A Novel Amino Acid Sequence-based Computational Approach to Predicting Cell-penetrating Peptides

INTRODUCTION: Machine Learning is a useful tool for the prediction of cell-penetration compounds as drug candidates. MATERIALS AND METHODS: In this study, we developed a novel method for predicting Cell-Penetrating Peptides (CPPs) membrane penetrating capability. For this, we used orthogonal encoding to encode amino acid and each amino acid position as one variable. Then a software of IBM spss modeler and a dataset including 533 CPPs, were used for model screening. RESULTS: The results indicated that the machine learning model of Support Vector Machine (SVM) was suitable for predicting membrane penetrating capability. For improvement, the three CPPs with the most longer lengths were used to predict CPPs. The penetration capability can be predicted with an accuracy of close to 95%. CONCLUSION: All the results indicated that by using amino acid position as a variable can be a perspective method for predicting CPPs membrane penetrating capability.