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

点击翻译按钮获取中文摘要

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.