从AlphaFold输出导出的Metadaptics的集合变量
Collective Variable for Metadynamics Derived from AlphaFold Output
摘要：
AlphaFold是一种基于神经网络的工具，用于预测蛋白质的3D结构。在CASP 14盲结构预测挑战赛中，它的表现明显优于其他竞争对手，这使它成为最好的结构预测工具。AlphaFold的输出之一是残差-残差距离的概率分布。这使得可以对所研究蛋白质的任何构象进行评分，以表达其与AlphaFold模型的符合性。在这里，我们展示了如何使用这个分数来驱动蛋白质折叠模拟的元模型和并行回火元模型。利用平行回火元自洽方法模拟了一个小蛋白Trp笼β发夹的折叠过程，并预测了它们的折叠平衡。我们看到了基于AlphaFold的集合变量在结构预测之外的应用中的潜力，例如结构优化或预测突变的结果。
英文摘要：
AlphaFold is a neural-network-based tool for the prediction of 3D structures of protein. In CASP14, a blind structure prediction challenge, it performed significantly better than other competitors, which makes it the best available structure prediction tool. One of the outputs of AlphaFold is the probability profile of residue-residue distances. This makes it possible to score any conformation of the studied protein to express its compliance with the AlphaFold model. Here we show how this score can be used to drive protein folding simulation by metadynamics and parallel tempering metadynamics. By parallel tempering metadynamics, we simulated folding of a mini-protein Trp-cage beta hairpin and predicted their folding equilibria. We see the potential of AlphaFold-based collective variable in applications beyond structure prediction, such as in structure refinement or prediction of the outcome of a mutation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
