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ToxDL 2.0:使用预训练的语言模型和图形神经网络预测蛋白质毒性

Lin Zhu¹, Yi Fang^2,3, Shuting Liu¹

¹School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

Computational and structural biotechnology journal

|April 25, 2025

概括

ToxDL 2.0,一种新的深度学习模型,通过整合进化和结构数据,准确地预测蛋白质毒性. 这种计算方法超越了现有的方法,有助于治疗和农业应用.

科学领域:

计算生物学是一种计算生物学.
生物信息学是一种生物信息学.
在蛋白质科学中的机器学习

背景情况:

蛋白质毒性评估对于药物开发和农业至关重要.
实验方法缓慢而昂贵,需要计算解决方案.
现有的蛋白质毒性深度学习模型往往忽略了关键的进化和结构数据.

研究的目的:

开发一种新的多式联机深度学习模型,ToxDL 2.0,用于增强蛋白质毒性预测.
整合进化和结构蛋白质信息以提高准确性.
为开发的模型提供一个公开可访问的Web服务器.

主要方法:

开发了多模式深度学习架构ToxDL 2.0.
使用图形卷积网络 (GCN) 来进行基于结构的嵌入,从AlphaFold2.2开始.
嵌入式域嵌入式和用于毒性预测的密集模块.
创建了一个全面的蛋白质毒性基准数据集用于验证.

主要成果:

ToxDL 2.0 在独立的测试组上表现出比最先进的方法更优异的性能.
该模型成功地整合了进化和结构信息,以进行准确的预测.
综合梯度分析确定了已知的有毒蛋白质动机.

结论:

ToxDL 2.0 在计算蛋白质毒性预测方面取得了重大进展.
该模型集成多式联运数据的能力提高了预测准确度.
公开可用的Web服务器有助于在研发中更广泛的应用.

关键词:

图形神经网络的神经网络语言模型语言模型多模式深度学习多模式深度学习蛋白质毒性蛋白质毒性

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