Targeted protein-ligand binding interactions drive the metabolic processes essential for life and biochemical manufacturing. Binding interactions between enzymes and small molecules are mediated by the sum of weak, non-covalent interactions including: hydrophobic packing, steric effects, electrostatics, and hydrogen-bonding. Characterization of these interactions is limited by the difficulty in obtaining high resolution structural data of the active binding poses. Furthermore, static models from crystallography are unable to capture the dynamic conformational changes that occur during the transition from the protein unbound to bound states. By resolving how these transitory contacts affect protein function, we accelerate the design of enzymes with target activities and discovery of small molecule inhibitors.
We investigate protein-ligand interactions from two directions: 1) From the perspective of protein engineering in answering the question, what mutations should be made in a protein’s amino acid sequence to enhance its binding affinity toward a target ligand. 2) From the field of drug design, how can we accurately predict the absolute binding free energies of small molecules. This work demonstrates how computational methods utilizing physical model- ing can be applied in combination with high-throughput, directed-evolution experiments to advance biomolecular design.
Molecular dynamics (MD) simulations account for the effects of atomic flexibility and explicit solvent that are key to biomolecular interactions. In Chapter 1, we review the basis of free energy calculations based on the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA), Linear Interaction Energy (LIE), and alchemical simulation approaches in drug development. We perform absolute alchemical simulations in Chapter 2 with inhibitors targeting the Urokinase Plasminogen Activator (UPA) system and analyze how a range of simulation parameters such as counter-ion concentration and alternative binding pocket protonation states impact the binding free energy predictions. We improve predictive accuracy by adapting the protocol to utilize the continuum PBSA solvent model with charge polarization corrections through scaling of the solute dielectric.
In Chapter 3, we describe current approaches to engineering proteins for altered redox cofactor specificity, which has industrial value in specific delivery of electron energy and reduction of feedstock costs in biomanufacturing. We integrate molecular modeling with site-saturated mutagenesis to efficiently navigate protein sequence space with Escherichia coli glyceraldehyde 3-phosphate dehydrogenase (Ec gapA) to enable utilization of the artificial redox cofactor nicotinamide mononucleotide (NMN+) in Chapter 4. Lastly, we investigate how mutations fine-tune oxygenase conformational dynamics to modify substrate specificity and turnover in Chapter 5.
Metabolic pathway engineering with enzymes specific for NMN/H provides direct control over electron flow in living organisms. Application of our developed molecular modeling tools will improve the accuracy and speed of MD simulations, facilitating routine usage to reduce the costs required to construct and screen protein variants, expedite identification of potential pharmaceuticals, and allow study of dynamic biomolecular interactions that are inaccessible through experiment.
