使用基于相互作用组的深度学习来推断药物对细胞信号传导的脱靶效应的协议。

IF 1.3 Q4 BIOCHEMICAL RESEARCH METHODS
STAR Protocols Pub Date : 2025-03-21 Epub Date: 2025-01-16 DOI:10.1016/j.xpro.2024.103573
Nikolaos Meimetis, Douglas A Lauffenburger, Avlant Nilsson
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引用次数: 0

摘要

针对特定蛋白质的药物通常会产生脱靶效应。我们提出了一种使用人工神经网络来模拟细胞对药物的转录反应的方案,旨在了解它们的作用机制。我们详细介绍了预测转录活性、推断药物-靶标相互作用和解释脱靶作用机制的步骤。作为案例研究,我们分析了来司替尼对A375细胞系FOXM1的脱靶效应。有关本协议使用和执行的完整细节,请参阅Meimetis et al.1。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Protocol to infer off-target effects of drugs on cellular signaling using interactome-based deep learning.

Drugs that target specific proteins often have off-target effects. We present a protocol using artificial neural networks to model cellular transcriptional responses to drugs, aiming to understand their mechanisms of action. We detail steps for predicting transcriptional activities, inferring drug-target interactions, and explaining the off-target mechanism of action. As a case study, we analyze the off-target effects of lestaurtinib on FOXM1 in the A375 cell line. For complete details on the use and execution of this protocol, please refer to Meimetis et al.1.

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来源期刊
STAR Protocols
STAR Protocols Biochemistry, Genetics and Molecular Biology-General Biochemistry, Genetics and Molecular Biology
CiteScore
2.00
自引率
0.00%
发文量
789
审稿时长
10 weeks
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