Table 1.
Sequence based-prediction methods, according to the rationale behind their analysis. *Registration prior to analysis is required.
| Method* | Underlying rationale | Webserver, software or equation |
|---|---|---|
| Phenomenological methods | ||
| AGGRESCAN [63], [64] | Prediction is assayed against an aggregation propensity scale for the 20 proteinogenic amino acids derived from in vivo experiments. | http://bioinf.uab.es/aggrescan/ |
| Zyggregator [65] | Prediction of a 21-residue sliding window from an equation accounting for hydrophobicity, secondary structure propensity, and net charge built upon changing aggregation rate on mutations.It also considers the presence of gatekeeper residues or hydrophobic patches | http://www-mvsoftware.ch.cam.ac.uk/index.php/login* |
| Theoretical methods | ||
| TANGO [7], [117] | Evaluation of the population of random coil, native conformation or aggregated species from empirically and statistically derived conformational amino acidic preferences, along with physico-chemical variables. | http://tango.crg.es/* |
| PASTA 2.0 [66], [118] | Energetic function derived from high-resolution protein structures, which considers interaction potential and H-bond formation between all non-consecutive residues for parallel and anti-parallel β-pairing. | http://protein.bio.unipd.it/pasta2/ |
| FoldAmyloid[67] | A protein structure derived scale; from the notion that hydrophobic stretches exhibit higher “packing density” and H-bonding propensity. | http://bioinfo.protres.ru/fold-amyloid/ |
| WALTZ[68] | Application of a position specific matrix derived from a large group of hexapeptides, for predicting amyloid-like formation. | https://waltz.switchlab.org/ |
| Pafig [119] | Analysis of six-residue sliding window for a scale derived from machine supervised learning over 531 physicochemical properties, which led to best discrimination using 41 of them. | Code can be downloaded from their web page http://www.mobioinfor.cn/pafig/ (Requires MS Windows) |
| Betascan [120] | Evaluation of β-strand pairing propensity, obtained from probabilities of residues to be H-bonded in amphiphilic β-sheets. | http://cb.csail.mit.edu/cb/betascan/ hosts the web server and allows download of the Perl scipt. |
| GAP [121] | Discriminates amyloid-like or β-amorphous hexapeptides from position-specific pairing frequencies. | https://www.iitm.ac.in/bioinfo/GAP/ |
| 3D Profile [122] | Energetic impact on the spatial accommodation to the backbone of the fibril forming Sup35 hexapeptide is assessed. | http://services.mbi.ucla.edu/zipperdb/* |
| Machine learning methods | ||
| APPNN [71] | Machine learning approach based on the analysis of seven physicochemical and biochemical features such as β-sheet frequency, hydrophobic moment, helix termination parameters or isoelectric point. | http://cran.r-project.org/web/packages/appnn/index.html |
| NetCSSP [72] | Analysis of contact-dependent secondary structure prediction to identify hidden β-propensities. | http://cssp2.sookmyung.ac.kr/ |
| FiSH Amyloid [73] | Classification of amyloidogenic stretches based on co-ocurrence patterns in protein sequences. | http://www.comprec.pwr.wroc.pl/COMPREC_home_page.html |
| Consensus methods | ||
| AmylPred2 [74] | Generates consensus predictions over 11 algorithms but allows user-customized predictions as some methodologies can have a certain degree of redundancy, thus biasing the consensus prediction. | http://aias.biol.uoa.gr/AMYLPRED2/* |
| MetAmyl [75] | Score is obtained applying a linear combination of four predictors’ (which showed lower redundancy) outcome, weighting the individual contribution of each method. | http://metamyl.genouest.org |
*
This list intends to be illustrative and not to provide an extensive enumeration and description of all available methods. Programs in this list are not necessarily more accurate than those absent.