Structure-Based Optimization of Molecules of Potential Inhibitors for a Pancreatic Cancer–Related Protein
Session Number
3
Advisor(s)
John Thurmond, Illinois Mathematics and Science Academy
Location
A155
Discipline
Chemistry
Start Date
15-4-2026 2:15 PM
End Date
15-4-2026 3:00 PM
Abstract
Pancreatic cancer is one of the most difficult cancers to detect and treat because it often develops quickly and shows few symptoms in its early stages. Because of this, researchers are interested in finding molecules that can better target proteins involved in the disease. In this project, a computational approach called structure-based lead optimization was used to design potential drug molecules that may bind more strongly to a pancreatic cancer–related protein. The three-dimensional structure of the protein (PDB ID: 1M17) was obtained from the Protein Data Bank and analyzed using the molecular modeling software SeeSAR. Starting with the original molecule bound to the protein, new molecules were created by modifying parts of the structure within the protein’s binding site. These modified molecules were then evaluated using the software to estimate how strongly they might bind to the target protein. Several of the designed molecules showed stronger predicted binding than the original molecule, with some estimated affinities reaching the picomolar range. While these results are promising, laboratory testing will be necessary to determine whether these molecules are effective in real biological systems and could contribute to future pancreatic cancer treatments.
Structure-Based Optimization of Molecules of Potential Inhibitors for a Pancreatic Cancer–Related Protein
A155
Pancreatic cancer is one of the most difficult cancers to detect and treat because it often develops quickly and shows few symptoms in its early stages. Because of this, researchers are interested in finding molecules that can better target proteins involved in the disease. In this project, a computational approach called structure-based lead optimization was used to design potential drug molecules that may bind more strongly to a pancreatic cancer–related protein. The three-dimensional structure of the protein (PDB ID: 1M17) was obtained from the Protein Data Bank and analyzed using the molecular modeling software SeeSAR. Starting with the original molecule bound to the protein, new molecules were created by modifying parts of the structure within the protein’s binding site. These modified molecules were then evaluated using the software to estimate how strongly they might bind to the target protein. Several of the designed molecules showed stronger predicted binding than the original molecule, with some estimated affinities reaching the picomolar range. While these results are promising, laboratory testing will be necessary to determine whether these molecules are effective in real biological systems and could contribute to future pancreatic cancer treatments.