Modeling conformational states of proteins with AlphaFold
Modeling conformational states of proteins with AlphaFold
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DOI：
10.1016/j.sbi.2023.102645
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发表时间：
2023-06-29
影响因子：
6.8
通讯作者：
Meiler, J.
中科院分区：
文献类型：
--
作者：
Sala, D.;Engelberger, F.;Meiler, J.
许多蛋白质通过在不同结构之间切换来发挥其功能。了解与这些状态相关的构象集合对于阐明控制蛋白质功能的关键机制至关重要。虽然实验测定工作仍然受到成本、时间和技术挑战的瓶颈，但机器学习技术 AlphaFold 在预测单体蛋白质的三维结构方面显示出接近实验的准确性。然而，AlphaFold 模型集合通常代表具有最小结构异质性的单一构象状态。因此，已经提出了几种管道来扩展系综的结构宽度或将预测偏向所需的构象状态。在这里，我们分析这些管道如何工作、它们可以预测什么、不能预测什么以及未来的方向。
Many proteins exert their function by switching among different structures. Knowing the conformational ensembles affiliated with these states is critical to elucidate key mechanistic aspects that govern protein function. While experimental determination efforts are still bottlenecked by cost, time, and technical challenges, the machine-learning technology AlphaFold showed near experimental accuracy in predicting the three-dimensional structure of monomeric proteins. However, an AlphaFold ensemble of models usually represents a single conformational state with minimal structural heterogeneity. Consequently, several pipelines have been proposed to either expand the structural breadth of an ensemble or bias the prediction toward a desired conformational state. Here, we analyze how those pipelines work, what they can and cannot predict, and future directions.