伪困惑在一个落的扫描中,用于估计蛋白质适应性
Pranav Kantroo1,2, Günter P Wagner3,4,5, Benjamin B Machta6,2
1Computational Biology and Bioinformatics Program, Yale University, New Haven, CT-06520, USA.
ArXiv
|July 23, 2024
概括
蛋白质语言模型使用一种新的One Fell Swoop (OFS) 方法高效地估计序列适应性. 这种方法通过快速探索功能序列空间,改善了蛋白质工程和祖先序列分析.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 蛋白质语言模型 (PLMs) 通过掩面语言建模来学习上下文残留表征.
- 这些嵌入式捕获各种下游任务的关键信息.
- 从PLM中有效估计序列属性是一个持续的挑战.
研究的目的:
- 开发一种有效的方法来估计使用PLM嵌入的掩盖概率和序列伪复杂性.
- 为了评估这种方法作为蛋白质适应性的代理.
- 探索其在祖先序列重建和功能序列空间探索中的应用.
主要方法:
- 开发了One Fell Swoop (OFS) 方法来估计单次前进传球中的掩盖概率.
- 使用OFS衍生的伪复杂性作为适应性估计.
- 在ProteinGym Indels数据集上的基准OFS性能.
- 应用OFS来评估重建的祖先蛋白序列的适用性.
主要成果:
- OFS伪复杂性非常接近适应性估计的真伪复杂性.
- 在ProteinGym Indels基准指标上,OFS取得了最先进的表现.
- 与现存的相比,对重建的祖先序列的OFS估计适应性更高.
- 计算效率可以通过蒙特卡洛方法快速探索功能序列空间.
结论:
- OFS方法提供了一种高效和有效的方法来估计语言模型中的蛋白质序列适应性.
- OFS增强了蛋白质工程,祖先序列分析和功能基因组学的下游应用.
- 这种技术为大规模探索蛋白质序列功能关系开辟了新的途径.
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