蛋白质稳定性预测通过微调蛋白质语言模型在大型数据集上的微调
Simon K S Chu1, Kush Narang2, Justin B Siegel3,4,5
1Biophysics Graduate Program, University of California Davis, Davis, California, United States of America.
PLoS computational biology
|July 22, 2024
概括
研究人员开发了ESMtherm,一种蛋白质语言模型,以预测蛋白质折叠稳定性. 这种人工智能模型有助于设计更稳定的蛋白质,用于食品,医药和疾病研究中的应用.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 蛋白质工程是指蛋白质工程.
背景情况:
- 蛋白质稳定性对于食品加工,治疗和识别致病突变的应用至关重要.
- 提高蛋白质稳定性通常需要广泛的工程活动,需要精简的优化方法.
研究的目的:
- 开发一种优化用于预测蛋白质折叠稳定性的蛋白质语言模型.
- 为了快速优化高稳定性蛋白质,使用更少的代.
主要方法:
- 在来自461个蛋白质域的528,000个自然和de novo蛋白质序列的大数据集上训练了一种蛋白质语言模型 (ESMtherm).
- 该模型在折叠稳定性数据上进行了训练,并设计用于处理删除,插入和多点突变等序列变异.
主要成果:
- ESMtherm展示了微调蛋白质语言模型的能力,以准确预测折叠稳定性.
- 该模型在小蛋白域上显示了合理的性能,并将其推广到训练集之外的序列.
- 与最先进的方法相比,观察到对更大的蛋白质支架进行概括的局限性.
结论:
- 蛋白质语言模型可以有效地微调稳定性预测.
- 对各种数据集进行大规模稳定性测量对于推进蛋白质工程至关重要.
- 需要进一步开发以改善对不同蛋白质大小和支架的概括.
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