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

Abstract:

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.

authors

Tang J,Ning J,Liu X,Wu B,Hu R

doi

10.2174/1573409914666180925100355

subject

Has Abstract

pub_date

2019-01-01 00:00:00

pages

206-211

issue

3

eissn

1573-4099

issn

1875-6697

pii

CAD-EPUB-93201

journal_volume

15

pub_type

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