Spiwok Vojtěch, Kurečka Martin, Křenek Aleš
Department of Biochemistry and Microbiology, Faculty of Food and Biochemical Technology, University of Chemistry and Technology, Prague, Czechia.
Institute of Computer Science, Masaryk University, Brno, Czechia.
Front Mol Biosci. 2022 Jun 13;9:878133. doi: 10.3389/fmolb.2022.878133. eCollection 2022.
AlphaFold is a neural network-based tool for the prediction of 3D structures of proteins. In CASP14, a blind structure prediction challenge, it performed significantly better than other competitors, making 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. Using parallel tempering metadynamics, we simulated the folding of a mini-protein Trp-cage and hairpin and predicted their folding equilibria. We observe the potential of the AlphaFold-based collective variable in applications beyond structure prediction, such as in structure refinement or prediction of the outcome of a mutation.
AlphaFold是一种基于神经网络的工具，用于预测蛋白质的三维结构。在CASP14（一项盲法结构预测挑战赛）中，它的表现明显优于其他竞争对手，成为目前最佳的结构预测工具。AlphaFold的输出之一是残基-残基距离的概率分布。这使得对所研究蛋白质的任何构象进行评分以表达其与AlphaFold模型的契合度成为可能。在此，我们展示了如何利用该评分通过元动力学和平行回火元动力学来驱动蛋白质折叠模拟。使用平行回火元动力学，我们模拟了小蛋白Trp-cage和发夹的折叠，并预测了它们的折叠平衡。我们观察到基于AlphaFold的集体变量在结构预测之外的应用潜力，例如在结构优化或突变结果预测方面。
