The group develops and applies methods of computational chemistry to model important processes in synthesis and in biochemical systems. Our expertise includes high-level ab initio calculations, hybrid quantum mechanics/molecular mechanics (QM/MM), molecular dynamics simulations and solvation modelling. Our research involves development, benchmarking and application of these tools to better understand reaction mechanisms, structure-activity relationships, and fundamental physical chemistry. Please see below for an overview of our research activites. We welcome informal enquires from prospective students/postdocs interested in working with us. 

Mechanisms and Catalysis

We employ high-level ab initio quantum chemical methods to investigate the mechanisms of important synthetic reactions. Often, the aim is to identify the rate-limiting step so that we can propose chemical modifications to improve selectivity and/or yield of the reaction. As part of these studies, we are also interested in understanding the fundamental physical (in)organic chemistry that governs reactivity. This includes predicting radical stability, nucleophilicity and basicities, and multi-state reactivity especially in transition-metal systems. 


See, for example: (a)  Hock, K. J.; Mertens, L.; Ho, J.*; Nguyen, T. V.*; Koenigs, R. M.* Corey Chaykovsky Reactions of Nitro Styrenes Enable Cis-Configured Trifluoromethyl Cyclopropanes J. Org. Chem. (2017), 82, 8220-8227. (b) Ho, J.; Zheng, J. J.; Meana-Pañeda, R.; Truhlar, D. G.; Ko, E. J.; Savage, P. G.; Williams, C.M.; Coote, M. L.; Tsanaktsidis, J. Chloroform as a Hydrogen Atom Donor in Barton Reductive Decarboxylation Reactions. J. Org. Chem., (2013), 78, 6677-6687.

Modelling Solvation

Many chemical reactions take place in the presence of solvent molecules. Yet, we do not currently have a rigorous framework for modelling the effects of solvent on kinetics, thermodynamics or excited state chemistry. Because of the abundance of experimental solution phase equilibirum acidities and redox potentials in the literature, we often use this data to validate the performance of solvation models. Currently, we are also very interested in developing and validating multi-scale quantum mechanics / molecular mechanics / implicit solvation methods with the view to developing a framework for systematic modelling of solvent effects. 


See for example: (a)  Ho, J.*; Ertem, M. Z.; Calculating Free Energy Changes in Continuum Solvation Models J. Phys. Chem. B (2016), 120, 1319-1329. (b)  Ho, J.* Are thermodynamic Cycles Necessary for Continuum Solvent Calculation of pKas and Reduction Potentials? Phys. Chem. Chem. Phys. (2015), 17, 2859-2868. (c)  Ho, J.; Coote, M. L. A Universal Approach for Continuum Solvent pKa Calculation: Are We There Yet? Theor. Chem. Acc. (2010), 125, 3-21.

Anion Receptor Chemistry

Anion receptors are molecules that bind anions. Some of these receptors can also facilitate the transfer of anions into cells and are called anion transporters. This transport process perturbs the ionic gradient in cells and has been shown to lead to cell death. The transport efficiency depends on how tightly these receptors bind anions and also on their lipophilicity. We use quantum chemical calculations to understand how structural and electronic factors control their anion binding affinities, and molecular dynamics simulations to simulate the transport process. Our aim is to use computational simulations to help develop these molecules into potential therapeutic agents for the treatment of diseases such as cystic fibrosis and cancer. 


See for example: (a) Ho, J.*; Zwicker, E.; Jolliffe, K. A. Quantum Chemical Prediction of Equilibrium Acidities of Urea, Deltamides, Squramides and Croconamides. J. Org. Chem. (Accepted 23 Aug 2017). (b)  Howe, E. N.; Busschaert, N.; Wu, X.; Berry, S. N.; Ho, J.; Light, M. E.; Czech, D. D.; Klein, H. A.; Kitchen, J. A.; Gale, P. A. pH Regulated Non-electrogenic Anion Transport by Phenylthiosemicarbazones. J. Am. Chem. Soc. (2016), 138, 8301-8308

Chemistry at Interfaces

Knowledge of the preferred orientation of molecules on surfaces is critical for understanding reactivity and sometimes the selectivity of many heterogeneous processes. Sum frequency generation (SFG) spectroscopy is a powerful tool for studying molecular orientation at interfaces. In collaboration with experimentalists, we build models of surfaces (e.g. amorphous silica) and run large-scale molecular dynamics simulations to determine the thermodynamics and rotational dynamics of molecules physisorbed to surfaces. We make predictions that our colleagues can test in the laboratory and this synergy is essential for assisting the interpretation of SFG data, and for improving the reliability of our computational predictions.


See for example: (a)  Chase, H. M.; Ho, J.#; Upshur, A.; Thomson, R. J.; Batista, V. S.; Geiger, F. M. Unanticipated Stickiness of alpha-Pinene J. Phys. Chem. A 2017, 121, 3239-3246.  (b)  Ho, J.; Psicuk, B. T.; Chase, H. M.; Upshur, M. A.; Rudshteyn, B.; Thomson, R. J.; Wang, H.; Geiger, F. M.; Batista, V. S. Sum Frequency Generation Spectroscopy and Molecular Dynamics Simulations Reveal a Rotationally Fluid Adsorption State of alpha-Pinene on Fused Silica. J. Phys. Chem. C (2016), 120, 12578-12589

Biomolecular Simulations

We develop and apply methods to model the structure, function and dynamics of biomolecules. This includes G-DNA quadruplexes, light-harvesting proteins and various enzymes. We co-developed the Moving-Domain QM/MM method which is a linear-scaling method that can model the electrostatic potential of large macromolecules such as DNA and enzymes. This method has been successfully applied in the refinement of the crystal structure of Oxytricha nova G-quadruplex, and the resulting structure was validated through comparison with NMR data (see below). We also apply large scale MD simulations and network analysis to identify information flow within proteins (e.g. allosteric interactions), and propose chemical modifications that can modulate the activity of biomolecules. In an ongoing project, we are working with an experimental group at Yale on understanding how subtle modifications, i.e point mutations, can dramatically alter the activity of a protein known as Macrophage Inhibitory Factor (MIF).


See for example: (a)  Ho, J.; Kish, E.; Méndez-Hernández, D.; WongCarter, K.; Pillai, S.; Kodis, G.; Gust, D.; Moore, T. A.; Moore, A. L.; Batista, V. S.; Robert, B. Triplet-triplet Energy Transfer in Artificial Photosynthetic Antennas and Light-Harvesting Proteins. Proc. Nat. Acad. Sci. 2017, 114, E5513-E5521. (b)  Ho, J.; Newcomer, M. B.; Ragain, C. M.; Gascon, J. A.; Batista, E. R.; Loria, P. J.; Batista, V. S. The Self-Consistent MoD-QM/MM Structural Refinement Methodology: Characterization of the Oxytricha nova G-quadruplex. J. Chem. Theory and Comput. (2014), 10, 5125-5135