MMPatho:利用多层次共识和进化信息进行增强的误解突变病原性预测
Fang Ge1,2, Muhammad Arif3,4, Zihao Yan5
1School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, 9 Wenyuanlu, Nanjing 210023, China.
Journal of chemical information and modeling
|November 10, 2023
概括
我们开发了MMPatho,这是一个计算工具,用于预测误解突变 (MM) 病原性. MMPatho使用变异和蛋白质语言模型特征准确地识别引起疾病的突变,帮助遗传疾病研究.
科学领域:
- 基因组学和生物信息学
- 计算生物学 计算生物学
- 分子遗传学 分子遗传学
背景情况:
- 误解突变 (MMs) 对于理解遗传疾病和个体变异至关重要.
- 准确预测MM病原性是必不可少的,但具有挑战性.
研究的目的:
- 开发一种新的计算方法,MMPatho,用于增强误解突变病原性预测.
- 创建可靠的基准和盲测试数据集,用于评估MM病原性预测模型.
主要方法:
- 建立了一个大规模的,非冗余的MM基准数据集和一个聚焦的盲测试集.
- 使用Ensembl VEP和dbNSFP.提取了变异级,氨基酸级和基因组级特征.
- 利用蛋白质序列编码和从ESM-1b和ProtTrans-T5中提取突变部位的嵌入.
- 开发了两个模型,ConsMM (XGBoost与SHAP) 和EvoIndMM (包含蛋白质语言嵌入).
主要成果:
- 在MMPatho模型 (ConsMM和EvoIndMM) 中,在一个盲测试集 (AUROC 0.9836-0.9854,AUPR 0.9852-0.9902) 上实现了高性能.
- 这些模型在预测误解突变致病性方面表现出优越性.
- 开发了一个Web服务器,以便公众访问MMPatho的预测和数据.
结论:
- MMPatho提供了一种卓越的计算方法,用于预测误解突变的致病性.
- 开发的数据集和网络服务器有助于进一步研究遗传疾病和变异解释.
- 整合进化信息和蛋白质语言嵌入,大大提高了预测能力.
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