T0) is shown.

Table 2 | Summary table showing reactivity of the bactericidal human monoclonal antibodies—P02-1A1, P02-5E10, and P02-6E9—in immunoassays (ELISA and western blot), and SBA activity versus the 17-strain MenB panel.

*I, immunoassay (ELISA and/or western blotting); SBA, serum bactericidal assay. Plus (*+*) denotes positive reactivity in immunoassays or bactericidal activity in SBA. Dash (–) denotes no discernible reactivity in immunoassays or bactericidal activity in SBA.*

that while our current data suggest that the 35 kDa antigen is absent from the NZ OMV component of 4CMenB, further work is required to determine its unequivocal absence. Given the need for protein antigens that would compose improved or entirely novel cross-serogroup antimeningococcal vaccines, data from this study show that reverse vaccinology 2.0 can be employed as a useful tool in identifying functionally immunogenic antimeningococcal antigens.

# ETHICS STATEMENT

Studies with human blood samples were approved by the London—Fulham Research Ethics Committee (Ref.: 11/LO/ 1982). Informed written consent was obtained from patients or their representatives in accordance with the Declaration of Helsinki. Patients were recruited following admission to the Imperial Healthcare (St. Mary's Hospital) Paediatric Intensive Care Unit (PICU), London, UK.

# AUTHOR CONTRIBUTIONS

Conceptualization: PL, JK, SN, and GS; investigation: FB and PL; writing—original draft: FB; writing—reviewing and editing: FB, PL, SK, SN, and GS.

# ACKNOWLEDGMENTS

We thank Stuart Gormley, Sobia Mustafa, and Michael Levin for their help in obtaining the patient sample, Hedda Wardemann for permitting the use of the AbVec vectors, and the Mongkolsapaya group for their help with the hmAb cloning protocol. This work was funded by a research grant from John and Michelle Bresnahan *via* MeningitisNow (awarded to PL, JK, SN, and GS) and an Imperial College Confidence-in-Concept Award (PL and FB).

# REFERENCES

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The reviewer MP and handling Editor declared their shared affiliation.

*Copyright © 2018 Bidmos, Nadel, Screaton, Kroll and Langford. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# A Web Resource for Designing Subunit Vaccine Against Major Pathogenic Species of Bacteria

Gandharva Nagpal 1,2†, Salman Sadullah Usmani 1,3† and Gajendra P. S. Raghava1,3 \*

*1 Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India, 2 Centre for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India, 3 Center for Computational Biology, Indraprastha Institute of Information Technology, Okhla, India*

Evolution has led to the expansion of survival strategies in pathogens including bacteria and emergence of drug resistant strains proved to be a major global threat. Vaccination is a promising strategy to protect human population. Reverse vaccinology is a more robust vaccine development approach especially with the availability of large-scale sequencing data and rapidly dropping cost of the techniques for acquiring such data from various organisms. The present study implements an immunoinformatic approach for screening the possible antigenic proteins among various pathogenic bacteria to systemically arrive at epitope-based vaccine candidates against 14 pathogenic bacteria. Thousand four hundred and fifty nine virulence factors and Five hundred and forty six products of essential genes were appraised as target proteins to predict potential epitopes with potential to stimulate different arms of the immune system. To address the self-tolerance, self-epitopes were identified by mapping on 1000 human proteome and were removed. Our analysis revealed that 21proteins from 5 bacterial species were found as virulent as well as essential to their survival, proved to be most suitable vaccine target against these species. In addition to the prediction of MHC-II binders, B cell and T cell epitopes as well as adjuvants individually from proteins of all 14 bacterial species, a stringent criteria lead us to identify 252 unique epitopes, which are predicted to be T-cell epitopes, B-cell epitopes, MHC II binders and Vaccine Adjuvants. In order to provide service to scientific community, we developed a web server VacTarBac for designing of vaccines against above species of bacteria. This platform integrates a number of tools that includes visualization tools to present antigenicity/epitopes density on an antigenic sequence. These tools will help users to identify most promiscuous vaccine candidates in a pathogenic antigen. This server VacTarBac is available from URL (http://webs.iiitd. edu.in/raghava/vactarbac/).

Keywords: reverse vaccinology, vaccine designing, immunotherapeutic, epitopes, antigen, virulence factor, essential genes

# INTRODUCTION

Evolution of existing bacterial pathogens and emergence of new pathogenic strains are continuously causing problems to mankind. To address the pathogenic challenges to the human health, researchers developed various vaccines and antibiotics during the twentieth century. A worldwide usage of such therapeutic strategies led to the expansion of structural as well as

#### Edited by:

*Pedro A. Reche, Complutense University of Madrid, Spain*

#### Reviewed by:

*Giampiero Pietrocola, University of Pavia, Italy Ghita Ghislat, INSERM U1104 Centre d'immunologie de Marseille-Luminy, France*

> \*Correspondence: *Gajendra P. S. Raghava<EMAIL_ADDRESS>
*†These authors have contributed equally to this work*

#### Specialty section:

*This article was submitted to Vaccines and Molecular Therapeutics, a section of the journal Frontiers in Immunology*

> Received: *29 June 2018* Accepted: *13 September 2018* Published: *02 October 2018*

#### Citation:

*Nagpal G, Usmani SS and Raghava GPS (2018) A Web Resource for Designing Subunit Vaccine Against Major Pathogenic Species of Bacteria. Front. Immunol. 9:2280. doi: 10.3389/fimmu.2018.02280*

**27**

behavioral properties in various bacterial species. Diverse defense mechanisms were acquired by the known bacterial species that resulted in antibiotic tolerance or drug resistance, currently a major concern during administration of known antibiotics in pathogenic conditions. Lord Jim O'Neil and his team estimated that antimicrobial resistance could cause 10 million deaths a year by 2050 (1).

Vaccination has proved to be a promising strategem to protect human population from dreadful diseases like smallpox, polio, etc. Yet, vaccines are currently unavailable for many infectious diseases such as melioidosis caused by Burkholderia pseudomallei (2). Even among the existing vaccines, BCG which is heavily used vaccine against tuberculosis, is less effective in the immunocompromised and adults in tropical and subtropical region (3) and its efficacy is rapidly decreasing (4). Anthrax, DPT, Hib, Meningococcal vaccine, Pneumococcus heptavalent conjugate polysaccharide vaccine (PCV7), Pneumococcus 13 valent conjugate polysaccharide vaccine (PCV13) etc are generally considered effective at controlling disease symptoms. However, a major concern of recent vaccination programs tends to be the preference of vaccine escape mutants, i.e., the repertoire of immune targets in the pathogen is different from that used in the vaccine formulation (5). Antigenic variation and continuous bacterial evolution has necessitated designing of better vaccine candidates.

Virulence factors (VFs) have been explored to design vaccines against several pathogens such as Porcine enteric coronaviruses (CoVs) (6), Vibrio cholerae, enterotoxigenic Escherichia coli (7) etc. Bacterial virulence factors (VFs) are captivating, as they play a major role in establishment of infection to cause pathological conditions while surviving in a hostile environment (8). Virulence factors are thus defined as the gene products enabling the pathogen to enter, replicate, and persist in a host even in a small inoculum. These include cell surface proteins that facilitate bacterial attachment, cell surface carbohydrates which protect a bacterium, bacterial toxins, and hydrolytic enzymes that subsidize pathogenicity of bacterium (8).

In addition to virulence factors, genes essential for the living of the bacterial cell, have proved to be attractive drug targets in various pathogens such as the fungus Aspergillus fumigatus (9, 10), protozoan Leishmania species, (11) as well as bacteria like Streptococcus pneumoniae, Haemophilus influenza, and Moraxella catarrhalis (11). Essential genes are indispensable for cellular metabolism.

The last two decades witnessed an embracement of a more revolutionary vaccine development approach, namely the information driven "reverse vaccinology" which involves identification of vaccine candidates by genome or proteome sequence analysis as the primary step. High cost and practical limitations of feasibility and time have engendered a predilection toward computational techniques for rational reduction in the number of vaccine candidates to be experimentally tested. The epitope prediction methods have made possible searching of sequence sets as large as the whole proteomes of organisms with various restriction filters to arrive at novel vaccine candidates.

In order to facilitate the scientific community, researchers have developed numerous pipelines, databases, and tools for identifying vaccine candidates against specific types of diseases (12, 13). Recently, researchers have proposed potential vaccine candidates against diverse pathogens like Ebola (14), Zika (15), M. tuberculosis (16), Shigella (17), Hepatitis B Virus (18), Helicobacter pylori (19), Vibrio cholera (20), Dengue (21), etc. using in silico predictions. These predicted candidates help reduce the burden in experimental labs for the researchers, thus saving time and cost of vaccine development. Most of the abovementioned studies only focus on a single pathogen and lacked identification of adjuvant candidates.

In this study, we have systemically arrived at epitope based vaccine candidate against major pathogenic bacterial species. **Table 1** provides a summary of the major pathogenic manifestations of the bacterial species considered in the present study. Both, the virulence factors and the essential genes, have been considered to predict epitopes. It is a well-known fact that, diverse bacterial species and strains have evolved multiple mechanisms for evading the host immune responses, not only by producing likes of host inhibition pathway receptor ligands ("molecular mimicry") but also by producing virulence factors resembling the intermediates of the host inhibitory pathways. Hence, peptides found in proteomes of the 1000 human genomes were removed while implementing the pipeline. To assist the scientific community all the potential vaccine candidates have been compiled and presented in the form of a database (http:// webs.iiitd.edu.in/raghava/vactarbac/).

# MATERIALS AND METHODS

#### Raw Data Source

Proteins were taken as raw data to predict the potential vaccine candidates. We considered both virulence factors as well as essential genes of pathogenic bacteria.

#### Virulence Factors

The Virulence Factor Database (VFDB) is a resource for housing the Virulence Factors (VFs) that enable a microorganism to establish itself on or within a host thus bestowing the pathogen with a potential to cause disease (22). VFDB provides two hierarchical non-redundant datasets, i.e., sets A and B. Set A is a core dataset, covering genes associated with experimentally verified virulence factors. Therefore, we have selected protein sequences of core dataset A for further analysis. The link http://www.mgc.ac.cn/VFs/Down/VFDB\_setA\_pro.fas. gz provides protein sequences of the VFDB database in FASTA format when uncompressed using the command "gunzip" on a unix command line or using the tool "Winzip" on a Windows platform.

#### Essential Genes/ Proteins

Database of Essential Genes (DEG) maintains currently known essential genomic elements, such as protein-coding genes and non-coding RNAs, among bacteria, archaea, and eukaryotes (22). These genes are mandatory for survival of the corresponding bacterium. DEG contains 18286 proteins encoded form genes experimentally found to be essential for the survival of the 32 bacteria. For our study, we restricted the analysis of bacterial TABLE 1 | Bacterial species considered in the study with the pathological conditions caused by them.

essential genes belonging to 14 species that have also been included in VFDB, for the convenience of comparison and combining with VFDB analysis.

#### Target Selection for Vaccine Identification

The most important aspect of subunit vaccine designing is vaccine target selection. In case of data downloaded from VFDB (set A), it contains 2595 proteins (11) from 46 bacterial species. We have matched these bacterial species into DEG database that resulted into 14 common bacterial species. Therefore, our dataset contains only those bacterial species, which have atleast one virulence factor and one protein encoded by essential genes from VFDB and DEG respectively. DEG contains 9664 proteins corresponding to these pathogenic bacteria. These are the essential proteins of pathogenic bacteria. Some specific bacterial components, because of their function proved to be better possible candidate for designing subunit vaccine (20, 23– 25) therefore; we have selected some specific significant proteins.

#### Membrane Protein

These are the protein emerged from the membrane of various pathogenic bacteria. There are evidences that membrane proteins mediate pathogen entry and colonization and are also the primary target of adaptive immune response (24). A total of 420 membranous proteins were selected from DEG for further processing.

#### Envelope Protein

These proteins are derived from the envelope of various pathogenic bacteria and are essential for their survival. These are important for attachment to host cell surface and also responsible for facilitating an immune response in the host cell (25). A total of 36 proteins were screened for further processing.

#### Secretion Protein

Fifty-two secretory proteins corresponding to bacterial species of our interest were deposited in DEG and play important role in their survival. Secretory system of pathogenic bacteria has been heavily exploited for screening of new possible vaccine (26, 27). These proteins play many roles in promoting bacterial virulence, from enhancing attachment to eukaryotic cells, to scavenging resources in an environmental niche, to directly intoxicating target cells and disrupting their functions (28).

#### Repair Protein

Bacterial damage repair mechanisms have broader roles encompassing responses to virulence as well as stress (29). Therefore we have also considered 38 proteins associated with the repair mechanism of various pathogenic bacteria.

# Epitope Prediction Pipeline Generation of Nona-Peptides

In order to design new vaccine candidate, first we have generated nona-peptides. Nona-peptides were 9-mer sequences (9 residues continuous stretch of peptide) originated from the essential or virulent proteins selected for the study. In order to avoid redundancy, we have removed all duplicated nona-peptides.

#### Removal of Self-Epitopes

Self-tolerance must also be considered in vaccine designing, as body's immune system rarely acts against self-epitopes**.** Therefore, all the nona-peptides, which are also present in human body need to be removed. To achieve this, we mapped nonapeptides on 1000 human proteome, and removed all the nonapeptides which are 100% identical.

#### Epitope Prediction

Our subsequent objective was to select peptides, which could activate human immune system, and generate memory cells. Therefore, we have used a pipeline for predicting different kinds of immune epitopes among these nona-peptides. This pipeline predicts; (i) B-cell epitopes using LBtope (30) (ii) MHC class II binders using ProPred (31) (iii) T-cell epitopes using CTLpred (32) and (iv) vaccine adjuvant using VaxinPAD (1).

#### IEDB Mapping

In addition to these tools, the experimentally reported epitopes present in the Immune Epitope Database (IEDB) were also mapped on the vaccine target proteins selected for this study. **Figure 1** shows the complete workflow of this study.

#### Database Web Interface

All the predicted epitopes have been stored and presented on a web-portal named as VacTarBac. It is built on Apache HTTP server (version 2.2.17), which is installed on machine with Ubuntu as operating system. The responsive front-end, which is suitable for mobile, tablet, and desktop, was developed using HTML5, CSS3, PHP5, and JavaScript. MySQL (a relational database management system, version 5.0.51 b) was used at the back-end to manage the data.

## RESULTS

# Screening of Bacterial Species

The VFDB virulence factor proteins download link is available at http://www.mgc.ac.cn/VFs/Down/VFDB\_setA\_pro.fas.gz, providing all the sequences in FASTA format with the headers providing the information for the protein, for example, the VFDB id, source organism, etc. From these headers, the source organism names were extracted. The DEG is available for download at http://www.essentialgene.org in the form of FASTA sequences too. Only those sequences from DEG were extracted that sourced from organism species (indicated by the headers) present in the VFDB headers too. Finally, the protein sequences from the VFDB and DEG belonged to 14 bacterial species.

# Screening of Target Proteins

These 14 bacterial species contain 1459 virulence factor as stated in VFDB. All these 1459 virulent factors were selected for the further study. Previous studies have advocated that bacterial proteins at specific cellular locations (20, 23–25, 33) or performing specific functions essential for cell (34) are potential candidates for designing subunit vaccine. Therefore, on the basis of function and localization with in the cell, 546 Proteins from the 14 bacterial species, were categorized as membrane, envelope, repair, and secretory proteins. **Table 2** shows distribution of screened target proteins among selected 14 bacteria.

The most important aspect of this rational determination of vaccine targets was to extract proteins common in both VFDB and DEG dataset. Our analysis shows that 21 IDs from VFDB dataset were identical to 35 IDs of DEG dataset. (**Supplementary Table 1**) These are 21 proteins from 5 bacterial species. We consider these as the most suitable vaccine targets, being both, virulence factors as well as gene products essential for survival.

### Epitope Predictions

After generating the nona-peptides, redundant nona-peptides as well as self-antigens were removed by mapping on 1000 human proteome. For rest of the peptides, we predicted the immunogenicity using our prediction pipeline. LBtope, ProPred, CTLpred, and VaxinPAD helped us identify B-cell epitopes, MHC class II binders, T-cell epitopes, and adjuvants respectively. These tools integrated and implemented as a pipeline helped identify the immunogenic regions of the individual proteins in the form of nona-mer epitopes. On the other hand, the visualization tools integrated in the platform "VacTarBac" helped envisage the

stretches of lengths longer than nona-mer having high density of predicted epitopes. The user may anticipate that these regions could prove to be antigens effective in the form of vaccines. Thus, for experimental investigation of the sequences that may prove to be effective vaccines, sequences longer than 9-mers containing numerous predicted epitopes could be taken up as a better strategy for the development of vaccines.

The epitopes predicted individually by each tool of our pipeline are too many to be tested experimentally. (**Table 3**) Therefore, we have applied an intuitive method to arrive at a reasonable number. (**Figure 2** and **Supplementary Table 2**) Epitopes were separately identified for T-Cell and B-Cell categories. In case of T-Cell epitopes only those were retained that were predicted T-Cell epitopes, MHC-II Binders as well as adjuvants. For B-Cell epitopes, peptides predicted to be B-Cell epitopes, MHC-II Binders and adjuvants were finalized for recommendation as therapeutics.

#### Epitopes Assessed to be the Best Vaccine Candidates

The aim of this study is to enhance the vaccine designing against various pathogenic bacteria by predicting potential MHC binder, B cell and T cell epitopes as well as vaccine adjuvants using a prediction pipeline. Yet, the idea of using a pipeline instead of the prediction tools individually was intended to pose stepwise filters on the numerous peptides that may be predicted as epitopes for different arms of the immune system. These filters reduce the number of epitopes to be experimentally tested and lead to a final set of peptide sequences with more possibility of activating immune system as these are recommended by all the individual tools in the prediction pipeline. Epitopes positively predicted by all the prediction tools, provided coverage of 13 out of 14 bacterial species. An organism-wise as well as vaccine target protein category-wise representation as shown in **Table 4** provides more comprehensive analysis of the epitopes assessed

#### TABLE 2 | Bacterial species-wise distribution of Target Proteins.

TABLE 3 | Distribution of proteins, nona-peptides and predicted epitopes among various categories.

to be the "best vaccine candidate" using all the prediction tools.

#### Best Possible Antigenic Proteins

As stated earlier, the study started with 1459 virulence factor and 546 essential proteins, we have predicted epitopes from all of the generated nona-peptides. Some proteins, have high number of predicted MHC binders, T cell eptiopes, B cell epitopes as well as vaccine adjuvants. For example, the peptide syntase (pyoverdine) protein, a virulence factor of Pseudomaonas aeruginosa results into 8197 predcited epitopes. Such proteins or regions within the proteins could be wisely used as potential antigens to stimulate the immune system (**Supplementary Table 3**). **Figure 3** shows the portion of an essential protein from Bacillus subtilis, which has a high number of predicted epitopes. This indicates that instead of experimenatlly validating a single nona-peptide, taking a longer portion of the protein, having overlapping predicted epitopes, could prove to be a better approach.

*Considering all the epitopes together in this table, these belonged to 13 bacterial species out of the total 14 species considered for the study.*

FIGURE 3 | Mapping of predcited epitopes on one of the essential protein of *Bacillus subtilis* as (A) user friendly interactive Java-enabled view and (B) traditional simpler view. The blue colored sequences are the predicted 9-mer epitopes starting from red colored amino acid.

#### Webserver Implementation

To assist the scientific community in expediting the peptidebased vaccine designing against pathogenic bacteria, all the predicted potential epitopes were compiled in the form of a database and were provided as a web-based service (http:// webs.iiitd.edu.in/raghava/vactarbac/). A user can browse all the

recommended potential B cell and T cell epitopes designed by targeting virulence factors as well as proteins encoded by essential genes of all 14 bacterial species. Browsing by the pathogenic bacteria as well as targeted proteins considered while designing of potential epitopes is also implemented. A user can also browse the top 5 antigens from the enlisted bacteria. In addition, a list of proposed vaccine candidates is also provided in the browse option (**Figure 4**). For the ease of user, results have been displayed in tabular and graphical form as well as an interactive visualization.

# DISCUSSION AND CONCLUSION

The twentieth century witnessed a remarkable success in development of antibiotics against various pathogenic bacteria but the emergence of drug resistance and toxicity caused a shift in the antimicrobial strategies toward peptide-based therapies. Several anti-bacterial peptides have been studied to check the pestiferous effects of pathogenic bacteria (35, 36). Rapid bacteriocidal activity and low propensity for resistance development are some of the major assets of these peptides, but high cost, limited stability and unknown toxicology, and pharmacokinetics are the major disadvantages (37). Contrary to antibiotics or peptide-based drugs, instigating host immune responses against the pathogen offers multifaceted management of the invasion and has been successfully achieved in the past by the use of vaccines like DPT, BCG etc against few deadly infectious diseases like tenaus, diptheria, tuberculosis etc. Consequently, active search for vaccines is underway currently for many infectious diseases caused by pathogenic bacteria, for example, melioidosis caused by Burkholderia pseudomallei (2). Even in case of tuberculosis, for which multiple lines of drugs and BCG vaccines are available; reduced efficacy of BCG and antibiotic resistance forced investigation of better and more potent vaccines. A recent computational effort has gone up to the strain level comparison and considered proteins as vaccine targets that were shared among tuberculoid, non-tuberculoid, and vaccine strains (16). Continuous efforts are being made by researchers to identify novel vaccine candidates against several pathogens such as Ebola virus (14), Zika virus (15), vaccinia virus (38), Neisseria meningitidis (39), Corynebacterium pseudotuberculosis (40) Edwardsiella tarda, and Flavobacterium columnare; fish pathogens (41, 42).

In past, several in silico, protein-based vaccine candidates have been identified, using peptide conservation score and predicted peptide-MHC binding (43) as well as molecular docking and MHC-peptide complex stabilization assay (18). In this study, we employed immunoinformatic tools to identify antigen and epitope-based vaccine candidates, having the potential to evoke one of the many arms of immune system. The present study includes proteins; virulence factors as well as proteins encoded by genes essential for survival from multiple pathogenic bacteria. The pipeline created for the identification of vaccine candidates, consists of widely used, accurate and recommended tools (12, 13). Our pipeline also includes a tool to identify peptide-based adjuvant candidates, a major advantage over other studies. The numbers of epitopes predicted by the individual tools within the pipeline that are not present in the proteome derived from the 1000 human genomes, were in thousands. For arriving at economical number of epitopes that could be tested experimentally, an intuitive rational criterion was to extract epitopes predicted positive by more than one tool in the pipeline. This would effectively mean that the selected epitope could have a predictive capability of evoking different arms of the immune system.

Upon executing the rational criteria, 1459 virulent factors from VFDB yielded 3622 nona-peptides having ability to bind with MHC-II, activate T-cell response and could act as selfadjuvants. Beside this, peptides are predicted to bind with MHC-II, activate B-cell response as well as self-adjuvants. Similarly, 420 membrane proteins from the DEG provided 1307 predicted T-cell epitopes and 155 predicted B-cell epitopes that are also MHC II binders and adjuvants. The 36 DEG envelope proteins when subjected to the epitope prediction pipeline, yield 104 predicted T-cell epitopes and 20 predicted B-cell epitopes with probability that these will bind MHC Class II molecules and would also be adjuvants. In cases of 52 secretory proteins and 38 repair proteins taken from the DEG, the predicted T-cell epitopes, and B-cell epitopes were respectively 173 and 23 for secretory proteins and 138 and 44 for the repair proteins, all of them being also the positively predicted MHC II binders as well as adjuvants.

In conclusion, the ultimate aim of the present study was the identification of epitopes capable of activating multiple wings of the human immune system arrived at by filtering the epitopes through stringent criteria. Combining the results of the VFDB and DEG datasets, this study was able to identify 252 unique epitopes predicted to be T-cell epitopes, B-cell epitopes, MHC II binders and Vaccine Adjuvants not present in the human proteome (belonging to the 1000 genomes) extracted from proteins (essential gene products and/or virulence factors) of 13 bacterial species. All the recommended vaccine candidates have been stored in a repository; VacTarBac (http://webs.iiitd.edu.in/ raghava/vactarbac/) freely available on the world-wide web.

Peptides are the promising candidate as immunotherapeutic but are less immunogenic when used alone as vaccine. They need potent immunostimulatory adjuvants to effectively activate the innate and adaptive arms of immune system. In this study, we have implemented VaxinPAD in our prediction pipeline, which aids in predicting based vaccine adjuvant, thus strengthening the study. Beside this, peptide formulation is a critical task and a formulation scientist must overcome the chemical instability of peptides. The conformational fluxionality and propensity to self-associate makes peptide more difficult to formulate. Various computational tools predict the aggregation propensities of polypeptide chains such as Zyggregator (44), PASTA 2.0 (45), etc. These can be used prior to formulating the peptide-based vaccine. THPdb is a database of FDA approved protein and peptide therapeutics, and provide detailed formulation strategies of peptide-based drug available in the market (46). Although little literature exist about formulating peptide drug product but strategies used in successful peptide drugs will be helpful in designing newer formulation techniques and constituent for peptide based vaccines.

#### REFERENCES

1. Neill JO'. Antimicrobial Resistance: Tackling a Crisis for the Health and Wealth of Nations The Review on Antimicrobial Resistance Chaired (2014).

We have predicted epitopes based on highly cited, published and accurate immune epitope prediction tools. Yet, these prediction algorithms have their own limitations. Thus, the antigen or epitope should be experimentally validated before suggesting it for medical purpose. Apart from this, the platform, VacTarBac, would require continuous revamp owing to the rapidly growing sequencing data particularly that of the novel and emerging pathogenic strains. A future work for this platform may include comparison of the pathogenic proteomes at the strain level. This may lead to identification of recently acquired proteins/peptides of the pathogen that may have rendered existing therapeutic strategies against the emergent strain ineffective. Moreover, the platform, VacTarBac, still lacks information of vaccine formulations that may be added in future to help in the actual development of the vaccines. Despite such limitations, we anticipate that the current study and the current data in the repository VacTarBac, will be helpful for researchers and will boost and hasten the vaccine designing against pathogenic bacteria considered in the study.

## AVAILABILITY AND UPDATE OF THE RESOURCE

VacTarBac is freely available at http://webs.iiitd.edu.in/raghava/ vactarbac/. We will update the platform at every 6 months depending upon the availability of virulence factor and essential genes information in other resources.

#### AUTHOR CONTRIBUTIONS

SU and GN downloaded and processed the data. GN prepared the pipeline and predicted epitopes. SU and GN analyzed results and prepared tables and figures. SU, GN, and GR wrote the manuscript. SU developed web interface. GR conceived the idea and coordinated the project.

### FUNDING

Authors are thankful to funding agencies J.C. Bose National Fellowship (DST), Department of Biotechnology (DBT) and Council of Scientific and Industrial research (CSIR) for fellowships and financial support.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu. 2018.02280/full#supplementary-material

2. Peacock SJ, Limmathurotsakul D, Lubell Y, Koh GCKW, White LJ, Day NPJ, et al. Melioidosis vaccines: a systematic review and appraisal of the potential to exploit biodefense vaccines for public health purposes. PLoS Negl Trop Dis. (2012) 6:e1488. doi: 10.1371/journal.pntd.0001488

multi-epitope peptide vaccine against Staphylococcus aureus. Infect Genet Evol. (2017) 48:83–94. doi: 10.1016/j.meegid.2016.12.010

binding and peptide conservation scores. PLoS ONE (2014) 9:e115745. doi: 10.1371/journal.pone.0115745

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Nagpal, Usmani and Raghava. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Bacterial Vaccine Antigen Discovery in the Reverse Vaccinology 2.0 Era: Progress and Challenges

Fadil A. Bidmos\*, Sara Siris, Camilla A. Gladstone and Paul R. Langford

Department of Medicine, Imperial College London, London, United Kingdom

The ongoing, and very serious, threat from antimicrobial resistance necessitates the development and use of preventative measures, predominantly vaccination. Polysaccharide-based vaccines have provided a degree of success in limiting morbidity from disseminated bacterial infections, including those caused by the major human obligate pathogens, Neisseria meningitidis, and Streptococcus pneumoniae. Limitations of these polysaccharide vaccines, such as partial coverage and induced escape leading to persistence of disease, provide a compelling argument for the development of protein vaccines. In this review, we briefly chronicle approaches that have yielded licensed vaccines before highlighting reverse vaccinology 2.0 and its potential application in the discovery of novel bacterial protein vaccine candidates. Technical challenges and research gaps are also discussed.

#### Edited by:

Pedro A. Reche, Complutense University of Madrid, Spain

#### Reviewed by:

Sudheer Gupta, All India Institute of Medical Sciences Bhopal, India Pietro Speziale, Università degli Studi di Pavia, Italy Paola Massari, Tufts University School of Medicine, United States

> \*Correspondence: Fadil A. Bidmos<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Vaccines and Molecular Therapeutics, a section of the journal Frontiers in Immunology

> Received: 30 June 2018 Accepted: 17 September 2018 Published: 08 October 2018

#### Citation:

Bidmos FA, Siris S, Gladstone CA and Langford PR (2018) Bacterial Vaccine Antigen Discovery in the Reverse Vaccinology 2.0 Era: Progress and Challenges. Front. Immunol. 9:2315. doi: 10.3389/fimmu.2018.02315 Keywords: reverse vaccinology 2.0, human monoclonal antibodies, bacterial pathogens, vaccine candidate antigens, immunotherapy

## BACKGROUND: VACCINE DISCOVERY IN THE PRE-WHOLE GENOME SEQUENCING (WGS) ERA

Precedent to advancements in genomics, vaccines were developed based on Pasteur's rules of vaccinology. A 230-fold serial passage of a bovine bacillus in bile medium produced the live attenuated Bacillus Calmette-Guerin (BCG) vaccine against Mycobacterium tuberculosis (MTB) (1). Then, a trivalent blend of poliovirus inactivated in <0.5% formalin was used by Salk et al. as a safe and effective vaccine against poliovirus (2). The inability to culture some pathogens in vitro (owing to safety or lack of suitable culture conditions), extensive antigenic variability, and molecular mimicry limit the broad applicability of traditional culture-based techniques in the development of vaccines targeting other economically-important pathogens such as Mycobacterium leprae and Neisseria meningitidis.

The inadequacies of culture-based techniques caused a shift in focus to the use of subunit components as vaccine candidates. Identification of these subunit vaccine candidates was largely hypothesis-driven, targeted cellular components and were often well-known virulence factors: for example, the pertussis toxin and fimbriae in the acellular pertussis vaccine (3); and the meningococcal porin, PorA, in the epidemic-specific detergent-extracted outer membrane vesicle (OMV) vaccines of Chile, Norway, and New Zealand (4–6). With complex biosynthetic methods, bacterial capsular polysaccharides served as prime components of effective vaccines, used singly, for example, in the 23-valent pneumococcal polysaccharide vaccine, PPSV23 (7). In addition, these capsular antigens have been conjugated to carrier proteins in a dose-sensitive manner for enhanced immunogenicity, as found in the Haemophilus influenzae type b (Hib) (8), pneumococcal

**38**

(9), and meningococcal (10) conjugate vaccines. The limited capacity of these hypothesis-driven studies to focus on only a handful of candidates at a time was costly in time, labor and financial terms. This is especially because of the large pool from which prospective candidates for individual bacterial pathogens are screened, coupled with the low likelihood of targets satisfying key vaccine candidacy criteria (abundantly-expressed, surfaceexposed, functionally-immunogenic, and highly-conserved). Thus, alternative high throughput methods were sought to accelerate the pre-clinical vaccine development phase, especially in situations requiring rapid curtailment of disease transmission.

## WHOLE GENOMIC AND PROTEOMIC APPROACHES

# Reverse Vaccinology (RV)

The publication of the first complete bacterial genome sequence in 1995 [for H. influenzae (11)] heralded a revolution in approaches to vaccine development. By using genomic data and preset bioinformatic screens, putative surface-associated antigens of a pathogen were identified. The subsequent recombinant expression of these genes and immunization of animals with recombinant proteins, for the determination of active and passive levels of protection, provided data that substantiated or annulled the vaccine candidacy of selected antigens (12, 13). This "classical" RV approach led to the development of the multicomponent meningococcal serogroup B vaccine (4CMenB) (14). While 4CMenB has potential for crossserogroup protection (15), it has been argued that pan-genomic in silico analysis is more appropriate because of the high degree of intraspecific diversity exhibited by many bacterial pathogens (16). Using this pan-genomic approach, Maione et al. (17) identified four protective antigens from the analysis of an octa-genomic panel derived from the most prevalent diseasecausing Streptococcus agalactiae strains. The main attraction of RV lies in its applicability to any pathogen with WGS data and to which antibody-mediated immunity for protection against disease is crucial. Its use in the discovery of candidate antigens comprising vaccines targeting other bacterial pathogens, including the multidrug-resistant Acinetobacter baumanii, has been demonstrated (18–20). However, important non-classical surface-associated proteins may be missed due to the parameters of the bioinformatic screen(s).

Related to RV is the use of transcriptomics to identify novel vaccine antigens. For example, the comparative analysis of the meningococcal transcriptome in ex vivo human whole blood and in vitro nasopharyngeal colonization models revealed three antigens that were differentially regulated between invasive disease and asymptomatic colonization, and were thus subjects for further vaccine candidacy studies (21) However, this transcriptomics-based approach has not been widely employed.

#### Surfome and Secretome Analysis

Whole proteomic approaches, involving enzymatic processing of whole cells or extracellular exudates followed by liquid-chromatography mass spectrometry (LC-MS) or peptide fragment fingerprinting, also allow for high-throughput screening of the antigenic repertoire of a pathogen (22). The power of these proteomic methods in identifying rare protective antigens missed by the in silico screens of RV makes them appealing [as exemplified by the case of the cell wall-anchored antigen, SAN\_1485, of S. agalactiae (23)]. Converse to RV, proteolytic digestion is more suited toward Gram-positive bacteria, since Gram-negative bacteria are more susceptible to proteolysis-induced cell lysis.

# REVERSE VACCINOLOGY 2.0

The majority of currently-available bacterial vaccines provide protection by inducing pathogen-specific antibodies. Therefore, harnessing the antibody component of a potent human humoral response to disseminated infection is valuable for the identification of novel protective antigens. This approach, termed reverse vaccinology 2.0 (RV 2.0) (24, 25), relies on the isolation and recombinant expression of the variable regions of heavy (VH) and light (VL = κ or λ) chain genes of immunoglobulin (focus has centerd on IgG) using a variety of molecular tools. Enriched by the development of high-throughput technologies, the screening of large numbers of antibody-secreting cells (ASCs) is also advancing knowledge of host-pathogen interactive biology and auto-immunity (26, 27).

# Monoclonal Antibody (mAb) Generation From ASCs

The first, and perhaps most crucial, phase of RV 2.0 is the cloning of human monoclonal antibodies (mAbs) from ASCs. Previously, immortalization of these ASCs via myeloma fusions or Epstein Barr virus (EBV) transformation were valuable to mAb production (28, 29). Because these were culture-based methods, the survival of all B-cells was not guaranteed and the omission of ASCs expressing antibodies cognate to crucial antigens was probable. Other techniques such as phage-display technology (30) and proteomic mining (31, 32) circumvent the unique issues affecting ASC immortalization techniques by focusing on recombinant antibody expression. However, the small proportion of antigen-specific antibodies (estimated at 10– 15%) that are produced (33) because of the random pairing of VH and VL sequences make phage display and proteomic mining imprecise.

A more favored approach to mAb cloning is the single-cell sorting of ASCs into multi-well plates using flow cytometry, followed by the cloning of mAbs from each well (34, 35). To clone a high proportion of antigen-specific antibodies, this approach, termed expression cloning, requires blood sampling during the peak immune response and is thus more suited to short-lived plasmablasts (CD3−, CD14−, CD19+, CD20−, CD56−, CD27high, and CD38high), since higher circulating numbers of these are indicative of very recent history of infection (36). Notwithstanding, several studies have demonstrated its applicability to memory B-cells (37, 38). Further in vitro selection of antigen-specific plasmablasts or memory B-cells using eGFP-bound viral-like particles (39), labeled-antigen probes (40, 41) or in vivo antigen-specific plasmablast enrichment in irradiated SCID/beige chimera mice (42) enhance the pathogen-specific mAb output of the approach. Converse to phage display and proteomic mining technologies, expression cloning yields mAbs with natural, host-like VH+VL pairings. Further refinements to this elegant method include: substituting restriction endonuclease cloning with Gibson assembly to enhance cloning precision (43); assembly of both VH and VL fragments into a single expression vector (44); and succeeding cell sorting with paired-chain antibody repertoire sequencing, thereby encompassing all V gene families, including unique clones expressed at low frequencies (33, 45, 46).

#### Assessment of Recombinant mAb Function

Subsequent to cloning, the clinical relevance of mAbs is assessed in in vivo investigations of passive immunity (47) or in vitro functional assays: for example, the well-established viral neutralization (48) and serum bactericidal assays (49), some of which have provided data employed in vaccine licensure (50). The cognate antigens targeted by functional mAbs can subsequently be determined using protein array screens or classical immunoproteomic approaches.

## Application of RV 2.0 to Viral Vaccine Development

The power of RV 2.0 (see **Figure 1**) in the identification of viral vaccine candidates has been demonstrated in several studies focussing on human cytomegalovirus (HCMV), respiratory syncytial virus (RSV), HIV, influenza and dengue viruses (25, 51). Some of these candidate antigens, discovered using RV 2.0, include a novel pentameric glycoprotein complex, the gHgLpUL128L pentamer, which induces high neutralizing titres against HCMV in mice (52) and the F protein of RSV stabilized to the prefusion conformation (53, 54). Accruing data from phase 1b/2a clinical trials show that a mAb (MEDI8897) reactive with prefusion F epitopes is effective when used prophylactically in preterm infants (55). Like MEDI8897, mAb MHAA4549A, cloned from a healthy vaccinee and which targets and neutralizes all known influenza A strains (56), demonstrated significant antiviral activity in a phase 2 human influenza A virus challenge (57). Thus, these studies have signified the use of RV 2.0 in producing broadly-neutralizing mAbs for post-infection prophylaxis in addition to identifying functionally-immunogenic vaccine candidates.

# POTENTIAL APPLICATION TO ANTIBACTERIAL VACCINE DISCOVERY

Judging by the progress made with the development of novel and effective viral immunotherapies, RV 2.0 is showing promise and is equally applicable to bacterial vaccinology. RV 2.0 was employed by Lu et al. (58) to identify functional anti-Staphylococcus aureus mAbs induced during bacteraemia. A total of ten mAbs were produced, four of which enhanced opsonophagocytosis of Wood46, a S. aureus reference strain. While three of the four functional mAbs targeted S. aureus antigens with known identities, the fourth mAb reacted with a novel antigen. Recently, Zimmermann et al. (59) also demonstrated that functional anti-MTB surface antigen antibodies can be cloned from patient-derived plasmablasts of reactivated memory B-cell origins, providing further evidence for a role for antibodies in the modulation of potent immune responses toward MTB. Taken together with other studies investigating the importance of antibody-mediated neutralization of intracellular pathogens (60), a role for the vaccine-induced generation of antibodies against pathogens such as MTB and Chlamydia trachomatis, using antigens derived with RV 2.0 is, thus, evidenced. Similarly, Bidmos et al. (61) and Blum et al. (45) cloned functional antibodies from sufferers of meningococcal and Lyme disease, respectively; thus, underscoring the utility of the approach for identifying novel targets in different classes of bacteria. Continued use of RV 2.0 in bacterial vaccine discovery is, therefore, encouraged following the surmounting of technical challenges and filling of research gaps. In the following sections of this mini-review, emphasis will be placed on human mAb cloning and serological correlates of protection, since other related technical aspects of RV 2.0 such as recombinant protein expression, high-throughput sequencing of bacterial genomes and antibody repertoires, antigen identity determination and structure-based antigen design have been reviewed elsewhere (62–66).

# Pathogen-Specific mAb Output

To identify novel antigens using the expression cloning method with precision, plasmablasts from patients convalescing from bacterial disease are required. Fundamental to the application of the expression cloning approach, therefore, is the determination of the magnitude and peak duration of the plasmablast response in these patients. The information on the duration of peak plasmablast circulation instructs optimum sampling time, which in turn impacts on the precision of pathogen-specific mAb generation. Studies assessing this magnitude of circulating plasmablast following bacterial infection have reported similar durations of peak response to those reported for primary or secondary viral infections [6–7 days for primary infections and ∼10 days post-infection for secondary infections; reviewed in (36)]. Recently, Band et al. (67) reported a significant induction of differentiating (Ki-67+) plasmablasts in patients of nosocomial bacterial infections compared to healthy controls. This induction peaked between days 8 and 16 post-culture positivity in A. baumanii-infected patients reaching levels as high as 21% of the total lymphocyte population. Perhaps unsurprisingly, it was also observed that this induction was markedly different in individuals, reflecting differences in immunocompetence, as peak plasmablast levels ranged from: 1 to 21% among A. baumaniiinfected patients; and 5–40% in Escherichia coli-infected patients. Consistent with the findings of Band et al. (67), a plasmablast response presented to: MTB infection in 38% of a patient cohort with levels ranging from 1 to 4% in those with strong serum IgG responses (59); S. aureus bacteraemia with mean levels of ∼3.2% (1–7% range) (58); and up to 4% of circulating CD19+ cells in untreated sufferers of Lyme borreliosis (45).

The implications of these data for the precision of the pathogen-specific mAb output are considerable. Firstly, there