title: 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.
authors: Washington University School of Medicine, St. Louis, MO, United States.
authors: M I
authors: MI
authors: Zimmerman
authors: Washington University School of Medicine, St. Louis, MO, United States; Center for Biological Systems Engineering, Washington University School of Medicine, St. Louis, MO, United States. Electronic address: bowman@biochem.wustl.edu.
authors: G R
authors: GR
authors: Bowman
mesh_terms: D000465:Algorithms; D000494:Allosteric Regulation; D020134:Catalytic Domain; D016000:Cluster Analysis; D007700:Kinetics; D008390:Markov Chains; D056004:Molecular Dynamics Simulation; D011487:Protein Conformation; D017510:Protein Folding; D013816:Thermodynamics; D013997:Time Factors; D001618:beta-Lactamases / Q000737:chemistry*
chemical_list: D001618:beta-Lactamases; C034413:beta-lactamase TEM-1
keywords: Adaptive sampling; Allostery; Conformational change; Cryptic site; Goal-oriented sampling; Markov state model; Molecular dynamics simulations