da Silva Gabriel Monteiro, Cui Jennifer Y, Dalgarno David C, Lisi George P, Rubenstein Brenda M
Brown University Department of Molecular Biology, Cell Biology, and Biochemistry, Providence, RI, USA.
Dalgarno Scientific LLC, Brookline, MA, USA.
bioRxiv. 2023 Dec 19:2023.07.25.550545. doi: 10.1101/2023.07.25.550545.
This paper presents a novel approach for predicting the relative populations of protein conformations using AlphaFold 2, an AI-powered method that has revolutionized biology by enabling the accurate prediction of protein structures. While AlphaFold 2 has shown exceptional accuracy and speed, it is designed to predict proteins' ground state conformations and is limited in its ability to predict conformational landscapes. Here, we demonstrate how AlphaFold 2 can directly predict the relative populations of different protein conformations by subsampling multiple sequence alignments. We tested our method against NMR experiments on two proteins with drastically different amounts of available sequence data, Abl1 kinase and the granulocyte-macrophage colony-stimulating factor, and predicted changes in their relative state populations with more than 80% accuracy. Our subsampling approach worked best when used to qualitatively predict the effects of mutations or evolution on the conformational landscape and well-populated states of proteins. It thus offers a fast and cost-effective way to predict the relative populations of protein conformations at even single-point mutation resolution, making it a useful tool for pharmacology, NMR analysis, and evolution.
本文提出了一种使用AlphaFold 2预测蛋白质构象相对丰度的新方法，AlphaFold 2是一种人工智能驱动的方法，通过实现蛋白质结构的准确预测，给生物学带来了变革。虽然AlphaFold 2已展现出卓越的准确性和速度，但它旨在预测蛋白质的基态构象，在预测构象格局方面能力有限。在此，我们展示了AlphaFold 2如何通过对多序列比对进行二次抽样，直接预测不同蛋白质构象的相对丰度。我们用两种具有截然不同可用序列数据量的蛋白质——Abl1激酶和粒细胞-巨噬细胞集落刺激因子，针对核磁共振实验测试了我们的方法，并以超过80%的准确率预测了它们相对状态丰度的变化。当用于定性预测突变或进化对蛋白质构象格局和高丰度状态的影响时，我们的二次抽样方法效果最佳。因此，它提供了一种快速且经济高效的方法，甚至能以单点突变分辨率预测蛋白质构象的相对丰度，使其成为药理学、核磁共振分析和进化研究的有用工具。
