How to Run FAST Simulations

Abstract

Molecular dynamics (MD) simulations are a powerful tool for understanding enzymes' structures and functions with full atomistic detail. These physics-based simulations model the dynamics of a protein in solution and store snapshots of its atomic coordinates at discrete time intervals. Analysis of the snapshots from these trajectories provides thermodynamic and kinetic properties such as conformational free energies, binding free energies, and transition times. Unfortunately, simulating biologically relevant timescales with brute force MD simulations requires enormous computing resources. In this chapter we detail a goal-oriented sampling algorithm, called fluctuation amplification of specific traits, that quickly generates pertinent thermodynamic and kinetic information by using an iterative series of short MD simulations to explore the vast depths of conformational space.

Keywords: Adaptive sampling; Allostery; Conformational change; Cryptic site; Goal-oriented sampling; Markov state model; Molecular dynamics simulations.