The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China.
Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
BMC Biol. 2024 Apr 19;22(1):86. doi: 10.1186/s12915-024-01883-4.
The blood-brain barrier serves as a critical interface between the bloodstream and brain tissue, mainly composed of pericytes, neurons, endothelial cells, and tightly connected basal membranes. It plays a pivotal role in safeguarding brain from harmful substances, thus protecting the integrity of the nervous system and preserving overall brain homeostasis. However, this remarkable selective transmission also poses a formidable challenge in the realm of central nervous system diseases treatment, hindering the delivery of large-molecule drugs into the brain. In response to this challenge, many researchers have devoted themselves to developing drug delivery systems capable of breaching the blood-brain barrier. Among these, blood-brain barrier penetrating peptides have emerged as promising candidates. These peptides had the advantages of high biosafety, ease of synthesis, and exceptional penetration efficiency, making them an effective drug delivery solution. While previous studies have developed a few prediction models for blood-brain barrier penetrating peptides, their performance has often been hampered by issue of limited positive data.
In this study, we present Augur, a novel prediction model using borderline-SMOTE-based data augmentation and machine learning. we extract highly interpretable physicochemical properties of blood-brain barrier penetrating peptides while solving the issues of small sample size and imbalance of positive and negative samples. Experimental results demonstrate the superior prediction performance of Augur with an AUC value of 0.932 on the training set and 0.931 on the independent test set.
This newly developed Augur model demonstrates superior performance in predicting blood-brain barrier penetrating peptides, offering valuable insights for drug development targeting neurological disorders. This breakthrough may enhance the efficiency of peptide-based drug discovery and pave the way for innovative treatment strategies for central nervous system diseases.
血脑屏障作为血液和脑组织之间的关键界面，主要由周细胞、神经元、内皮细胞和紧密连接的基底膜组成。它在保护大脑免受有害物质侵害方面起着至关重要的作用，从而保护神经系统的完整性并维持大脑整体的内稳态。然而，这种显著的选择性传递在中枢神经系统疾病治疗领域也构成了一个巨大的挑战，阻碍了大分子药物进入大脑。针对这一挑战，许多研究人员致力于开发能够穿透血脑屏障的药物传递系统。在这些系统中，血脑屏障穿透肽已成为有前途的候选物。这些肽具有高生物安全性、易于合成和出色的穿透效率等优点，是一种有效的药物传递解决方案。虽然之前的研究已经开发出几种血脑屏障穿透肽的预测模型，但它们的性能往往受到阳性数据有限的问题的影响。
在这项研究中，我们提出了 Augur，这是一种使用基于边界-SMOTE 的数据扩充和机器学习的新型预测模型。我们提取了血脑屏障穿透肽的高度可解释的物理化学性质，同时解决了小样本量和正负样本不平衡的问题。实验结果表明，Augur 在训练集上的 AUC 值为 0.932，在独立测试集上的 AUC 值为 0.931，具有优越的预测性能。
新开发的 Augur 模型在预测血脑屏障穿透肽方面表现出优越的性能，为针对神经疾病的药物开发提供了有价值的见解。这一突破可能会提高基于肽的药物发现的效率，并为中枢神经系统疾病的创新治疗策略铺平道路。
