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] |