Mapping Multiple Minima in Protein Energy Landscapes with Evolutionary Algorithms

Abstract

Many proteins involved in human proteinopathies exhibit complex energy landscapes with multiple thermodynamicallystable and semi-stable structural states. Landscape reconstruction is crucial to understanding functional modulations, but one is confronted with the multiple minima problem. While traditionally the objective for evolutionary algorithms (EAs) is to find the global minimum, here we present work on an EA that maps the various minima in a protein's energy landscape. Specifically, we investigate the role of initialization of the initial population in the rate of convergence and solution diversity. Results are presented on two key proteins, H-Ras and SOD1, related to human cancers and familial Amyotrophic lateral sclerosis (ALS).

Research on the Mechanical Automation Technology based on Evolutionary Algorithms and Artificial Intelligence Theory

“… The analysis method of shift. System information obtained by the aid of sensors into in the state model [4][5][6].  The structure of the control object.…”