Cagiada Matteo, Ovchinnikov Sergey, Lindorff-Larsen Kresten
Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Protein Sci. 2025 Jan;34(1):e5233. doi: 10.1002/pro.5233.
While there has been substantial progress in our ability to predict changes in protein stability due to amino acid substitutions, progress has been slower in methods to predict the absolute stability of a protein. Here, we show how a generative model for protein sequence can be leveraged to predict absolute protein stability. We benchmark our predictions across a broad set of proteins and find a mean error of 1.5 kcal/mol and a correlation coefficient of 0.7 for the absolute stability across a range of natural, small- to medium-sized proteins up to ca. 150 amino acid residues. We analyze current limitations and future directions including how such a model may be useful for predicting conformational free energies. Our approach is simple to use and freely available at an online implementation available via https://github.com/KULL-Centre/_2024_cagiada_stability.
虽然我们在预测氨基酸取代导致的蛋白质稳定性变化方面取得了重大进展，但在预测蛋白质绝对稳定性的方法上进展较慢。在这里，我们展示了如何利用蛋白质序列生成模型来预测蛋白质的绝对稳定性。我们在广泛的蛋白质组上对预测结果进行基准测试，发现对于一系列天然的、中小规模的蛋白质（最多约150个氨基酸残基），绝对稳定性的平均误差为1.5千卡/摩尔，相关系数为0.7。我们分析了当前的局限性和未来方向，包括这样一个模型如何有助于预测构象自由能。我们的方法使用简单，可通过https://github.com/KULL-Centre/_2024_cagiada_stability在线实现免费获取。
