Improved Prediction of Cell-Penetrating Peptides via Effective Orchestrating Amino Acid Composition Feature Representation

Abstract

Cell-penetrating peptides (CPPs) promote the transport of pharmacologically active molecules, such as nanoparticles, plasmid DNA and short interfering RNA. Accurate prediction of new CPPs is a prerequisite for in-depth study of such molecules. Biological experimental predictions can provide an accurate description of the penetrating properties of CPPs. However, predicting CPPs by wet laboratory experiments is both resource-intensive and time-consuming. Therefore, the development of effective calculation method prediction has become an important topic in the study of CPPs. Recently, numerous methods developed for predicting CPPs use amino acid composition, alone and the accuracies of such methods have been limited. In this study, we proposed a new CPP prediction framework, which integrates four amino acid composition features, and utilizes these features to help train Support Vector Machine (SVM) model as a classifier to predict CPPs. When performing on the training dataset CPP924, the proposed method achieves an accuracy of 92.3%, which is significantly better than the state-of-the-art methods. These results suggest that the framework can orchestrate various amino acid composition features predicted models flexibly with good performances.

Keywords

• Cell-penetrating peptides
• sequence-based prediction
• feature representation
• amino acid composition
• support vector machine