Identification of Interactions of ABT-341 to Dipeptidyl Peptidase IV during Molecular Dynamics Simulations

Abstract

Background: Dipeptidyl Peptidase IV (DPP-IV) is an established drug discovery target for type 2 diabetes mellitus (T2DM) therapy. On the other hand, molecular dynamics (MD) simulations have been widely employed to obtain insights of the protein-ligand interactions in structure-based drug design research projects. Moreover, a software to identify protein-ligand interactions called PyPLIF HIPPOS was made publicly available recently. Employing PyPLIF HIPPOS to identify the interactions of DPP-IV and its ligand ABT-341 during MD simulations was then of considerable interest. Objectives: The main aim of this study was to identify protein-ligand interactions of ABT-341 to DPP-IV during MD simulations. Material and Methods: The crystal structure of DPP-IV co-crystallized with ABT-341 obtained from the Protein Data Bank with code of 2I78 was used as the main material. YASARA-Structure was employed for performing 10 ns prodution run MD simulations with snapshots in every 100 ps and PyPLIF HIPPOS was used to identify the protein-ligand interactions. Results: There were 23 interactions involving 13 residues identified by employing PyPLIF HIPPOS during the MD simulations. Two of them identified in all snapshots, i.e., hydrophobic interactions to PHE357 and TYR666. Conclusions: PyPLIF HIPPOS was succesfully employed to identify the interactions of ABT-341 to DPP-IV during MD simulations.

Molecular Dynamics Simulations of the Caffeic Acid Interactions to Dipeptidyl Peptidase Iv

“…The results from PyPLIF HIPPOS analysis (all.nobb.plif.xlsx; Supplementary File S9) confirmed the visual inspection. The previously identified DPP4 active site was composed of Glu206, Phe357, and Tyr666 [12,16]. Caffeic acid interacted with at least one of them from the beginning until the simulation time reached 9.05 ns.…”

“…Assisted by PyPLIF HIPPOS, we increased the prediction quality of SBVS by targeting some G-protein coupled receptors (GPCR), identified the molecular determinants of the protein-ligand binding in the retrospective SBVS campaigns [9]. We recently reported the usefulness of PyPLIF HIPPOS in identifying dominant DPP4-ABT341 interactions during 10 ns production run of molecular dynamics (MD) simulations [12]. The non-hydrophobic interactions identified in more than 50% of snapshots were aromatic interactions to Phe357, aromatic interactions to Tyr666, and hydrogen bond to Glu206 when the residue serves as the acceptor [12].…”

“…We recently reported the usefulness of PyPLIF HIPPOS in identifying dominant DPP4-ABT341 interactions during 10 ns production run of molecular dynamics (MD) simulations [12]. The non-hydrophobic interactions identified in more than 50% of snapshots were aromatic interactions to Phe357, aromatic interactions to Tyr666, and hydrogen bond to Glu206 when the residue serves as the acceptor [12]. The identified dominant interactions reflect the binding of DPP4 to a phenolic moiety.…”

Molecular Dynamics Simulations of the Stk630921 Interactions to Interleukin-17a

“…, 7,9,11 (27),12,14,22,25-nonaene-20-carboxamide or known as compound 63Q [18] is used as the control ligand.…”

“…PyPLIF HIPPOS was originally designed to identify the outputs of molecular docking simulations, namely AutoDock Vina [25] and PLANTS [26]. In its development, this tool has also been successfully used to identify the output of the MD simulation [27]. Therefore, it can be used to cover the weakness of molecular docking simulations that treat proteins and ligands as rigid entities without considering external forces such as temperature and pressure according to the real nature of biomolecules in the body that are always dynamic.…”