中文版 | English
题名

蛋白质-蛋白质结合亲和力预测

其他题名
PROTEIN-PROTEIN BINDING AFFINITY PREDICTION
姓名
姓名拼音
WU Ruiping
学号
12132804
学位类型
硕士
学位专业
0856 材料与化工
学科门类/专业学位类别
0856 材料与化工
导师
贾铁争
导师单位
化学系
外机构导师
周雷
外机构导师单位
深圳湾实验室
论文答辩日期
2023-05-24
论文提交日期
2023-06-27
学位授予单位
南方科技大学
学位授予地点
深圳
摘要

蛋白质-蛋白质相互作用(protein-protein interactions,PPIs)是生物体内一类关键的相互作用。深入了解PPIs对理解机体生理功能,药物开发都有着重要意义,因此它一直受到广泛关注。结合亲和力是一个用来衡量相互作用强度和结合紧密程度的关键性热力学指标,除了一些测定结合亲和力的湿实验方法外,目前已经开发出了多种计算方法来预测蛋白质-蛋白质的结合亲和力。这些计算方法可以简单分为两类,一类是以分子力学/泊松-玻尔兹曼表面积(Molecular Mechanics/Poisson-Boltzmann Surface Area,MM/PBSA)为代表的传统方法,它们基于基本的化学和物理原理,从能量和热力学角度来计算结合自由能,具有坚实的理论基础。另一类是基于深度学习的现代方法,它们利用深度学习高效执行、高水平特征提取的能力来得到那些没有被传统方法的能量函数捕捉到的特征,并且还大大缩短了预测时间。针对蛋白质-蛋白质亲和力预测,本文在传统方法和现代方法上都做了相关改进,提出了一种基于MM/PBSA的Interfacial Water方法和一种基于图神经网络的交互结合图网络(Mutual Bind Graph Network,MBGN)模型。在基于MM/PBSA的Interfacial Water方法中,通过显示计算分子动力学模拟过程中结合面上的动态水分子,发现了结合面上的水分子对抗体和新冠病毒结合的关键性作用,与Standard MM/PBSA方法相比其皮尔森相关系数也大大提高,并且分布更加收敛。在基于图神经网络的MBGN模型中,构建了一个由图嵌入层,交互注意力层和前馈网络层构成的结合亲和力预测模型,证明了图神经网络在预测蛋白质-蛋白质结合亲和力上的可行性和巨大潜力。

关键词
语种
中文
培养类别
独立培养
入学年份
2021
学位授予年份
2023-06
参考文献列表

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