一种基于氨基酸序列的预测细胞穿透肽的新型计算方法。
A Novel Amino Acid Sequence-based Computational Approach to Predicting Cell-penetrating Peptides.
作者信息
Tang Jihui, Ning Jie, Liu Xiaoyan, Wu Baoming, Hu Rongfeng
机构信息
School of Pharmacy, Anhui Medical University, 81 Meishan Road, Hefei 230032, China.
Department of Oncology, The First Affiliated Hospital, Anhui Medical University, Hefei 230022, China.
出版信息
Curr Comput Aided Drug Des. 2019;15(3):206-211. doi: 10.2174/1573409914666180925100355.
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.
引言
机器学习是预测作为候选药物的细胞穿透化合物的有用工具。
材料与方法
在本研究中，我们开发了一种预测细胞穿透肽（CPP）膜穿透能力的新方法。为此，我们使用正交编码将氨基酸和每个氨基酸位置编码为一个变量。然后使用IBM SPSS Modeler软件和一个包含533种CPP的数据集进行模型筛选。
结果
结果表明，支持向量机（SVM）机器学习模型适用于预测膜穿透能力。为了改进，使用了长度最长的三种CPP来预测CPP。穿透能力的预测准确率接近95%。
结论
所有结果表明，将氨基酸位置用作变量可能是预测CPP膜穿透能力的一种有前景的方法。
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