Multivariate Minimization and Molecular Dynamic Techniques for Predicting Nucleic Acid and Protein Structures on Supercomputers

塔玛·石里克博士得到了理论研究基金会的资助。 化学系的计算化学课程， 数据库、软件开发和计算生物学 仪器和资源部的计划，以及 高级技术部的新技术计划 科学计算开发新的计算机算法， 进行分子力学和分子动力学 计算。 这些新算法将被用来预测 核酸和蛋白质结构， 超级计算机 在这项研究中，Schlick和同事将继续解决 一些基本的计算和理论问题 在分子建模领域，通过：1）设计更多 有效的非线性最小化技术复杂大 规模问题; 2）在分子中使用较大的时间步长 动力学模拟; 3）包括量子力学效应 在分子动力学模拟;和4）减少 两两非键相互作用的计算时间。 的 所开发的算法将用于研究 闭合环状DNA的序列依赖性折叠途径 双链体。 本研究的最终目的是探索 详细的折叠途径和重要的转变， 酸和蛋白质。

Dr. Tamar Schlick is supported by a grant from the Theoretical and Computational Chemistry Program in the Chemistry Division, the Databases, Software Development and Computational Biology Program in the Division of Instrumentation and Resources, and the New Technologies Program in the Division of Advanced Scientific Computing to develop new computer algorithms for performing molecular mechanics and molecular dynamics calculations. These new algorithms will be used to predict nucleic acid and protein structures with the aid of supercomputers. In this research Schlick and coworkers will continue to address some of the fundamental computational and theoretical problems in the field of molecular modeling by: 1) devising more efficient nonlinear minimization techniques for complex large scale problems; 2) using larger time steps in molecular dynamics simulations; 3) including quantum-mechanical effects in molecular dynamics simulations; and 4) reducing the computation time for the pairwise nonbonded interactions. The algorithms which are developed will be used to study sequence-dependent folding pathways of closed circular DNA duplexes. The ultimate goal of this research is to explore the detailed folding pathways and important transitions in nucleic acids and proteins.