The Development of Machine Learning Methods in Cell-Penetrating Peptides Identification: A Brief Review

Abstract

Background: Cell-penetrating Peptides (CPPs) are important short peptides that facilitate cellular intake or uptake of various molecules. CPPs can transport drug molecules through the plasma membrane and send these molecules to different cellular organelles. Thus, CPP identification and related mechanisms have been extensively explored. In order to reveal the penetration mechanisms of a large number of CPPs, it is necessary to develop convenient and fast methods for CPPs identification.

Methods: Biochemical experiments can provide precise details for accurately identifying CPP, but these methods are expensive and laborious. To overcome these disadvantages, several computational methods have been developed to identify CPPs. We have performed review on the development of machine learning methods in CPP identification. This review provides an insight into CPP identification.

Results: We summarized the machine learning-based CPP identification methods and compared the construction strategies of 11 different computational methods. Furthermore, we pointed out the limitations and difficulties in predicting CPPs.

Conclusion: In this review, the last studies on CPP identification using machine learning method were reported. We also discussed the future development direction of CPP recognition with computational methods.

Keywords: Cell-penetrating peptide; machine learning method; membrane; modeling; prediction; uptake efficiency..