Molecular Mechanism of Selective Binding of NMS-P118 to PARP-1 and PARP-2: A Computational Perspective.

NMS-P118 PARP alanine scanning binding free energy interaction entropy selectivity

Journal

Frontiers in molecular biosciences
ISSN: 2296-889X
Titre abrégé: Front Mol Biosci
Pays: Switzerland
ID NLM: 101653173

Informations de publication

Date de publication:
2020
Historique:
received: 24 02 2020
accepted: 13 03 2020
entrez: 7 5 2020
pubmed: 7 5 2020
medline: 7 5 2020
Statut: epublish

Résumé

The nuclear protein poly (ADP-ribose) polymerase-1 (PARP-1) inhibitors have been proven effective to potentiate both chemotherapeutic agents and radiotherapy. However, a major problem of most current PARP inhibitors is their lack of selectivity for PARP-1 and its closest isoform PARP-2. NMS-P118 is a highly selective PARP inhibitor that binds PARP-1 stronger than PARP-2 and has many advantages such as excellent pharmacokinetic profiles. In this study, molecular dynamics (MD) simulations of NMS-P118 in complex with PARP-1 and PARP-2 were performed to understand the molecular mechanism of its selectivity. Alanine scanning together with free energy calculation using MM/GBSA and interaction entropy reveal key residues that are responsible for the selectivity. Although the conformation of the binding pockets and NMS-P118 are very similar in PARP-1 and PARP-2, most of the hot-spot residues in PARP-1 have stronger binding free energy than the corresponding residues in PARP-2. Detailed analysis of the binding energy shows that the 4'4-difluorocyclohexyl ring on NMS-P118 form favorable hydrophobic interaction with Y889 in PARP-1. In addition, the H862 residue in PARP-1 has stronger binding free energy than H428 in PARP-2, which is due to shorter distance and stronger hydrogen bonds. Moreover, the negatively charged E763 residue in PARP-1 forms stronger electrostatic interaction energy with the positively charged NMS-P118 than the Q332 residue in PARP-2. These results rationalize the selectivity of NMS-P118 and may be useful for designing novel selective PARP inhibitors.

Identifiants

pubmed: 32373627
doi: 10.3389/fmolb.2020.00050
pmc: PMC7179655
doi:

Types de publication

Journal Article

Langues

eng

Pagination

50

Informations de copyright

Copyright © 2020 Wang, Cong, Li, Bao, Qi and Zhang.

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Auteurs

Ran Wang (R)

Shanghai Key Laboratory of Green Chemistry and Chemical Process, Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University at Shanghai, Shanghai, China.

Yalong Cong (Y)

Shanghai Key Laboratory of Green Chemistry and Chemical Process, Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University at Shanghai, Shanghai, China.

Mengxin Li (M)

Shanghai Key Laboratory of Green Chemistry and Chemical Process, Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University at Shanghai, Shanghai, China.

Jinxiao Bao (J)

Shanghai Key Laboratory of Green Chemistry and Chemical Process, Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University at Shanghai, Shanghai, China.

Yifei Qi (Y)

Shanghai Key Laboratory of Green Chemistry and Chemical Process, Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University at Shanghai, Shanghai, China.
NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, China.

John Z H Zhang (JZH)

Shanghai Key Laboratory of Green Chemistry and Chemical Process, Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University at Shanghai, Shanghai, China.
NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, China.
Department of Chemistry, New York University, New York, NY, United States.
Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, China.

Classifications MeSH