CAREER: Scalable Mathematical and Computational Models for Biomolecular Modeling

职业：生物分子建模的可扩展数学和计算模型

• 批准号: 0135195
• 负责人: Jesus Izaguirre
• 金额: $ 31.25万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-02-01 至 2008-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0135195&HistoricalAwards=false
• 关键词: CAREER; Scalable; Mathematical; Computational; Models

The proposed activities aim to create mathematical and computational methods for modeling large biological molecules. Computational simulation of dynamics and sampling of proteins, DNA, and nucleic acids promise to be a tool for understanding the relationship between structure and function, and for computer assisted drug design. Processes of interest include protein dynamics and folding and the study of other cellular components. Despite significant progress in the field, there is still a gap between the simulations that can be routinely performed with current technology and the complexity of processes and systems of biological interest. The proposed mathematical and computational methods will overcome the size and time scale limitations inherent in atomistic dynamics and sampling, while preserving the atomistic resolution of the biological systems. The new methods will be disseminated for research and educational purposes through an open source and scalable software framework called ProtoMol, and tested in a range of systems, from small proteins to potassium channels that reside in lipid bilayers. These new algorithms will translate into speedups of one or more orders of magnitude over current methodologies. This technology will enable simulations that are sorely needed in the expanding field of proteomics and the processing of data from the human genome project. To study dynamical processes, trajectories of large biomolecules are generated using molecular dynamics (MD). In an attempt to overcome the time scale limitations, multiple time stepping (MTS) integrators have been introduced. Nevertheless, even these methods have been limited by stability, and thus the time steps used in MD have not been dramatically increased. The PI proposes to devise multiscale algorithms for MD that are not limited by stability. To accomplish this goal, research will proceed in two phases: the _rst will extend the PI's work on stabler MTS numerical integrators by overcoming instabilities present in these methods. This will allow an estimated two- to eight-fold speedup over current methods for MD. The second phase involves the use of a symplectic semi-implicit method for MD using a splitting that separates cleanly many time scales, and incorporates the faster and less interesting ones in a more approximate manner. Speedups of two orders of magnitude or more are possible, depending on the degree of accuracy desired. This proposal will also tackle the related problem of statistical sampling. The large conformational space of biomolecular systems causes many difficulties to traditional sampling methodologies such as MD and Monte Carlo methods, or hybrids of both, all of which super performance degradation as the system size increases. We will use a biased hybrid Monte Carlo method that scales nearly linearly with system size. This will produce speedups of one or two orders of magnitude over MD, MC, or conventional hybrid MC methods. Synergy between this research project and teaching will occur at several levels. There will be an enhancement of materials of undergraduate and graduate courses taught by the PI, on data structures and applied algorithms, numerical methods, and computational methods for biomolecular modeling. Learning modules for engineering and science students will be developed in ProtoMol to facilitate an understanding of the behavior of large biological molecules.

拟议的活动旨在创建数学和计算方法来模拟大型生物分子。动力学的计算模拟和蛋白质、DNA和核酸的采样有望成为理解结构和功能之间关系以及计算机辅助药物设计的工具。感兴趣的过程包括蛋白质动力学和折叠以及其他细胞成分的研究。尽管在该领域取得了重大进展，但在利用现有技术进行的常规模拟与生物学过程和系统的复杂性之间仍然存在差距。所提出的数学和计算方法将克服原子动力学和采样中固有的大小和时间尺度限制，同时保留生物系统的原子分辨率。这些新方法将通过一个名为ProtoMol的开源和可扩展的软件框架传播，用于研究和教育目的，并在一系列系统中进行测试，从小蛋白质到脂质双层中的钾通道。这些新的算法将转化为一个或多个数量级的速度比目前的方法。这项技术将使蛋白质组学和人类基因组计划数据处理领域迫切需要的模拟成为可能。为了研究动力学过程，使用分子动力学（MD）生成大生物分子的轨迹。在试图克服时间尺度的限制，多时间步进（MTS）积分器已被引入。然而，即使这些方法也受到稳定性的限制，因此MD中使用的时间步长没有显著增加。PI建议设计不受稳定性限制的MD多尺度算法。为了实现这一目标，研究将分两个阶段进行：第一阶段将通过克服这些方法中存在的不稳定性来扩展PI在更稳定的MTS数值积分器上的工作。这将允许估计的2到8倍的速度比目前的方法MD。第二阶段涉及使用一个辛半隐式方法MD使用分裂干净地分离许多时间尺度，并结合更快，更不感兴趣的一个更近似的方式。两个数量级或更多的加速是可能的，这取决于所需的准确度。这项建议也将解决有关的统计抽样问题。生物分子系统的大构象空间给传统的采样方法如分子动力学和蒙特卡罗方法或两者的混合方法带来了许多困难，所有这些方法都随着系统尺寸的增加而性能下降。我们将使用有偏混合蒙特卡罗方法，该方法与系统大小几乎呈线性关系。这将产生一个或两个数量级的加速比MD，MC，或传统的混合MC方法。这个研究项目和教学之间的协同作用将发生在几个层面。将有一个由PI教授的本科生和研究生课程的材料的增强，在数据结构和应用算法，数值方法和生物分子建模的计算方法。将在ProtoMol中开发工程和科学学生的学习模块，以促进对大生物分子行为的理解。