predicting virulence factors of immunological interest

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31 Predicting Virulence Factors of Immunological Interest Sudipto Saha and Gajendra P. S. Raghava Summary In this chapter, three prediction servers used for predicting virulence factors, bacterial toxins, and neurotoxins have been described. VICMpred server predicts the functional proteins of gram- negative bacteria that include virulence factors, information molecule, cellular process, and metabolism molecule. BTXpred server allows users to predict bacterial toxins, its release, and further classification of exotoxins. NTXpred server allows prediction of neurotoxins and further classifying them based on their function and source. Key Words: Virulence factors; bacterial toxins; exotoxins; endotoxins; toxoid; toxin- neutralizing antibodies; neurotoxins; vaccine 1. Introduction Most of the proteins in an organism involve in cellular process, metabolism, and information storage, the remaining can be classified under virulence factors that allow the germs to establish themselves in the host. Virulence factors include adhesions, toxins, and hemolytic molecules. VICMpred server predicts the functional proteins of Gram-negative bacteria using amino acid patterns and composition. The ability of the toxoid vaccine to induce toxin-neutralizing antibodies has provided the basis for the use of therapeutic antitoxins and immunoglobulins for the prophylaxis and treatment of diseases caused by bacterial toxin. The discovery of an effective method to detoxify tetanus and diphtheria toxins by formaldehyde treatment allowed the introduction of mass immunization that led to almost complete elimination of both diseases from developed countries. BTXpred server predicts bacterial toxins and classifying From: Methods in Molecular Biology, vol. 409: Immunoinformatics: Predicting Immunogenicity In Silico Edited by: D. R. Flower © Humana Press Inc., Totowa, NJ 407

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31

Predicting Virulence Factors of Immunological Interest

Sudipto Saha and Gajendra P. S. Raghava

Summary

In this chapter, three prediction servers used for predicting virulence factors, bacterial toxins,and neurotoxins have been described. VICMpred server predicts the functional proteins of gram-negative bacteria that include virulence factors, information molecule, cellular process, andmetabolism molecule. BTXpred server allows users to predict bacterial toxins, its release, andfurther classification of exotoxins. NTXpred server allows prediction of neurotoxins and furtherclassifying them based on their function and source.

Key Words: Virulence factors; bacterial toxins; exotoxins; endotoxins; toxoid; toxin-neutralizing antibodies; neurotoxins; vaccine

1. IntroductionMost of the proteins in an organism involve in cellular process, metabolism,

and information storage, the remaining can be classified under virulence factorsthat allow the germs to establish themselves in the host. Virulence factorsinclude adhesions, toxins, and hemolytic molecules. VICMpred server predictsthe functional proteins of Gram-negative bacteria using amino acid patternsand composition. The ability of the toxoid vaccine to induce toxin-neutralizingantibodies has provided the basis for the use of therapeutic antitoxins andimmunoglobulins for the prophylaxis and treatment of diseases caused bybacterial toxin. The discovery of an effective method to detoxify tetanus anddiphtheria toxins by formaldehyde treatment allowed the introduction of massimmunization that led to almost complete elimination of both diseases fromdeveloped countries. BTXpred server predicts bacterial toxins and classifying

From: Methods in Molecular Biology, vol. 409: Immunoinformatics: Predicting Immunogenicity In SilicoEdited by: D. R. Flower © Humana Press Inc., Totowa, NJ

407

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them based on release (exotoxins and endotoxins) and function as (i) activateadenylate cyclase, (ii) activate guanylate cyclase, (iii) food poisioning, (iv)neurotoxins, (v) macrophage cytotoxin, (vi) vacuolating cytotoxin, (vii) thiolactivated, and (viii) hemolysin. The knowledge of neurotoxins is very importantfor the development of drugs against pain and epilepsy. A number of pharmacompanies are working on the use of these neurotoxins for the development ofpotent drugs. NTXpred server predicts neurotoxins and classifies them basedon source as (i) eubacteria (produced by genus Clostridium), (ii) cnidarians(where cnidoblast organelles store and deliver toxins), (iii) molluscans (cone),(iv) arthropods (mainly scorpion and spider), (v) chordates (snake) and onfunction as (i) ion channel blockers, (ii) blockers of acetylcholine receptors,(iii) inhibitors of neurotransmitter release through metalloproteolytic activity,(iv) inhibitors of acetylcholine release with phospholipase A2 activity, and (v)facilitators of acetylcholine release. Thus, identification of virulence factors iscrucial for vaccine and drug development.

2. Materials and Methods2.1. Usage of Web Servers

The users are required to fill a request form available athttp://www.imtech.res.in/errors/noauth.html for using web servers developedby raghava’s group (http://www.imtech.res.in/raghava/). The user name (e-mailID) and password are provided through e-mail. The old users can directly accessthe database by providing the user name and password.

2.2. Description of VICMpred

The web-based server allows prediction of broad function of a protein(e.g., virulence factors, information molecule, cellular process, and metabolismmolecule) from its amino acid sequences. The common gateway interface (CGI)script for the server has been written using PERL version 5.03. The server hasbeen installed on a Sun Server (420E) under a UNIX (Solaris 7) environment.Users can enter the primary amino acid sequence for prediction using fileuploading or cut-and-paste options. The server accepts the protein sequences inany standard format such as EMBL, GCG, and FASTA or in plain text format.Web servers use the readseq program to read the input sequences. The resultsprovide summarized information about the query sequence and prediction.

VICMperd is freely available at http:www.imtecg.res.in/raghava/vicmpred/and mirror site available at http://bioinformatic.uams.edu/mirror/vicmpred/. Theavailable menus in VICMpred server are help page, submission, algorithm,

Predicting Virulence Factors 409

references, developers, and contact. The help page describes the general infor-mation and stepwise help to submit sequence in the submission page. Thesubmission menu links to the server submission form, shown in Fig. 1A.

2.2.1. Types of Prediction

The server allows the prediction on the basis of two different approaches:

1. Pattern based2. Pattern based combined with amino acid composition and dipeptide composition

(see Note 1).

2.2.2. About VICMpred

The detailed information on methods used in developing the server isavailable at algorithm menu. The output format of the server has been shown inFig. 1B (see Note 2). It is important in drug and vaccine point of view to selectvirulence proteins from the pool of proteins or the proteome of an organism.

2.3. Description of BTXpred Server

The aim of BTXpred server is to predict bacterial toxins and its functionfrom primary amino acid sequence using SVM, HMM, and PSI-Blast. The CGIscript for the server has been written using PERL version 5.03. The server hasbeen installed on a Sun Server (420E) under a UNIX (Solaris 7) environment.Users can enter the primary amino acid sequence for prediction using fileuploading or cut-and-paste options. The server accepts the protein sequencesin any standard format such as EMBL, GCG, and FASTA or in plain textformat. Web servers use the readseq program to read the input sequences.The results provide summarized information about the query sequence andprediction.

BTXpred server and related information is available fromhttp://www.imtech.res.in/raghava/btxpred. The mirror site of BTXpred serveris accessible from http:bioinformatics.uams.edu/mirror/btxpred/. The serverallows users to predict bacterial toxins, its release, and further classificationof exotoxins. The server provides the option of predicting toxins either onthe basis of amino acid or dipeptide composition-based SVM method (1) orPSI-BLAST (2) and classifies exotoxins using HMM (3) and PSI-BLAST.The server predicts bacterial toxins, classifies bacterial toxins into exotoxinsand endotoxins, and further classifies exotoxins into seven different functionsdepending on their molecular targets (i) activate adenylate cyclase, (ii) activateguanylate cyclase, (iii) food poisoning, (iv) neurotoxins, (v) macrophage

410 Saha and Raghava

(A)

(B)

Fig. 1. The snapshot of the (A) submission and (B) output format of VICMpredserver.

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cytotoxin, (vi) vacuolating cytotoxin, and (vii) thiol-activated cytotoxin. Theavailable menus in the server are submission form, help page, supplementary,epitope prediction, and developers link. The submission menu links to thesubmission form of the server as shown in Fig. 2A.

(A)

(B)

Fig. 2. The snapshot of the (A) submission and (B) output format of BTXpred server.

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(A)

(B)

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2.3.1. Types of Prediction

The server allows three types of prediction (see Note 3).

1. Bacterial toxin or non-toxin.2. Types of toxin—endotoxin or exotoxin.3. Function of exotoxins.

2.3.2. Different Approaches Provided by BTXpred

The server allows the prediction on the basis of three different approaches.

1. SVM (for toxin and types of toxin).2. PSI-Blast (for toxin, types of toxin, and functions of exotoxins).3. HMM (only for function of exotoxins).

2.3.3. About BTXpred Server

The help page describes the general information and stepwise help to submitsequence in submission page. Additional information of this server is linkedto supplementary menu. The epitope prediction menu links to Bcepred server(4) for prediction of B-cell epitope in the bacterial toxin protein. This will helpthe users interested in generating antibodies against the toxin. The output ofthe server provides summarized information about the query sequence and theprediction. The snapshot of the output format has been shown in Fig. 2B.

2.4. Description of NTXpred Server

The aim of NTXpred server is to predict neurotoxins and its source andprobable function from primary amino acid sequence using SVM based oncomposition and PSI-Blast. The CGI script for the server has been writtenusing PERL version 5.03. The server has been installed on a Sun Server (420E)under a UNIX (Solaris 7) environment. Users can enter the primary aminoacid sequence for prediction using file uploading or cut-and-paste options. Theserver accepts the protein sequences in any standard format such as EMBL,GCG, and FASTA or in plain text format. Web servers use the readseq programto read the input sequences. The results provide summarized information aboutthe query sequence and prediction.

The server and related information is available at http://www.imtech.res.in/raghava/ntxpred and mirror site at http://bioinformatics.uams.edu/mirror/

�Fig. 3. The snapshot of the (A) submission and (B) output format of NTXpred

server.

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ntxpred/. The server predicts neurotoxins, its source mainly eubacteria, cnidaria(sea anemone), mollusca (cone), arthropoda (scorpion and spider) and chordata(snake), probable function mainly the ion channel blockers, blockers of acetyl-choline receptors, inhibitors acetylcholine release throughmetalloproteolyticactivity or through phospholipase A2 activity and facilitators acetylcholinerelease and further sub-classification of ion channels blockers into calcium,sodium, potassium, and chloride ion channels inhibitors. The available menusin the server are submission form, help page, data set, algorithm, B-cell epitopeprediction, supplementary information, developers, and contact information.The submission menu links to the submission form of the server as shown inFig. 3A.

2.4.1. Types of Prediction

The server allows four types of prediction.

1. Neurotoxins or non-toxin2. Source of the neurotoxin3. Function of the neurotoxin4. Sub-classification of ion channel inhibitors

2.4.2. Different Approaches Provided by NTXpred

The server provides the prediction on the basis of five different approaches(see Note 4).

1. SVM module based on amino acid composition.2. SVM module based on amino acid composition and length.3. SVM module based on dipeptide.4. SVM module based on dipeptide and length.5. PSI-Blast.

2.4.3. About NTXpred Server

The help page describes the general information and stepwise help to submitsequence in the submission page. Additional information of this server islinked to supplementary menu. The epitope prediction menu links to Bcepredserver (4) for prediction of B-cell epitope in the bacterial toxin protein.The results provide summarized information about the query sequence andprediction. The snapshot of the submission and output format is shown inFig. 3B.

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AcknowledgmentsWe acknowledge the financial support from the Council of Scientific and

Industrial Research (CSIR) and Department of Biotechnology (DBT), Govt. ofIndia.

Notes1. Combined approach of pattern based and composition of amino acid give higher

accuracy than pattern based alone.2. Scores of four different classes are given in tabular form. The highest score achieved

by individual class is the predicted functional class.3. For choosing types of prediction by BTXpred, SVM does not allow predicting

function of exotoxins, and PSI-BLAST allows all the three, but HMM allows onlyprediction of function of exotoxins.

4. SVM module based on amino acid composition and length give higher accuracy inpredicting neurotoxins, source, and function.

References1. Joachims, T. 1999. Making large-scale SVM learning particle. In Scholkopf, B.,

Burges, C., and Smola, A. (eds), Advances in Kernal Methods Support VectorLearning. MIT Press, Cambridge, MA and London, pp. 42–56.

2. Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W., andLipman, D.J. 1997. Gapped BLAST and PSI-BLAST: a new generation of proteindatabase search programs. Nucleic Acids Res. 25: 3389–3402.

3. Eddy, S.R. 1998. Profile hidden Markov models. Bioinformatics 14: 755–763.4. Saha, S. and Raghava, G.P.S. 2004. BcePred: prediction of continuous B-cell

epitopes in antigenic sequences using physico-chemical properties. In ArtificialImmune Systems, Nicosia, G., Cutello, V., Bentley, P.J., and Timis, J. (eds.)ICARIS, LNCS 3239, pp. 197–204.