Computer simulations require accurate representations of intermolecular interactions. In collaboration with the Johnson group at Dalhousie University, we are developing new representations of intermolecular interactions that describe dispersion interactions in matter more realistically. Recently, our group has begun to explore machine-learned neural network potentials as a radically different way to represent intermolecular interactions.

1. S.-L. J. Lahey, C. N. Rowley, Simulating Protein-Ligand Binding with Neural Network Potentials. Chem. Sci., 2020, doi: 10.1039/C9SC06017K

2. Walters, E., Mohebifar, M., Johnson, E.R., Rowley, C. N., Evaluating the London Dispersion Coefficients of Protein Force Fields Using the Exchange-Hole Dipole Moment Model, J. Phys. Chem B. 2018, doi: 10.1021/acs.jpcb.8b02814

3. Mohebifar, M., Johnson, E.R., Rowley, C. N. J. Chem. Theory Comput., 2017, doi: 10.1021/acs.jctc.7b00522

Covalent-modifier drugs act on their target by forming a chemical bond with a side-chain of the targeted protein. These covalent modifiers account for 26% of enzyme-targeting drugs, including widely used drugs such as penicillin and aspirin. Recently, this mode of action has been used to develop a new class of anti-cancer drugs that contain an electrophilic group that forms a chemical bond with the target kinase.

Modeling the activity of these inhibitors requires a more sophisticated set of simulation tools than the tools that are used to model conventional reversible-binding drugs. These include pKa calculations to determine which amino acids are the most reactive and QM/MM simulations to model the chemical reaction between the drug and its target. We are currently studying the kinase family of proteins, which contain many important targets for anti-cancer drugs. Covalent-modifier drugs have the potential to improve the selectivity for target kinases.

Further Reading

1. Covalent Modifiers Literature Blog

2. Awoonor-Williams, E., Walsh, A. G., Rowley, C. N. Modeling Covalent-Modifier Drugs, BBA Proteins and Proteom. 2017, Invited review, doi: 10.1016/j.bbapap.2017.05.009

3. Smith, J. M., Rowley, C.N. Automated computational screening of the thiol reactivity of substituted alkenes. J. Comput. Aided Mol. Des. 2015, doi: 10.1007/s10822-015-9857-0

4. Smith, J. M., Jami Alahmadi, Y., Rowley, C.N. Range-Separated DFT Functionals are Necessary to Model Thio-Michael Additions. J. Chem. Theory Comput. 2013, 9 (11), 4860

5. A review of irreversible inhibitors in medicinal chemistry: Potashman, M. H.; Duggan, M. E. Covalent Modifiers: An Orthogonal Approach to Drug Design. J. Med. Chem. 2009, 52, 1231

The solvation of ions is central to biochemistry and marine chemistry. Our group has developed an interface between the molecular dynamics code CHARMM and the quantum chemistry program TURBOMOLE. This CHARMM-TURBOMOLE interface allows us to perform extended QM/MM molecular dynamics simulations using high-level QM methods and polarizable MM force fields. This work is performed in parallel to development of our polarizable force fields.

Further Reading

1. Riahi, S., Rowley C.N. The CHARMM-TURBOMOLE Interface for Efficient and Accurate QM/MM Molecular Dynamics, Free Energies, and Excited State Properties. J. Comput. Chem. 2014, 35, 2076–2086. doi: 10.1002/jcc.23716

2. Riahi, S., Roux, B., Rowley, C.N. QM/MM Molecular Dynamics Simulations of the Hydration of Mg(II) and Zn(II) Ions. Can. J. Chem. 2013, 91(7), 552–558

3. Rowley, C.N., Roux, B. The Solvation Structure of Na+ and K+ in Liquid Water Determined from High Level Ab Initio Molecular Dynamics Simulations. J. Chem. Theory Comput., 2012, 8 (10), 3526–3535

Our group has contributed to simulation of how chemical toxins like hydrogen sulfide, and biological agents like antimicrobial peptides can permeate cell membranes.

Realistic simulations of these complex systems require that we use more sophisticated simulation methods. Traditional methods assume that the distribution of electron density within the molecules is constant, neglecting electron polarization. Our group develops models that include the effects of electron polarization, which allows us to model these liquids more realistically.

Further Reading

1. Riahi, S., Rowley C.N. Why Can Hydrogen Sulfide Permeate Cell Membranes? J. Am. Chem. Soc. 2014, 136 (43), 1511

2. Riahi, S., Rowley, C.N. Solvation of Hydrogen Sulfide in Liquid Water and at the Water/Vapor Interface Using a Polarizable Force Field J. Phys. Chem. B 2014, 118 (5), 1373

3. Adluri, A. N. S., Murphy, J. N, Tozer, T. Rowley C.N. A Polarizable Force Field with a Sigma-Hole for Liquid and Aqueous Bromomethane. J. Phys. Chem. B 2015 doi: 10.1021/acs.jpcb.5b09041