Design of allosteric inhibitors to restore the efficacy of existing antibiotics
We’ve developed a combination of computational and experimental approaches for identifying cryptic allosteric sites, which are druggable pockets that only open when a protein fluctuates away from its crystal structure. The ability to target these sites could create a wealth of new opportunities for drug design, so now we’re in the process of developing a tool chain for discovering small molecules that bind cryptic sites.
Our primary model system is TEM-1 beta-lactamase, which contributes to antibiotic resistance by degrading beta-lactam antibiotics, such as penicillin. This is an important application as bacteria are rapidly evolving resistance to all of our existing antibiotics and most pharmaceutical companies are no longer investing in new antibiotics. Together, these trends have raised concerns that we may be entering a post-antibiotic era, in which common bacterial infections are life-threatening events.
We’re using rational drug design tools developed to target crystal structures to target experimentally-validated structures of cryptic allosteric sites from our simulations. Then we’re using a battery of experimental tests to see if our best hits inhibit beta-lactamase by binding in their predicted poses. Successful compounds may be useful for restoring the efficacy of existing antibiotics.
Molecular mechanisms of degenerative processes associated with aging
We’re just beginning to apply our tools for understanding protein conformational changes and allosteric communication to degenerative processes associated with aging. One area of particular interest to us is phototransduction, the process by which the eye converts light into electrical signals. Our first objective is to understand how the proteins involved in phototransduction convey information. Once we have this reference point, we plan to begin studying how mutations that cause blinding diseases disrupt this information flow. In the future, we hope to design drugs that reverse the effects of these deleterious mutations.
New simulation methods
Molecular simulations are an important component of all of our work. Therefore, we are also working on new methods for running these simulations as efficiently as possible. Rather than trying to run one long simulation, our main focus is on developing adaptive sampling methods. These algorithms run many independent simulations in batches. The starting points for each batch of simulations are chosen by analyzing all previous batches of simulations and determining where more simulation data would be most beneficial. Running simulations in this manner allows us to make effective use of commodity hardware and to ensure that we don’t keep simulating the same events over and over again, which is a common problem with long, conventional simulations. Deciding where to run new simulations and how to integrate the information from many independent simulations into a single, statistically rigorous model gets into lots of interesting physical chemistry and machine learning.