基于深度学习方法发现靶向 TLR4 治疗肝脏炎症疾病的银翘解毒片有效成分

IF 3.9 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Sizhe Zhang, Peng Han, Haiqing Sun, Ying Su, Chen Chen, Cheng Chen, Jinyao Li, Xiaoyi Lv, Xuecong Tian, Yandan Xu
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引用次数: 0

摘要

银翘散(YCHD)是传统中药中的经典方剂,被认为具有通过调节Toll样受体4(TLR4)靶点治疗肝病的潜力。因此,深入探讨 "养生堂 "中针对 TLR4 的有效成分和治疗机制,是治疗肝病的一项前景广阔的策略。本研究提出了AIGO-DTI深度学习框架来预测YCHD中主要成分对TLR4的靶向概率。与四种机器学习模型(RF、SVM、KNN、XGBoost)和两种深度学习模型(GCN、GAT)的比较评估表明,AIGO-DTI 框架表现出最佳的整体性能,其 Recall 和 AUC 分别达到 0.968 和 0.991。随后的湿实验表明,异莨菪亭可通过TLR4影响由脂多糖(LPS)诱导的树突状细胞(DCs)的成熟,这表明它对肝脏疾病,尤其是肝炎具有治疗潜力。此外,基于 AIGO-DTI 框架,本研究建立了一个名为 TLR4-Predict 的在线平台,以方便领域专家发现更多与 TLR4 相关的化合物。总之,所提出的 AIGO-DTI 框架能准确预测 YCHD 中与 TLR4 相互作用的独特化合物,为识别和筛选靶向 TLR4 的先导化合物提供了新的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Discovery of Active Ingredient of Yinchenhao Decoction Targeting TLR4 for Hepatic Inflammatory Diseases Based on Deep Learning Approach.

Yinchenhao Decoction (YCHD), a classic formula in traditional Chinese medicine, is believed to have the potential to treat liver diseases by modulating the Toll-like receptor 4 (TLR4) target. Therefore, a thorough exploration of the effective components and therapeutic mechanisms targeting TLR4 in YCHD is a promising strategy for liver diseases. In this study, the AIGO-DTI deep learning framework was proposed to predict the targeting probability of major components in YCHD for TLR4. Comparative evaluations with four machine learning models (RF, SVM, KNN, XGBoost) and two deep learning models (GCN, GAT) demonstrated that the AIGO-DTI framework exhibited the best overall performance, with Recall and AUC reaching 0.968 and 0.991, respectively.This study further utilized the AIGO-DTI model to identify the potential impact of Isoscopoletin, a major component of YCHD, on TLR4. Subsequent wet experiments revealed that Isoscopoletin could influence the maturation of Dendritic Cells (DCs) induced by Lipopolysaccharide (LPS) through TLR4, suggesting its therapeutic potential for liver diseases, especially hepatitis. Additionally, based on the AIGO-DTI framework, this study established an online platform named TLR4-Predict to facilitate domain experts in discovering more compounds related to TLR4. Overall, the proposed AIGO-DTI framework accurately predicts unique compounds in YCHD that interact with TLR4, providing new insights for identifying and screening lead compounds targeting TLR4.

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来源期刊
Interdisciplinary Sciences: Computational Life Sciences
Interdisciplinary Sciences: Computational Life Sciences MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
8.60
自引率
4.20%
发文量
55
期刊介绍: Interdisciplinary Sciences--Computational Life Sciences aims to cover the most recent and outstanding developments in interdisciplinary areas of sciences, especially focusing on computational life sciences, an area that is enjoying rapid development at the forefront of scientific research and technology. The journal publishes original papers of significant general interest covering recent research and developments. Articles will be published rapidly by taking full advantage of internet technology for online submission and peer-reviewing of manuscripts, and then by publishing OnlineFirstTM through SpringerLink even before the issue is built or sent to the printer. The editorial board consists of many leading scientists with international reputation, among others, Luc Montagnier (UNESCO, France), Dennis Salahub (University of Calgary, Canada), Weitao Yang (Duke University, USA). Prof. Dongqing Wei at the Shanghai Jiatong University is appointed as the editor-in-chief; he made important contributions in bioinformatics and computational physics and is best known for his ground-breaking works on the theory of ferroelectric liquids. With the help from a team of associate editors and the editorial board, an international journal with sound reputation shall be created.
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