The emerging threat of drug resistant bacteria in hospitals is one of the most pressing pandemics clinicians are currently facing. A recent study of cardiac ICUs found that 75% of Staphylococcus aureus and 95% of coagulase-negative staphylococci were identified as methicillin resistant. The beta-lactam class of antibiotics (e.g. penicillins, cephalosporins) work by disrupting the activity of bacterial transpeptidases which are largely responsible for creating bacterial cell walls. To combat these drugs bacteria employ beta-lactamases, which are their most common defense against antibiotics. In response to this problem inhibitors are added to commercially available antibiotics which bind and depress beta-lactamase activity. The long term goals of this work are to assist in the development and understanding of beta-lactam based antibiotics and inhibitors. This can be accomplished by better understanding the underlying mechanisms that govern drug resistance and proposing ways to exploit this information. I hypothesize that the development and application of novel computational methods can evaluate the effectiveness of current drug development strategies. Specifically, should new antibiotics be targeted toward better binding in peptidases or should an alternative strategy be employed? Our initial aim is to develop and validate novel methods for calculating the free energies of protein assisted chemical reactions. Upon completion we will employ these methods to determine whether future beta-lactam based antibiotics should be targeted toward greater binding affinity in native bacterial peptidases or whether it is more advantageous to design drugs that preferentially stabilize protein based chemical reactions.
Our final aim will involve computing the free energy of native and mutant beta-lactamase states and examining mechanisms by which these proteins sustain the ability to break down antibiotics while repressing inhibitor activity. Completion of these aims will result in an improved description of drug resistance mechanisms and will allow researchers to exploit this information in the creation of new antibiotics and inhibitors. The funding from this grant will support the my career development via additional training (e.g. courses, advisement, presentations, grant writing, research). In addition, I plan to take advantage of the teaching opportunities at NIH which will give a unique boost to my desire to transition into an independent assistant professor position.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Career Transition Award (K22)
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Special Emphasis Panel (ZHL1-CSR-X (O1))
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Werner, Ellen
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University of South Florida
Schools of Arts and Sciences
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White, Justin K; Handa, Sumit; Vankayala, Sai Lakshmana et al. (2016) Thiamin Diphosphate Activation in 1-Deoxy-d-xylulose 5-Phosphate Synthase: Insights into the Mechanism and Underlying Intermolecular Interactions. J Phys Chem B 120:9922-34
Nichols, Derek A; Hargis, Jacqueline C; Sanishvili, Ruslan et al. (2015) Ligand-Induced Proton Transfer and Low-Barrier Hydrogen Bond Revealed by X-ray Crystallography. J Am Chem Soc 137:8086-95
Weidlich, Iwona E; Pevzner, Yuri; Miller, Benjamin T et al. (2015) Development and implementation of (Q)SAR modeling within the CHARMMing web-user interface. J Comput Chem 36:62-7
Hudson, Phillip S; White, Justin K; Kearns, Fiona L et al. (2015) Efficiently computing pathway free energies: New approaches based on chain-of-replica and Non-Boltzmann Bennett reweighting schemes. Biochim Biophys Acta 1850:944-53
Hargis, Jacqueline C; White, Justin K; Chen, Yu et al. (2014) Can molecular dynamics and QM/MM solve the penicillin binding protein protonation puzzle? J Chem Inf Model 54:1412-24
Pevzner, Yuri; Frugier, Emilie; Schalk, Vinushka et al. (2014) Fragment-based docking: development of the CHARMMing Web user interface as a platform for computer-aided drug design. J Chem Inf Model 54:2612-20
Miller, Benjamin T; Singh, Rishi P; Schalk, Vinushka et al. (2014) Web-based computational chemistry education with CHARMMing I: Lessons and tutorial. PLoS Comput Biol 10:e1003719
Vankayala, Sai Lakshmana; Hargis, Jacqueline C; Woodcock, H Lee (2013) How does catalase release nitric oxide? A computational structure-activity relationship study. J Chem Inf Model 53:2951-61
Vankayala, Sai Lakshmana; Hargis, Jacqueline C; Woodcock, H Lee (2012) Unlocking the binding and reaction mechanism of hydroxyurea substrates as biological nitric oxide donors. J Chem Inf Model 52:1288-97
Santiago, Daniel N; Pevzner, Yuri; Durand, Ashley A et al. (2012) Virtual target screening: validation using kinase inhibitors. J Chem Inf Model 52:2192-203

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