Research

Research in the Carlson lab focuses broadly on the molecular recognition between ligands and proteins, from the fundamental biophysics of ligand binding to applied inhibitor design.  Understanding protein flexibility, allosteric control, and ligand binding all require dynamic information at the atomic level.  The technique that we prefer is molecular dynamics (MD) simulations.  Our most recent projects have focused on developing mixed-solvent MD (MixMD), a surface-mapping technique which samples protein motion in a solution of water and organic probe molecules.  This technique identifies possible binding hotspots for ligands and protein-protein interactions.  It requires many MD simulations, typically 10-20 independent simulations of 20-100ns, to properly sample the possible configurations.  This series of MD simulations must be done for each possible probe types.  It is a resource intensive method, but it has the promise of identifying previously unknown regulatory sites on proteins.  This could significantly increase the number of drug targets available to treat a wide variety of medical disorders.

This work is funded by the NIH. 

We have created one of the largest database of protein-ligand complexes. Other databases of protein-ligand complexes have been limited to a few hundred entries. These are compiled in a ground-up fashion, adding new entries as they are gleaned from the literature. We are using a top-down approach to gather all relevant entries from the Protein Databank (PDB). After the 2014 update, Binding MOAD has 25,771 entries. BLAST searches were used to divide the dataset into related protein structures, resulting in 7599 unique protein systems. After painstakingly searching the crystallography literature, we have collected binding affinity data for 9,142 complexes (36% of the structures).

We are mining the structures in MOAD to deduce fundamental properties that govern binding in enzymes and non-enzymes. This has involved in-depth statistical analysis and writing our own code to assess fit and exposure of ligand.  The project was originally funded by Carlson's Beckman Young Investigator Award and the NSF and is now funded by the NIH. 

Protein flexibility and dynamics are crucial to ligand recognition and catalysis. We use molecular dynamics (MD) simulations to gain new and detailed insight into reaction mechanisms, binding events, and conformational changes in enzyme systems. We also utilize Langevin dynamics to accelerate conformational searching in systems where increased sampling of protein flexibility is necessary. Our group also applies statistical mechanics and quantum mechanics calculations to examine recognition processes.