Novel machine learning application for prediction of membrane insertion potential of cell-penetrating peptides

Abstract

Cell-penetrating peptides (CPPs) are often used as transporter systems to deliver various therapeutic agents into the cell. We developed a novel machine learning application which can quantitatively screen the insertion/interaction potential of various CPPs into three model phospholipid monolayers. An artificial neural network (ANN) was designed, trained, and ultimately tested on an external dataset using Langmuir experimental data for 13 CPPs (hydrophilic and amphiphilic) together with various features related to the insertion/interaction efficiency of CPPs. The trained ANN provided accurate predictions of the maximum change in surface pressure of CPPs when injected below three membrane models at pH 7.4. The accuracy of predictions was high for the dataset which was used to construct the model (r² = 0.986) as well as for the external "prospective" dataset (r² = 0.969). In conclusion, this study demonstrates the promising potential of ANNs for screening the insertion potential of CPPs into membrane models for efficient intracellular delivery of therapeutic agents.

Keywords: Artificial neural networks; Cell-penetrating peptide; Langmuir; Machine learning; Phospholipid; Surface pressure.