Calculation of the standard binding free energy of sparsomycin to the ribosomal peptidyl-transferase P-site using molecular dynamics simulations with restraining potentials

2010 ◽  
Vol 23 (2) ◽  
pp. 128-141 ◽  
Author(s):  
Xiaoxia Ge ◽  
Benoît Roux
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Molecular Dynamics Simulations ◽  
Binding Free Energy ◽  
Peptidyl Transferase ◽  
Dynamics Simulations

Molecular dynamics simulations and binding free energy analysis of DNA minor groove complexes of curcumin

2011 ◽  
Vol 17 (11) ◽  
pp. 2805-2816 ◽  
Author(s):  
Mathew Varghese Koonammackal ◽  
Unnikrishnan Viswambharan Nair Nellipparambil ◽  
Chellappanpillai Sudarsanakumar
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Molecular Dynamics Simulations ◽  
Energy Analysis ◽  
Binding Free Energy ◽  
Minor Groove ◽  
Dna Minor Groove ◽  
Dynamics Simulations ◽  
Free Energy Analysis

scholarly journals How quickly can we predict trimethoprim resistance using alchemical free energy methods?

2020 ◽  
Vol 10 (6) ◽  
pp. 20190141
Author(s):  
Philip W. Fowler
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Molecular Dynamics Simulations ◽  
Clinical Microbiology ◽  
Binding Free Energy ◽  
Single Mutation ◽  
Energy Methods ◽  
Synonymous Mutations ◽  
Dynamics Simulations ◽  
Alchemical Free Energy

The emergence of antimicrobial resistance threatens modern medicine and necessitates more personalized treatment of bacterial infections. Sequencing the whole genome of the pathogen(s) in a clinical sample offers one way to improve clinical microbiology diagnostic services, and has already been adopted for tuberculosis in some countries. A key weakness of a genetics clinical microbiology is it cannot return a result for rare or novel genetic variants and therefore predictive methods are required. Non-synonymous mutations in the S. aureus dfrB gene can be successfully classified as either conferring resistance (or not) by calculating their effect on the binding free energy of the antibiotic, trimethoprim. The underlying approach, alchemical free energy methods, requires large numbers of molecular dynamics simulations to be run. We show that a large number ( N = 15) of binding free energies calculated from a series of very short (50 ps) molecular dynamics simulations are able to satisfactorily classify all seven mutations in our clinically derived testset. A result for a single mutation could therefore be returned in less than an hour, thereby demonstrating that this or similar methods are now sufficiently fast and reproducible for clinical use.

Sign in / Sign up

Export Citation Format

Share Document