Computational modeling, cell biological, biophysical, chemical experimentation, and animal models will be combined in a novel approach to prescreening of compounds for drug design, which is rendered to be more efficient and rapid than current approaches. This interdisciplinary study integrates: (i) computational modeling incorporating protein dynamics to screen for compounds that favor a particular protein conformation and with that best induce cell death, followed by refinement to predict more efficient compounds;(ii) chemical synthesis of the most promising compounds;(iii) cell biological experimentation to verify predictions, aid in the refinement of the computational predictions and perform preclinical evaluations;followed by (iv) screening of the most promising compounds in vivo. We are targeting a specific protein, the MMR protein MSH2 (MutS homolog 2), since we have shown that this protein undergoes specific conformational changes in response to DNA damage, specifically due to platinum damage, which ultimately results in the induction of cell death. We are specifically designing compounds that will induce the same conformational changes in MSH2 and ultimately trigger cell-death. Our multi-disciplinary approach has multiple novel aspects. First, we are using a novel combination of computational modeling and cell biology as an efficient way of prescreening promising anticancer drugs. Second, in targeting the MSH2-dependent apoptotic pathway, we are targeting an important cellular pathway whose existence and importance has only recently come to light. Third, we are targeting specific """"""""death"""""""" conformations of MSH2, discovered computationally, to eliminate the majority of compounds in the first step of the screen and induce a specific cell death pathway. With this approach, the compounds will be designed to activate a naturally occurring pathway, as opposed to inhibiting a pathway. Fourth, this project closely integrates computational modeling of the molecular details of the response to damage by chemotherapeutics with biological experimentation. PUBLIC HEALTH REVELANCE:The eventual goal of this research is the discovery and design of improved chemotherapeutics, ones that are both safer and more effective, based on a MSH2-dependent apoptotic pathway.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Drug Discovery and Molecular Pharmacology Study Section (DMP)
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Fu, Yali
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Wake Forest University Health Sciences
Schools of Arts and Sciences
United States
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Godwin, Ryan C; Macnamara, Lindsay M; Alexander, Rebecca W et al. (2018) Structure and Dynamics of tRNAMet Containing Core Substitutions. ACS Omega 3:10668-10678
Godwin, Ryan C; Gmeiner, William H; Salsbury Jr, Freddie R (2018) All-atom molecular dynamics comparison of disease-associated zinc fingers. J Biomol Struct Dyn 36:2581-2594
Dutta, Samrat; Rivetti, Claudio; Gassman, Natalie R et al. (2018) Analysis of single, cisplatin-induced DNA bends by atomic force microscopy and simulations. J Mol Recognit 31:e2731
Melvin, Ryan L; Gmeiner, William H; Salsbury Jr, Freddie R (2017) All-Atom MD Predicts Magnesium-Induced Hairpin in Chemically Perturbed RNA Analog of F10 Therapeutic. J Phys Chem B 121:7803-7812
Godwin, Ryan C; Melvin, Ryan L; Gmeiner, William H et al. (2017) Binding Site Configurations Probe the Structure and Dynamics of the Zinc Finger of NEMO (NF-?B Essential Modulator). Biochemistry 56:623-633
Melvin, Ryan L; Godwin, Ryan C; Xiao, Jiajie et al. (2016) Uncovering Large-Scale Conformational Change in Molecular Dynamics without Prior Knowledge. J Chem Theory Comput 12:6130-6146
Melvin, Ryan L; Salsbury Jr, Freddie R (2016) Visualizing ensembles in structural biology. J Mol Graph Model 67:44-53
Godwin, Ryan; Gmeiner, William; Salsbury Jr, Freddie R (2016) Importance of long-time simulations for rare event sampling in zinc finger proteins. J Biomol Struct Dyn 34:125-34
Lu, Yan; Salsbury Jr, Freddie R (2015) Autoinhibitory mechanisms of ERG studied by molecular dynamics simulations. AIP Adv 5:017130
Lu, Yan; Salsbury, Freddie R (2015) Recapturing the Correlated Motions of Protein Using Coarse- Grained Models. Protein Pept Lett 22:654-9

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