Network Analyses Based on Machine Learning Methods to Quantify Effects of Peptide-Protein Complexes as Drug Targets Using Cinnamon in Cardiovascular Diseases and Metabolic Syndrome as a Case Study.
Network Analyses Based on Machine Learning Methods to Quantify Effects of Peptide-Protein Complexes as Drug Targets Using Cinnamon in Cardiovascular Diseases and Metabolic Syndrome as a Case Study.
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DOI：
10.3389/fgene.2021.816131
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发表时间：
2021
影响因子：
3.7
通讯作者：
Zhao H
中科院分区：
文献类型：
--
作者：
Wang Y;Wang L;Liu Y;Li K;Zhao H
肽-蛋白复合物在心血管疾病（CVD）和代谢综合征（MetS）等多种疾病中发挥重要作用。这些肽可能是设计抑制剂或药物靶点的关键分子。许多中药在不同的疾病中发挥着不同的作用，应该使用网络进行综合分析，它可以提供比单一水平产生的结果更多的信息。在这项研究中，基于机器学习方法设计了一个网络分析管道，以量化肽-蛋白质复合物作为药物靶标的作用。以肉桂（CA）在心血管疾病和代谢综合征中的应用为例，进行了途径筛选、组合网络构建和基于肽的生物标志物预测与验证三个步骤。结果表明，17个肽-蛋白复合物，包括6个肽和4个蛋白质被确定为CA的目标。在构建的小鼠模型中使用qRT-PCR测试AKT 1、AKT 2和ENOS的表达。AKT 2被证明是CA指示生物标志物，而E2 F1和ENOS是CA治疗靶点。AKT 1被认为是糖尿病反应性生物标志物，因为它在糖尿病患者中下调，但与CA无关。总之，该管道可以根据生物功能分析确定新的药物靶标。这可以提供对药物在不同疾病中的作用的深入理解，这可能促进基于肽-蛋白质复合物的治疗方法的发展。
Peptide–protein complexes play important roles in multiple diseases such as cardiovascular diseases (CVDs) and metabolic syndrome (MetS). The peptides may be the key molecules in the designing of inhibitors or drug targets. Many Chinese traditional drugs are shown to play various roles in different diseases, and comprehensive analyses should be performed using networks which could offer more information than results generated from a single level. In this study, a network analysis pipeline was designed based on machine learning methods to quantify the effects of peptide–protein complexes as drug targets. Three steps, namely, pathway filter, combined network construction, and biomarker prediction and validation based on peptides, were performed using cinnamon (CA) in CVDs and MetS as a case. Results showed that 17 peptide–protein complexes including six peptides and four proteins were identified as CA targets. The expressions of AKT1, AKT2, and ENOS were tested using qRT-PCR in a mouse model that was constructed. AKT2 was shown to be a CA-indicating biomarker, while E2F1 and ENOS were CA treatment targets. AKT1 was considered a diabetic responsive biomarker because it was down-regulated in diabetic but not related to CA. Taken together, the pipeline could identify new drug targets based on biological function analyses. This may provide a deep understanding of the drugs’ roles in different diseases which may foster the development of peptide–protein complex–based therapeutic approaches.
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DOI：
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发表时间：
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期刊：
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影响因子：
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DOI：
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