Assessing AF2’s ability to predict structural ensembles of proteins

IF 4.4 2区生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY

Structure Pub Date : 2024-09-26 DOI:10.1016/j.str.2024.09.001

Jakob R. Riccabona, Fabian C. Spoendlin, Anna-Lena M. Fischer, Johannes R. Loeffler, Patrick K. Quoika, Timothy P. Jenkins, James A. Ferguson, Eva Smorodina, Andreas H. Laustsen, Victor Greiff, Stefano Forli, Andrew B. Ward, Charlotte M. Deane, Monica L. Fernández-Quintero

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Abstract

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.

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评估 AF2 预测蛋白质结构组合的能力

蛋白质结构预测领域的最新突破提高了确定蛋白质构型的精度和速度。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，为了解蛋白质的结构波动提供了宝贵的见解。然而，分子动力学模拟的范围往往受到可访问时间尺度和可用计算资源的限制，这给全面探索蛋白质行为带来了挑战。最近新出现的方法侧重于扩展 AlphaFold2（AF2）预测蛋白质构象亚态的能力。在此，我们将对采用 AF2 进行集合预测的各种工作流程的性能进行基准测试，并将获得的结构与通过 MD 模拟和 NMR 获得的集合进行比较。我们概述了目前基于机器学习（ML）的集合生成所能达到的性能水平和可访问的时间尺度。自由能表面的重要最小值仍未被发现。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Structure 生物-生化与分子生物学

CiteScore

8.90

自引率

1.80%

发文量

155

审稿时长

3-8 weeks

期刊介绍： Structure aims to publish papers of exceptional interest in the field of structural biology. The journal strives to be essential reading for structural biologists, as well as biologists and biochemists that are interested in macromolecular structure and function. Structure strongly encourages the submission of manuscripts that present structural and molecular insights into biological function and mechanism. Other reports that address fundamental questions in structural biology, such as structure-based examinations of protein evolution, folding, and/or design, will also be considered. We will consider the application of any method, experimental or computational, at high or low resolution, to conduct structural investigations, as long as the method is appropriate for the biological, functional, and mechanistic question(s) being addressed. Likewise, reports describing single-molecule analysis of biological mechanisms are welcome. In general, the editors encourage submission of experimental structural studies that are enriched by an analysis of structure-activity relationships and will not consider studies that solely report structural information unless the structure or analysis is of exceptional and broad interest. Studies reporting only homology models, de novo models, or molecular dynamics simulations are also discouraged unless the models are informed by or validated by novel experimental data; rationalization of a large body of existing experimental evidence and making testable predictions based on a model or simulation is often not considered sufficient.