对LDLR蛋白结构的深度生成模型,以预测变异性病原性
Jose K James1, Kristjan Norland1, Angad S Johar1
1Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
Journal of lipid research
|October 11, 2023
概括
像进化规模建模 (ESM) 和变体效应进化模型 (EVE) 这样的深度学习模型有效地预测低密度脂蛋白受体 (LDLR) 变体的致病性. 这些模型与AlphaFold 2相比,与实验数据和临床结果有更好的相关性.
科学领域:
- 基因组学和生物信息学
- 心血管疾病遗传学 心血管疾病遗传学
- 蛋白质变体效应预测预测
背景情况:
- 归类低密度脂蛋白受体 (LDLR) 蛋白质编码误解变体是具有挑战性的,因为受体的复杂结构和功能.
- 深度生成模型,包括进化规模建模 (ESM),变异效应进化模型 (EVE) 和AlphaFold 2 (AF2),在预测蛋白质结构和功能方面显示出希望.
- ESM和EVE估计变体的可能性,而AF2预测结构变化,提出不同的解释性和应用挑战.
研究的目的:
- 评估深度生成模型 (ESM,EVE,AF2) 在预测LDLR变异的致病性方面的有效性.
- 将这些模型的性能与已建立的变异预测工具和实验措施进行比较.
- 评估这些模型与临床表型的关联,特别是血清LDL-C水平和动脉样硬化心血管疾病.
主要方法:
- 测试了ESM,EVE和AF2用于预测变异致病性,并将其性能与Polyphen-2,SIFT,REVEL和灵长类AI等既定方法进行了比较.
- 评估模型与LDL吸收的实验测量结果的相关性.
- 利用英国生物银行数据,将模型关联与临床表型进行比较,包括血清LDL-C和动脉样硬化心血管疾病.
主要成果:
- AF2预测了LDLR结构,模拟不良的变体致病性.
- ESM和EVE的性能与ClinVar二元分类的既定方法相似,但与实验性LDL吸收的相关性更强.
- ESM和EVE与血清LDL-C的关联比Polyphen-2更强;ESM确定了具有更极端LDL-C水平的变体,并与动脉样硬化心血管疾病的关联更强.
结论:
- AF2预测的LDLR结构对于模拟变体致病性是不可靠的.
- ESM和EVE与ClinVar分类的现有方法具有竞争力,并且在与实验试验和临床表型的相关性方面更优越.
- ESM和EVE代表了先进的工具来预测LDLR变体的功能影响及其与心血管风险的关联.
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