Molecular Consequences of PCOS-Linked Aromatase Variants: Structural Modeling and Inhibitor Docking
Faculty Sponsors
Dr. Emily Schmitt Lavin, Dr. Arthur Sikora
Project Type
Event
Location
Alvin Sherman Library
Start Date
1-4-2026 2:55 PM
End Date
2-4-2026 12:00 PM
Molecular Consequences of PCOS-Linked Aromatase Variants: Structural Modeling and Inhibitor Docking
Alvin Sherman Library
Polycystic ovary syndrome (PCOS) affects approximately 10% of reproductive-age women and involves hormonal imbalance partly driven by impaired estrogen synthesis. Aromatase (CYP19A1) catalyzes conversion of testosterone and androstenedione to estrogen, but structural consequences of PCOS-associated variants remain unclear. We investifated Val161Asp (V161D), a PCOS-linked substitution that replaces a nonpolar valine with a negatively charged aspartate in a hydrophobic helical region. Using the wild-type structure (PDB 3EQM), we generated a 3D-printed model highlighting the mutation and key catalytic features (heme, substrate-binding channel, conserved monooxygenase motifs). We then produced an AlphaFold V161D model and compared it with wild type in Jmol to assess mutation-specific disruptions. V161D is predicted to destabilize local helix packing, alter substrate-channel geometry, and perturb androstenedione positioning in the active site, suggesting reduced catalytic efficiency and impaired estrogen production in PCOS. To extend this workflow, we modeled additional variants with AlphaFold, refind alignments in PyMOL, and performed SwissDock docking with three non-steroidal aromatase inihibitors (anastrozole, letrozole, fadrozole). Across inhibitors, R264C produced the largest predicted reduction in binding affinity, likely reflecting loss of critical hydrogen-bond and ionic interactions, whereas V161D and R375C showed moderate or minimal effects. This integrative approach systematically evaluates how PCOS-associated mutations may alter inhibitor interactions, identifying Arg264 as a potential liability for drug efficacy and motivating experimental validation.
