Goethe Martin, Fita Ignacio, Rubi J Miguel
Department of Condensed Matter Physics, University of Barcelona, Carrer Martí i Franquès 1, 08028 Barcelona, Spain.
Department of Inorganic and Organic Chemistry, University of Barcelona, Carrer Martí i Franquès 1, 08028 Barcelona, Spain.
Entropy (Basel). 2018 Aug 7;20(8):580. doi: 10.3390/e20080580.
is a method for configurational entropy estimation of proteins based on machine-learning. Entropy is predicted with an artificial neural network which was trained on simulation trajectories of a large set of representative proteins. is extremely fast compared to other approaches based on the sampling of a multitude of microstates. Consequently, can be incorporated into a large class of protein software which currently neglects configurational entropy for performance reasons. Here, we apply to various conformations of the Cas4 protein SSO0001 of , a protein that assembles to a decamer of known toroidal shape. We provide numerical evidence that the native state (NAT) of a SSO0001 monomer has a similar structure to the protomers of the oligomer, where NAT of the monomer is stabilized mainly entropically. Due to its large amount of configurational entropy, NAT has lower free energy than alternative conformations of very low enthalpy and solvation free-energy. Hence, SSO0001 serves as an example case where neglecting configurational entropy leads to incorrect conclusion. Our results imply that no refolding of the subunits is required during oligomerization which suggests that configurational entropy is employed by nature to largely enhance the rate of assembly.
是一种基于机器学习的蛋白质构象熵估计方法。通过在大量代表性蛋白质的模拟轨迹上训练的人工神经网络来预测熵。与其他基于大量微状态采样的方法相比，它极其快速。因此，它可以被纳入目前因性能原因而忽略构象熵的一大类蛋白质软件中。在这里，我们将其应用于嗜盐栖热孢菌（Thermotoga maritima）的Cas4蛋白SSO0001的各种构象，该蛋白组装成已知环形形状的十聚体。我们提供了数值证据，表明SSO0001单体的天然状态（NAT）与寡聚体的原体具有相似的结构，其中单体的NAT主要通过熵来稳定。由于其大量的构象熵，NAT比极低焓和溶剂化自由能的替代构象具有更低的自由能。因此，SSO0001是一个忽略构象熵会导致错误结论的示例案例。我们的结果表明，在寡聚化过程中不需要亚基重新折叠，这表明自然界利用构象熵在很大程度上提高了组装速率。
