基因型-表型景观计算基于第一个原则和深度学习
Yuexing Liu1, Yao Luo2, Xin Lu1
1Guangzhou Laboratory, Guangzhou, Guangdong Province 510005, China.
Briefings in bioinformatics
|May 3, 2024
概括
我们开发了一个新的表型嵌入定理,将基因型映射到适应性,从而能够准确预测病毒进化和免疫逃生突变. 这种计算生物学方法创建了基因型-表型景观.
科学领域:
- 理论和计算生物学理论和计算生物学
- 进化生物学 进化生物学
- 遗传学 是一个遗传学.
背景情况:
- 将基因型映射到表型和适应性对于理解进化至关重要,但仍然是一个重大挑战.
- 现有的方法在弥合基因型适应性差距方面缺乏定量精度.
研究的目的:
- 引入表型嵌入 (P-E) 定理作为一种新的框架,用于定量地将基因型与表型联系起来.
- 建立一个可计算的基础,以建立一个将基因型和表型相关联的一般第一原则.
- 开发一种用于模拟病毒演变和预测免疫逃生突变的计算模型.
主要方法:
- 使用编码器-解码器深度学习框架开发了表型嵌入 (P-E) 定理.
- 提出了基因型-表型相关性的第一个一般原则.
- 创建了一个基于Co-attention的变压器模型,用于基因型-适应性映射.
- 应用了P-E定理来计算SARS-CoV-2的基本复制数 (R0).
主要成果:
- 精确模拟病毒的中性进化和预测免疫逃生突变.
- 病毒免疫逃脱与病毒健康有数量联系.
- 成功地从第一原则中推导出SARS-CoV-2的基本复制数 (R0).
- 构建了一个基因型-适应性景观.
结论:
- P-E定理为构建基因型-表型景观提供了一个一般的,可解释的方法.
- 这项工作为理论和计算生物学建立了新的范式.
- 开发的模型准确地预测了病毒演变和免疫逃脱,这对公共卫生和疫苗开发有影响.
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