Presentation Type
Poster Presentation
Category
STEM (science, technology, engineering, mathematics)
Abstract/Artist Statement
Peroxisome proliferator-activated receptor gamma (PPARγ) is a promising target for the treatment of type 2 diabetes because activating this receptor improves insulin sensitivity and helps manage glucose levels in patients. However, currently approved drugs that activate this receptor have side effects, such as weight gain, weakening of bones, and cardiac failure, which have limited or halted their use. The goal of my study is to compare computational methods that estimate the binding affinity of compounds to PPARγ. To achieve this, I will estimate the binding affinities of compounds that have published experimental binding affinities to determine if the estimated binding affinities correlate with the experimental binding affinities. The computational methods I will evaluate include free energy perturbation, docking, and a modified docking method that incorporates a minimization step. Each method has its own advantages and disadvantages, such as allowing movement of the compound within a rigid receptor or movement of both the receptor and compound, but the latter requires significant computational resources. The use of computational methods to estimate binding affinities of compounds to proteins will reduce the number of compounds that need to be synthesized and screened to discover new drug candidates that target a protein, in this case, PPARγ.
Mentor Name
Travis Hughes
Comparing Computational Design Methods for a Type 2 Diabetes Medication
UC North Ballroom
Peroxisome proliferator-activated receptor gamma (PPARγ) is a promising target for the treatment of type 2 diabetes because activating this receptor improves insulin sensitivity and helps manage glucose levels in patients. However, currently approved drugs that activate this receptor have side effects, such as weight gain, weakening of bones, and cardiac failure, which have limited or halted their use. The goal of my study is to compare computational methods that estimate the binding affinity of compounds to PPARγ. To achieve this, I will estimate the binding affinities of compounds that have published experimental binding affinities to determine if the estimated binding affinities correlate with the experimental binding affinities. The computational methods I will evaluate include free energy perturbation, docking, and a modified docking method that incorporates a minimization step. Each method has its own advantages and disadvantages, such as allowing movement of the compound within a rigid receptor or movement of both the receptor and compound, but the latter requires significant computational resources. The use of computational methods to estimate binding affinities of compounds to proteins will reduce the number of compounds that need to be synthesized and screened to discover new drug candidates that target a protein, in this case, PPARγ.