Schlick Tamar
Department of Chemistry and Courant Institute of Mathematical Sciences, New York University 251 Mercer Street, New York, NY 10012 USA.
F1000 Biol Rep. 2009 Jul 8;1:51. doi: 10.3410/B1-51.
The rugged energy landscape of biomolecules together with shortcomings of traditional molecular dynamics (MD) simulations require specialized methods for capturing large-scale, long-time configurational changes along with chemical dynamics behavior. In this report, MD-based methods for biomolecules are surveyed, involving modification of the potential, simulation protocol, or algorithm as well as global reformulations. While many of these methods are successful at probing the thermally accessible configuration space at the expense of altered kinetics, more sophisticated approaches like transition path sampling or Markov chain models are required to obtain mechanistic information, reaction pathways, and/or reaction rates. Divide-and-conquer methods for sampling and for piecing together reaction rate information are especially suitable for readily available computer cluster networks. Successful applications to biomolecules remain a challenge.
生物分子崎岖的能量图景以及传统分子动力学(MD)模拟的缺点,需要专门的方法来捕捉大规模、长时间的构型变化以及化学动力学行为。在本报告中,对基于MD的生物分子方法进行了综述,包括势函数、模拟协议或算法的修改以及全局重新公式化。虽然这些方法中的许多在以改变动力学为代价探测热可及构型空间方面取得了成功,但需要更复杂的方法,如过渡路径采样或马尔可夫链模型,来获取机理信息、反应途径和/或反应速率。用于采样和拼凑反应速率信息的分治方法特别适用于现成的计算机集群网络。在生物分子上的成功应用仍然是一个挑战。