Sampling alternative conformational states of transporters and receptors with AlphaFold2.
Sampling alternative conformational states of transporters and receptors with AlphaFold2.
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DOI：
10.7554/elife.75751
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发表时间：
2022-03-03
期刊：
影响因子：
7.7
通讯作者：
Meiler, Jens
中科院分区：
文献类型：
--
作者：
del Alamo, Diego;Sala, Davide;Mchaourab, Hassane S.;Meiler, Jens
平衡波动和触发的构象变化往往是膜蛋白功能循环的基础。例如，转运蛋白通过在向内和向外状态之间交替来介导分子穿过细胞膜，而受体经历启动信号级联的细胞内结构重排。虽然这些蛋白质的构象可塑性在历史上对传统的从头蛋白质结构预测管道提出了挑战，但最近AlphaFold 2（AF 2）在CASP 14中的成功最终导致了对多种构象中的转运蛋白的高准确性建模。鉴于AF 2被设计用于预测蛋白质的静态结构，目前尚不清楚该结果是否代表了准确预测多种构象和/或结构异质性的未充分探索的能力。在这里，我们提出了一种方法来驱动AF 2采样替代构象的拓扑多样的转运蛋白和G-蛋白偶联受体，是不存在的AF 2训练集。尽管使用默认AF 2管道生成的大多数蛋白质的模型在构象上是同质的并且彼此几乎相同，但是通过随机子采样来减少输入多序列比对的深度导致在多个构象中生成准确的模型。在我们的基准测试中，这些构象跨越了两个感兴趣的实验结构之间的范围，观察到这些构象分布的极端模型是最准确的（平均模板建模得分为0.94）。这些结果表明了一种识别类似原生替代状态的简单方法，同时也强调了设计下一代深度学习算法来预测生物学相关状态的必要性。
Equilibrium fluctuations and triggered conformational changes often underlie the functional cycles of membrane proteins. For example, transporters mediate the passage of molecules across cell membranes by alternating between inward- and outward-facing states, while receptors undergo intracellular structural rearrangements that initiate signaling cascades. Although the conformational plasticity of these proteins has historically posed a challenge for traditional de novo protein structure prediction pipelines, the recent success of AlphaFold2 (AF2) in CASP14 culminated in the modeling of a transporter in multiple conformations to high accuracy. Given that AF2 was designed to predict static structures of proteins, it remains unclear if this result represents an underexplored capability to accurately predict multiple conformations and/or structural heterogeneity. Here, we present an approach to drive AF2 to sample alternative conformations of topologically diverse transporters and G-protein-coupled receptors that are absent from the AF2 training set. Whereas models of most proteins generated using the default AF2 pipeline are conformationally homogeneous and nearly identical to one another, reducing the depth of the input multiple sequence alignments by stochastic subsampling led to the generation of accurate models in multiple conformations. In our benchmark, these conformations spanned the range between two experimental structures of interest, with models at the extremes of these conformational distributions observed to be among the most accurate (average template modeling score of 0.94). These results suggest a straightforward approach to identifying native-like alternative states, while also highlighting the need for the next generation of deep learning algorithms to be designed to predict ensembles of biophysically relevant states.