Evolution of Alchemical Free Energy Methods in Drug Discovery

2020 ◽  
Vol 60 (11) ◽  
pp. 5308-5318 ◽  
Author(s):  
Lin Frank Song ◽  
Kenneth M. Merz
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Energy Methods ◽  
Alchemical Free Energy

scholarly journals Alchemical free energy methods for drug discovery: progress and challenges

2011 ◽  
Vol 21 (2) ◽  
pp. 150-160 ◽  
Author(s):  
John D Chodera ◽  
David L Mobley ◽  
Michael R Shirts ◽  
Richard W Dixon ◽  
Kim Branson ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Energy Methods ◽  
Alchemical Free Energy

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.

scholarly journals Uncertainty Quantification in Alchemical Free Energy Methods

2018 ◽  
Vol 14 (6) ◽  
pp. 2867-2880 ◽  
Author(s):  
Agastya P. Bhati ◽  
Shunzhou Wan ◽  
Yuan Hu ◽  
Brad Sherborne ◽  
Peter V. Coveney
Keyword(s):  
Free Energy ◽  
Uncertainty Quantification ◽  
Energy Methods ◽  
Alchemical Free Energy

scholarly journals Predicting Ligand Binding Affinity with Alchemical Free Energy Methods in a Polar Model Binding Site

2009 ◽  
Vol 394 (4) ◽  
pp. 747-763 ◽  
Author(s):  
Sarah E. Boyce ◽  
David L. Mobley ◽  
Gabriel J. Rocklin ◽  
Alan P. Graves ◽  
Ken A. Dill ◽  
...  
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Binding Site ◽  
Binding Affinity ◽  
Energy Methods ◽  
Polar Model ◽  
Alchemical Free Energy

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

2020 ◽  
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

AbstractThe emergence of antimicrobial resistance (AMR) threatens modern medicine and necessitates more personalised 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.

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