Division of Immunology, Beckman Research Institute of the City of Hope, Duarte, California 91010, USA.
J Comput Chem. 2013 Apr 30;34(11):904-14. doi: 10.1002/jcc.23200. Epub 2013 Jan 23.
Internal coordinate molecular dynamics (ICMD) methods provide a more natural description of a protein by using bond, angle, and torsional coordinates instead of a Cartesian coordinate representation. Freezing high-frequency bonds and angles in the ICMD model gives rise to constrained ICMD (CICMD) models. There are several theoretical aspects that need to be developed to make the CICMD method robust and widely usable. In this article, we have designed a new framework for (1) initializing velocities for nonindependent CICMD coordinates, (2) efficient computation of center of mass velocity during CICMD simulations, (3) using advanced integrators such as Runge-Kutta, Lobatto, and adaptive CVODE for CICMD simulations, and (4) cancelling out the "flying ice cube effect" that sometimes arises in Nosé-Hoover dynamics. The Generalized Newton-Euler Inverse Mass Operator (GNEIMO) method is an implementation of a CICMD method that we have developed to study protein dynamics. GNEIMO allows for a hierarchy of coarse-grained simulation models based on the ability to rigidly constrain any group of atoms. In this article, we perform tests on the Lobatto and Runge-Kutta integrators to determine optimal simulation parameters. We also implement an adaptive coarse-graining tool using the GNEIMO Python interface. This tool enables the secondary structure-guided "freezing and thawing" of degrees of freedom in the molecule on the fly during molecular dynamics simulations and is shown to fold four proteins to their native topologies. With these advancements, we envision the use of the GNEIMO method in protein structure prediction, structure refinement, and in studying domain motion.
内坐标分子动力学(ICMD)方法通过使用键、角度和扭转坐标而不是笛卡尔坐标表示来提供对蛋白质的更自然描述。在 ICMD 模型中冻结高频键和角度会产生约束 ICMD(CICMD)模型。为了使 CICMD 方法稳健且广泛可用,需要开发几个理论方面。在本文中,我们设计了一个新框架,用于(1)为非独立的 CICMD 坐标初始化速度,(2)在 CICMD 模拟中有效计算质心速度,(3)在 CICMD 模拟中使用高级积分器,如龙格-库塔、洛巴托和自适应 CVODE,以及(4)消除 Nosé-Hoover 动力学中有时出现的“飞冰块效应”。广义牛顿-欧拉逆质量算子(GNEIMO)方法是我们开发的用于研究蛋白质动力学的 CICMD 方法的实现。GNEIMO 允许基于刚性约束任何原子组的能力构建层次化的粗粒化模拟模型。在本文中,我们对洛巴托和龙格-库塔积分器进行测试,以确定最佳模拟参数。我们还使用 GNEIMO Python 接口实现了自适应粗粒化工具。该工具能够在分子动力学模拟过程中实时对分子中的自由度进行基于二级结构的“冻结和解冻”,并展示了对四个蛋白质进行折叠到其天然拓扑结构。有了这些进展,我们设想在蛋白质结构预测、结构细化和研究域运动中使用 GNEIMO 方法。