Automated biophysical measurements to drive improvements in physical modeling accuracy


To drive improvements in quantitative accuracy, we use automated biophysical experiments to probe the physical determinants of small molecule affinity and selectivity. Using robotically driven site-directed mutagenesis to perturb the protein, rather than synthesize new small molecules, we can rapidly collect data to improve algorithms, forcefields, and the treatment of chemical effects in protein-ligand modeling, as well as address fundamental physical questions about what interactions are critical in determining small molecule affinity and selectivity.


David L. Mobley (University of California, Irvine): Model binding systems and best practices for alchemical free energy calculations
David D. L. Minh (Illinois Institute of Technology): Bayesian inference for biophysical experiments (ITC, WAXS/SAXS)
James Fraser (UCSF): Room-temperature crystallography, WAXS/SAXS, X-ray fragment screening


Mehtap Isik (TPCB Graduate Student)
Ariën S. ("Bas") Rustenburg (TPCB Graduate Student)


Results of the 2017 Roadmap Survey of the Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenge community
David L. Mobley, John D. Chodera, Michael K. Gilson
SAMPL community survey, made available online [DOI] [PDF]

Advancing predictive modeling through focused development of model systems to drive new modeling innovations.
David L Mobley, John D Chodera, Lyle Isaacs, and Bruce C Gibb.
NIH R01 proposal, made available online [DOI] [PDF]

Measuring experimental cyclohexane-water distribution coefficients for the SAMPL5 challenge
Ariën S. Rustenburg, Justin Dancer, Baiwei Lin, Jianweng A. Feng, Daniel F. Ortwine, David L. Mobley, and John D. Chodera.
Solicited manuscript for special issue of the Journal of Computer Aided Molecular Design on the SAMPL5 Challenge.
preprint: [bioRxiv] [PDF] // data: [GitHub]

Identifying ligand binding sites and poses using GPU-accelerated Hamiltonian replica exchange molecular dynamics
Kai Wang K, John D. Chodera, Yanzhi Yang, and Michael R. Shirts. 
J. Comput. Aid. Mol. Des. 27:989, 2013. [DOI] [PDF]

Free energy methods in drug discovery and design: Progress and challenges
John D. Chodera, David L. Mobley, Michael R. Shirts, Richard W. Dixon, Kim M. Branson, and Vijay S. Pande.
Curr. Opin. Struct. Biol. 21:150, 2011. [DOI] [PDF]

Predicting absolute ligand binding free energies to a simple model site
David L. Mobley, Alan P. Graves, John D. Chodera, Andrea C. McReynolds, Brian K. Shoichet, and Ken A. Dill
J. Mol. Biol. 371:1118, 2007. [DOI] [PDF]

Predicting small-molecule solvation free energies: A blind challenge test for computational chemistry
Anthony Nicholls, David L. Mobley, J. Peter Guthrie, John D. Chodera, and Vijay S. Pande.
J. Med. Chem. 51:769, 2008. [DOI] [PDF]

Comparison of charge models for fixed-charge force fields: Small-molecule hydration free energies in explicit solvent
David L. Mobley, Élise Dumont, John D. Chodera, Christopher I. Bayly, Matthew D. Cooper, and Ken A. Dill.
J. Phys. Chem. B 111:2242, 2007. [DOI] [PDF]