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超快的古典遗传学方法击败了大型蛋白质语言模型的变异效应预测

Sebastian Prillo¹, Wilson Wu¹, Yun S Song¹

¹University of California, Berkeley.

Advances in neural information processing systems

|June 9, 2025

概括

我们开发了一种快速的方法,直接从蛋白质序列对齐中估计氨基酸替代率,显著加快了家族遗传学分析. 这种方法使得一个新的特定站点模型能够在预测变异效应方面表现出色,优于复杂模型.

科学领域:

进化生物学是进化的生物学.
计算生物学是一种计算生物学.
生物信息学是一种生物信息学.

背景情况:

氨基酸替代率矩阵对于遗传学至关重要,但估计计算密集.
目前的方法需要从大规模的蛋白质对齐中重建树木,从而创建了一个重要的瓶.

研究的目的:

开发一种计算效率高的方法,直接从多个序列对齐 (MSAs) 中估计氨基酸替换率矩阵.
引入一种利用这种高效估计的新型特定地点的遗传学模型 (SiteRM).
评估SiteRM在变异效应预测中的性能.

主要方法:

开发了一个近线性时间算法,FastCherries,用于从MSA重建桃.
直接从MSA估计的利率矩阵,绕过了预先计算树的需要.
引入了SiteRM模型用于特定站点的速率矩阵估计.
应用SiteRM用于使用临床和深度突变扫描数据进行变异效应预测任务.

主要成果:

FastCherries算法能够从MSA中近线性时间估计速率矩阵,比现有方法快数量级.
使用特定站点速率矩阵的SiteRM模型在变异效应预测方面表现出优异的性能,与大型蛋白质语言模型相比.
该方法成功处理数百万个序列的MSA,展示了前所未有的可扩展性.

结论:

开发的方法显著加速了基本进化参数的估计.
SiteRM提供了一种强大的新工具,用于变异效应预测,其性能优于现有的最先进模型.
这项工作通过高效的算法和新型建模方法推进了统计类遗传学和计算变异效应预测.

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