- Research Overview
- Chemical Biology and Medicinal Chemistry
- Chemical Biology and Medicinal Chemistry Overview
- Discovering Enzyme Substrates and Functions
- Discovering Protein Ligands to Probe and Alter Function
- Discovering Enzyme Activators
- Analyzing Mechanisms of Drug Resistance via Chemical Biology
- Analyzing Enzyme Conformational Dynamics, Substrate Binding, and Catalysis
- Effective Drug Targeting of Pathogens via Medicinal Chemistry
- Computational Chemistry and Biology
- Computational Chemistry and Biology Overview
- Modeling protein regulation via allostery and post-translational modifications
- Visualizing and integrating bioinformatics and biomolecular data
- Modeling membrane permeation to optimize pharmacokinetics
- Determining enzyme function by predicting substrate specificity
- Physical Biology
- Protein and Cellular Engineering
- Protein and Cellular Engineering Overview
- Monitoring enzyme activity and disease biomarkers
- Generating human proteome antibodies via phage display and directed evolution
- Globally analyzing and dissecting apoptosis
- Proximity tagging of protein-protein interactions
- Investigating cellular interactions in tissues
- Creating fluorescent probes targeting the genome and key bio-pathways
- De novo design of catalytic and membrane proteins
- Probing and modulating membrane proteins
Modeling membrane permeation to optimize pharmacokinetics
Examples of our research and methods include
Department research develops computer programs that model the biophysics of membrane permeation to predict and improve the ability of potential drug compounds to pass through bodily barriers and enter cells.
The biophysical approach taken here is distinct from methods primarily now in use for drug design. These methods seek to predict a molecule’s membrane permeability by statistically comparing its features to chemically similar ones with experimentally determined permeability. Such quantitative structure-permeability relationship (QSPR) methods can be limited in applicability to novel compounds and may not provide the mechanistic insight needed to improve a given drug lead’s permeability.
Department scientists seek more broadly applicable and systematically improvable modeling that emphasizes the role of molecules’ conformational flexibility (performing extensive conformational sampling) and their ability to form internal hydrogen bonds. These bonds increase the molecules’ greasy water-repelling quality (hydrophobicity), allowing it to more readily pass through also-hydrophobic cell membranes.
Research here also develops programs to predict the binding of molecules as substrates by membrane-associated efflux transporter proteins such as P-glycoproteins (P-gp), which prevent exogenous substances, including drugs, from crossing the intestinal lining or entering other sensitive organs, including the brain. Beyond affecting drugs’ distribution and elimination, the efflux proteins’ overexpression in cancer cells has been linked with multi-drug resistance in tumors. Existing experimental assays are expensive, time-consuming, and limited in their accuracy for certain substrates, while quantitative structure-activity relationship (QSAR) modeling (i.e. comparing structural features to those of existing drug molecules less subject to efflux) is complicated by the chemical diversity of P-gp substrates.
Cyclic peptides are being used as a challenging system for studying membrane permeation because:
- Their larger size and relative complexity could provide scaffolds for oral drugs targeting protein-protein interactions as well as proteins without well-defined small molecule binding sites.
- Although more than 100 macrocyclic drugs are in use, their greater molecular weights (i.e., 800 daltons vs. less than 500 Da for most small molecule drugs) and complexity (e.g., number of polar groups) are not traditionally drug-like. Thus they require new ways to understand, predict, and improve their pharmacokinetics, including gut absorption.
- They can be readily synthesized with well-defined variations in factors, such as the spatial arrangement of key atoms (stereochemistry), rigidity, hydrophobicity, etc., and so predictive computational models can be promptly followed and iteratively improved by experimental testing.
Examples of such computational chemistry and biology research by department scientists include
Predicting blood-brain barrier penetration
Department scientists developed a new computational approach combining mechanistic modeling of passive membrane permeation and active efflux by P-glycoprotein to better predict which compounds can penetrate the blood-brain barrier. This included demonstrating that the dual-parameter approach is better than either alone at predicting experimentally derived high vs. low efflux ratios and the ability to permeate the barrier. It was also shown that the approach could be used to rank-order similar compounds for permeability and provide guidance into modifying molecules to improve their ability to enter the brain.
Researchers here developed a new algorithm for predicting the binding specificity—and thus likely substrates—of the P-glycoprotein (P-gp) efflux transporter protein, using flexible receptor docking as opposed to QSAR. The approach was driven by structural studies of mouse P-gp bound with drug-like substrates that revealed a large, hydrophobic binding pocket without clearly defined sub-sites. This is supportive of the induced-fit binding model in which an enzyme’s active site substantially changes conformation in accommodating diverse ligands. The computational method for predicting P-gp substrates was tested and confirmed against experimental assay data, as well as with a series of peptidic cysteine protease inhibitors that are being developed as anti-parasitic drug leads.
Improving membrane permeability of peptides
- Department scientists predicted that the ability to form intramolecular hydrogen bonds could improve membrane permeation, using a molecular modeling approach. This computational finding was confirmed experimentally by the synthesis of pairs of very similar molecules, differing primarily in their ability to form hydrogen bonds. Thus researchers here demonstrated a way to improve membrane permeability while retaining or improving other favorable drug-like properties.
- Department scientists and collaborators used computer modeling to predict the effect of specific configurations of backbone N-methylation on the membrane permeability and thus the oral bioavailability of cyclic peptides. Their model worked from the hypothesis, drawn from naturally bioactive cyclic peptides, that such N-methylation at specific locations alters conformation to stabilize intramolecular hydrogen bonding in the peptides’ membrane-associated state, improving their hydrophobicity and thus their permeability. This conformational hypothesis was experimentally tested by synthesizing N-methylated cyclic hexapeptide scaffolds that had in vitro permeability comparable to small molecule drugs.