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Cyclic Peptide Inhibitors of the Tsg101 UEV Protein Interactions Refined through Global Docking and Gaussian Accelerated Molecular Dynamics Simulations.
Lin, Wen-Wei; Wang, Yu-Jen; Ko, Cheng-Wen; Cheng, Tain-Lu; Wang, Yeng-Tseng.
Afiliação
  • Lin WW; School of Post-Baccalaureate Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan.
  • Wang YJ; Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung 807, Taiwan.
  • Ko CW; Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan.
  • Cheng TL; Department of Mechanical and Electromechanical Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan.
  • Wang YT; Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung 804, Taiwan.
Polymers (Basel) ; 12(10)2020 Sep 28.
Article em En | MEDLINE | ID: mdl-32998394
Tsg101 UEV domain proteins are potential targets for virus infection therapy, especially for HIV and Ebola viruses. Peptides are key in curbing virus transmission, and cyclic peptides have a greater survival time than their linear peptides. To date, the accurate prediction of cyclic peptide-protein receptors binding conformations still is challenging because of high peptide flexibility. Here, a useful approach combined the global peptide docking, Gaussian accelerated molecular dynamics (GaMD), two-dimensional (2D) potential of mean force (PMF), normal molecular dynamics (cMD), and solvated interaction energy (SIE) techniques. Then we used this approach to investigate the binding conformations of UEV domain proteins with three cyclic peptides inhibitors. We reported the possible cyclic peptide-UEV domain protein binding conformations via 2D PMF free energy profiles and SIE free energy calculations. The residues Trp145, Tyr147, and Trp148 of the native cyclic peptide (CP1) indeed play essential roles in the cyclic peptides-UEV domain proteins interactions. Our findings might increase the accuracy of cyclic peptide-protein conformational prediction, which may facilitate cyclic peptide inhibitor design. Our approach is expected to further aid in addressing the challenges in cyclic peptide inhibitor design.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article