Literature DB >> 29536226

Structural insights of dipeptidyl peptidase-IV inhibitors through molecular dynamics-guided receptor-dependent 4D-QSAR studies.

Rajesh B Patil1, Euzebio G Barbosa2, Jaiprakash N Sangshetti3, Vishal P Zambre4, Sanjay D Sawant4.   

Abstract

Dipeptidyl peptidase-IV (DPP-IV) inhibitors are promising antidiabetic agents. Currently, several DPP-IV inhibitors have been approved for therapeutic use in diabetes mellitus. Receptor-dependent 4D-QSAR is comparatively a new approach which uses molecular dynamics simulations to generate conformational ensemble profiles of compounds representing a dynamic state of compounds at a target's binding site. This work describes a receptor-dependent 4D-QSAR study on triazolopiperazine derivatives. QSARINS multiple linear regression method was adopted to generate 4D-QSAR models. A model with 9 variables was found to have better predictive accuracy with [Formula: see text], [Formula: see text] (leave-one-out) = 0.592 and [Formula: see text] predicted = 0.597. The location of these 9 variables at the binding site of DPP-IV revealed the importance of the residues Val711, Tyr662, Tyr666, Val202, Asp200 and Thr199 in making critical interactions with DPP-IV inhibitors. The study of these critical interactions revealed the structural features required in DPP-IV inhibitors. Thus, in this study the importance of a halogen substituent on a phenyl ring, the extent of substitution on the triazolopiperazine ring, the presence of an ionizable amino group and the presence of a hydrophobic substituent that can bind deeper in binding pocket of DPP-IV were revealed.

Entities:  

Keywords:  4D QSAR; DPP-IV; Diabetes; Molecular dynamics; Sitagliptin

Mesh:

Substances:

Year:  2018        PMID: 29536226     DOI: 10.1007/s11030-018-9815-6

Source DB:  PubMed          Journal:  Mol Divers        ISSN: 1381-1991            Impact factor:   2.943


  55 in total

1.  Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection.

Authors:  Alexander Golbraikh; Alexander Tropsha
Journal:  J Comput Aided Mol Des       Date:  2002 May-Jun       Impact factor: 3.686

2.  PRODRG: a tool for high-throughput crystallography of protein-ligand complexes.

Authors:  Alexander W Schüttelkopf; Daan M F van Aalten
Journal:  Acta Crystallogr D Biol Crystallogr       Date:  2004-07-21

3.  Fragment-based quantitative structure-activity relationship (FB-QSAR) for fragment-based drug design.

Authors:  Qi-Shi Du; Ri-Bo Huang; Yu-Tuo Wei; Zong-Wen Pang; Li-Qin Du; Kuo-Chen Chou
Journal:  J Comput Chem       Date:  2009-01-30       Impact factor: 3.376

4.  (3R)-4-[(3R)-3-Amino-4-(2,4,5-trifluorophenyl)butanoyl]-3-(2,2,2-trifluoroethyl)-1,4-diazepan-2-one, a selective dipeptidyl peptidase IV inhibitor for the treatment of type 2 diabetes.

Authors:  Tesfaye Biftu; Dennis Feng; Xiaoxia Qian; Gui-Bai Liang; Gerard Kieczykowski; George Eiermann; Huaibing He; Barbara Leiting; Kathy Lyons; Aleksandr Petrov; Ranabir Sinha-Roy; Bei Zhang; Giovanna Scapin; Sangita Patel; Ying-Duo Gao; Suresh Singh; Joseph Wu; Xiaoping Zhang; Nancy A Thornberry; Ann E Weber
Journal:  Bioorg Med Chem Lett       Date:  2006-10-05       Impact factor: 2.823

5.  Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity.

Authors:  G Klebe; U Abraham; T Mietzner
Journal:  J Med Chem       Date:  1994-11-25       Impact factor: 7.446

Review 6.  Dipeptidyl peptidase IV and its inhibitors: therapeutics for type 2 diabetes and what else?

Authors:  Lucienne Juillerat-Jeanneret
Journal:  J Med Chem       Date:  2013-10-24       Impact factor: 7.446

7.  Design, synthesis, and biological evaluation of triazolopiperazine-based beta-amino amides as potent, orally active dipeptidyl peptidase IV (DPP-4) inhibitors.

Authors:  Jennifer E Kowalchick; Barbara Leiting; KellyAnn D Pryor; Frank Marsilio; Joseph K Wu; Huaibing He; Kathryn A Lyons; George J Eiermann; Aleksandr Petrov; Giovanna Scapin; Reshma A Patel; Nancy A Thornberry; Ann E Weber; Dooseop Kim
Journal:  Bioorg Med Chem Lett       Date:  2007-08-23       Impact factor: 2.823

Review 8.  Inhibition of the activity of dipeptidyl-peptidase IV as a treatment for type 2 diabetes.

Authors:  J J Holst; C F Deacon
Journal:  Diabetes       Date:  1998-11       Impact factor: 9.461

9.  Novel inhibitor design for hemagglutinin against H1N1 influenza virus by core hopping method.

Authors:  Xiao-Bo Li; Shu-Qing Wang; Wei-Ren Xu; Run-Ling Wang; Kuo-Chen Chou
Journal:  PLoS One       Date:  2011-11-30       Impact factor: 3.240

10.  iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences.

Authors:  Wei Chen; Pengmian Feng; Hui Yang; Hui Ding; Hao Lin; Kuo-Chen Chou
Journal:  Oncotarget       Date:  2017-01-17
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  1 in total

1.  Exploring the potential mechanism of emetine against coronavirus disease 2019 combined with lung adenocarcinoma: bioinformatics and molecular simulation analyses.

Authors:  Kun Zhang; Ke Wang; Chaoguo Zhang; Xiuli Teng; Dan Li; Mingwei Chen
Journal:  BMC Cancer       Date:  2022-06-22       Impact factor: 4.638

  1 in total

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