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碳水化合物化学基因组学的系统研究。

A systematic study of chemogenomics of carbohydrates.

作者信息

Gu Jiangyong, Luo Fang, Chen Lirong, Yuan Gu, Xu Xiaojie

机构信息

Beijing National Laboratory for Molecular Sciences, State Key Lab of Rare Earth Material Chemistry and Applications, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, P. R. China.

出版信息

Mol Biosyst. 2014 Mar 4;10(3):391-7. doi: 10.1039/c3mb70534j. Epub 2014 Jan 28.

Abstract

Chemogenomics focuses on the interactions between biologically active molecules and protein targets for drug discovery. Carbohydrates are the most abundant compounds in natural products. Compared with other drugs, the carbohydrate drugs show weaker side effects. Searching for multi-target carbohydrate drugs can be regarded as a solution to improve therapeutic efficacy and safety. In this work, we collected 60 344 carbohydrates from the Universal Natural Products Database (UNPD) and explored the chemical space of carbohydrates by principal component analysis. We found that there is a large quantity of potential lead compounds among carbohydrates. Then we explored the potential of carbohydrates in drug discovery by using a network-based multi-target computational approach. All carbohydrates were docked to 2389 target proteins. The most potential carbohydrates for drug discovery and their indications were predicted based on a docking score-weighted prediction model. We also explored the interactions between carbohydrates and target proteins to find the pathological networks, potential drug candidates and new indications.

摘要

化学基因组学专注于生物活性分子与蛋白质靶点之间的相互作用以用于药物发现。碳水化合物是天然产物中最丰富的化合物。与其他药物相比,碳水化合物药物的副作用较弱。寻找多靶点碳水化合物药物可被视为提高治疗效果和安全性的一种解决方案。在这项工作中,我们从通用天然产物数据库(UNPD)中收集了60344种碳水化合物,并通过主成分分析探索了碳水化合物的化学空间。我们发现碳水化合物中有大量潜在的先导化合物。然后我们使用基于网络的多靶点计算方法探索了碳水化合物在药物发现中的潜力。所有碳水化合物都与2389种靶蛋白进行对接。基于对接分数加权预测模型预测了最具药物发现潜力的碳水化合物及其适应症。我们还探索了碳水化合物与靶蛋白之间的相互作用,以找到病理网络、潜在的候选药物和新的适应症。

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