Parui Sridip, Brini Emiliano, Dill Ken A
Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States.
School of Chemistry and Materials Science, 85 Lomb Memorial Drive, Rochester, New York 14623, United States.
J Chem Theory Comput. 2023 Oct 10;19(19):6839-6847. doi: 10.1021/acs.jctc.3c00679. Epub 2023 Sep 19.
Some proteins are conformational switches, able to transition between relatively different conformations. To understand what drives them requires computing the free-energy difference Δ between their stable states, and . Molecular dynamics (MD) simulations alone are often slow because they require a reaction coordinate and must sample many transitions in between. Here, we show that modeling employing limited data (MELD) x MD on known endstates and is accurate and efficient because it does not require passing over barriers or knowing reaction coordinates. We validate this method on two problems: (1) it gives correct relative populations of α and β conformers for small designed chameleon sequences of protein G; and (2) it correctly predicts the conformations of the C-terminal domain (CTD) of RfaH. Free-energy methods like MELD x MD can often resolve structures that confuse machine-learning (ML) methods.
一些蛋白质是构象开关,能够在相对不同的构象之间转变。要理解驱动它们的因素,需要计算其稳定状态之间的自由能差Δ。仅分子动力学(MD)模拟通常很慢,因为它们需要一个反应坐标,并且必须对其间的许多转变进行采样。在这里,我们表明,在已知终态上使用有限数据建模(MELD)x MD既准确又高效,因为它不需要越过势垒或知道反应坐标。我们在两个问题上验证了该方法:(1)对于蛋白质G的小设计变色龙序列,它给出了α和β构象异构体的正确相对丰度;(2)它正确地预测了RfaH的C端结构域(CTD)的构象。像MELD x MD这样的自由能方法通常可以解析那些使机器学习(ML)方法感到困惑的结构。