Literature DB >> 27497168

How to Run FAST Simulations.

M I Zimmerman1, G R Bowman2.   

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.
© 2016 Elsevier Inc. All rights reserved.

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

Mesh:

Substances:

Year:  2016        PMID: 27497168     DOI: 10.1016/bs.mie.2016.05.032

Source DB:  PubMed          Journal:  Methods Enzymol        ISSN: 0076-6879            Impact factor:   1.600


  6 in total

1.  Spatial and temporal alterations in protein structure by EGF regulate cryptic cysteine oxidation.

Authors:  Jessica B Behring; Sjoerd van der Post; Arshag D Mooradian; Matthew J Egan; Maxwell I Zimmerman; Jenna L Clements; Gregory R Bowman; Jason M Held
Journal:  Sci Signal       Date:  2020-01-21       Impact factor: 8.192

2.  Prediction of New Stabilizing Mutations Based on Mechanistic Insights from Markov State Models.

Authors:  Maxwell I Zimmerman; Kathryn M Hart; Carrie A Sibbald; Thomas E Frederick; John R Jimah; Catherine R Knoverek; Niraj H Tolia; Gregory R Bowman
Journal:  ACS Cent Sci       Date:  2017-11-21       Impact factor: 14.553

3.  SARS-CoV-2 Nsp16 activation mechanism and a cryptic pocket with pan-coronavirus antiviral potential.

Authors:  Neha Vithani; Michael D Ward; Maxwell I Zimmerman; Borna Novak; Jonathan H Borowsky; Sukrit Singh; Gregory R Bowman
Journal:  bioRxiv       Date:  2020-12-10

4.  Naturally Occurring Genetic Variants in the Oxytocin Receptor Alter Receptor Signaling Profiles.

Authors:  Manasi Malik; Michael D Ward; Yingye Fang; Justin R Porter; Maxwell I Zimmerman; Thomas Koelblen; Michelle Roh; Antonina I Frolova; Thomas P Burris; Gregory R Bowman; Princess I Imoukhuede; Sarah K England
Journal:  ACS Pharmacol Transl Sci       Date:  2021-09-08

5.  SARS-CoV-2 simulations go exascale to predict dramatic spike opening and cryptic pockets across the proteome.

Authors:  Maxwell I Zimmerman; Justin R Porter; Michael D Ward; Sukrit Singh; Neha Vithani; Artur Meller; Upasana L Mallimadugula; Catherine E Kuhn; Jonathan H Borowsky; Rafal P Wiewiora; Matthew F D Hurley; Aoife M Harbison; Carl A Fogarty; Joseph E Coffland; Elisa Fadda; Vincent A Voelz; John D Chodera; Gregory R Bowman
Journal:  Nat Chem       Date:  2021-05-24       Impact factor: 24.427

6.  SARS-CoV-2 Nsp16 activation mechanism and a cryptic pocket with pan-coronavirus antiviral potential.

Authors:  Neha Vithani; Michael D Ward; Maxwell I Zimmerman; Borna Novak; Jonathan H Borowsky; Sukrit Singh; Gregory R Bowman
Journal:  Biophys J       Date:  2021-03-29       Impact factor: 4.033

  6 in total

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