The primary goal of the research is to develop and use computational tools to make quantitative predictions on the structure, reactivity and inhibition of proteins. Sophisticated software is being created for drug design with specific applications to cancer, arthritis, bacterial infections, sleep disorders, and Parkinson's disease. The research includes fundamental advances in the development of methods and software for modeling proteins and features applications on protein-ligand binding, inhibitor design, protein folding, and enzymatic reactions. Atomic-level computer simulations are the primary tool at three levels of complexity: a scoring function approach with the GenMol program, which can rapidly build combinatorial libraries of protein-ligand complexes, and extended linear response (ELR) and rigorous free-energy perturbation (FEP) calculations using Monte Carlo statistical mechanics (MC). The MC simulations are performed for the protein-ligand complexes in the presence of explicit water molecules and involve sampling of all degrees of freedom for the systems. The resultant detailed structural and energetic information helps elucidate variations in binding affinities as either the structure of the ligand or the protein sequence is modified. In turn, this knowledge forms the basis for the design of high-affinity, protein-selective ligands. In order to refine the predictive abilities of the methods, training and testing will use a database of ca. 2000 protein-ligand complexes with known activity data for analog series. Specific extensive studies of protein-ligand binding and drug-lead optimization are targeted for several proteins including CDK2 kinase, cyclooxygenases, DNA gyrase, and fatty acid amide hydrolase. The drug design is enhanced by application of the QikProp program, which analyzes the drug-likeness of input organic molecules through estimation of pharmaceutically relevant properties including aqueous solubility, cell permeabilities, blood-brain barrier permeability, and serum protein binding. Other activities are the development of an improved """"""""force field"""""""" for the description of intra and inter-molecular energetics, examination of the mechanisms of enzymatic reactions, and prediction of the structures of polypeptides in aqueous solution using new MC sampling methods with a continuum solvent model.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM032136-27
Application #
7647433
Study Section
Special Emphasis Panel (ZRG1-BCMB-N (90))
Program Officer
Preusch, Peter C
Project Start
1990-07-01
Project End
2011-06-30
Budget Start
2009-07-01
Budget End
2010-06-30
Support Year
27
Fiscal Year
2009
Total Cost
$345,112
Indirect Cost
Name
Yale University
Department
Chemistry
Type
Schools of Arts and Sciences
DUNS #
043207562
City
New Haven
State
CT
Country
United States
Zip Code
06520
Trivedi-Parmar, Vinay; Jorgensen, William L (2018) Advances and Insights for Small Molecule Inhibition of Macrophage Migration Inhibitory Factor. J Med Chem 61:8104-8119
Trivedi-Parmar, Vinay; Robertson, Michael J; Cisneros, José A et al. (2018) Optimization of Pyrazoles as Phenol Surrogates to Yield Potent Inhibitors of Macrophage Migration Inhibitory Factor. ChemMedChem 13:1092-1097
Cabeza de Vaca, Israel; Qian, Yue; Vilseck, Jonah Z et al. (2018) Enhanced Monte Carlo Methods for Modeling Proteins Including Computation of Absolute Free Energies of Binding. J Chem Theory Comput 14:3279-3288
Dodda, Leela S; Tirado-Rives, Julian; Jorgensen, William L (2018) Unbinding Dynamics of Non-Nucleoside Inhibitors from HIV-1 Reverse Transcriptase. J Phys Chem B :
Dawson, Thomas K; Dziedzic, Pawel; Robertson, Michael J et al. (2017) Adding a Hydrogen Bond May Not Help: Naphthyridinone vs Quinoline Inhibitors of Macrophage Migration Inhibitory Factor. ACS Med Chem Lett 8:1287-1291
Chan, Albert H; Lee, Won-Gil; Spasov, Krasimir A et al. (2017) Covalent inhibitors for eradication of drug-resistant HIV-1 reverse transcriptase: From design to protein crystallography. Proc Natl Acad Sci U S A 114:9725-9730
Dodda, Leela S; Vilseck, Jonah Z; Tirado-Rives, Julian et al. (2017) 1.14*CM1A-LBCC: Localized Bond-Charge Corrected CM1A Charges for Condensed-Phase Simulations. J Phys Chem B 121:3864-3870
Robertson, Michael J; Tirado-Rives, Julian; Jorgensen, William L (2017) Improved Treatment of Nucleosides and Nucleotides in the OPLS-AA Force Field. Chem Phys Lett 683:276-280
Yan, Xin Cindy; Robertson, Michael J; Tirado-Rives, Julian et al. (2017) Improved Description of Sulfur Charge Anisotropy in OPLS Force Fields: Model Development and Parameterization. J Phys Chem B 121:6626-6636
Dodda, Leela S; Cabeza de Vaca, Israel; Tirado-Rives, Julian et al. (2017) LigParGen web server: an automatic OPLS-AA parameter generator for organic ligands. Nucleic Acids Res 45:W331-W336

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