BioDesign Research / 2022 / Article / Tab 7

Review Article

Design of Protein Segments and Peptides for Binding to Protein Targets

Table 7

Overview of methods developed for prediction of AMP activity. Note that the reported accuracies are against different benchmarks and thus cannot be compared.

MethodTraining setResult/OutputAccuracy

14 ML algorithms including RF, CART, SVM, K-nearest neighbor, Logistic Regression (LOGREG), Adaptive Boosting (AB) [157]HemoPI-1, HemoPI-2 and HemoPI-3Predicts hemolytic nature with more accuracy than hemolytic activityHemoPI-1: 94-95%
HemoP2/3- 75-77%
AI4AMP [158]: PC6 encoded Deep Learning ModelAPD3, LAMP, CAMP3, DRAMPConsiders physicochemical properties of aminoacids as features90%
Deep-AmPEP30 [159]: CNN, Reduced AAC (RAAC)Subset of a training set used in AmPEP [160] (experimentally validated AMPs from APD3, LAMP, CAMPR3)Predicts AMP activity of short peptides from genomic DNA.77.13%
AntiBP [161]: ANN, Quantitative Matrices (QM), SVM486 peptides shorter than 61 residues from the APD databasePreference for certain residues at the N- and C-terminals that demarcate them from non antibacterial peptidesANN based: 88.17%, QM based: 90.37% SVM based: 92.11%
AMPA [162]: Web application to assess the antimicrobial domain in protein. Can serve as template for AMP designHigh throughput screening results from amino acid substitutions on bactenecin 2AQuick discovery of new AMP patterns in protein sequencesaccuracy of 85% and a sensitivity of 90% [163]
AVPpred [164]: SVM1245 experimentally validated antiviral peptidesPredicts highly effective AVPs86%
iAMP-2L [165]: Pseudo-amino acid composition, Fuzzy K-Nearest neighborCurated set of AMP and non-AMP peptides collected from UniprotPredicts if a given sequence is an AMP or not and if yes, the next classification is done on the type of AMP92.23%
AMP_Scanner [166]: Deep Neural Network with the Keras framework ( using a sequential model and a TensorFlow deep learning library back-endAPD vr.3 databaseThis model automatically extracts expert-free features and hence removes the reliance on domain experts for feature construction91.01%