The greatest limitation for structure-based drug discovery (SBDD) is the need to neglect water and protein flexibility in most modeling. Here, we outline simulation methods that overcome these limitations. This proposal focuses on developing MixMD, our method for mixed-solvent molecular dynamics (MD). MixMD identifies critical binding sub-sites on protein surfaces (hotspots). Proteins are simulated in a box of explicit water with 5% small, organic probe cosolvents. The waters and probes sample the local environments along the protein surface, and sites with high occupancy of probes are identified as hotspots. MixMD has superior performance over other cosolvent MD methods like MacKerell?s SILCS and Barril?s MDmix. Other methods are plagued by many spurious, misleading, ?extra? sites that indistinguishable from real binding sites, which greatly hinders prospective applications. Our long-term goal is to improve SBDD by developing methods that more accurately model protein-ligand binding. Our underlying hypotheses are 1) MixMD?s more complete description of the physics of binding yields better hotspot predictions than traditional SBDD methods and 2) both qualitative and quantitative data from MixMD can be used in SBDD. This proposal outlines two areas for developing MixMD and increasing its impact on SBDD.
Specific Aim 1 develops methods for calculating the free energies, entropies, and enthalpies of the hotspot probes. Comparisons will be made between occupancy-based, energy-based, and kinetics-based methods for calculating those key binding properties.
Specific Aim 2 will address a series of key challenges in SBDD. First, MixMD will be used to identify bridging water molecules in binding sites. Clearly, hotspot locations ascertain displaceable water, but it is just as important to pinpoint required, bridging waters in binding sites. Second, the accessibility of difficult, cryptic sites will be examined. While mapping the sites, we will determine whether pocket opening and probe binding are sequential events where probes ?capture? open states or concerted events where probes ?induce? open states by pushing against the malleable torsions of the cryptic pocket. Lastly, MixMD data will be used to predict druggabilities of binding sites. The Non-Redundant set of Druggable and Less Druggable binding sites (NRDLD) will be used to derive a druggability index based on number of hotspots, their affinities, their proximities, and their degree of burial in the protein.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM065372-11
Application #
9637404
Study Section
Macromolecular Structure and Function D Study Section (MSFD)
Program Officer
Lyster, Peter
Project Start
2002-04-01
Project End
2022-01-31
Budget Start
2019-02-01
Budget End
2020-01-31
Support Year
11
Fiscal Year
2019
Total Cost
Indirect Cost
Name
University of Michigan Ann Arbor
Department
Pharmacology
Type
Schools of Pharmacy
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
Graham, Sarah E; Smith, Richard D; Carlson, Heather A (2018) Predicting Displaceable Water Sites Using Mixed-Solvent Molecular Dynamics. J Chem Inf Model 58:305-314
Graham, Sarah E; Leja, Noah; Carlson, Heather A (2018) MixMD Probeview: Robust Binding Site Prediction from Cosolvent Simulations. J Chem Inf Model 58:1426-1433
Ghanakota, Phani; Carlson, Heather A (2017) Comparing pharmacophore models derived from crystallography and NMR ensembles. J Comput Aided Mol Des 31:979-993
Ghanakota, Phani; Carlson, Heather A (2016) Driving Structure-Based Drug Discovery through Cosolvent Molecular Dynamics. J Med Chem 59:10383-10399
Ung, Peter M U; Ghanakota, Phani; Graham, Sarah E et al. (2016) Identifying binding hot spots on protein surfaces by mixed-solvent molecular dynamics: HIV-1 protease as a test case. Biopolymers 105:21-34
Graham, Sarah E; Tweedy, Sara E; Carlson, Heather A (2016) Dynamic behavior of the post-SET loop region of NSD1: Implications for histone binding and drug development. Protein Sci 25:1021-9
Ghanakota, Phani; Carlson, Heather A (2016) Moving Beyond Active-Site Detection: MixMD Applied to Allosteric Systems. J Phys Chem B 120:8685-95
Xu, Hao; Majmudar, Jaimeen D; Davda, Dahvid et al. (2015) Substrate-Competitive Activity-Based Profiling of Ester Prodrug Activating Enzymes. Mol Pharm 12:3399-407
Lexa, Katrina W; Goh, Garrett B; Carlson, Heather A (2014) Parameter choice matters: validating probe parameters for use in mixed-solvent simulations. J Chem Inf Model 54:2190-9
Ung, Peter M-U; Dunbar Jr, James B; Gestwicki, Jason E et al. (2014) An allosteric modulator of HIV-1 protease shows equipotent inhibition of wild-type and drug-resistant proteases. J Med Chem 57:6468-78

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