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通过多任务学习解密孤儿GPCR药物发现

Wei-Cheng Huang¹, Wei-Ting Lin¹, Ming-Shiu Hung¹

¹Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli County, 35053, Taiwan.

Journal of cheminformatics

|January 23, 2024

概括

这项研究引入了多任务模型来预测G蛋白合受体 (GPCRs) 的药物疗效,通过利用蛋白质特征进行数据传输来加速对孤儿GPCRs的治疗方法的发现.

科学领域:

计算化学是一种计算化学.
药理学药理学是指药理学的学科.
结构生物学是结构生物学.

背景情况:

对G蛋白结合受体 (GPCRs) 的药物发现受到有限的3D结构和生物活性数据的阻碍,特别是对于孤儿GPCRs.
由于数据稀缺,现有的计算模型在预测配体-GPCR相互作用方面面临挑战.

研究的目的:

开发和验证用于预测化学-GPCR对半最大有效度 (EC50) 的多任务模型.
通过利用蛋白序列和化学性质信息,使人类孤儿GPCRs的药物发现成为可能.

主要方法:

利用蛋白质多个序列对齐特征和化学物理化学特性/指纹进行编码.
根据特征相似性,雇佣多任务学习将已知GPCRs的数据传输到孤儿受体.
在200个GPCRs的agonist和antagonist数据上训练模型.

主要成果:

在验证数据集上获得了0.24的优异平均平方误差 (MSE).
在独立的孤儿数据集上显示了1.51的相当好的MSE,可通过基于特征的可转移性改进到0.53.
识别了信息特征,并将它们映射到3D结构中,以了解GPCR-配体相互作用.

结论:

拟议的多任务模型为GPCR超级家族中的联体生物活性学习提供了一种新的方法.
这种方法可以显著加速对孤儿GPCRs的治疗剂的发现.
通过分析信息特征和它们的结构映射,获得了对GPCR-连接物相互作用的洞察力.

关键词:

功能选择功能选择与G蛋白结合的受体是G蛋白结合的受体.在GPCR中,GPCR是指GPCR.基于连接体的虚拟查.多任务学习多任务学习

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