Peter Emanuel K, Shea Joan-Emma
Department of Pharmacy and Chemistry, Institute of Physical and Theoretical Chemistry, University of Regensburg, Germany.
Phys Chem Chem Phys. 2017 Jul 5;19(26):17373-17382. doi: 10.1039/c7cp03035e.
In this paper, we present a novel hybrid Molecular Dynamics/kinetic Monte Carlo (MD/kMC) algorithm and apply it to protein folding and aggregation in explicit solvent. The new algorithm uses a dynamical definition of biases throughout the MD component of the simulation, normalized in relation to the unbiased forces. The algorithm guarantees sampling of the underlying ensemble in dependency of one average linear coupling factor 〈α〉. We test the validity of the kinetics in simulations of dialanine and compare dihedral transition kinetics with long-time MD-simulations. We find that for low 〈α〉 values, kinetics are in good quantitative agreement. In folding simulations of TrpCage and TrpZip4 in explicit solvent, we also find good quantitative agreement with experimental results and prior MD/kMC simulations. Finally, we apply our algorithm to study growth of the Alzheimer Amyloid Aβ 16-22 fibril by monomer addition. We observe two possible binding modes, one at the extremity of the fibril (elongation) and one on the surface of the fibril (lateral growth), on timescales ranging from ns to 8 μs.
在本文中，我们提出了一种新颖的混合分子动力学/动力学蒙特卡罗（MD/kMC）算法，并将其应用于在显式溶剂中的蛋白质折叠和聚集。新算法在模拟的MD部分使用偏差的动态定义，并相对于无偏差力进行归一化。该算法保证了根据一个平均线性耦合因子〈α〉对基础系综进行采样。我们在丙氨酸二肽模拟中测试了动力学的有效性，并将二面角跃迁动力学与长时间MD模拟进行了比较。我们发现，对于低〈α〉值，动力学在定量上具有良好的一致性。在显式溶剂中对TrpCage和TrpZip4进行折叠模拟时，我们还发现与实验结果和先前的MD/kMC模拟在定量上具有良好的一致性。最后，我们应用我们的算法通过单体添加来研究阿尔茨海默病淀粉样蛋白Aβ 16-22原纤维的生长。我们观察到两种可能的结合模式，一种在原纤维的末端（伸长），一种在原纤维的表面（横向生长），时间尺度从纳秒到8微秒。
