期刊
PHARMACEUTICS
卷 14, 期 1, 页码 -出版社
MDPI
DOI: 10.3390/pharmaceutics14010122
关键词
tumor-homing peptide; therapeutic peptide; scoring card method; propensity score; machine learning; bioinformatics
资金
- National Research Foundation of Korea (NRF) Korean government (MSIT) [2021R1A2C1014338]
- National Research Foundation of Korea [2021R1A2C1014338] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
In this study, a new computational approach called SCMTHP was proposed for identifying and analyzing the tumor-homing activities of peptides. The results showed that SCMTHP achieved comparable performance to state-of-the-art methods and outperformed other machine learning methods. By using SCMTHP, better understanding of the biophysical and biochemical properties of tumor-homing peptides can be obtained.
Tumor-homing peptides (THPs) are small peptides that can recognize and bind cancer cells specifically. To gain a better understanding of THPs' functional mechanisms, the accurate identification and characterization of THPs is required. Although some computational methods for in silico THP identification have been proposed, a major drawback is their lack of model interpretability. In this study, we propose a new, simple and easily interpretable computational approach (called SCMTHP) for identifying and analyzing tumor-homing activities of peptides via the use of a scoring card method (SCM). To improve the predictability and interpretability of our predictor, we generated propensity scores of 20 amino acids as THPs. Finally, informative physicochemical properties were used for providing insights on characteristics giving rise to the bioactivity of THPs via the use of SCMTHP-derived propensity scores. Benchmarking experiments from independent test indicated that SCMTHP could achieve comparable performance to state-of-the-art method with accuracies of 0.827 and 0.798, respectively, when evaluated on two benchmark datasets consisting of Main and Small datasets. Furthermore, SCMTHP was found to outperform several well-known machine learning-based classifiers (e.g., decision tree, k-nearest neighbor, multi-layer perceptron, naive Bayes and partial least squares regression) as indicated by both 10-fold cross-validation and independent tests. Finally, the SCMTHP web server was established and made freely available online. SCMTHP is expected to be a useful tool for rapid and accurate identification of THPs and for providing better understanding on THP biophysical and biochemical properties.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据