Katie Mitchell-Koch

(With collaborator Prof. Vinh Nguyen, Virginia Tech, dielectric spectroscopy)

Water molecules around proteins have heterogeneous dynamics: at some parts of the surface, water moves as quickly as it does far away from the protein, while other regions of the protein surface exhibit slow hydration dynamics.  We are looking at connections between:

• Solvent structure and dynamics: Water moves faster in regions of higher solvation layer density around the enzyme Candida antarctica lipase B (CALB).  This result seems unexpected.  We’ve shown that around this enzyme, the hydration layer water molecules, like many bulk liquids, obey Rosenfeld scaling: where diffusion, D α ln S₂.  In other words, diffusion is faster where the excess, or pairwise, entropy is higher.  Water at lower density has analogies to ice (though it is still very much a liquid!), with more tetrahedrality and lower entropy, and concomitantly, slower diffusion.¹

• Protein structure and solvent dynamics: For water around proteins, our results show slower water dynamics are favored by concave curvature and hydrophobic regions,¹ in agreement with other scientists’ results, although strong biomolecule-water hydrogen bonds also generally slow down water reorientation.² We’re also interested in biocatalysis in organic solvents, and we see that regions of faster and slower dynamics in the solvation layer shift around when you change from water to tert-butanol to cyclohexane, for example.³  This is due to differences in the protein-solvent interactions as solvent shape, size, polarity, and hydrogen bonding abilities change. Work is underway to correlate properties of the protein structure with regional solvent dynamics of organic solvents in which CALB is stable and enzymatically active.

• Solvent dynamics and protein dynamics: Protein dynamics are a diffusive, or Brownian, process.  Slower moving solvent represents a higher friction environment.  We have shown that the conformational dynamics of CALB (opening/closing for substrate binding and release) vary by the location of crystallographic waters that are kept when the enzyme is simulated in organic solvent.⁴ We also see that for CALB in different solvents, there is a correlation between protein flexibility and the mobility of local solvent molecules.³ Work is underway to further explore these connections.  Funding from National Science Foundation and ACS Petroleum Research Fund

(With collaborator Prof. Jim Bann, Wichita State, fluorolabelled protein studies)

Our goals include:

• Using small molecules to develop a framework to understand fluorine chemical shifts.  Changes in electronic structure, characterized with DFT calculations, can rationalize chemical shifts,⁵ and chemical shifts report on other molecular properties.  For example, calculations indicate that ¹⁹F chemical shifts can identify tautomeric forms of fluorohistidines.^{6 7}
• Developing methods for computational assignment and interpretation of fluorine chemical shifts in protein environments.  Fluorolabeled amino acids can be incorporated into proteins, serving as valuable reporters on protein structure, function, and interactions.  We have determined the best electronic structure methods to calculate fluorine chemical shifts of different classes of fluorinated amino acids,⁸ and work is underway to calculate fluorine chemical shifts in proteins using a combination of molecular dynamics simulations and electronic structure calculations. Funding from K-INBRE

• Mechanistic investigations in organometallic reactions and development of P-31 NMR spectroscopy as a reporter on catalytic intermediates: With Prof. Kami Hull (University of Texas-Austin), we are carrying out DFT calculations to understand selectivity and mechanism in transition metal-catalyzed reactions.  Some of these processes involve phosphine ligands. Our calculations indicate that P-31 NMR reports on the identity of axial ligands and the coordination environment of catalytic intermediates (i.e. distinct changes in P-31 chemical shift of phosphine ligands arise from changing the metal’s coordination environment).  Work is underway to develop computationally-assisted assignment and interpretation of phosphorous chemical shifts in organometallic complexes.   Funding from National Science Foundation for theoretical work (Mitchell-Koch, PI)

• Small molecule interactions with lipids and amphiphiles: With Prof. Doug English (Wichita State), we have investigated the interactions of substrates with different micelles for selective oxidation reactions using bromide.  This work has involved development of a force field for simulations of cetyl pyridinium bromide (CPB) micelles.⁹  With Prof. Dennis Burns (Wichita State), Prof. English, and Prof. Erika Lutter (Oklahoma State) we are studying liptins: several families of small molecules developed by Prof. Burns that target the anionic lipid head group phosphatidylglycerol (PG), a component of bacterial membranes.  We are simulating the interactions of liptins with a lipid bilayer patch that contains a mixture of PG and other lipids.  Liptins have shown antibiotic activity in the laboratory, and we are developing computational methods for in silico screening of potential synthetic modifications to the PG-binding molecules for enhanced affinity.

1. Jayangika N. Dahanayake and Katie R. Mitchell-Koch “Entropy Connects Water Structure and Dynamics in Protein Hydration Layer” Physical Chemistry Chemical Physics 2018, 20 (21), 14765-14777.
https://doi.org/10.1039/c8cp01674g
https://www.ncbi.nlm.nih.gov/pubmed/29780979

2. Deepu K. George, Ali Charkhesht, Olivia A. Hull, Archana Mishra, Daniel G. S. Capelluto, Katie R. Mitchell-Koch, Nguyen Q. Vinh, “New Insights into the Dynamics of Micelles and their Hydration Waters by Gigahertz-to-Terahertz Spectroscopy” Journal of Physical Chemistry B, 2016, 120, 10757-10767.
https://doi.org/10.1021/acs.jpcb.6b06423

3. Jayangika N. Dahanayake and Katie R. Mitchell-Koch “How Does Solvation Layer Mobility Affect Protein Structural Dynamics?” Frontiers in Molecular Biosciences, 2018, 5:65. invited submission to special issue on “Structural Dynamics of Enzymes”  
https://www.frontiersin.org/articles/10.3389/fmolb.2018.00065/full

4. Jayangika Dahanayake, Devaki N. Gautam, Rajni Verma, Katie R. Mitchell-Koch, “To Keep or Not to Keep? The Question of Crystallographic Waters for Enzyme Simulations in Organic Solvent”, Molecular Simulation, 2016, 42 (12), 1001-1013. https://doi.org/10.1080/08927022.2016.1139108
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4937824/

5. Chandana Kasireddy, James G. Bann, Katie R. Mitchell-Koch “Demystifying Fluorine Chemical Shifts: Electronic Structure Calculations Reveal Origins of Seemingly Anomalous Fluorohistidine 19F-NMR Spectra”, Physical Chemistry Chemical Physics, 2015, 17, 30606-30612.
https://doi.org/10.1039/C5CP05502Dh
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4643390/

6. Chandana Kasireddy, Jonathan M. Ellis, James G. Bann, Katie R. Mitchell-Koch, “Tautomeric Stabilities of 4-fluorohistidine Shed New Light on Mechanistic Experiments with Labeled Ribonuclease A” Chemical Physics Letters, 2016, 666c, 58-61.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5461937/
https://doi.org/10.1016/j.cplett.2016.10.072

7. Chandana Kasireddy, Jonathan M. Ellis, James G. Bann, Katie R. Mitchell-Koch “Spectroscopic Signatures, Molecular Properties, and Relative Stabilities of the Tautomers of 2-fluorohistidine and 4-fluorohistidine”, Scientific Reports, 2017, 42651.
https://www.nature.com/articles/srep42651

8. Jayangika N. Dahanayake, Chandana Kasireddy, Jonathan M. Ellis, Derek Hildebrandt, Olivia A. Hull, Joseph P. Karnes, Dylan Morlan, and Katie R. Mitchell‐Koch. "Evaluating Electronic Structure Methods for Accurate Calculation of 19F Chemical Shifts in Fluorinated Amino Acids." Journal of Computational Chemistry 2017, 38 (30), 2605-2617.
https://doi.org/10.1002/jcc.24919
https://www.ncbi.nlm.nih.gov/pubmed/28833293

9. Rajni Verma, Archana Mishra, Katie R. Mitchell-Koch “Molecular Modeling of Cetylpyridinium Bromide, a Cationic Surfactant, in Solutions and Micelle” Journal of Chemical Theory and Computation 2015,  11 (11), 5415-5425.
https://doi.org/10.1021/acs.jctc.5b00475