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.
bioRxiv : the preprint server for biology
|July 19, 2024
概括
蛋白质语言模型可以有效地估计序列适应性,使用一种新的One Fell Swoop (OFS) 方法. 这种方法改善了蛋白质稳定性的预测,并有助于探索功能序列空间.
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.
- 蛋白质工程是指蛋白质工程.
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.
- 这些嵌入式捕获下游任务的基本序列信息.
- 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从PLM中掩盖的概率和序列伪复杂性.
- 评估这种方法作为蛋白质适应性和稳定性的代理.
- 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:
- 提出了一次倒下扫描 (OFS) 方法来估计单次前进传递中的掩盖概率.
- 使用OFS方法计算伪复杂性作为适应性估计.
- 在ProteinGym Indels数据集上对比OFS伪复杂性与真伪复杂性.
- 使用OFS伪复杂性评估重建的祖先蛋白质序列的适应性.
主要成果:
- OFS方法有效地估计了伪复杂性,作为可靠的健康代理.
- OFS伪复杂性表现与适应性估计的真伪复杂性密切匹配.
- 在ProteinGym Indels基准上取得了最先进的结果.
- OFS伪复杂性成功地将祖先蛋白质重建列为比现存序列更合适的.
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.
- 这种技术通过快速探索功能序列空间和改进祖先序列分析来推进蛋白质工程.
- OFS增强了蛋白质语言模型的实用性,用于预测蛋白质的稳定性和设计.
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