Biomolecular Interaction Centre, School of Physical and Chemical Sciences, University of Canterbury, Christchurch, New Zealand.
Department of Physics, Durham University, Durham, UK.
Protein Sci. 2022 Jun;31(6):e4333. doi: 10.1002/pro.4333.
The advent of machine learning-based structure prediction algorithms such as AlphaFold2 (AF2) and RoseTTa Fold have moved the generation of accurate structural models for the entire cellular protein machinery into the reach of the scientific community. However, structure predictions of protein complexes are based on user-provided input and may require experimental validation. Mass spectrometry (MS) is a versatile, time-effective tool that provides information on post-translational modifications, ligand interactions, conformational changes, and higher-order oligomerization. Using three protein systems, we show that native MS experiments can uncover structural features of ligand interactions, homology models, and point mutations that are undetectable by AF2 alone. We conclude that machine learning can be complemented with MS to yield more accurate structural models on a small and large scale.
基于机器学习的结构预测算法（如 AlphaFold2 （AF2）和 RoseTTa Fold）的出现，使得为整个细胞蛋白质机器生成准确的结构模型成为可能。然而，蛋白质复合物的结构预测是基于用户提供的输入，可能需要实验验证。质谱（MS）是一种通用的、高效的工具，可以提供关于翻译后修饰、配体相互作用、构象变化和高级寡聚化的信息。我们使用三个蛋白质系统表明，天然 MS 实验可以揭示配体相互作用、同源模型和点突变的结构特征，而这些特征是仅使用 AF2 无法检测到的。我们得出结论，机器学习可以与 MS 互补，从而在小尺度和大尺度上产生更准确的结构模型。
