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评估使用新数据集预测误解突变后蛋白质稳定性变化的计算工具

Feifan Zheng¹, Yang Liu¹, Yan Yang¹

¹MOE Key Laboratory of Geriatric Diseases and Immunology, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou, China.

Protein science : a publication of the Protein Society

|December 12, 2023

概括

预测蛋白质稳定突变仍然是计算方法的挑战. 一个新的数据集和对27个工具的评估揭示了当前准确预测稳定性变化的方法的局限性.

科学领域:

结构生物学结构生物学
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

了解突变对蛋白质稳定性的影响对于蛋白质工程,疾病研究和进化研究至关重要.
预测突变对蛋白质稳定性的影响的现有计算方法在直接比较方面面临挑战,原因是训练数据的多样化.
当前的预测工具通常在破坏突变稳定方面表现更好,而不是稳定突变.

研究的目的:

创建一个新的,不重叠的蛋白质突变数据集,用于评估计算预测方法.
评估27种计算方法的性能,包括最近的深度学习方法,用于预测蛋白质稳定性的变化.
确定当前方法的局限性,特别是预测稳定突变的方法.

主要方法:

编制了来自ThermoMutDB,FireProtDB和ProThermDB的4038个单点突变的新型数据集,不包括与S2648数据集的重叠.
使用这个新数据集评估了27个计算预测工具,确保与其训练数据不重叠.
使用皮尔森相关系数分析了预测准确度,并评估了稳定与不稳定突变的性能趋势.

主要成果:

对未见数据的测试工具的皮尔森相关系数在0.20到0.53.3之间.
没有一种测试方法能够准确预测稳定突变,即使在其他分析中表现良好.
破坏稳定的突变显示了所有属性的一致预测趋势,而稳定的突变缺乏明确的模式.

结论:

当前的计算方法难以准确预测突变对蛋白质稳定性的影响,特别是稳定突变.
仅仅解决训练数据集偏差可能不足以改善稳定突变的预测.
需要开发更精确的计算方法,专门用于预测稳定突变.

关键词:

计算工具是计算工具.错误的感觉突变的突变.蛋白质稳定性变化蛋白质稳定性变化稳定突变的发生.

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