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. 2019 May 14;20(10):2383. doi: 10.3390/ijms20102383

Table 1.

Overview of prediction methods for peptide solubility and cell-penetrating peptides.

Method Learning Machine Model Input Length (aa) Input Format Multiple Entry Database Web Server Refs
Peptide Solubility
ccSOL omics Super vector machine (SVM) FASTA Yes (up to 104) Target Track (non-redundant) (http://sbkb.org/tt/) http://s.tartaglialab.com/static_files/shared/tutorial_ccsol_omics.html [39]
PROSO II Super vector machine (SVM) 21 to 2000 FASTA Yes (up to 50) Target Track (http://sbkb.org/tt/) http://mbiljj45.bio.med.uni-muenchen.de:8888/prosoII/prosoII.seam [40]
Cell-Penetrating Peptides
CPPpred Artificial neural networks (ANN) 5 to 30 FASTA Yes CPPsite http://bioware.ucd.ie/cpppred [47]
CPPpred-RF Random forest (RF) FASTA Yes CPP924 and CPPsite3 http://server.malab.cn/CPPred-RF [46]
KELM-CPPpred Kernel extreme learning model (KELM) 5 to 30 FASTA Yes Curated 408 CPP/non-CPP http://sairam.people.iitgn.ac.in/KELM-CPPpred.html [48]
CellPPD Super vector machine (SVM) FASTA Yes CPPsite1,2,3 http://crdd.osdd.net/raghava/cellppd/multi_pep.php [50]
CPPsite 2.0 FASTA Yes 1855 uniquely curated http://crdd.osdd.net/raghava/cppsite/ [51]