Integrated Computational Omics Approach to Identify Novel Natural Product Inhibitors Targeting KIF11 in Endometrial Carcinoma

Abstract

Endometrial carcinoma (EC) presents a growing global health challenge, characterized by significant molecular heterogeneity and the overexpression of the mitotic motor protein Kinesin Family Member 11 (KIF11). To address the critical need for targeted therapeutics, this study employed an integrated computational framework combining multiomics expression analysis with structural bioinformatics to identify novel natural product-based inhibitors. Validation using TCGA and CPTAC datasets confirmed significant upregulation of KIF11 in EC at both transcriptomic and proteomic levels. A pharmacophore-based virtual screening of approximately 400,000 natural compounds was conducted against the KIF11 active site (PDB ID: 2X7C), followed by ADMET profiling and molecular docking. While glycodeoxycholic acid (GDCA) exhibited the highest docking affinity (−9.5 kcal/mol), subsequent 100 ns molecular dynamics simulations revealed that glycocholic acid (GCA) possessed superior conformational stability. GCA demonstrated a stable RMSD profile (approximately 1.5 Å) and persistent hydrogen bonding with key residues LYS111 and GLU118, unlike the fluctuating profile observed in GDCA. This study identifies GCA as a robust lead candidate, underscoring the efficacy of integrating transcriptomic and proteomic data with structural biology to drive precision therapeutics for EC.