Prediction of multiple conformational states by combining sequence clustering with AlphaFold2
Prediction of multiple conformational states by combining sequence clustering with AlphaFold2
复制标题
结合序列聚类与 AlphaFold2 预测多种构象状态
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
D. Kern
中科院分区：
文献类型：
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作者：
H. Wayment;S. Ovchinnikov;Lucy J. Colwell;D. Kern
AlphaFold 2（AF 2）通过准确预测蛋白质和蛋白质-蛋白质复合物的单一结构，彻底改变了结构生物学。然而，生物学功能植根于蛋白质对不同构象子状态进行采样的能力，并且致病点突变通常是由于这些子状态的群体变化。这引发了人们对扩大AF 2预测构象亚态的能力的极大兴趣。我们证明，聚类输入多序列比对（MSA）的序列相似性，使AF 2样本交替状态的已知变质蛋白质，包括昼夜节律蛋白KaiB，转录因子RfaH，和纺锤体检查点蛋白Mad 2，并评分这些国家的高置信度。此外，我们使用AF 2来识别预测在其两种状态之间切换KaiB的两个点突变的最小集合。最后，我们使用我们的聚类方法，AF-集群，筛选蛋白质家族中的替代状态，而不知道折叠开关，并确定了一个假定的替代状态的氧化还原酶DsbE。与KaiB类似，DsbE被预测在硫氧还蛋白样折叠和新折叠之间切换。这一预测是未来实验测试的主题。这种生物信息学方法与实验相结合的进一步发展可能会对预测蛋白质能量景观产生深远的影响，这对于揭示生物功能至关重要。
AlphaFold2 (AF2) has revolutionized structural biology by accurately predicting single structures of proteins and protein-protein complexes. However, biological function is rooted in a protein’s ability to sample different conformational substates, and disease-causing point mutations are often due to population changes of these substates. This has sparked immense interest in expanding AF2’s capability to predict conformational substates. We demonstrate that clustering an input multiple sequence alignment (MSA) by sequence similarity enables AF2 to sample alternate states of known metamorphic proteins, including the circadian rhythm protein KaiB, the transcription factor RfaH, and the spindle checkpoint protein Mad2, and score these states with high confidence. Moreover, we use AF2 to identify a minimal set of two point mutations predicted to switch KaiB between its two states. Finally, we used our clustering method, AF-cluster, to screen for alternate states in protein families without known fold-switching, and identified a putative alternate state for the oxidoreductase DsbE. Similarly to KaiB, DsbE is predicted to switch between a thioredoxin-like fold and a novel fold. This prediction is the subject of future experimental testing. Further development of such bioinformatic methods in tandem with experiments will likely have profound impact on predicting protein energy landscapes, essential for shedding light into biological function.
影响因子：
48
作者：
Mirdita, Milot;Schutze, Konstantin;Moriwaki, Yoshitaka;Heo, Lim;Ovchinnikov, Sergey;Steinegger, Martin
通讯作者：
Steinegger, Martin