A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English
Year: 2026
Type: article
Abstract: Polo-like Kinase 1 (PLK1) plays essential roles in the S, G2, and M phases of the cell cycle, and its overexpression is frequently observed in multiple cancers, including breast cancer, where it contributes to genomic instability and dysregulated apoptosis. Unlike conventional ATP-competitive inhibitors that target the kinase domain, selective inhibition of PLK1's polo-box domain (PBD) offers a promising strategy to disrupt protein-ligand interactions critical for mitotic progression, thereby triggering apoptosis in cancer cells. However, the high structural similarity between PLK1 and its homologs (PLK2 and PLK3), which are vital for neurological function and the stress response, respectively, necessitates exceptional selectivity to avoid off-target effects. To address this challenge, the protocol entails a bilingual (American Sign Language and English) computational workflow that integrates virtual screening, structural clustering, protein-ligand docking, binding affinity prediction, ADMET-S profiling, and quantum mechanical (QM) stability analysis. Starting from the SuperNatural 3.0 natural product database, compounds were filtered using breast cancer relevance and drug-likeness criteria, clustered to ensure chemical diversity, and evaluated their interactions with PLK1-, PLK2-, and PLK3-PBD structures. While virtual docking and in silico ADMET-S assessments cannot definitively confirm selectivity or mechanism of action, this study generates testable hypotheses and prioritizes a focused set of natural-product-derived candidates for future molecular dynamics simulations, biochemical validation, or experimental screening.
DOI: 10.3791/67979
Authors: Blessed P. Mbogo, Samantha R. Fernsebner, Monsurat M. Lawal, Daniel J. Lundberg, Tugba G. Kucukkal
Institutions: Gallaudet University, University of Connecticut
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G1453181110, Designing Plk1 Inhibitors through multiscale computational and experimental methods, G1732709109, Track 4: Advanced CI Coordination Ecosystem: Monitoring and Measurement Services, G34258105, G3562386289, Track 1: ACCESS Resource Allocations Marketplace and Platform Services (RAMPS), G4010157413, G4090037298, G4275568664, ACO: An Open CI Ecosystem to Advance Scientific Discovery (OpenCI), Track 3: COre National Ecosystem for CyberinfrasTructure (CONECT), G5097243881, G5156331529, G5367127336, G5464919783, G560129009, G63540804, G7526614437, Track 2: Customized Multi-tier Assistance, Training, and Computational Help (MATCH) for End User ACCESS to CI, G8499740153
OpenAlex ID: W7148799376
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