Table 1.
Methods for the prediction of MoRFs and related binding regions including SLiMs (short linear motifs that bind proteins) and disordered protein-binding regions (DPBRs). The methods sorted by the publication year in the ascending order within each group. The ‘Type’ column indicates whether a given method is available as the online webserver (WS) and/or standalone source code (SC); NA means that neither webserver nor source code is available. The ‘URL’ column gives the page where the method can be found as of January 7, 2019. The ‘Citations Total’ column gives the number of citations collected from Google Scholar on March 20, 2019. To avoid duplicate counting of citations for methods that are published in multiple articles, we use the one with the highest number of citations. The ‘Citations Annual’ column gives an average number of citations per year since a given method was published. The ‘Predictive model’ column categorizes the models into two groups: those generated with machine learning (ML) algorithms and those that rely on a scoring function (SF) generated either by an empirical formula or using an alignment score. The machine learning models include neural network (NN), support vector machine (SVM), naïve Bayes (NB), and logistic regression (LR).
| Target of predictions | Method name | Ref. | Year published | Predictive model | Meta predictor | Availability |
Citations |
||
|---|---|---|---|---|---|---|---|---|---|
| Type | URL | Total | Annual | ||||||
| MoRF regions | α-MoRFpred | [40,96] | 2005 | ML (NN) | No | NA | NA | 454 | 32 |
| retro-MoRFs | [98] | 2010 | SF (alignment) | No | NA | NA | 27 | 3 | |
| MoRFpred | [86,87] | 2012 | ML (SVM) | No | WS | http://biomine.cs.vcu.edu/servers/MoRFpred/ | 194 | 28 | |
| MFSPSSMpred | [102] | 2013 | ML (SVM) | No | WS + SC | The website does not work as of January 2019 | 32 | 5 | |
| MoRFCHiBi | [88] | 2015 | ML (SVM) | No | WS + SC | https://gsponerlab.msl.ubc.ca/software/morf_chibi/ | 37 | 9 | |
| DISOPRED3 | [90] | 2015 | ML (SVM) | No | WS + SC | http://bioinf.cs.ucl.ac.uk/disopred | 218 | 54 | |
| fMoRFpred | [9] | 2016 | ML (SVM) | No | WS | http://biomine.cs.vcu.edu/servers/fMoRFpred/ | 36 | 12 | |
| MoRFCHiBiLight | [89] | 2016 | ML (NB) | No | WS + SC | https://gsponerlab.msl.ubc.ca/software/morf_chibi/ | 23 | 8 | |
| MoRFCHiBiWeb | [89] | 2016 | ML (NB) | Yes | WS + SC | https://gsponerlab.msl.ubc.ca/software/morf_chibi/ | 23 | 8 | |
| Predict-MoRFs | [103] | 2016 | ML (SVM) | No | SC | https://github.com/roneshsharma/Predict-MoRFs | 6 | 2 | |
| Fang et al. | [101] | 2018 | ML (SVM) | No | NA | NA | 0 | 0 | |
| MoRFPred-plus | [104] | 2018 | ML (SVM) | No | SC | https://github.com/roneshsharma/MoRFpred-plus/wiki/MoRFpred-plus | 8 | 8 | |
| OPAL | [91] | 2018 | ML (SVM) | Yes | WS + SC | http://www.alok-ai-lab.com/tools/opal/ | 9 | 9 | |
| OPAL+ | [100] | 2018 | ML (SVM) | Yes | WS + SC | http://www.alok-ai-lab.com/tools/opal_plus/ | 0 | 0 | |
| DPBRs | DisoRDPbind | [94,95] | 2015 | ML (LR) | No | WS | http://biomine.cs.vcu.edu/servers/DisoRDPbind/ | 47 | 12 |
| ANCHOR | [39,41,93] | 2009 | SF | No | WS + SC | http://anchor.enzim.hu | 395 | 39 | |
| SLiMs | PepBindPred | [105] | 2013 | ML (NN) | No | WS | http://bioware.ucd.ie/~compass/biowareweb/Server_pages/pepbindpred.php | 17 | 3 |
| SLiMPred | [106] | 2012 | ML (NN) | No | WS | http://bioware.ucd.ie/~compass/biowareweb/Server_pages/slimpred.php | 55 | 8 | |
| Semi-disorder | SPINE-D | [107] | 2013 | ML (NN) | No | WS + SC | http://sparks-lab.org/SPINE-D/ | 32 | 5 |
| SPOT-Disorder | [108] | 2017 | ML (NN) | No | WS + SC | http://sparks-lab.org/server/SPOT-disorder/ | 47 | 23 | |