NHC_MDynamics: High-Throughput Tools for Simulations of Complex Fluids Using Nosé-Hoover Chains and Big Data Analytics

Abstract

One of the most promising alternatives to acquire knowledge of the behavior of biological systems are the methods of molecular simulation such as Molecular Dynamics (MD). MD has become one of the most powerful numerical tools to study thermodynamic properties of macromolecules such as proteins and other biomolecules. However, MD has two major limitations when it comes to study large systems such as proteins at the molecular level, these are: the size of the system (the number of atoms to study) and the integration time used to evaluate the equations of motion. Therefore, this work is focused on the implementation of the NVT algorithm of molecular dynamics based on the Nosé-Hoover thermostat chains with the high performance computing technology such as Graphical Processing Units (GPUs) and Big Data analytics for the generation of knowledge to help understand the functioning of thermodynamic properties in simulated systems of Lennard-Jones fluids, as well as the study of the behavior of proteins, such as diabetes, to refine their structures to formulate and integrate them into the improvement of food in an improved diet for people with this condition.