From: FungalRV: adhesin prediction and immunoinformatics portal for human fungal pathogens
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. | |
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] |