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.
 [9]
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
 [11]
3. TMHMM Server v. 2.0
 Predicts the transmembrane helices in proteins based on Hidden Markov Model.
 Transmembrane helices prediction
 [12]
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
 [13]
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.
 [14]
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.
 [15]
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.
 [16]
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.
 [17]
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
 [18]
10. ABCPred
 Predict B cell epitope(s) in an antigen sequence, using artificial neural network.
 Linear B Cell Epitope Prediction.
 [19]
11. BcePred
 Predicts linear B-cell epitopes, using physico-chemical properties.
 Linear B Cell Epitope Prediction.
 [20]
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.
 [21]
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
 [22]
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.
 [23]
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.
 [24]
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.
 [25]
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.
 [26]
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
 [27]
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
 [28]
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
 [29]

Chaudhuri et al. Malaria Journal 2008 7:184   doi:10.1186/1475-2875-7-184

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