预测单个突变对蛋白质稳定性和对突变类型的结合的影响
Preeti Pandey1, Shailesh Kumar Panday1, Prawin Rimal1
1Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA.
International journal of molecular sciences
|August 12, 2023
概括
预测对蛋白质的突变影响至关重要,但目前在非随机数据上训练的方法可能无法准确评估人类单核酸变体 (SNV). 性能因突变类型而异,而且这些方法往往低估了能量变化.
科学领域:
- 计算生物学是一种计算生物学.
- 生物物理学的生物物理.
- 基因组学就是基因组学.
背景情况:
- 对蛋白质稳定性和相互作用的突变影响的准确预测对于蛋白质工程和理解遗传疾病至关重要.
- 现有的预测模型在很大程度上依赖于实验性自由能量变化数据进行培训.
- 当前的训练数据集往往包含非随机突变,这可能会限制对人类单核酸变体 (SNV) 的适用性.
研究的目的:
- 评估现有的算法在预测突变效应方面的性能,特别关注单核酸变体 (SNVs).
- 评估当前培训数据库是否适合预测人类人口SNVs对蛋白质热力学的影响.
- 识别当前预测模型中的潜在偏差和局限性,当应用于现实世界的遗传变异时.
主要方法:
- 分析现有的数据库,实验测量折叠和结合自由能量变化.
- 在包含SNV与非SNV的数据集上,预测模型性能的比较.
- 评估模型对突变化学性质的敏感性.
- 评估预测自由能量变化的准确性.
主要成果:
- 用于培训预测模型的数据库并不平等地代表SNV和非SNV.
- 自由能量变化的分布在SNV和非SNV之间有所不同.
- 预测模型显示SNV与非SNV相比,SNV的性能较低 (皮尔森相关系数较小).
- 模型准确性对突变氨基酸之间的化学差异敏感,性能变化高达四倍.
- 所有测试的方法都倾向于低估大约50%的自由能量变化大小.
结论:
- 目前的预测算法,训练在有偏见的数据集,可能无法可靠地预测人类SNVs对蛋白质稳定性和相互作用的影响.
- 突变的化学性质显著影响预测的准确性,突出了当前模型的局限性.
- 现有方法对热力学变化的显著低估需要进一步精细化,以获得准确的临床和工程应用.
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