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生物信息学门户网站用于预测蛋白质与核酸相互作用中的结合区域和模式

Xiao Zhang¹, Wenbo Guo¹, Jiaxin Liu²

¹State Key Laboratory of Green Pesticide; Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China.

Genomics, proteomics & bioinformatics

|December 2, 2025

概括

本研究介绍了蛋白质核酸相互作用 (PNI) 的计算工具箱,提高了结合点和动态的预测. 这些工具有助于理解生物机制,并推进核酸疗法.

科学领域:

生物信息学是一种生物信息学.
计算生物学计算生物学
分子生物学分子生物学

背景情况:

蛋白质核酸相互作用 (PNI) 对基因调节和DNA修复等基本生物过程至关重要.
了解PNI结合部位和模式是阐明分子机制和识别疾病相关异常的关键.
现有的计算工具需要系统的评估,以确保可靠性和促进PNI研究的创新.

研究的目的:

开发和介绍一个全面的计算工具箱来分析蛋白质-核酸相互作用.
利用机器学习和深度学习算法来预测PNI结合点和构造动态.
探索PNI在药物发现和核酸疗法的设计中的应用.

主要方法:

策划PNI数据库,包括交互类型,来源和跨领域数据.
开发和应用机器学习 (ML) 和深度学习 (DL) 模型用于PNI绑定站点预测.
通过计算建模和预测算法研究PNI结构动力学.

主要成果:

一个全面的PNI工具箱,包括精心策划的数据库和先进的预测算法.
通过ML/DL方法成功预测PNI结合点和形状动态.
确定PNI在药物研究和治疗开发中的潜在应用.

结论:

开发的工具箱为PNI分析提供了先进的计算工具,增强了对分子机制的理解.
该研究强调了ML/DL在预测PNI特征方面的实用性,为生物信息学资源做出了贡献.
这些发现支持通过改进的计算建模和药物设计来推进核酸疗法.

关键词:

具有约束性的形状结构.结合残留物和结合场所深度学习模型深度学习模型大分子对接对接结构生物信息学结构生物信息学

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