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.
Highlights
Cell-penetrating peptides (CPPs) are short peptides that reach 5–50 amino acids in length and can pass through cell membranes and carry proteins, peptides, nucleic acids and other molecules into cells
1850 CPP sequences have been included in the largest CPP database, CPPsite 2.0 [4]; 80% of CPPs are derived from protein sequences
PERFORMANCE OF DIFFERENT FEATURES To evaluate the effect of different feature information on the prediction performance of CPPs, we compared the experimental subsets of each feature subset on the benchmark dataset
Summary
Cell-penetrating peptides (CPPs) are short peptides that reach 5–50 amino acids in length and can pass through cell membranes and carry proteins, peptides, nucleic acids and other molecules into cells. The cell membrane is a semi-permeable barrier between cells and the extracellular environment and features selective permeability, which strongly ensures a relatively constant intracellular environment [1]. This phospholipid bilayer is essential for cell survival and function, it creates barriers to the exchange of cargo molecules inside and outside cells. Considering that macromolecules, such as proteins, peptides and nucleic acids, hardly enter cells and reach sufficient concentration, the therapeutic effect of drugs is unsatisfactory, hindering the application of these.
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