In Silico B Cell and T Cell Epitopes Evaluation of lipL32 and OmpL1 Proteins for Designing a Recombinant Multi-Epitope Vaccine Against Leptospirosis

Nazifi, Narges; Mousavi, Seyyed Mojtaba; Moradi, Saeedeh; Jaydari, Amin; Jahandar, Mohammad Hassan; Forouharmehr, Ali

doi:10.5812/iji.63255

International Journal of Infection

The Official Journal of Zahedan University of Medical Sciences

e-ISSN 2383-1421

p-ISSN 2383-1413

Cover image of the article: In Silico B Cell and T Cell Epitopes Evaluation of lipL32 and OmpL1 Proteins for Designing a Recombinant Multi-Epitope Vaccine Against Leptospirosis

Image Credit:Int J Infect

https://doi.org/10.5812/iji.63255

In Silico B Cell and T Cell Epitopes Evaluation of lipL32 and OmpL1 Proteins for Designing a Recombinant Multi-Epitope Vaccine Against Leptospirosis

Authors

Narges Nazifi¹, Seyyed Mojtaba Mousavi², Saeedeh Moradi³, Amin Jaydari⁴, Mohammad Hassan Jahandar⁵, Ali Forouharmehr^2,*

1Department of Animal Science, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

2Department of Animal Science, Faculty of Agriculture, Lorestan University, Khorramabad, Iran

3Department of Animal Science, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

4Department of Microbiology, Faculty of Veterinary Medicine, Lorestan University, Khorramabad, Iran

5Department of Agriculture, Bam Branch, Islamic Azad University, Bam, IR Iran

*Corresponding Author: Corresponding author: Ali Forouharmehr, Department of Animal Science, Faculty of Agriculture, Lorestan University, Khorramabad, Iran. Tel: +98-9120724710, E-mail: Email: forouharmehr@gmail.com

International Journal of Infection:Vol. 5, issue 2; e63255

Published online:Apr 28, 2018

Article type:Research Article

Received:Oct 25, 2017

Accepted:May 02, 2018

How to Cite:Nazifi N, Mousavi SM, Moradi S, Jaydari A, Jahandar MH, et al. In Silico B Cell and T Cell Epitopes Evaluation of lipL32 and OmpL1 Proteins for Designing a Recombinant Multi-Epitope Vaccine Against Leptospirosis. Int J Infect. 2018;5(2):e63255. doi: https://doi.org/10.5812/iji.63255

Abstract

Leptospirosis is a widespread zoonotic disease caused by Leptospira interrogans. The conventional vaccines have some major problems. Therefore, recombinant vaccines such as multiple-epitope vaccine are suggested. OmpL1 and lipL32 are the most important proteins of Leptospira interrogans bacteria that can be used in epitope prediction process to design a multiple-epitope vaccine. Hence, in this study, the most reliable and accurate online servers were applied to predict B cell and T cell epitopes, the secondary and tertiary structures, enzyme digestion, and antigenicity score of ompL1 and lipL32. The results showed that epitopes located at 103 - 122, 210 - 232, and 272 - 291 amino acid residues are the common epitopes between T cell (MHCI) and B cell. 288 - 308 amino acid residues were introduced as common epitopes to stimulate both T cell (MHCI and MHCII) and B cell of ompL1 protein. In the case of LipL32 protein, 80 - 96 amino acid residues are recommended for T cell epitopes and 63-81 amino acid residues for stimulation of both B and T cells. All the mentioned epitopes can be considered as linear epitopes in designing a recombinant vaccine based on chimeric epitopes. It appears that these epitopes can be applied to design recombinant multiple-epitope vaccines against leptospirosis.

Highlights

KeywordsEpitope Leptospira Vaccine Zoonosis

1. Background

Leptospirosis is a widespread zoonotic disease that could be found in both developed and developing countries. This disease is usually seen among mammalians (in particular, human, and livestock) that are in contact with rodents or living in polluted areas. There are more reports of this disease during unforeseen events such as floods, earthquakes, etc. (1-3). Leptospirosis is a bacterial disease caused by Leptospira interrogans. In fact, Leptospira genus is classified into Leptospira biflexa, which comprises all non-pathogenic strains, and Leptospira interrogans that consists of all pathogenic strains (4). Leptospirosis can be transferred through direct or indirect contact with the infected cases (5). The most prevalent symptoms of this disease are meningitis, hepatitis, and nephritis and sometimes, it leads to death (6, 7). Conventional vaccines (Bacterin -type) used to prevent Leptospirosis have some side effects (fever, pain), short-term immunity and serovar- resisted protection (4); hence, it is necessary to apply new strategies to solve these problems. One of the most recent strategies for preventing leptospirosis is to use recombinant vaccines that are epitope-based or protein vaccines with a vital role in antigenicity of a pathogen (8-10). Outer membrane proteins (OMPs) are the most important proteins diagnosed by the immune system in different bacterial infections. OMPs are widely applied to design recombinant vaccines such as subunit and epitope vaccines (4). There are three classes of OMPs in Leptospira: Lip (lipoprotein) that includes lipL32, lipL42, and lipL24; transmembrane proteins that include ompL1s, and finally peripheral proteins that include lipl42 (11-15). Many studies have shown that ompL1 and lipL32 are the most conserved OMPs in most pathogenic strains of Leptospira and they can be applied to design recombinant vaccines to prevent leptospirosis (16). Studies also showed that the application of an OMP as a subunit recombinant vaccine could not successfully prevent leptospirosis since it is not antigenic enough to stimulate the immune system. Consequently, it seems that applying multi-epitope vaccines, which use epitopes of several OMPs, is the solution to vaccine antigenicity (9, 17). Epitopes are amino acid sequences of OMPs recognized by antibodies of the immune system. In general, epitopes are divided into B cells (continuous and discontinuous) and T cells (MHCI and MHCII) (18, 19). Nowadays, by the progress in biological data, researchers are widely using intelligent methods such as machine learning for data analysis. Such methods not only are affordable, but also provide reliable data for the experiment (20). Therefore, in the present study, the most reliable and appropriate online tools and servers were applied to predict B cell and T cell epitopes of ompL1 and LipL32 (as the most important leptospirosis’ OMPs) (20).

2. Methods

To predict B and T cell epitopes of ompL1 (accession number: JX532100.1) and LipL32 (accession number: JN886739.1), their amino acid sequences were collected from the National Center for Biotechnology Information (NCBI).

2.1. B Cell Epitopes Prediction of Ompl1 and Lip32

In order to predict B cell epitopes of ompL1 and LipL32 using their primary amino acid sequences, the most reliable and accurate online servers were employed. These online servers include IEDB, ABCpred, BepiPred, BCPREDS, and SVMTrip. It must be noted that all servers have been designed to predict discontinuous B cell epitopes. Required parameters of each server such as the desired length to predict epitopes were adjusted as default (18).

2.2. T Cell Epitopes Prediction of Ompl1 and Lip32

In case of T cell epitopes prediction of ompL1 and LipL32, both MHCI and MHCII epitopes were evaluated by the most precise online servers: IEDB, SYFPEITH, NetCTL, NetMHC, Propred, and MHC2Pred (19).

2.3. Evaluation of the Most Important Features of the Predicted Epitopes

To investigate the antigenicity score of the predicted B and T cell epitopes, VaxiJen 2.0 server was employed with the desired threshold (0.5). Also, the most important features of the predicted epitopes such as enzymatic digestion sites, PI, and the Mass of the predicted epitopes through specialized servers were analyzed by Protein Digest server (18, 19).

2.4. Secondary and Tertiary Structure Prediction of ompL1 and Lip32

To predict secondary structures of ompL1 and LipL32 proteins, based on their primary amino acid sequences, improved self-optimized prediction method (SOPMA) server was applied and their helices, sheets, turns, and coil were evaluated. Tertiary structures of ompL1 and lipL32 proteins were designed by iterative threading assembly refinement (I-TASSER) online server which uses hierarchical approach to predict the structure and function of proteins. PDB formats of the predicted tertiary structures of the studied proteins were visualized by PyMOL V1 Viewer software (21).

3. Results

3.1. B Cell Epitopes Prediction of ompL1 and LipL32

As shown in Table 1, the initial results were theoretically obtained, based on the highest score and the most frequent epitopes among all the mentioned specialized servers. It must be mentioned that in order to predict discontinuous B cell epitopes, physic-chemical properties of amino acids such as hydrophilicity, charge, flexibility, polarity, and the exposed surface area were considered.

Table 1.

The List of High Scored Predicted T Cell Epitopes Using Online Software and Their VaxiJen Score^a

Sequence	Server	SCORE	Vaxijen Score	Sequence	Server	SCORE	Vaxijen Score
MHCI
LipL32				OmpL1
₆₃VKPGQAPDGLVDGNKKA₇₉	IEDB	98	1.0042 (Probable ANTIGEN)	₇₂PACFQNPAKPTG₈₃	IEDB	99	NON-ANTIGEN
₁₄₈IAKAAKAKPVQKLDDDDDGDDTYKEERHNK₁₇₇		99.5	1.2556 (Probable ANTIGEN)	₇₄CFQNPAKPTGEG₈₅		100	NON-ANTIGEN
₁₇₉NSLTRIKIPNPPKS₁₉₂		98	-0.1699 (Probable NON-ANTIGEN)	202FNGGWSLNGSNN₂₁₃		100	0.6696 (Probable ANTIGEN)
₁₈₅ KIPNPPKSFDDLKN198		97	-0.3322 (Probable NON-ANTIGEN)	₁₀₉TGGAINARSTKG₁₂₀		99	2.2206 (Probable ANTIGEN)
₉₈ISPTGEIGEPGDGDLVSDAFKAATPEEKSMPHWFD₁₃₂		95.87	0.4244 (Probable NON-ANTIGEN)	₂₅₈LIGTQARVTDKGH₂₇₀		95	0.9339 (Probable ANTIGEN)
₁₅₂AKAKPVQKLDDDDDGDDTYKEERHNKYNS₁₇₀		100	1.1642 (Probable ANTIGEN)	₆₇VGPSDPACFQNP₇₈		100	NON-ANTIGEN
₃₁ SSFVLSEDTIPGTNETVKT₄₉		96.25	0.5263 (ProbableANTIGEN)	₈₁PTGEGNYIGVAP₉₂		99	1.4730 (Probable ANTIGEN)
₉₇MISPTGEIGEPGDGDLVSDA₁₁₇		99	0.4157 (ProbableNON-ANTIGEN)	₁₇₈PATVGIKLNVTE₁₈₉		99	1.6638 (Probable ANTIGEN)
₁₅₃KAKPVQKLDDDDDGDDTYKEERHNK₁₇₇		99.88	1.2193 (ProbableANTIGEN)	₂₉₉PFPAYPIVVGGQ₃₁₀		100	0.8669 (Probable ANTIGEN)
₁₈₇ PNPPKSFDDLKNIDT₂₀₁		99	0.0200 (Probable NON-ANTIGEN)	₂₇₂FIELETIMSAAY₂₈₃	NetMHC	0.432	0.5005 (Probable ANTIGEN)
₂₃₅PPGIPGVSPLIHSNPEELQKQAIAAEES₂₆₂		95.62	0.4033 (Probable NON-ANTIGEN)	₂₉₀SVGGATNLSPFPAY₃₀₃		0.353	0.7905 (Probable ANTIGEN)
₂₃₈IPGVSPLIHSNPEE₂₅₁		100	0.5522 (Probable ANTIGEN)	₁₂₀GAMVGGNLMV₁₂₉		0.513	0.8084 (Probable ANTIGEN)
₃₄VLSEDTIPGTNETV₄₇	NetMHC	0.537	-0.3071 (Probable NON-ANTIGEN)	₂₃1NLLSDGTDPV₂₄₀		0.649	NON-ANTIGEN
₇₆NKKAYYLYVWIPAV₈₉		0.751	-0.3071 (Probable NON-ANTIGEN)	₂₅₆NFLIGTQARV₂₆₅		0.594	NON-ANTIGEN
₈₀YYLYVWIPAVIAEM₉₃		0.600	-0.3071 (Probable NON-ANTIGEN)	₂₇₂FIELETIMSA₂₈₁		0.668	0.5322 (Probable ANTIGEN)
₂₀₇ RGLYRISFTTYKPG₂₂₀		0.425	0.6082 (ProbableANTIGEN)	₂₉₆NLSPFPAYPI₃₀₅		0.582	1.2997 (Probable ANTIGEN)
₄₅ETVKTLLPY₅₃	NetCTL	1.42	0.0981 (Probable NON-ANTIGEN)	₂₁₀GSNNIKGGY₂₁₈	NetCTL	1.67	1.8330 (Probable ANTIGEN)
₅₄GSVINYYGY₆₂	NetCTL	1.24	-0.1594 (Probable NON-ANTIGEN)	₂₆₅VTDKGHVFI₂₇₃	NetCTL	1.27	NON-ANTIGEN
₇₀DGLVDGNKKAYYLY₈₃	Syfpeithi	17	0.3399 (Probable NON-ANTIGEN)	₂₇₄ ELETIMSAAY₂₈₃	Syfpeithi	24	NON-ANTIGEN
₂₀₀DTKKLLVRGLY₂₁₀		24	-0.3071 (Probable NON-ANTIGEN)	₂₉₄ATNLSPFPAY₃₀₃		23	NON-ANTIGEN
₁₄₂AIMPDQIAKA₁₅₁		25	0.4060 (Probable NON-ANTIGEN)	₁₇₆ VIPATVGIKLNVTEDAAI₁₉₃		22	1.1858 (Probable ANTIGEN)
₂₀₃KLLVRGLYRI₂₁₂		26	-0.5296 (Probable NON-ANTIGEN)	₉₂PRKAIPAENR₁₀₁		23	NON-ANTIGEN
₂₃₂LLFPPGIPGV₂₄₁		31	0.7159 (Probable ANTIGEN)	₁₄₁WRVAAEYTQK₁₅₀		24	0.9085 (Probable ANTIGEN)
				₂₄₈FRTSGIAPNF₂₅₇	Syfpeithi	24	1.1327 (Probable ANTIGEN)
MHCII
₆₄KPGQAPDGLVDGNKKAYY₈₁	IEDB	89.97	0.7241 (Probable ANTIGEN)	₇₅FQNPAKPTGEGNYIGVAPR₉₃	IEDB	91.61	0.7 (Probable ANTIGEN)
₁₁₈KAATPEEKSMPHWFDTW₁₃₄	IEDB	92.65	0.2905 (Probable NON-ANTIGEN)	₂₃₃LSDGTDPVTTREHVRFRTS₂₅₁	IEDB	85.64	1.1127 (Probable ANTIGEN)
₁₈₆IPNPPKSFDDLKNID₂₀₀			-0.0292 (Probable NON-ANTIGEN)	₅₉VRSSNTCTV₆₇	Propred	3.4	1.4325 (Probable ANTIGEN)
₉₉SPTGEIGEPGDGDLVSD₁₁₅		83.37	0.4243 (Probable NON-ANTIGEN)	₁₈₃IKLNVTEDA₁₉₁		4.1	0.9238 (Probable ANTIGEN)
₁₅₀KAAKAKPVQKLDDDDDGDDTYKEER₁₇₄		83.58	1.1462 (Probable ANTIGEN)	₂₁₈YDILTAAGA₂₂₆		1.1	NON-ANTIGEN
₈₂YVWIPAVIA₉₀	Propred	55	1.1663 (Probable ANTIGEN)	₂₇₂FIELETIMS₂₈₀		3.38	0.5775 (Probable ANTIGEN)
₁₇₇YNSLTRIKI₁₈₅		23.33	0.1128 (Probable NON-ANTIGEN)	₁₄₂RVAAEYTQK₁₅₀	MHC2Pred	1.528	0.8696 (Probable ANTIGEN)
₂₂₉VGLLFPPGI₂₃₇		32.83	0.2905 (Probable NON-ANTIGEN)	₁₅₅VTKADIAGY₁₆₃		1.009	0.6261 (Probable ANTIGEN)
₂₂FGGLPSLKS₃₀		45.35	0.4137 (Probable NON-ANTIGEN	₁₇₂SIVIPATVG₁₈₂		1.793	NON-ANTIGEN
₃₃VLSEDTIPG₄₁		31.16	0.0022 (Probable NON-ANTIGEN)	₂₂₇GAVANLLSD₂₃₅		1.431	NON-ANTIGEN
₈₂YVWIPAVIA₉₀		34.65	1.1663 (Probable ANTIGEN)	₂₅₇FLIGTQARV₂₆₅		1.507	0.5925 (Probable ANTIGEN)
₂₀₉YRISFTTYK₂₁₇		69.77	0.7222 (Probable ANTIGEN)	₂₉₀SVGGATNLS₂₉₈		1.464	0.9973 (Probable ANTIGEN)
₈₃YVWIPAVIA₄₁	MHC2Pred	1.607	1.1663 (Probable ANTIGEN)	₁₀₃ITLDRTTGG₁₁₁		1.105	1.5169(Probable ANTIGEN)
₈₈AVIAEMGVR₉₆		1.52	0.8893 (Probable ANTIGEN)
₁₃₆RVERMSAIM₁₄₄		1.65	-0.4057 (Probable NON-ANTIGEN)
₁₁₅DAFKAATPE₁₄₃		1.406	0.8056 (Probable ANTIGEN)
₁₃₀WFDTWIRVE₁₃₈			0.0030 (Probable NON-ANTIGEN)
₁₃₈ERMSAIMPD₁₄₆			0.2385 (Probable NON-ANTIGEN)

^aHighlight parts related to selected epitopes with more than 0.5 score of VaxiJen for digestion analysis.

3.2. T Cell Epitopes Prediction of ompL1 and LipL32

To predict T cell epitopes, the most frequent Iranian alleles of MHCI (A-0101, A0201, and B-2705) and MHCII (DRB1-0101 and DRB1-0401) were used. It must be mentioned that in each server, the predicted epitopes with the highest scores were selected (data not shown). Then, T cell predicted epitopes were evaluated using the above mentioned servers and used in the following analysis in order to identify the conserved sequences in both MHCI and MHCII epitopes (Table 2).

Table 2.

The List of High Scored Predicted B Cell Epitopes Using Online Software and Their VaxiJen Score^a

Sequence	Server	SCORE	VaxiJen Score	Sequence	Server	SCORE	VaxiJen Score
LipL32				OmpL1
₁₁₉AATPEEKS₁₂₆	IEDB	1.368	0.9680 (ProbableANTIGEN)	₄₅ITKDGLDAATHYGPVRSS₆₂	IEDB	0.816	NON-ANTIGEN
₁₅₈QKLDDDDDGDDTYKEE₁₇₃	IEDB	1.762	1.5742 (Probable ANTIGEN)	₆₆TVGPSDPACFQNPAKPTGEGN₈₆	IEDB	1.46	NON-ANTIGEN
₁₃₇VEERMSAIMPDQIAKA₁₅₂	ABCpred	0.9	0.2977 (Probable NON-ANTIGEN)	₁₄₁WRVAAEYTQKISGG₁₅₄	ABCpred	0.88	1.0726 (Probable ANTIGEN)
₁₂₇MPHWFDTWIRVEERMS₁₄₂		0.77	0.3736 (Probable NON-ANTIGEN)	₂₁₃NIKGGYDILTAAGAGAVANL₂₃₂		0.84	0.8346 (Probable ANTIGEN)
₁₄₄MPDQIAKAAKAKPV₁₅₇		0.91	0.3754 (Probable NON-ANTIGEN)	₈₉GVAPRKAIPAENRLITLDRTTG₁₁₀		0.78	-0.1147 (Probable NON-ANTIGEN)
₆₃VKPGQAPDGLVD₇₄	BepiPred	1.39	1.1553 (Probable ANTIGEN)	₃₄LQLDLGQLGGTITK₄₇	BCPREDS (BCPred)	0.977	0.5749 (Probable ANTIGEN)
₁₀₂GEIGEPGDGDL₁₁₂		1.71	0.8654 (Probable ANTIGEN)	₅₃ATHYGPVRSSNTCTVGPSDP₇₂	BCPREDS (AAP)	1	0.8607 (Probable ANTIGEN)
₁₁₇FKAATPEEKSMP₁₂₈		1.11	0.2487 (Probable NON-ANTIGEN)	₁₀₃ITLDRTTGGAINARSTKGAM₁₂₂		1	1.4181 (Probable ANTIGEN)
₂₃₀VGLLFPPGIPGVSPLIHSNP₂₄₉	BCPREDS (BCPred)	1	0.3560 (Probable NON-ANTIGEN)	₁₃₀GYESDFGKYFFWRVAAEYTQ₁₄₉		0.973	NON-ANTIGEN
₃₉TIPGTNETVKTLLPYGSVIN₅₈	BCPREDS (AAP)	1	0.0138 (Probable NON-ANTIGEN)	₁₆₅IVDMTWGFSSIVIPATVGIK₁₆₄		1	0.7011 (Probable ANTIGEN)
₁₀₇PGDGDLVSDAFKAATPEEKS₁₂₆		1	0.8044 (Probable ANTIGEN)	₂₀₂FNGGWSLNGSNNIKGGYDIL₂₂₁		0.979	0.9594 (Probable ANTIGEN)
₁₈₄IKIPNPPKSFDDLKNIDTKK₂₀₃		1	0.2565 (Probable NON-ANTIGEN)	₂₂₃AAGAGAVANLLSDGTDPVTT₂₄₂		1	NON-ANTIGEN
₅₈NYYGYVKPGQAPDG₇₁	BCPREDS (FBCPred)	0.999	0.5762 (Probable ANTIGEN)	₂₈₈TQSVGGATNLSPFPAYPIVV₃₀₇		1	1.1034 (Probable ANTIGEN)
₁₀₀PTGEIGEPGDGDLV₁₁₂	BCPREDS (FBCPred)	1	0.6637 (Probable ANTIGEN)	₂₂₇FIELETIMSAAYAVGKTQSV₂₉₁	SVMTrip	1	0.5332 (Probable ANTIGEN)
₄₇VKTLLPYGSVINYYGYVKPG₆₆	SVMTrip	1.000	-0.3640 (Probable NON-ANTIGEN)

^aHighlight areas related to selected epitopes with more than 0.5 score of VaxiJen for digestion analysis.

3.3. Evaluation of the Most Important Features of Predicted Epitopes

After prediction of B cell and T ell epitopes using out put of different online servers, their antigenicity scores were evaluated, as shown in Tables 1 and 2. Epitopes with a score above 0.5 were considered as the most antigenic epitopes. The obtained results from this step were applied in further study to evaluate the enzymatic digestion. PI and Mass, reported in Table 3, were calculated by protein digestion server. According to these results, the antigenic epitopes that had the largest number of non-digestive enzymes were selected as final B cell and T cell epitopes (Table 3) (colored epitopic regions indicate the selected ones). Epitopes with the same caption are common among different categories (Table 4). In the case of ompL1 protein, pink, green, and red regions are common between T Cell (MHCI) or B Cell epitopes, but the blue color is common among both T and B cells that are arranged between 288 and 307aa residues (Table 3). Finally, those selected sequences from lipL32 protein have the gray colored in both T cell categories (MHCI & MHCII), which are located at 80 - 93aa residues. Moreover, the blue regions indicate common epitopes between both T (MHCI & MHCII) and B cells and are located at an amino acid range of 64 - 81 (Table 3).

Table 3.

Protein Digestion Analysis of Final B- and T-Cell Epitopes^a

Sequence	PI	Mass	Non-Digestive Enzyme	Sequence	PI	Mass	Non-Digestive Enzyme
T Cell, MHCI
OmpL1				LipL32
₂₁₀GSNNIKGGY₂₁₈	8.59	908.97	Chymotrypsin, Clostripain, Cyanogen_Bromide, Proline_Endopept, Staph_Protease, IodosoBenzoate, Trypsin_R, AspN , Chymotrypsin (modified)	₃₁SSFVLSEDTIPGTNETVK₄₈	4.14	1924.09	Trypsin,Clostripain, Cyanogen_Bromide, IodosoBenzoate, Trypsin_K,Trypsin_R,
₂₇₂FIELETIMSAAY₂₈₃	3.79	1387.61	Trypsin, Chymotrypsin, Clostripain, IodosoBenzoate, Proline_Endopept, Trypsin_K, Trypsin_R, AspN,	₆₃ VKPGQAPDGLVDGNKKA₇₉	8.47	1693.92	Chymotrypsin,Clostripain, Cyanogen_Bromide,IodosoBenzoate, Staph_Protease,Trypsin_R,
₂₉₀SVGGATNLSPFPAY₃₀₃	5.24	1380.52	Trypsin, Chymotrypsin, Clostripain, Cyanogen_Bromide, IodosoBenzoate, Staph_Protease,Trypsin_K, Trypsin_R, AspN,	₇₆ NKKAYYLYVWIPAV₈₉	9.40	1728.07	Clostripain, Cyanogen_Bromide, Staph_Protease, Trypsin_R,AspN,
₁₀₉TGGAINARSTKG₁₂₀	11.00	1132.24	Chymotrypsin, Cyanogen_Bromide, IodosoBenzoate, Proline_Endopept, Staph_Protease, AspN, Chymotrypsin (modified),	₈₀YYLYVWIPAVIAEM₉₃	4.00	1731.08	Trypsin, Clostripain,Cyanogen_Bromide, Trypsin_K,Trypsin_R, AspN
₈₁PTGEGNYIGVAP₉₂	4.00	1077.16	Trypsin, Cyanogen_Bromide, Clostripain, IodosoBenzoate, Trypsin_K, Trypsin_R,AspN,	₁₅₃ KAKPVQKLDDDDDGDDTYKEERHNK₁₇₇	4.70	2960.12	Cyanogen_Bromide, IodosoBenzoate
₁₇₈PATVGIKLNVTE₁₈₉	6.64	1241.45	Chymotrypsin, Clostripain, Cyanogen_Bromide, IodosoBenzoate, Staph_Protease, Trypsin_R, AspN	₂₀₇ RGLYRISFTTYKPG220	10.28	1658.92	Cyanogen_Bromide, IodosoBenzoate
₂₄₈FRTSGIA PNF₂₅₇	9.75	1109.25	Cyanogen_Bromide, Trypsin_K, AspN, IodosoBenzoate, Staph_Protease,	₂₃₂LLFPPGIPGV₂₄₁	5.52	1009.26	Trypsin ,Chymotrypsin, Clostripain,Cyanogen_Bromide,IodosoBenzoate, Staph_Protease,Trypsin_K, Trypsin_R, AspN
				₂₃₈IPGVSPLIHSNPEE₂₅₁	4.81	1488.66	Trypsin, Chymotrypsin, Clostripain, Cyanogen_Bromide, IodosoBenzoate,Trypsin_K, Trypsin_R, AspN,
				₃₁SSFVLSEDTIPGTNETVK₄₈	4.14	1924.09	Trypsin,Clostripain, Cyanogen_Bromide, IodosoBenzoate, Trypsin_K,Trypsin_R,
T Cell, MHCII
OmpL1				LipL32
₁₀₃ITLDRTTGG₁₁₁	5.84	933.03	Chymotrypsin, Cyanogen_Bromide, IodosoBenzoate, Proline_Endopept, Staph_Protease,Trypsin_K,	₆₄KPGQAPDGLVDGNKKAY₈₁	8.43	1757.96	Clostripain, Cyanogen_Bromide, IodosoBenzoate, Staph_Protease, Trypsin_R,
₁₄₂RVAAEYTQK₁₅₀	8.59	1065.19	Cyanogen_Bromide, IodosoBenzoate, Proline_Endopept, Trypsin_K, AspN	₈₃YVWIPAVIA₉₁	5.52	1031.26	Trypsin, Clostripain, Cyanogen_Bromide, Staph_Protease, Trypsin_K, Trypsin_R, AspN,
₇₅FQNPAKPTGEGNYIGVAPR₉₃	8.59	2016.24	Trypsin, Clostripain, Cyanogen_Bromide, IodosoBenzoate,Trypsin_K, Trypsin_R, AspN	₈₈AVIAEMGVR₉₆	6.05	945.14	Trypsin, Chymotrypsin, Clostripain, IodosoBenzoate, Proline_Endopept, Trypsin_K, Trypsin_R, AspN, Chymotrypsin(modified),
₁₅₅VTKADIAGY₁₆₃	5.81	937.06	Chymotrypsin, Clostripain, Cyanogen_Bromide, IodosoBenzoate, Proline_Endopept, Staph_Protease, Trypsin_R, Chymotrypsin (modified)	₁₁₅DAFKAATPE	4.75	949.03	Cyanogen_Bromide, IodosoBenzoate, Staph_Protease, Trypsin_R, AspN,
₁₈₃IKLNVTEDA₁₉₃	4.37	1002.13	Chymotrypsin, Clostripain, Cyanogen_Bromide, IodosoBenzoate, Proline_Endopept, Trypsin_R	₁₅₀KAAKAKPVQKLDDDDDGDDTYKE₁₇₂	4.49	2565.73	Clostripain, Cyanogen_Bromide, IodosoBenzoate, Staph_Protease,Trypsin_R,
₂₃₃LSDGTDPVTTREHVRFRTS₂₅₁	6.76	2174.36	Cyanogen_Bromide, IodosoBenzoate, Trypsin_K	₂₀₉YRISFTTYK₂₁₇	9.70	1178.35	Cyanogen_Bromide, IodosoBenzoate, Proline_Endopept, Staph_Protease, Trypsin_K, AspN,
₂₉₀SVGGATNLS₂₉₈	5.24	804.86	Trypsin, Chymotrypsin, Clostripain, Cyanogen_Bromide, IodosoBenzoate, Proline_Endopept, Staph_Protease, Trypsin_K, Trypsin_R, AspN
B Cell
OmpL1				LipL32
₃₄LQLDLGQLGGTITK₄₇	5.84	1456.70	Trypsin, Chymotrypsin, Clostripain, Cyanogen_Bromide, IodosoBenzoate, Proline_Endopept, Staph_Protease, Trypsin_K,Trypsin_R	₅₈NYYGYVKPGQAPDG₇₁	5.83	1528.64	Trypsin, Clostripain, Cyanogen_Bromide,IodosoBenzoate,Staph_Protease, Trypsin_K, Trypsin_R,
₂₉ATHYGPVRSSNTCTVGPSDP₄₈	6.79	2046.20	Cyanogen_Bromide, IodosoBenzoate, Staph_Protease, Trypsin_K	₆₃VKPGQAPDGLVD₇₄	4.54	1195.34	Trypsin, Chymotrypsin, Clostripain, Cyanogen_Bromide, IodosoBenzoate, Staph_Protease, Trypsin_K, Trypsin_R,
₁₀₃ITLDRTTGGAINARSTKGAM₁₂₂	10.84	1987.22	Chymotrypsin, Cyanogen_Bromide, IodosoBenzoate, Proline_Endopept, Staph_Protease	₁₀₀PTGEIGEPGDGDL₁₁₂	3.43	1256.29	Trypsin, Chymotrypsin, Clostripain, Cyanogen_Bromide, IodosoBenzoate, Trypsin_K,Trypsin_R, Chymotrypsin(modified)
₁₆₅IVDMTWGFSSIVIPATVGIK₁₈₄	5.84	2134.56	Trypsin, Clostripain, Staph_Protease, Trypsin_K, Trypsin_R	₁₁₉AATPEEKS₁₂₆	4.53	831.88	Chymotrypsin, Clostripain, Cyanogen_Bromide, IodosoBenzoate, Trypsin_R, AspN, Chymotrypsin(modified),
₂₀₂FNGGWSLNGSNNIKGGYDIL₂₂₁	5.83	2126.31	Clostripain, Cyanogen_Bromide, Proline_Endopept, Staph_Protease, Trypsin_R	₁₅₈QKLDDDDDGDDTYKE₁₇₂	3.85	1771.72	Clostripain, Cyanogen_Bromide, IodosoBenzoate, Proline_Endopept, Staph_Protease, Trypsin_R,
₂₁₃NIKGGYDILTAAGAGAVANL₂₃₂	5.83	1889.14	Cyanogen_Bromide, IodosoBenzoate, Proline_Endopept, Staph_Protease, Trypsin_K,Trypsin_R,
₂₇₂FIELETIMSAAYAVGKTQSV₂₉₁	4.53	2158.49	Clostripain, IodosoBenzoate, Proline_Endopept, Trypsin_R, AspN,
₂₈₈TQSVGGATNLSPFPAYPIVV₃₀₇	5.18	2018.30	Trypsin, Chymotrypsin, Clostripain, Cyanogen_Bromide, IodosoBenzoate, Staph_Protease, Trypsin_K, Trypsin_R, AspN

^aAll colored epitopes related to final selected epitopes with maximum of Non-digestive enzymes; ompL1 protein: pink, green and red region are common epitopes between T cell MHCI class and B cell epitopes but the blue one is the common epitopes among both T and B cells which arranged between 288-307 amino acid residuals. LipL32 protein; gray colored epitopes are common ones among the T cell epitopes and located in 80-93 amino acid residuals. And the blue ones indicate the common ones between both T and B cell epitopes and are in amino acid 64-81 residuals.

Table 4.

Final B and T Cell Predicted Epitopes^a

OMPL1	lip32
T Cell (MHCI)
₁₀₉TGGAINARSTKG₁₂₀	-
₂₁₀GSNNIKGGY₂₁₈	₆₃VKPGQAPDGLVDGNKKA₇₉
₂₇₂FIELETIMSAAY₂₈₃	₈₀YYLYVWIPAVIAEM₉₃
₂₉₀SVGGATNLSPFPAY₃₀₃	₂₃₈IPGVSPLIHSNPEE₂₅₁
T Cell (MHCII)
₇₅FQNPAKPTGEGNYIGVAPR₉₃	₆₄KPGQAPDGLVDGNKKAY₈₁
₁₅₅VTKADIAGY₁₆₃	₈₈AVIAEMGVR₉₆
₂₉₀SVGGATNLS₂₉₈	₂₀₉YRISFTTYK₂₁₇
B Cell
₃₄LQLDLGQLGGTITK₄₇	₆₃VKPGQAPDGLVD₇₄
₁₀₃ITLDRTTGGAINARSTKGAM₁₂₂	₁₀₀PTGEIGEPGDGDL₁₁₂
₂₁₃NIKGGYDILTAAGAGAVANL₂₃₂	-
₂₇₂FIELETIMSAAYAVGKTQSV₂₉₁	-
₂₈₈TQSVGGATNLSPFPAYPIVV₃₀₇	-

^aIn first column; green, pink and red highlighted regions related to common epitopes between T cell MHCI class and B cell, and the blue ones related to the common ones among both T and B cells. In second column; the gray epitopes are common between MCHI and MHCII classes and the blue highlighted epitopes are common epitopes among both T and B cells

3.4. Secondary and Tertiary Structure Prediction of ompl1 and Lip32

As reported earlier, in order to predict the secondary structure of candidate proteins, SOPMA server was applied. OmpL1 protein included 23.79% Alpha helix, 38.26% Random coil, 27.33% Extended strand, and 10.61% Beta-turn structures (Figure 1). In addition, lipL32 protein included 38.06% Alpha helix, 19.03% Extended strand, 9.33% Beta-turn, and 33.58% Random coil structures (Figure 2). As shown in the results, these two proteins involved a high proportion of random coil structures indicating the concentration of epitopes in the mentioned areas, rather than total protein. The results of the 3DLigandSite show that all the final predicted B and T cell epitopes of ompL1 and lipL32 can be exposed on the surfaces (Figure 3A and 3B).

Figure 1.
Secondary structure prediction results of OMPL protein. Amino acids with different colors represent different secondary structures. Blue: α helix, green: β turn, red: extended strand, and yellow: random coil.

Figure 2.
Secondary structure prediction results of Lip32 protein. Amino acids with different colors represent different secondary structures. Blue: α helix, green: β turn, red: extended strand, and yellow: random coil.

Figure 3.
Tertiary structure prediction results for the OMPL1 (A) and Lip32 (B) proteins. A: green, pink, and red highlighted regions related to common epitopes between T cell MHCI class and B cell, and the blue ones related to the common ones among T and B cells. B: the gray regions are common between T cell MCHI and MHCII classes and the blue highlighted regions are common epitopes among both T and B cells.

4. Discussion

Vaccines prevent infectious diseases. Although conventional vaccines (attenuated and killed vaccines) are able to save millions of lives, there are some drawbacks such as long production process (around 15 years), the diverse effects on different cases, and their side effects (even death) (22-24). Therefore, it seems that the new generation of vaccines must be substituted with the conventional ones. Today, bioinformatics is widely being used. In fact, bioinformatics uses both computer and biology to accelerate data analysis and decrease the expenditure of the experiments. One of the most important applications of bioinformatics is vaccine production. In fact, advancements in bioinformatics tools along with the advances in recombinant DNA technology and genetics can decrease the time (around 2 years) and expenses of vaccine production (20, 25). As noted before, since Leptospirosis is a zoonotic and widespread disease in most developing countries including Iran, certain considerations should be taken into account to fight this disease. Many studies have reported that subunit recombinant vaccines, which have been designed based on LipL32 and ompL1 proteins (as the most important OMPs to design subunit vaccine), cannot be successfully applied to treat Leptospirosis, because these recombinant vaccines are not strong enough to stimulate the immune system against the disease (16, 26). However more recently, it has been reported that the use of chimeric epitope vaccines designed based on epitopes of OmpL1 and Lep32 is a promising method of fighting leptospirosis (16). Therefore, it seems in order to produce chimeric epitope vaccine, the epitope prediction of ompL1 and LipL32 for Leptospirosis is of great importance. In this study, the most accurate and reliable bioinformatics tools were applied to predict B cell and T cell epitopes of ompL1 and LipL32 (18-20). As our result showed, 20 to 25 amino acids, at the beginning of both proteins, cannot be epitopes because these amino acid sequences are usually considered as a signal peptide to translocate proteins to the endoplasmic reticulum and they are then cleaved by signal peptidase (27, 28). Therefore, these amino acid sequences cannot be exposed as epitopes on the surface of the bacteria to stimulate the immune system.

The results of the current study obviously showed that the epitopic region of ompL1 protein including 103 - 122, 210 - 232, and 272 - 291 aa residues are the most common epitopes between T cell (MHCI) and B cell. In addition, 288-308 aa residues could be considered as a unique epitopic region to stimulate both T cell (MHCI&MHCII) and B cell. Moreover, the amino acids that have been arranged in 80 - 96aa residues are recommended for T cell epitope and 63 - 8196aa residues are suggested for both B and T cells in LipL32 protein. All of them can be considered to design a chimerical epitopic vaccine.

According to our prediction, it appears these epitopes could not only evade from protease system but also provide enough immune response against Leptospirosis. In fact, the evasion of protease system can increase epitopes half-life and can lead to the stimulation of immune system. It should be noted that the final epitopes, which have been predicted in this study, could simultaneously stimulate B and T cells. Therefore, the use of these epitopes with the appropriate arrangement could lead to a proper immune response.

4.1. Conclusions

Nowadays, bioinformatics is widely being used to analyze biological data. This area of science can accelerate data analysis and decrease the expenses at the same time. In this study, a wide variety of the most reliable and precise online tools and servers were applied to predict B cell, T cell, and common B and T cell epitopes of ompL1 and lipL32. In addition, 288 - 308aa residues of ompL1 protein can be considered as common epitopes to stimulate both T cell (MHCI and MHCII) and B cell. For LipL32 protein, 63-81aa residues are suggested for the epitopic region of both B and T cells. However, the results of this study need to be confirmed by further experimental studies.

Footnotes

Conflict of Interest:The authors declare that there is no conflict of interest.

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Copyright

Copyright © 2018, International Journal of Infection. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited

Outline

In Silico B Cell and T Cell Epitopes Evaluation of lipL32 and OmpL1 Proteins for Designing a Recombinant Multi-Epitope Vaccine Against Leptospirosis

Abstract

Highlights

1. Background

2. Methods

2.1. B Cell Epitopes Prediction of Ompl1 and Lip32

2.2. T Cell Epitopes Prediction of Ompl1 and Lip32

2.3. Evaluation of the Most Important Features of the Predicted Epitopes

2.4. Secondary and Tertiary Structure Prediction of ompL1 and Lip32

3. Results

3.1. B Cell Epitopes Prediction of ompL1 and LipL32

3.2. T Cell Epitopes Prediction of ompL1 and LipL32

3.3. Evaluation of the Most Important Features of Predicted Epitopes

3.4. Secondary and Tertiary Structure Prediction of ompl1 and Lip32

4. Discussion

4.1. Conclusions

Footnotes

References

Copyright

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