DeepB3P: A transformer-based model for identifying blood-brain barrier penetrating peptides with data augmentation using feedback GAN,Journal of Advanced Research

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DeepB3P: A transformer-based model for identifying blood-brain barrier penetrating peptides with data augmentation using feedback GAN
Journal of Advanced Research ( IF 11.4 ) Pub Date : 2024-08-05 , DOI: 10.1016/j.jare.2024.08.002
Qiang Tang ₁ , Wei Chen ₂

Affiliation

Introduction

The blood–brain barrier (BBB) serves as a critical structural barrier and impedes the entry of most neurotherapeutic drugs into the brain. This poses substantial challenges for central nervous system (CNS) drug development, as there is a lack of efficient drug delivery technologies to overcome this obstacle. BBB penetrating peptides (BBBPs) hold promise in overcoming the BBB and facilitating the delivery of drug molecules to the brain. Therefore, precise identification of BBBPs has become a crucial step in CNS drug development. However, most computational methods are designed based on conventional models that inadequately capture the intricate interaction between BBBPs and the BBB. Moreover, the performance of these methods was further hampered by unbalanced datasets.

Objectives

This study addresses the problem of unbalanced datasets in BBBP prediction and proposes a powerful predictor for efficiently and accurately identifying BBBPs, as well as generating analogous BBBPs.

Methods

A transformer-based deep learning model, DeepB³P, was proposed for predicting BBBP. The feedback generative adversarial network (FBGAN) model was employed to effectively generate analogous BBBPs, addressing data imbalance.

Results

The FBGAN model possesses the ability to generate novel BBBP-like peptides, effectively mitigating the data imbalance in BBBP prediction. Extensive experiments on benchmarking datasets demonstrated that DeepB³P outperforms other BBBP prediction models by approximately 9.09%, 4.55% and 9.41% in terms of specificity, accuracy, and Matthew’s correlation coefficient, respectively. For accelerating the progress in BBBP identification and CNS drug design, the proposed DeepB³P was implemented as a webserver, which is accessible at http://cbcb.cdutcm.edu.cn/deepb3p/.

Conclusion

The interpretable analyses provided by DeepB³P offer valuable insights and enhance downstream analyses for BBBP identification. Moreover, the BBBP-like peptides generated by FBGAN hold potential as candidates for CNS drug development.

中文翻译：

DeepB3P：一种基于 transformer 的模型，用于使用反馈 GAN 进行数据增强来识别血脑屏障穿透肽

介绍

血脑屏障（BBB）是一个关键的结构屏障，会阻碍大多数神经治疗药物进入大脑。这给中枢神经系统（CNS）药物开发带来了巨大挑战，因为缺乏有效的药物递送技术来克服这一障碍。BBB 穿透肽（BBBP）有望克服 BBB 并促进药物分子向大脑的递送。因此，精确鉴定 BBBP 已成为 CNS 药物开发的关键步骤。然而，大多数计算方法都是基于传统模型设计的，这些模型无法充分捕捉 BBBP 和 BBB 之间错综复杂的相互作用。此外，这些方法的性能进一步受到不平衡数据集的阻碍。

目标

本研究解决了 BBBP 预测中数据集不平衡的问题，并提出了一个强大的预测器，用于高效准确地识别 BBBP，以及生成类似的 BBBP。

方法

提出了一种基于 transformer 的深度学习模型 DeepB³P 来预测 BBBP。采用反馈生成对抗网络（FBGAN）模型有效地生成类似的 BBBP，解决数据不平衡问题。

结果

FBGAN 模型具有生成新型 BBBP 样肽的能力，有效缓解了 BBBP 预测中的数据不平衡。在基准测试数据集上的广泛实验表明，DeepB³P 在特异性、准确性和 Matthew 相关系数方面分别优于其他 BBBP 预测模型约 9.09%、4.55% 和 9.41%。为了加快 BBBP 鉴定和 CNS 药物设计的进展，提出的 DeepB³P 被实施为 Web 服务器，可在 http://cbcb.cdutcm.edu.cn/deepb3p/ 访问。