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进化引导的先进深度学习架构赋予哺乳动物GPCRome激进分子预测的力量

Aayushi Mittal¹, Mudit Gupta¹, Sanjay Kumar Mohanty¹

¹Department of Computational Biology, Indraprastha Institute of Information Technology-Delhi (IIIT-Delhi), Okhla, Phase III, New Delhi 110020, India.

|February 20, 2026

概括

我们开发了EvOlf,这是一个深度学习框架,用于预测连接体-受体相互作用,提高物种间的准确性. 这种工具有助于识别新的药物标和理解受体功能.

科学领域:

计算生物学是一种计算生物学.
药理学药理学是指药理学的学科.
基因组学就是基因组学.

背景情况:

G蛋白结合受体 (GPCR) 是重要的信号蛋白.
目前用于联体受体相互作用的计算方法在受体覆盖范围,气味受体含量和跨物种数据方面存在局限性.

研究的目的:

介绍EvOlf,一个以进化为导向的深度学习框架,用于预测连接体-受体相互作用.
通过整合多种受体类型和广泛的跨物种数据来克服现有方法的局限性.

主要方法:

开发了EvOlf,一个使用层次自我注意的深度学习框架,多分辨率功能编码和融合变压器.
利用了105,235个经过实验验证的24种哺乳动物相互作用的广泛数据集.
集成的气味和非气味受体.

主要成果:

在预测体受体相互作用方面,EvOlf取得了最先进的性能.
在一个独立的测试数据集上,EvOlf显著超过了现有的预测方法.
实验验证确定了三种针对β1-上腺体受体的新型全性联合激活剂,增强了激素诱导的信号传递.

结论:

EvOlf提供了一个强大的和可扩展的平台,用于受体脱化和新型配体的发现.
该框架的进化方法和全面的数据集提高了预测准确性和生物相关性.
已识别的联合激活剂证明了EvOlf在治疗标识方面的潜力.

关键词:

科普:分子生物学在GPCR-ligand的预测中.GPC 罗马罗马罗马深度学习是一种深度学习.甲化除甲化嗅觉嗅觉是一种嗅觉.

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