MalVac: Database of malarial vaccine candidates

1475-2875-7-184 1475-2875 Commentary MalVac: Database of malarial vaccine candidates Chaudhuri Rupanjali rupanjali.bhu@gmail.com Ahmed Shakil rajshakil@gmail.com Ansari Alam Faraz faraz7862@rediffmail.com Singh Vir Harinder harinder_vir_singh@yahoo.com Ramachandran Srinivasan ramuigib@gmail.com

G.N Ramachandran Knowledge Centre for Genome Informatics, Institute of Genomics and Integrative Biology (Council of Scientific and Industrial Research), Mall Road, Delhi 110007, India

Malaria Journal 1475-2875 2008 7 1 184 http://www.malariajournal.com/content/7/1/184 18811938 10.1186/1475-2875-7-184 26 7 2008 23 9 2008 23 9 2008 2008 Chaudhuri et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

The sequencing of genomes of the Plasmodium species causing malaria, offers immense opportunities to aid in the development of new therapeutics and vaccine candidates through Bioinformatics tools and resources.

Methods

The starting point of MalVac database is the collection of known vaccine candidates and a set of predicted vaccine candidates identified from the whole proteome sequences of Plasmodium species provided by PlasmoDb 5.4 release (31st October 2007). These predicted vaccine candidates are the adhesins and adhesin-like proteins from Plasmodium species, Plasmodium falciparum, Plasmodium vivax and Plasmodium yoelii. Subsequently, these protein sequences were analysed through 20 publicly available algorithms to obtain Orthologs, Paralogs, BetaWraps, TargetP, TMHMM, SignalP, CDDSearch, BLAST with Human Ref. Proteins, T-cell epitopes, B-cell epitopes, Discotopes, and allergen predictions. All of this information was collected and organized with the ORFids of the protein sequences as primary keys. This information is relevant from the view point of Reverse Vaccinology in facilitating decision making on the most probable choice for vaccine strategy.

Results

Detailed information on the patterning of the epitopes and other motifs of importance from the viewpoint of reverse vaccinology has been obtained on the most probable protein candidates for vaccine investigation from three major malarial species. Analysis data are available on 161 adhesin proteins from P. falciparum, 137 adhesin proteins from P. vivax and 34 adhesin proteins from P. yoelii. The results are displayed in convenient tabular format and a facility to export the entire data has been provided. The MalVac database is a "community resource". Users are encouraged to export data and further contribute by value addition. Value added data may be sent back to the community either through MalVac or PlasmoDB.

Conclusion

A web server MalVac for facilitation of the identification of probable vaccine candidates has been developed and can be freely accessed.

Background

Malaria is a major killer disease. Annually, about 500 million people get infected and an estimated 1 million deaths occur. Despite numerous efforts we still do not have effective vaccines 1. Among the parasites that cause malaria, the most common and widely distributed is Plasmodium vivax 2. But the most fatal form of malaria is caused by Plasmodium falciparum 3. Plasmodium yoelii is a commonly used rodent malaria parasite as a model to study malaria infection. Malaria caused by P. yoelii has similarities to that caused by P. falciparum and P. vivax 4.

Currently, several vaccines against multiple stages are in clinical development, including pre-erythrocytic, blood stage and others 3. Although these advancements raise the hopes of the availability of an effective vaccine, it is noted that our limited knowledge on the details of the immune responses is becoming a major handicap 1. The availability of complete genome sequences of Plasmodium falciparum 5, P. yoelii 4 and P. vivax has provided new opportunity for applying the principles of Reverse Vaccinology. Reverse vaccinology uses bioinformatics in the initial steps to identify potential antigens, which are subsequently examined for their efficacy and toxicity. In its maiden application, use of algorithm for prediction of sub-cellular location boosted the power of identifying potential vaccine candidates 6. Subsequently, enhancements have been proposed to reverse vaccinology by suggesting the use of additional algorithms to find probability of being an adhesin, of topology (transmembrane regions) and to find similarity with host protein 7.

Recently, integrative approaches are proposed for Reverse Vaccinology by including prediction of multiple features of proteins 8. Adopting this strategy, the following predictions were incorporated: of adhesins 9 and their orthologs 10, paralogs 11, transmembrane topologies 12, beta helix supersecondary structural motifs 13, subcellular localization 1415, similarity against Human proteins 16, antigenic regions 17, conserved domains18, epitopes 1920212223242526 and allergens 272829. The work flow started with adhesin prediction algorithm, which holds an important position in vaccine development process. The adhesin proteins mediate the adherence of malaria parasites to the host cells and facilitate invasion. Targeting these adhesins to abrogate the colonization process can prevent malaria infection 930.

The multiple features of potential vaccine candidates coupled with information on the current candidates being pursued can be queried through a user friendly interface. These data are housed in MalVac database, which can aid in the discovery of adhesin based vaccines.

Methods

Database architecture

The ORF identification tags (ORF ID) assigned to proteins of malaria parasites as given in PlasmoDB 5.4 release of 31^stOctober 2007 31 were used as primary keys. The database was developed using MySQL version 4.1.20 at back end and operated in Red Hat Enterprise Linux ES release 4. The web interfaces have been developed in HTML and PHP 5.1.4, which dynamically execute the MySQL queries to fetch the stored data and is run through Apache2 server. The overall layout of MalVac is provided in Figure 1.

Figure 1

The MalVac layout

The MalVac layout. All data are organized in relation to the primary key ORF ID.

The first step towards MalVac database creation is the collection of known vaccine candidates and a set of predicted vaccine candidates identified from the whole proteome sequences of Plasmodium species provided by PlasmoDB 5.4 release(31st October 2007). These predicted vaccine candidates are the adhesins and adhesin-like proteins from Plasmodium species, P. falciparum, P. vivax and P. yoelii using MAAP server 9. Subsequently these protein sequences were analysed with 20 algorithms important from the view of reverse vaccinology (Table 1).

Table 1

Algorithms used to predict molecular features of potential malarial vaccine candidates and housed in MalVac.

Algorithm

Principle

Role in MalVac

Reference

1. MAAP

Predicts Malarial adhesins and adhesins-like proteins based on Support Vector Machines

Adhesin and Adhesin like protein prediction.

2. BLASTCLUST

Clusters protein or DNA sequences based on pair wise matches found using the BLAST algorithm in case of proteins or Mega BLAST algorithm for DNA.

Paralogs finding

3. TMHMM Server v. 2.0

Predicts the transmembrane helices in proteins based on Hidden Markov Model.

Transmembrane helices prediction

4. BetaWrap

Predicts the right-handed parallel beta-helix supersecondary structural motif in primary amino acid sequences by using beta-strand interactions learned from non-beta-helix structures.

Betawrap finding

5. TargetP1.1

Predicts the subcellular location of eukaryotic proteins based on the predicted presence of any of the N-terminal presequences: chloroplast transit peptide (cTP), mitochondrial targeting peptide (mTP) or secretory pathway signal peptide (SP).

Localization Prediction.

6. SignalP 3.0

Predicts the presence and location of signal peptide cleavage sites in amino acid sequences from different organisms. The method incorporates a prediction of cleavage sites and a signal peptide/non-signal peptide prediction based on a combination of several artificial neural networks and hidden Markov models.

Signal Peptide Prediction.

7. BlastP

It uses the BLAST algorithm to compare an amino acid query sequence against a protein sequence database.

Prediction of similarity to human reference proteins.

8. Antigenic

Predicts potentially antigenic regions of a protein sequence, based on occurrence frequencies of amino acid residue types in known epitopes.

Antigenic region prediction.

9. Conserved Domain Database and Search Service, v2.13

The Database is a collection of multiple sequence alignments for ancient domains and full-length proteins. It is used to identify the conserved domains present in a protein query sequence.

Conserved Domain Finding

10. ABCPred

Predict B cell epitope(s) in an antigen sequence, using artificial neural network.

Linear B Cell Epitope Prediction.

11. BcePred

Predicts linear B-cell epitopes, using physico-chemical properties.

Linear B Cell Epitope Prediction.

12. Discotope 1.1

Predicts discontinuous B cell epitopes from protein three dimensional structures utilizing calculation of surface accessibility (estimated in terms of contact numbers) and a novel epitope propensity amino acid score.

Conformational B Cell Epitope Prediction.

13. CEP

The algorithm predicts epitopes of protein antigens with known structures. It uses accessibility of residues and spatial distance cut-off to predict antigenic determinants (ADs), conformational epitopes (CEs) and sequential epitopes (SEs).

Conformational B Cell Epitope Prediction

14. NetMHC 2.2

Predicts binding of peptides to a number of different HLA alleles using artificial neural networks (ANNs) and weight matrices.

HLA Class I Epitope prediction.

15. MHCPred 2.0

MHCPred uses the additive method to predict the binding affinity of major histocompatibility complex (MHC) class I and II molecules and also to the Transporter associated with Processing (TAP). Allele specific Quantitative Structure Activity Relationship (QSAR) models were generated using partial least squares (PLS).

MHC Class I and II epitope prediction.

16. Bimas

Ranks potential 8-mer, 9-mer, or 10-mer peptides based on a predicted half-time of dissociation to HLA class I molecules. The analysis is based on coefficient tables deduced from the published literature by Dr. Kenneth Parker, Children's Hospital Boston.

HLA Class I Epitope prediction.

17. Propred

Predicts MHC Class-II binding regions in an antigen sequence, using quantitative matrices derived from published literature. It assists in locating promiscous binding regions that are useful in selecting vaccine candidates.

Promiscous MHC Class II epitope prediction.

18. AlgPred

Predicts allergens in query protein based on similarity to known epitopes, searching MEME/MAST allergen motifs using MAST and assign a protein allergen if it have any motif, search based on SVM modules and search with BLAST search against 2890 allergen-representative peptides obtained from Bjorklund et al 2005 and assign a protein allergen if it has a BLAST hit.

Allergen Prediction

19. Allermatch

Predicts the potential allergenicity of proteins by bioinformatics approaches as recommended by the Codex alimentarius and FAO/WHO Expert consultation on allergenicity of foods derived through modern biotechnology.

Allergen Prediction

20. WebAllergen

Predicts the potential allergenicity of proteins. The query protein is compared against a set of pre-built allergenic motifs that have been obtained from 664 known allergen proteins.

Allergen Prediction

Database access and interface

MalVac Database is freely available 32. A user friendly web-based interface allows users to explore the site and fetch the data corresponding to their queries. For example, if the user needs to search database for data on a set of proteins given by their ORF identification tags one starts with clicking the "Database Search" button (Figure 2). This would take the user to the "MalVac Query Page". Here the user can search the database for adhesin proteins and their attributes corresponding to one or more ORF identification tags of a species or against a specific Keyword. To fetch the required data the corresponding checkboxes need to be toggled 'on' followed by clicking the submit button (Figure 3). The results are displayed in convenient tabular format and a facility to export the entire data has been provided. To get the Epitope and Allergen data the user must provide a specific ORF ID along with the species selected.

Figure 2

The Home page of MalVac

The Home page of MalVac. The "Database Search" facility can be used for first level search. Advanced search is provided in the "Search Tools" facility. "Other links" would take users to other websites of malaria for obtaining additional details and the "Known Vaccines" tab describes the details of the currently known vaccine candidates.

Figure 3

The MalVac Query Page

The MalVac Query Page. Default selections are MAAP score and ORF ID.

Advanced search facility of predicted malarial adhesins is also provided where the results can be filtered on the basis of Protein length, number of transmembrane spanning regions, localization and reliability class, presence or absence of betawraps, paralogs, orthologs, hits to Conserved Domain Database and Human Reference proteins (retrieved from NCBI through ftp on April 22, 2008). The results obtained can be exported by the user. The known vaccines link takes user to the page containing the list of known vaccine candidates provided in tabular form. This data can again be exported by the user. Facility to post comments by the user has been provided in MalVac web interface. Users can post their value added comments and suggestions on specific genes based on their own experience through the comment posting page of MalVac.

Results and Discussion

MalVac Database contains analysis data on 332 potential vaccine candidates on three most important Plasmodium species. Of these, 161 are from P. falciparum, 137 are from P. vivax and 34 are from P. yoelii. First level of searching and retrieval of data is possible either through ORF ID or keywords. Multiple ORF IDs can be submitted using comma separation. Keywords can be used singly. If multiple keywords are used then the search is implemented using the AND Boolean. In the case of searching for epitope data, due to their huge size, data are conveniently retrieved in a singular mode for each ORF ID specifically. All data can be exported conveniently as a text file.

The database houses detailed information on these vaccine candidates analysed through 20 algorithms important from the view of reverse vaccinology. The analysis through these algorithms provide a broad range of information regarding Orthologs, Paralogs, BetaWraps, Localization, Transmembrane spanning regions, Signal Peptides, Conserved domains, similarity to Human Reference Proteins, T-cell epitopes, B-cell epitopes, Discotopes, and Allergen predictions.

Advanced level searches are also provided. In this facility users can search using combined feature selection. The most immediate application of such a scheme is in filtering for candidate proteins meeting a certain set of specifications. For example users formulate their queries by selecting for proteins that have less (or greater) than a specified number of transmembrane domains and less (or greater) than a specified length of protein. The features on which users can formulate their search could be based on Protein length, number of transmembrane spanning regions, localization-reliability class, presence or absence of betawraps, paralogs, orthologs, hits to CDD and human reference proteins in the advanced search page. The results obtained can be exported by the user.

Conclusion

MalVac database was built as a community resource to aid malaria vaccinologists. MalVac is freely available with facility to export data and use for user's convenience 32.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

SR conceived the idea and provided guidance, suggestions, critical comments, and testing of MalVac. RG, SA, FAA, HVS collected data, organized systematically, prepared the codes for MalVac. SR and RG wrote the manuscript. All authors read and approved the final manuscript.

Acknowledgements

SR thanks the NMITLI project on Plasmodium falciparum annotation and CMM0017 Task force on "Drug target development using in silico biology" for financial support and Raghunandan for systems support. RG thanks ICMR for a fellowship.

What can transgenic parasites tell us about the development of <it>Plasmodium</it>-specific immune responses? Thompson J Millington OR Garside P Brewer JM Parasite Immunol 2008 30 223 233 18324925 Focus on <it>Plasmodium vivax</it> Sina B Trends Parasitol 2002 18 287 289 12379943 Malaria Vaccines: the stage we are at Todryk SM Hill AV Nat Rev Microbiol 2007 5 487 489 17571459 Genome sequence and comparative analysis of the model rodent malaria parasite <it>Plasmodium yoelii yoelii</it> Carlton JM Angiuoli SV Suh BB Kooij TW Pertea M Silva JC Ermolaeva MD Allen JE Selengut JD Koo HL Peterson JD Pop M Kosack DS Shumway MF Bidwell SL Shallom SJ van Aken SE Riedmuller SB Feldblyum TV Cho JK Quackenbush J Sedegah M Shoaibi A Cummings LM Florens L Yates JR Raine JD Sinden RE Harris MA Cunningham DA Preiser PR Bergman LW Vaidya AB van Lin LH Janse CJ Waters AP Smith HO White OR Salzberg SL Venter JC Fraser CM Hoffman SL Gardner MJ Carucci DJ Nature 2002 419 512 519 12368865 Genome sequence of the human malaria parasite <it>Plasmodium falciparum</it> Gardner MJ Hall N Fung E White O Berriman M Hyman RW Carlton JM Pain A Nelson KE Bowman S Paulsen IT James K Eisen JA Rutherford K Salzberg SL Craig A Kyes S Chan MS Nene V Shallom SJ Suh B Peterson J Angiuoli S Pertea M Allen J Selengut J Haft D Mather MW Vaidya AB Martin DM Fairlamb AH Fraunholz MJ Roos DS Ralph SA McFadden GI Cummings LM Subramanian GM Mungall C Venter JC Carucci DJ Hoffman SL Newbold C Davis RW Fraser CM Barrell B Nature 2002 419 498 511 12368864 Post-genomic vaccine development Serruto D Rappuoli R FEBS Lett 2006 580 2985 2992 16716781 Nerve: New Enhanced Reverse Vaccinology Environment Vivona S Bernante F Filippini F BMC Biotechnol 2006 6 35 1570458 16848907 Computer-aided biotechnology: from immuno-informatics to reverse vaccinology Vivona S Gardy JL Ramachandran S Brinkman FS Raghava GP Flower DR Filippini F Trends Biotechnol 2008 26 190 200 18291542 MAAP: Malarial adhesins and adhesin-like proteins predictor Ansari FA Kumar N Bala Subramanyam M Gnanamani M Ramachandran S Proteins 2008 70 659 666 17879344 OrthoMCL-DB: querying a comprehensive multi-species collection of ortholog groups Chen F Mackey AJ Stoeckert CJ Jr Roos DS Nucleic Acid Res 2006 34 Database D363 368 1347485 16381887 Selection in the evolution of gene duplications Kondrashov FA Rogozin IB Wolf YI Koonin EV Genome Biol 2002 3 RESEARCH0008 65685 11864370 Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes Krogh A Larsson B von Heijne G Sonnhammer EL J Mol Biol 2001 305 567 580 11152613 BETAWRAP: successful prediction of parallel beta-helices from primary sequence reveals an association with many microbial pathogens Bradley P Cowen L Menke M King J Berger B Proc Natl Acad Sci USA 2001 98 14819 14824 64942 11752429 Predicting subcellular localization of proteins based on their N-terminal amino acid sequence Emanuelsson O Nielsen H Brunak S von Heijne G J Mol Biol 2000 300 1005 1016 10891285 Improved prediction of signal peptides: SignalP 3.0 Bendtsen JD Nielsen H von Heijne G Brunak S J Mol Biol 2004 340 783 795 15223320 Basic local alignment search tool Altschul SF Gish W Miller W Myers EW Lipman DJ J Mol Biol 1990 215 403 410 2231712 A semi-empirical method for prediction of antigenic determinants on protein antigens Kolaskar AS Tongaonkar PC FEBS Lett 1990 276 172 174 1702393 CDD: a Conserved Domain Database for protein classification Marchler-Bauer A Anderson JB Cherukuri PF DeWeese-Scott C Geer LY Gwadz M He S Hurwitz DI Jackson JD Ke Z Lanczycki CJ Liebert CA Liu C Lu F Marchler GH Mullokandov M Shoemaker BA Simonyan V Song JS Thiessen PA Yamashita RA Yin JJ Zhang D Bryant SH Nucleic Acids Res 2005 33 Database D192 196 540023 15608175 Prediction of Continuous B-cell Epitopes in an Antigen Using Recurrent Neural Network Saha S Raghava GP Proteins 2006 65 40 48 16894596 Prediction methods for B-cell epitopes Saha S Raghava GP Methods Mol Biol 2007 409 387 394 18450017 Prediction of residues in discontinuous B cell epitopes using protein 3D structures Andersen H P Nielsen M Lund O Protein Science 2006 15 2558 2567 2242418 17001032 CEP: a conformational epitope prediction server Kulkarni-Kale U Bhosle S Kolaskar AS Nucleic Acids Res 2005 33 Web Server W168 171 1160221 15980448 Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach Buus S Lauemoller SL Worning P Kesmir C Frimurer T Corbet S Fomsgaard A Hilden J Holm A Brunak S Tissue Antigens 2003 62 378 384 14617044 MHCPred: bringing a quantitative dimension to the online prediction of MHC binding Guan P Doytchinova IA Zygouri C Flower DR Appl Bioinformatics 2003 2 63 66 15130834 Scheme for ranking potential HLA-A2 binding peptides based on independent binding of individual peptide side-chains Parker KC Bednarek MA Coligan JE J Immunol 1994 152 163 175 8254189 ProPred: Prediction of HLA-DR binding sites Singh H Raghava GP Bioinformatics 2001 17 1236 1237 11751237 AlgPred: prediction of allergenic proteins and mapping of IgE epitopes Saha S Raghava GP Nucleic Acids Res 2006 34 Web Server W202 209 1538830 16844994 Allermatch, a webtool for the prediction of potential allergenicity according to current FAO/WHO Codex alimentarius guidelines Fiers MW Kleter GA Nijland H Peijnenburg AA Nap JP van Ham RC BMC Bioinformatics 2004 5 133 522748 15373946 WebAllergen: a web server for predicting allergenic proteins Riaz T Hor HL Krishnan A Tang F Li KB Bioinformatics 2005 21 2570 2571 15746289 Adhesins as targets for vaccine development Wizemann TM Adamou JE Langermann S Emerg Infect Dis 1999 5 395 403 10341176 PlasmoDB: the Plasmodium genome resource. A database integrating experimental and computational data Bahl A Brunk B Crabtree J Fraunholz MJ Gajria B Grant GR Ginsburg H Gupta D Kissinger JC Labo P Li L Mailman MD Milgram AJ Pearson DS Roos DS Schug J Stoeckert CJ Jr Whetzel P Nucleic Acids Res 2003 31 212 215 165528 12519984 MalVac: Database of Malarial Vaccine Candidates A Community Resource http://malvac.igib.res.in/