Ideal, rigorous binding affinity computations, based on statistical mechanics, would fully account for ligand and receptor (protein) flexibility, as well as non-additive effects, which cannot properly be included in faster, more approximate estimates. Such ideal calculations, however, push the limits of present-day computational resources, and therefore have had limited practical impact. Furthermore, rigorous affinity estimates also suffer from errors in the assumed forcefields, which may lead to inaccuracies even when well-designed, well- converged calculations are performed. This proposal aims to take important steps toward overcoming these obstacles, and thus to make rigorous affinity estimation a practical, reliable part of the modeler's toolkit. The issue of computational cost will be addressed with innovative, efficient methods; accuracy will be addressed by the use of both standard and polarizable forcefields; and, lastly, molecular flexibility will be addressed using novel end-point (non- """"""""alchemical"""""""") methods and a new conformational sampling scheme. The estrogen receptor is a system which truly embodies all the challenges of affinity calculations, possessing a great diversity of ligands, some of which induce a large receptor conformational change. Beyond its far- reaching clinical importance, the estrogen-receptor is a key model system for understanding binding phenomena in nuclear hormone receptors. Our strategies for improving rigorous affinity calculations will be pursued in the estrogen receptor system, in a series of tasks of increasing complexity. First, estrogen receptor ligands will be studied in solution, then in simplified models of the receptor binding site, and finally the full system will be investigated. With a local experimental collaborator, we will attempt to engineer compounds of potential clinical importance. Successful computational approaches will be implemented in widely available software packages. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
1R01GM076569-01A1
Application #
7197577
Study Section
Special Emphasis Panel (ZRG1-BCMB-Q (90))
Program Officer
Preusch, Peter C
Project Start
2007-01-15
Project End
2010-12-31
Budget Start
2007-01-15
Budget End
2007-12-31
Support Year
1
Fiscal Year
2007
Total Cost
$246,881
Indirect Cost
Name
University of Pittsburgh
Department
Biology
Type
Schools of Medicine
DUNS #
004514360
City
Pittsburgh
State
PA
Country
United States
Zip Code
15213
Mamonov, Artem B; Lettieri, Steven; Ding, Ying et al. (2012) Tunable, mixed-resolution modeling using library-based Monte Carlo and graphics processing units. J Chem Theory Comput 8:2921-2929
Lettieri, Steven; Zuckerman, Daniel M (2012) Accelerating molecular Monte Carlo simulations using distance and orientation-dependent energy tables: tuning from atomistic accuracy to smoothed ""coarse-grained"" models. J Comput Chem 33:268-75
Bhatt, Divesh; Zuckerman, Daniel M (2011) Beyond microscopic reversibility: Are observable non-equilibrium processes precisely reversible? J Chem Theory Comput 7:2520-2527
Mamonov, Artem B; Zhang, Xin; Zuckerman, Daniel M (2011) Rapid sampling of all-atom peptides using a library-based polymer-growth approach. J Comput Chem 32:396-405
Cashman, D J; Mamonov, A B; Bhatt, D et al. (2011) Thermal motions of the E. coli glucose-galactose binding protein studied using well-sampled, semi-atomistic simulations. Curr Top Med Chem 11:211-20
Zuckerman, Daniel M (2011) Equilibrium sampling in biomolecular simulations. Annu Rev Biophys 40:41-62
Lettieri, Steven; Mamonov, Artem B; Zuckerman, Daniel M (2011) Extending fragment-based free energy calculations with library Monte Carlo simulation: annealing in interaction space. J Comput Chem 32:1135-43
Zhang, Xin; Bhatt, Divesh; Zuckerman, Daniel M (2010) Automated sampling assessment for molecular simulations using the effective sample size. J Chem Theory Comput 6:3048-3057
Bhatt, Divesh; Zhang, Bin W; Zuckerman, Daniel M (2010) Steady-state simulations using weighted ensemble path sampling. J Chem Phys 133:014110
Ding, Ying; Mamonov, Artem B; Zuckerman, Daniel M (2010) Efficient equilibrium sampling of all-atom peptides using library-based Monte Carlo. J Phys Chem B 114:5870-7

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