Degree Type

Thesis

Date of Award

2016

Degree Name

Master of Science

Department

Computer Science

Major

Computer Science

First Advisor

Guang Song

Abstract

Classical normal mode analysis (CNMA) has been widely acknowledged as one of the most useful simulation tools for studying protein dynamics. CNMA uses a fine-grained all-atom model of proteins and a complex empirical potential. In addition, CNMA requires a structure that must be energetically minimized, which makes the method cumbersome to use, especially for large proteins. In contrast, elastic network models (ENM) use coarse-grained protein models and adopt a simplified potential function. ENM is much faster than CNMA but is less accurate. To take the advantages of both CNMA and ENM, the spring-based normal mode analysis (sbNMA) was developed. It uses a fine-grained all-atom model for proteins and an all-atom empirical force field to maintain accuracy while reducing the computing complexity by eliminating the minimization step. In the previous work on sbNMA, only the CHARMM force field was explored. In this work, we extend the analyses to AMBER, another widely-used force field. We investigate the dependence of sbNMA's performance on force fields. This work provides also insightful understandings of the differences between CHARMM and AMBER.

Copyright Owner

Jaekyun Song

Language

en

File Format

application/pdf

File Size

46 pages

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