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oai:figshare.com:article/34031516 2026-09-30T11:04:32Z category_1 category_4 category_19 category_21 category_873 category_734 category_931 portal_63 item_type_6 month_year_09_2026

Computer-Aided Drug Design for Type 2 Diabetes-Related Enzymes: Polarizable and Nonpolarizable Simulations of Enzyme-Inhibitor Complexes Esmat Mohammadi (11974181) Justin A. Lemkul (1372401) Biophysics Biochemistry Pharmacology Biotechnology Chemical Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified type 2 diabetes regulating glucose production less directly coupled inhibitor complexes carbohydrate important therapeutic targets flexibility primarily localized drude model captures aided drug design ligands remain associated cooperative interaction network binding regions throughout based virtual screening apo enzymes show whereas ligand binding candidate inhibitors identified including pancreatic α inhibitors identified representative ligands regions surrounding interaction patterns computational screening related enzymes digesting enzymes small number results highlight reference data quantum mechanical parametrized using nonpolarizable charmm molecular dynamics largely driven findings provide dominant contacts dependent manner computational support catalytic residues catalytic pockets catalytic machinery Carbohydrate-digesting enzymes, including pancreatic α-amylase and maltase-glucoamylase (MGAM), are important therapeutic targets for type 2 diabetes due to their role in regulating glucose production. In this study, molecular dynamics (MD) simulations were performed to investigate the stability and interaction patterns of inhibitors identified through SILCS-based virtual screening. Representative ligands for the N-terminal and C-terminal domains of MGAM (NtMGAM, CtMGAM), and α-amylase were parametrized using both the nonpolarizable CHARMM and polarizable Drude force fields based on quantum mechanical (QM) reference data. MD simulations of the apo enzymes show that their overall structures remain stable, with flexibility primarily localized to regions surrounding the catalytic pockets. Simulations of the enzyme–ligand complexes demonstrate that the ligands remain associated with the binding regions throughout the trajectories. Comparison between the two force fields reveals distinct interaction patterns: CHARMM interactions are largely driven by a small number of dominant contacts, whereas the Drude model captures a more extensive and cooperative interaction network. Notably, the Drude model enables enhanced engagement with catalytic residues in NtMGAM, whereas ligand binding in CtMGAM and α-amylase remains more flexible and less directly coupled to the catalytic machinery. These results highlight the importance of polarization in describing enzyme–ligand interactions and suggest that polarization can influence the balance and persistence of interactions in a system-dependent manner. These findings provide further computational support for the candidate inhibitors identified through computational screening. 2026-09-30T00:00:00Z Text Journal contribution 10.1021/acs.jcim.6c01734.s001 https://figshare.com/articles/journal_contribution/Computer-Aided_Drug_Design_for_Type_2_Diabetes-Related_Enzymes_Polarizable_and_Nonpolarizable_Simulations_of_Enzyme-Inhibitor_Complexes/34031516 CC BY-NC 4.0