Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Forschungszentrum Jülich, 52425 Jülich, Germany; Institute of Theoretical and Computational Chemistry, Heinrich Heine University Düsseldorf, Universitätstrasse 1, 40225 Düsseldorf, Germany.
J Mol Biol. 2021 Oct 1;433(20):167182. doi: 10.1016/j.jmb.2021.167182. Epub 2021 Aug 3.
The protein folding problem was apparently solved recently by the advent of a deep learning method for protein structure prediction called AlphaFold. However, this program is not able to make predictions about the protein folding pathways. Moreover, it only treats about half of the human proteome, as the remaining proteins are intrinsically disordered or contain disordered regions. By definition these proteins differ from natively folded proteins and do not adopt a properly folded structure in solution. However these intrinsically disordered proteins (IDPs) also systematically differ in amino acid composition and uniquely often become folded upon binding to an interaction partner. These factors preclude solving IDP structures by current machine-learning methods like AlphaFold, which also cannot solve the protein aggregation problem, since this meta-folding process can give rise to different aggregate sizes and structures. An alternative computational method is provided by molecular dynamics simulations that already successfully explored the energy landscapes of IDP conformational switching and protein aggregation in multiple cases. These energy landscapes are very different from those of 'simple' protein folding, where one energy funnel leads to a unique protein structure. Instead, the energy landscapes of IDP conformational switching and protein aggregation feature a number of minima for different competing low-energy structures. In this review, I discuss the characteristics of these multifunneled energy landscapes in detail, illustrated by molecular dynamics simulations that elucidated the underlying conformational transitions and aggregation processes.
蛋白质折叠问题最近显然通过一种名为 AlphaFold 的深度学习方法得到了解决，该方法可用于预测蛋白质结构。然而，该程序无法对蛋白质折叠途径进行预测。此外，它只能处理人类蛋白质组的大约一半，因为其余的蛋白质本质上是无序的或含有无序区域。根据定义，这些蛋白质与天然折叠的蛋白质不同，在溶液中不会采用适当折叠的结构。然而，这些无序蛋白质（IDP）在氨基酸组成上也存在系统差异，并且通常在与相互作用伴侣结合时会发生折叠。这些因素排除了使用像 AlphaFold 这样的当前机器学习方法来解决 IDP 结构的问题，该方法也无法解决蛋白质聚集问题，因为这种元折叠过程会产生不同的聚集大小和结构。分子动力学模拟提供了一种替代的计算方法，该方法已经成功地在多种情况下探索了 IDP 构象转变和蛋白质聚集的能量景观。这些能量景观与“简单”蛋白质折叠的能量景观非常不同，在后者中，一个能量漏斗导致一个独特的蛋白质结构。相反，IDP 构象转变和蛋白质聚集的能量景观具有多个用于不同竞争的低能量结构的最小值。在这篇综述中，我详细讨论了这些多漏斗能量景观的特征，通过阐明潜在构象转变和聚集过程的分子动力学模拟来说明这些特征。
