Center for Molecular Biosciences Innsbruck, Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innsbruck, Austria.
Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.
Structure. 2024 Nov 7;32(11):2147-2159.e2. doi: 10.1016/j.str.2024.09.001. Epub 2024 Sep 26.
Recent breakthroughs in protein structure prediction have enhanced the precision and speed at which protein configurations can be determined. Additionally, molecular dynamics (MD) simulations serve as a crucial tool for capturing the conformational space of proteins, providing valuable insights into their structural fluctuations. However, the scope of MD simulations is often limited by the accessible timescales and the computational resources available, posing challenges to comprehensively exploring protein behaviors. Recently emerging approaches have focused on expanding the capability of AlphaFold2 (AF2) to predict conformational substates of protein. Here, we benchmark the performance of various workflows that have adapted AF2 for ensemble prediction and compare the obtained structures with ensembles obtained from MD simulations and NMR. We provide an overview of the levels of performance and accessible timescales that can currently be achieved with machine learning (ML) based ensemble generation. Significant minima of the free energy surfaces remain undetected.
近年来，蛋白质结构预测方面的突破提高了确定蛋白质结构的精度和速度。此外，分子动力学（MD）模拟是捕捉蛋白质构象空间的重要工具，可以深入了解其结构变化。然而，MD 模拟的范围通常受到可访问时间尺度和可用计算资源的限制，这对全面探索蛋白质行为提出了挑战。最近出现的方法侧重于扩展 AlphaFold2（AF2）预测蛋白质构象亚态的能力。在这里，我们对各种适用于集合预测的 AF2 工作流程的性能进行基准测试，并将获得的结构与从 MD 模拟和 NMR 获得的集合进行比较。我们概述了基于机器学习（ML）的集合生成目前可以达到的性能水平和可访问时间尺度。自由能表面的显著最小值仍然未被检测到。
