Table 5.

Algorithms used to analyse predicted adhesins for Immunoinformatics.

Algorithm	Principle	Reference
1. BLASTCLUST	Clusters protein or DNA sequences based on pairwise matches found using the BLAST algorithm in case of proteins or Mega BLAST algorithm for DNA.	[60]
2. OrthoMCL	OrthoMCL software was used to cluster proteins based on sequence similarity, using an all-against-all BLAST search of each species' proteome, followed by normalization of inter-species differences, and Markov clustering.	[61]
3. 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.	[62]
4. Antigenic	Predicts potentially antigenic regions of a protein sequence, based on occurrence frequencies of amino acid residue types in known epitopes.	[63]
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).	[64]
5. 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.	[65]
6. TMHMM Server v. 2.0	Predicts the transmembrane helices in proteins based on Hidden Markov Model.	[66]
7. Conserved Domain Database and Search Service, v2.22	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.	[67]
8. BlastP	It uses the BLAST algorithm to compare an amino acid query sequence against a protein sequence database.	[68]
9. ABCPred	Predict B cell epitope(s) in an antigen sequence, using artificial neural network.	[69]
10. BcePred	Predicts linear B-cell epitopes, using physico-chemical properties.	[70]
11. Discotope 1.2	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.	[71]
12. BEPro	BEPro, uses a combination of amino-acid propensity scores and half sphere exposure values at multiple distances to achieve state-of-the-art performance.	[72]
13. 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.	[73]
14. IEDB-AR (Average Relative Binding Method)	Predicts IC(50) values allowing combination of searches involving different peptide sizes and alleles into a single global prediction.	[74,75]
15. 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.	[76]
16. NetMHC 3.0	Predicts binding of peptides to a number of different HLA alleles using artificial neural networks (ANNs) and weight matrices.	[77]
17. 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.	[78]
18. 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.	[79]