通过扰动转录组学的知识图嵌入识别化合物与蛋白质之间的相互作用。

IF 11.1 Q1 CELL BIOLOGY Cell genomics Pub Date : 2024-10-09 Epub Date: 2024-09-19 DOI:10.1016/j.xgen.2024.100655
Shengkun Ni, Xiangtai Kong, Yingying Zhang, Zhengyang Chen, Zhaokun Wang, Zunyun Fu, Ruifeng Huo, Xiaochu Tong, Ning Qu, Xiaolong Wu, Kun Wang, Wei Zhang, Runze Zhang, Zimei Zhang, Jiangshan Shi, Yitian Wang, Ruirui Yang, Xutong Li, Sulin Zhang, Mingyue Zheng
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引用次数: 0

摘要

扰动转录组学的出现为药物发现提供了新的视角,但现有的分析方法存在性能不足和适用性有限的问题。在这项工作中,我们提出了 PertKGE,这是一种利用知识图嵌入从扰动转录组学中解构化合物与蛋白质相互作用的方法。通过考虑生物系统中具有相同语义背景的多层次调控事件,PertKGE 显著提高了在两个关键的 "冷启动 "环境中解卷的准确性:推断新化合物的靶点和进行新靶点的虚拟筛选。我们进一步证明了纳入多层次调控事件在减轻表征偏差方面的关键作用。值得注意的是,它使我们确定了外切核苷酸焦磷酸酶/磷酸二酯酶-1 是导致坦克酶抑制剂 K-756 产生独特抗肿瘤免疫治疗效果的靶点,并发现了针对新兴癌症治疗靶点醛脱氢酶 1B1 的五个新靶点,命中率高达 10.2%。这些发现凸显了 PertKGE 在加速药物发现方面的潜力。
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Identifying compound-protein interactions with knowledge graph embedding of perturbation transcriptomics.

The emergence of perturbation transcriptomics provides a new perspective for drug discovery, but existing analysis methods suffer from inadequate performance and limited applicability. In this work, we present PertKGE, a method designed to deconvolute compound-protein interactions from perturbation transcriptomics with knowledge graph embedding. By considering multi-level regulatory events within biological systems that share the same semantic context, PertKGE significantly improves deconvoluting accuracy in two critical "cold-start" settings: inferring targets for new compounds and conducting virtual screening for new targets. We further demonstrate the pivotal role of incorporating multi-level regulatory events in alleviating representational biases. Notably, it enables the identification of ectonucleotide pyrophosphatase/phosphodiesterase-1 as the target responsible for the unique anti-tumor immunotherapy effect of tankyrase inhibitor K-756 and the discovery of five novel hits targeting the emerging cancer therapeutic target aldehyde dehydrogenase 1B1 with a remarkable hit rate of 10.2%. These findings highlight the potential of PertKGE to accelerate drug discovery.

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