Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China.
Biophys J. 2010 May 19;98(10):2356-64. doi: 10.1016/j.bpj.2010.01.044.
We present a novel sampling approach to explore large protein conformational transitions by determining unique substates from instantaneous normal modes calculated from an elastic network model, and applied to a progression of atomistic molecular dynamics snapshots. This unbiased sampling scheme allows us to direct the path sampling between the conformational end states over simulation timescales that are greatly reduced relative to the known experimental timescales. We use adenylate kinase as a test system to show that instantaneous normal modes can be used to identify substates that drive the structural fluctuations of adenylate kinase from its closed to open conformations, in which we observe 16 complete transitions in 4 mus of simulation time, reducing the timescale over conventional simulation timescales by two orders of magnitude. Analysis shows that the unbiased determination of substates is consistent with known pathways determined experimentally.
我们提出了一种新的采样方法，通过从弹性网络模型计算的瞬时正常模式中确定独特的亚稳态，来探索大型蛋白质构象转变，并将其应用于一系列原子分子动力学快照。这种无偏采样方案允许我们在大大缩短的模拟时间尺度上引导构象末端状态之间的路径采样，与已知的实验时间尺度相比，这大大缩短了模拟时间尺度。我们使用腺苷酸激酶作为测试系统，表明瞬时正常模式可用于识别亚稳态，从而驱动腺苷酸激酶从其封闭构象到开放构象的结构波动，在 4 微秒的模拟时间内，我们观察到 16 次完整的转变，将时间尺度相对于传统模拟时间尺度缩短了两个数量级。分析表明，亚稳态的无偏确定与实验确定的已知途径一致。
