Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172 Shenzhen, China.
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, 518172 Shenzhen, China.
Anal Chem. 2024 Jan 30;96(4):1538-1546. doi: 10.1021/acs.analchem.3c04196. Epub 2024 Jan 16.
Tuberculosis (TB) is a severe disease caused by that poses a significant threat to human health. The emergence of drug-resistant strains has made the global fight against TB even more challenging. Antituberculosis peptides (ATPs) have shown promising results as a potential treatment for TB. However, conventional wet lab-based approaches to ATP discovery are time-consuming and costly and often fail to discover peptides with desired properties. To address these challenges, we propose a novel machine learning-based framework called ATPfinder that can significantly accelerate the discovery of ATP. Our approach integrates various efficient peptide descriptors and utilizes the deep forest algorithm to construct the model. This neural network-like cascading structure can effectively process and mine features without complex hyperparameter tuning. Our experimental results show that ATPfinder outperforms existing ATP prediction tools, achieving state-of-the-art performance with an accuracy of 89.3% and an MCC of 0.70. Moreover, our framework exhibits better robustness than baseline algorithms commonly used for other sequence analysis tasks. Additionally, the excellent interpretability of our model can assist researchers in understanding the critical features of ATP. Finally, we developed a downloadable desktop application to simplify the use of our framework for researchers. Therefore, ATPfinder can facilitate the discovery of peptide drugs and provide potential solutions for TB treatment. Our framework is freely available at https://github.com/lantianyao/ATPfinder/ (data sets and code) and https://awi.cuhk.edu.cn/dbAMP/ATPfinder.html (software).
结核病（TB）是一种由 引起的严重疾病，对人类健康构成重大威胁。耐药菌株的出现使得全球防治结核病的工作更加具有挑战性。抗结核肽（ATPs）作为结核病潜在治疗方法已显示出良好的效果。然而，传统的基于湿实验室的 ATP 发现方法耗时且昂贵，并且经常无法发现具有所需特性的肽。为了解决这些挑战，我们提出了一种称为 ATPfinder 的新型基于机器学习的框架，可显著加快 ATP 的发现速度。我们的方法集成了各种有效的肽描述符，并利用深度森林算法构建模型。这种类似于神经网络的级联结构可以有效地处理和挖掘特征，而无需复杂的超参数调整。我们的实验结果表明，ATPfinde 优于现有的 ATP 预测工具，其准确率为 89.3%，MCC 为 0.70，达到了最新的性能水平。此外，我们的框架比其他常用于其他序列分析任务的基线算法具有更好的稳健性。此外，我们模型的出色可解释性可以帮助研究人员了解 ATP 的关键特征。最后，我们开发了一个可下载的桌面应用程序，以简化研究人员使用我们框架的过程。因此，ATPfinde 可以促进肽药物的发现，并为结核病治疗提供潜在的解决方案。我们的框架可在以下网址免费获得：https://github.com/lantianyao/ATPfinder/（数据集和代码）和 https://awi.cuhk.edu.cn/dbAMP/ATPfinder.html（软件）。
