论文标题
从AlphaFold输出得出的元动力学的集体变量
Collective Variable for Metadynamics Derived from AlphaFold Output
论文作者
论文摘要
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CASP14中，盲目的结构预测挑战，其表现明显优于其他竞争对手，这使其成为最佳可用结构预测工具。 Alphafold的输出之一是残基占用距离的概率曲线。这使得有可能对所研究蛋白的任何构象进行评分，以表达其符合AlphaFold模型。在这里，我们展示了该分数如何通过元动力学和平行回火元动力来驱动蛋白质折叠模拟。通过平行回火元动力学，我们模拟了微型蛋白TRP型ββ发夹的折叠，并预测了它们的折叠平衡。我们看到基于Alphafold的集体变量在超出结构预测的应用中的潜力，例如在结构的细化或突变结果预测中。
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.
