Predicting protein flexibility with AlphaFold
Predicting protein flexibility with AlphaFold
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DOI：
10.1002/prot.26471
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发表时间：
2023-02-03
影响因子：
2.9
通讯作者：
Bruschweiler, Rafael
中科院分区：
文献类型：
--
作者：
Ma Puyi;Li Da-Wei;Bruschweiler, Rafael
AlphaFold 2彻底改变了从氨基酸序列预测蛋白质结构。除了蛋白质结构，关于蛋白质不同区域的高分辨率动力学信息对于理解蛋白质功能是重要的。虽然AlphaFold 2既没有被设计也没有被训练来预测蛋白质动力学，但这里显示了AlphaFold 2返回的信息如何用于预测单个残基水平的动态蛋白质区域。该方法被称为cdsAF 2，使用AlphaFold 2返回的3D蛋白质结构，使用局部接触模型来预测骨架NMR N-H S-2序参数，该模型考虑了每个肽平面沿着骨架与其环境的接触。通过使用局部接触模型将每个残基AlphaFold 2的结构预测准确性的pLDDT置信度得分与预测的S-2值相结合，获得了半定量地捕获在实验主链NMR N-H S-2序参数分布中观察到的许多动力学特征的估计量。该方法被证明为一组9种不同大小和变量的动态和无序的蛋白质。
AlphaFold2 has revolutionized protein structure prediction from amino-acid sequence. In addition to protein structures, high-resolution dynamics information about various protein regions is important for understanding protein function. Although AlphaFold2 has neither been designed nor trained to predict protein dynamics, it is shown here how the information returned by AlphaFold2 can be used to predict dynamic protein regions at the individual residue level. The approach, which is termed cdsAF2, uses the 3D protein structure returned by AlphaFold2 to predict backbone NMR N-H S-2 order parameters using a local contact model that takes into account the contacts made by each peptide plane along the backbone with its environment. By combining for each residue AlphaFold2's pLDDT confidence score for the structure prediction accuracy with the predicted S-2 value using the local contact model, an estimator is obtained that semi-quantitatively captures many of the dynamics features observed in experimental backbone NMR N-H S-2 order parameter profiles. The method is demonstrated for a set nine proteins of different sizes and variable amounts of dynamics and disorder.