Knotting Optimization and Folding Pathways of a Go-Model with a Deep Knot
Knotting Optimization and Folding Pathways of a Go-Model with a Deep Knot
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深结 Go 模型的结优化和折叠路径
DOI：
10.1021/acs.jpcb.2c05588
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Finke, John M.
中科院分区：
文献类型：
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作者：
Dahlstrom, Thomas J.;Capraro, Dominique T.;Jennings, Particia A.;Finke, John M.
蛋白质结的形成是蛋白质折叠问题的一个有趣的分支。由于结形成的实验分辨率有限，目前理论方法提供了对打结过程最详细的见解。虽然分子动力学模拟适用于浅结，但在捕获蛋白质中深结的形成时面临着挑战，例如来自海洋栖热菌 (Thermotoga maritima) 的最小束缚三叶草 α/β 甲基转移酶 (MTTTM)。为了提高 CαGo 模型模拟中 MTTTM 打结的效率，研究了 MTTTMGo 模型的突变变体。通过对打结和未打结状态进行基于结构的分析，确定了四个残基（K71、R72、E75、V76），当它们的接触能加倍并且结环周围的二面体强度增加时，打结效率从 2% 提高到 83%。该模型的关键特征是（i）C端活结中间体，将结穿入高度非结构化的中间体中，（ii）无法在类似天然的中间状态下打结，以及（iii）无法打结的长期陷阱中的少数群体。对残基 71-76 接触点的检查提供了一小部分潜在的突变体，可以直接测试模型的有效性。此外，这里开发的打结优化过程在生成其他打结蛋白的打结效率模型方面具有广泛的适用性。
Formation of protein knots is an intriguing offshoot of the protein folding problem. Since experimental resolution on knot formation is limited, theoretical methods currently provide the most detailed insights into the knotting process. While suitable for shallow knots, molecular dynamics simulations have faced challenges capturing the formation of deep knots in proteins such as the minimally tied trefoil α/β methyltransferase fromThermotoga maritima(MTTTM). To improve the efficiency of MTTTMknotting in CαGo-model simulations, mutant variants of the MTTTMGo-model were investigated. Through a structure-based analysis of knotted and unknotted states, four residues (K71, R72, E75, V76) were identified to increase the knotting efficiency from 2% to 83% when their contact energies were doubled and dihedral strength around the knot loop increased. The key features of this model are (i) a C-terminal slipknot intermediate that threads the knot in a highly unstructured intermediate, (ii) the inability to knot in native-like intermediate states, and (iii) a minor population in a long-lived trap that cannot knot. Examination of residue 71–76 contacts provides a small set of potential mutants that can directly test the model’s validity. In addition, the knotting optimization process developed here has broad applicability in generating knotting-efficient models of other knotted proteins.