selnikraj-reverse vaccinology

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Insilico Prediction of Vaccine Candidates against Botulism caused by Clostridium

botulinium by Reverse Vaccinology

Selvaraj C1, Shobana K2, Thiagarajan B3,

[email protected], [email protected], [email protected]+91-9894579639, 2 +91-9944936040, 3 +91-9944944356,

Department of Bioinformatics, Dr G R Damodaran College of Science, Coimbatore 641 014.

Abstract

Botulism is a neuroparalytic disease caused by neurotoxins produced by the bacteria Clostridium

botulinium. Botulinium neurotoxins(BoNTs) are amongst the most potent naturally occurring toxinsand are

biological threat agents. The seven toxin serotypesof BoNTs (serotypes AG) have different toxicities, act

through three different intracellular protein targets and exhibit different durations of effect. Botulism may

follow ingestionof food contaminated with BoNT, from toxin production ofClostridium botuliniumpresent

in the intestines or wounds or from the inhalation of aerosolizedtoxin. Intoxication classically presents as an

acute, symmetrical,descending flaccid paralysis.

The objective of the work is to predict the vaccine candidates against the disease Botulism caused by

Clostridium botulinium that could be used as a biological weapon against the civilian population. Predicting

vaccine candidates against Clostridium botulinium is possible with various reverse vaccinology methods,

such as genome analysis, localization prediction, predicting surface associated proteins and comparing with

human genome. The usage of various servers in various platforms could predict the description of the

proteome by the screening techniques. The epitope mapping could define the binding predictions and
mailto:[email protected]:[email protected]:[email protected]:[email protected]


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homology mapping. The results would encourage the researchers in selecting the antigens from the

predicted proteome of pathogens for which cell culture is difficult or mere impossibility.

1.1 Clostridium Botulism

Clostridium botulinium will only grow at low Eh values which are normally correlated with the absence of

oxygen. There are a number of neurotoxin types which differ markedly in their characteristics and ability to

cause disease in humans. Two groups are important in food:

Group I-Types A, B and F (proteolytic strains) Group II-Types B, E and F (nonproteolytic strains)

Because group I organisms are proteolytic their growth generally causes spoilage of the

Contaminated food. There are two manifestations of disease relevant to food; classic food borne botulism

and infant botulism

There are five clinical categories of botulism: 1) foodborne botulism; 2) wound botulism; 3) infant botulism;

4) adult infectious botulism; 5) inadvertent, following botulinium toxin injection, Botulism is caused by a

group of anaerobic spore-forming organisms called Clostridium botulinum. This is classified as a single

species but consists of at least three genetically distinguishable groups of organisms that have been

recognized as toxic for humans. They share the ability to produce neurotoxins with similar pharmacological

activities but diverse serologic properties. The toxin types are classified as A, B, C, D, E, F and G. Human

botulism has been described with the strains ofClostridium botulinum that produce toxin types A, B and E.

Less frequently, cases involving type F toxin produced by C. baratii and type E toxin produced by C.

butyricum have been published

1.2 Clostridium as Bioweapon

Clostridium botulinum is a spore forming , obligate anaerobe whose natural habitat is soil, from which it can

be isolated without undue difficulty. The species C botulinum consists of 4 genetically diverse groups that

would not otherwise be designated as a single species except for their common characteristic of producing

botulinum toxin.33,34 Botulinum toxin exists in 7 distinct antigenic types that have been assigned the letters

A through G. The toxin types are defined by their absence of cross neutralization (eg, anti-A antitoxin does

not neutralize toxin types B-G). The toxin types also serve as convenient epidemiological markers. In

addition to C botulinum, unique strains of Clostridium baratii and Clostridium butyricum have the capacity

to produce botulinum toxin.35-37 Botulinum toxin is a simple di chain polypeptide that consists of a 100-kd

heavy chain joined by a single disulfide bond to a 50-kd light chain; its 3-dimensional structure was

recently resolved to 3.3 A.38 The toxins light chain is a Zn++- containing endopeptidase that blocks

acetylcholine-containing vesicles from fusing with the terminal membrane of the motor neuron, resulting in


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flaccid muscle paralysis.8 Thelethal dose of botulinum toxin for humans is not known but can be estimated

from primate studies. By extrapolation, the lethal amounts of crystalline type A toxin for a 70-kg human

would be approximately 0.09-0.15 g intravenously or intramuscularly, 0.70-0.90 g inhalationally, and 70

g orally.10,39-41 Therapeutic botulinum toxin represents an impractical bioterrorist weapon because a vial

of the type A preparation currently licensed in the United States containsonly about 0.3% of the estimated

Fig 1.2.1 Mechanism of Action of Botulinum Toxin

1.3 Reverse vaccinology

Reverse vaccinology is one of the best examples of how bioinformatics can boost molecular immunology,

the conventional approach to design vaccines requires pathogens cultivation and dissection of its main

components before testing their ability to elicit protective immunity, reverse vaccinologys novelty consists


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in starting the search for immunogenic antigens from Insilico analyses of the pathogens genome instead of

culturing the microorganism. This allow scientist to save time and money while facing pathogens for which

cell culture is difficult and impossible. Reverse vaccinology potentially permits researchers to select ,in

addition to most in vivo expressed antigens, any proteins encoded by the genome of the pathogen. Reverse

vaccinology is less helpful with eukaryotes due to complexities of cellular and tissue organization, and it is

more effective with prokaryotes, extra and intracellular, indeed, the production of specific antibodies can

boost immunity not only against extra cellular pathogens , usually controlled by Th2-polorized responses,

but also against the either obligate or facultative intracellular ones, usually controlled by Th1-polorised

responses, even these later pathogens are susceptible to humoral immunity during the extra cellular phases

of their infectious cycle and are made vulnerable by antibody cross-linking that modifies the intracellular

millleu through signalling.

The focus of the reverse vaccinology is one of the in silico vaccine candidates from the list of predicted

proteins to address two issues such as the possiblities (i) of auto matizing the process with new criteria of

analyses and (ii) of reducing the percentage of vaccine candidates to less than 20 30 % reported until now,

indeed, the real goal of Insilico selection is to choose the minimal number of vaccine candidate sufficient to

find protective antigens during during experimental phases, thus designing a vaccine that conserves time

and money, therefore, it has to be stressed that obtaining a high recall of protective antigens in a very small

number of selected vaccine candidates is more convenient than trying to find all possible protective

antigens, according to this approach it is more productive to select the protective antigens that will most

likely be easily expressed, this reduces the risk of experimental troubles, the extra cellular loops of

multispanning membrane proteins can actually be significant targets especially if reasonably large, hence

one may focus on fragments likely to be exposed and accessible to antibodies rather than an entire protein in

order to overcome eventual cloning and expression problems, yet discarding non surface exposed predicting

antigens, thereby forwarding further bioinformatic analysis on selected ones, proved successful, however to

minimise the risk of loosing good vaccine candidates, the information concerning all other proteins is

retained, vaccine candidate are presented with corresponding integrated analyses. In order to rationalize

selection step, it is been taken into account the important of avoiding antigens that can potentially cause

autoimmunity in man. Since major histocompatiblity complex(MHC) ligands can be really short and as few

as eight residues. This problem may rise also from antigens sharing weak global similarity with host

proteins.


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Fig 1.3.1 Reverse Vaccinology

The first proteome scanning assign each sequence seven predictions. They are (i) subcellular localization of

proteins using PSORTb (ii) transmembrane topology prediction using using HMMMTOP (iii)signal peptide

prediction using signal P (iv) prediction of lip protein signal peptides using LipoP (v) prediction of non

classical protein secretion using secretomeP (vi) Tatsignal peptide prediction using TatP (vii) predicting

topology of beta barrel outer membrane proteins such as outer membrane using Pred TMBB, indeed,

surface- exposed proteins and especially adhesins are ideal targets for vaccine development. The second

step was determining the virulence of short listed candidates, this was done using virulentPred server.

The step addresses the problem of sharing similarity regions between pathogen and host proteins, it is well

known that in vaccine development this can either cause low immune response, tolerance or autoimmunity.


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The algorithm BLAST was chosen to compare each pathogens sequence as a query against the human

proteome, local alignment is suitable for this task because potential MHC ligands that help in predicting

potential interenference such as tolerance or autoimmunity with the immune system can be very short, in

this way it is not only taken in to account MHC II, commonly involed in presentation of exogenes antigens,

but also MHC I ,involved in cross prediction

2, Tools used for screening techniques

2. 1. PSORTb 2.0

Computational prediction of the subcellular localization of proteins is a valuble tool for genome analysis

and annotation, since proteins subcellular localization can provide clues regarding its function in an

organism, for bacterial pathogens, the prediction of proteins on the cell surface is of particular intrest due to

potential of such proteins to be primary drug or vaccine targets, the proteins subcellular localization is

influenced by several feautures present within the proteins primary structure, such as a presence of a signal

peptide or trans membrane spanning alpha helices,PSORT family of programs analyzes several feautures at

once, using information obtained from each analysis to generate an overall prediction of localization site,

developed by kenta nakai in 1991, PSORT is an algorithm which assigns a probable localization site to a

protein given an aminoacid sequence alone, originally developed for prediction for protein localization in

gram negative bacteria ,PSORT was expanded into a suite of programs i.e. PSORT, PSORT II ipSORT

capable of handling proteins from all classes of organisms.

2. 2. HMMTOP 2.0

Hidden markovs model for topology prediction(HMMTOP) method is based on the principle that the

topology of the transmembrane proteins are determined by the maximum divergence of aminoacid

composition of sequence segments in various structural parts of these proteins rather than by specific amino

acid composition of sequence segments in various in various structural parts of these proteins rather than by

specific aminoacid composition of these parts, it predicts both the localization of helical trans membrane

proteins, this hidden markovs model with special architecture was developed to search transmembrane

topology corresponding to the maximum likelihood among all the possible topologies of the given protein

2. 3. LipoP 1.0

The LipoP 1.0 server produces predictions of lipoproteins and discriminates between lipoprotein signal

peptides, other signal peptides and n-terminal membrane helices in Gram negative bacteria.

2. 4. SecretomeP 2.0

The SecretomeP 2.0 server produces ab initio predictions of non-classical i.e. not signal peptide triggered

protein secretion. The method queries a large number of other feature prediction servers to obtain


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information on various post-translational and localizational aspects of the protein, which are integrated into

the final secretion prediction. It represents the overview of bacterial non classical secretion and a prediction

method for identification of proteins following signal peptide independent secretion pathways, they have

compiled a list of proteins found extracellularly despite the absence of signal peptide

2. 5. VirulentPred

VirulentPred is a bacterial virulent protein prediction method based on bi-layer cascade Support Vector

Machine (SVM). The first layer SVM classifiers were trained and optimized with different individual

protein sequence features like amino acid composition, dipeptide composition (occurrences of the possible

pairs of i and i+1 amino acid residues), higher order dipeptide composition (pairs of i and i+2 residues) and

remote evolutionary relationships by use of Position-Specific Iterated BLAST (PSI-BLAST) generated

Position Specific Scoring Matrix (PSSM). A five-fold cross-validation technique was used for the

evaluation of various prediction strategies in the current work. The results from the first layer (SVM scores

and PSI-BLAST output) were cascaded to the second layer SVM classifier to train and generate the final

classifier. The cascade SVM classifier was able to accomplish a significantly higher accuracy of 81.8%,

covering 86% area in the Receiver Operator Characteristic (ROC) plot, better than that of either of the layer

one SVM classifiers based either on single or multiple sequence features.

VirulentPred can be used to serene virulent proteins in proteomes, together with experimentally verified

virulent proteins, several putative, non annotated and hypothetical protein sequences have been predicted to

be high scoring virulent proteins by the prediction method.

The prediction of virulent proteins will aid studies aimed at knowing more about bacterial virulence and

annotation of virulent genes for the identification of novel antimicrobial targets, hence in the present study

an attempt has been made to develop reliable SVM based on feautures such as compositions of amino acids,

dipeptides and higher order dipeptides , evolutionary information in the form of multiple sequence

alignment and similarity search , finally, the performance of cascade SVM module, developed using

individual feature modules were found to be much more efficient, hence used as default option for the

VirulentPred Server.

2. 6. BlastP

After finishing the first proteome screenings the fourth step address the problem of the sharing similarity

regions between pathogen and host proteins, it is well known that in vaccine development this can either

cause low immune response or autoimmunity. The algorithm BlastP is used


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3. Sequence Information

3.1. Source from Pedant database

The pedant genome database provides exhaustive automatic analysis of genomic sequences by a large

variety of bioinformatics tools. For example the following pre-computed analyses are available to analyse

protein function: Blast similarity searches against the non-redundant protein sequence database, motifsearches against the Pfam, BLOCKS and PROSITE databases. Prediction of cellular roles and functions are

made based on high stringency BLAST searches against protein sequences. Out of 1715 entries the 24

proteins are selected for the outlet as selected proteins and this protein is based on the membrane protein,

the membrane protein is selected because the membrane protein will efficiently bind to the host protein, thus

the membrane protein molecule is selected.

3.2. Clostridium botulinum A str. ATCC 3502

Table 3.1 selected protein information based on membrane protein


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4. Results and Discussion

4.1 Predicted vaccine candidates using reverse vaccinology

Using the reverse vaccinology methods the genome of Clostridium botulinumA strain was analysed, the

screening was done with the help of different servers such as VirulentPred, LipoP, SecretomeP, SignalP,PredTMBB etc, after the screening the proteins with significant scores are selected for comparing with

human genome with BLASTP, the proteins which shows more than 80% similarity were omitted

The criteria for selection are

Should be virulent Should have minimum secretomeP of 0.5 Should contain signal peptide cleavage sites Should be a membrane protein Transmembrane helices must be minimum Presence of Beta barrels are significant Non secretory proteins are omitted


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4.2 Epitope mapping results


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Three proteins were selected for the mapping experiments using IEDB tools, the selected proteins are given

below

Table 5.2 Selected Proteins List

4.3 Peptide binding to MHC Class II Molecules

This tool will take in an amino acid sequence, or set of sequences and determine each subsequence's ability

to bind to a specific MHC class I molecule

Table 4.3 Peptide binding to MHC Class II molecules


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Fig 4.1Epitope Prediction by Chow & Fasman Beta Turn Prediction

GI: 148378204 NC_009495 238008238652

Fig 4.2 Epitope Prediction by Bepipred Linear Epitope PredictionGI: 148378204 NC_009495 238008238652

Fig 4.3 through < 50 filter in Bepipred linear epitope Prediction


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5. Conclusion

The clostridium botulism is taken as the source organism and the sequence information is collected from the

pedant databases, and from that 24 protein sequence are collected on the basis of the membrane protein,

from that the tools are utilised and the virulent vaccine candidate has predicted, from the best three vaccine

candidates and examined for the experimental determinations, The predicted vaccine candidates alone arecloned and expressed using PCR, the resulting protein are purified and immunization experiments are done

with mice, this is followed by ELISA and Western blot test, after testing them with in vitro and in vivo assay

identifications are done with many experiments ,and the out of final vaccine development, out of these only

few are predicted as efficient vaccine candidates


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6. Bibliography

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10. R . Villar , S . Elliott , K . Davenport, Botulism: The Many Faces of Botulinum Toxin and its Potential

for Bioterrorism, Infectious Disease Clinics of North America, Volume 20 , Issue 2 , Pages 313 - 327

7. Websites

1, http://bioinfo.icgeb.res.in/virulent/

2, http://www.cbs.dtu.dk/services/SecretomeP/

3, http://www.cbs.dtu.dk/services/LipoP/

4, http://www.enzim.hu/hmmtop/html/submit.html

5, http://blast.ncbi.nlm.nih.gov/Blast.cgi

6, http://www.immuneepitope.org

7, http://www.ncbi.nlm.nih.gov/pubmed/

8, http://pedant.gsf.de/index.jsp

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