BioDesign Research / 2022 / Article / Tab 6

Review Article

Design of Protein Segments and Peptides for Binding to Protein Targets

Table 6

An overview of some of the methods developed to predict CPP function. Note that the reported accuracies are not on similar datasets and thus cannot be compared.

MethodTraining SetFeaturesResultAccuracy

CellPPD [148]: SVM, MEME/MSAT708 experimentally validated, unique
CPPs from the CPPsite database
Amino acid composition, Dipeptide Composition, Physicochemical Properties of amino acidsDetailed 120 CPP motifs identified from their dataset97.40%
SkipCPP-Pred [150]: K-skip-n-gram model, RF classifier1855 experimentally validated CPPs from CPPsite 2.0Input sequencesHigh quality dataset CPP924 with reduced redundancy90.6%
CPPred-FL [152]: RF classifierBenchmark dataset CPP924 containing 924 peptide sequences6 features based on class information and 19 features based on probabilistic informationNew feature representation algorithm that includes all sequence based feature descriptors91.2%
BChemRF-CPPred [154]: MLP architecture, SVM, Gaussian process classifierCPPsite 2.0, C2Pred serverStructure based descriptors including molecular weight, number of rotatable bonds, topological polar surface area, number of hydrogen bond donors and acceptors, etc. Amino acid composition-based descriptors including dipeptide composition.
Pseudo-amino acid composition related to hydrophobicity, hydrophilicity, side chain mass, etc
Evaluated the influence of structural and physicochemical properties in the permeability of peptides89.62%