Literature DB >> 19441865

Consensus superiority of the pharmacophore-based alignment, over maximum common substructure (MCS): 3D-QSAR studies on carbamates as acetylcholinesterase inhibitors.

Shailendra S Chaudhaery1, Kuldeep K Roy, Anil K Saxena.   

Abstract

In view of the nonavailability of complete X-ray structure of carbamates cocrystallized with AChE enzyme, the 3D-QSAR model development based on cocrystallized conformer (CCBA) as well as docked conformer-based alignment (DCBA) is not feasible. Therefore, the only two alternatives viz. pharmacophore and maximum common substructure-based alignments are left for the 3D-QSAR comparative molecular field analyses (CoMFA) and comparative molecular similarity indices analyses (CoMSIA) model development. So, in the present study, the 3D-QSAR models have been developed using both alignment methods, where CoMFA and CoMSIA models based on pharmacophore-based alignment were in good agreement with each other and demonstrated significant superiority over MCS-based alignment in terms of leave-one-out (LOO) cross-validated q(2) values of 0.573 and 0.723 and the r(2) values of 0.972 and 0.950, respectively. The validation of the best CoMFA and CoMSIA models based on pharmacophore (Hip-Hop)-based alignment on a test set of 17 compounds provided significant predictive r(2) [r(2)(pred(test))] of 0.614 and 0.788, respectively. The contour map analyses revealed the relative importance of steric, electrostatic, and hydrophobicity for AChE inhibition activity. However, hydrophobic factor plays a major contribution to the AChE inhibitory activity modulation which is in strong agreement with the fact that the AChE is having a wide active site gorge (approximately 20 A) occupied by a large number of hydrophobic amino acid residues.

Entities:  

Mesh:

Substances:

Year:  2009        PMID: 19441865     DOI: 10.1021/ci900049e

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  9 in total

1.  Rationalizing lead optimization by consensus 2D- CoMFA CoMSIA GRIND (3D) QSAR guided fragment hopping in search of γ-secretase inhibitors.

Authors:  Prabu Manoharan; Nanda Ghoshal
Journal:  Mol Divers       Date:  2012-08-14       Impact factor: 2.943

2.  Combined structure-based pharmacophore and 3D-QSAR studies on phenylalanine series compounds as TPH1 inhibitors.

Authors:  Liang Ouyang; Gu He; Wei Huang; Xiangrong Song; Fengbo Wu; Mingli Xiang
Journal:  Int J Mol Sci       Date:  2012-05-02       Impact factor: 6.208

3.  Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) studies on α(1A)-adrenergic receptor antagonists based on pharmacophore molecular alignment.

Authors:  Xin Zhao; Minsheng Chen; Biyun Huang; Hong Ji; Mu Yuan
Journal:  Int J Mol Sci       Date:  2011-10-20       Impact factor: 5.923

4.  Benefits of statistical molecular design, covariance analysis, and reference models in QSAR: a case study on acetylcholinesterase.

Authors:  C David Andersson; J Mikael Hillgren; Cecilia Lindgren; Weixing Qian; Christine Akfur; Lotta Berg; Fredrik Ekström; Anna Linusson
Journal:  J Comput Aided Mol Des       Date:  2014-10-29       Impact factor: 3.686

5.  Machine learning vs. field 3D-QSAR models for serotonin 2A receptor psychoactive substances identification.

Authors:  Giuseppe Floresta; Vincenzo Abbate
Journal:  RSC Adv       Date:  2021-04-20       Impact factor: 3.361

6.  Discovery of novel focal adhesion kinase inhibitors using a hybrid protocol of virtual screening approach based on multicomplex-based pharmacophore and molecular docking.

Authors:  Fengbo Wu; Ting Xu; Gu He; Liang Ouyang; Bo Han; Cheng Peng; Xiangrong Song; Mingli Xiang
Journal:  Int J Mol Sci       Date:  2012-11-23       Impact factor: 5.923

7.  Combining structure-based pharmacophore modeling, virtual screening, and in silico ADMET analysis to discover novel tetrahydro-quinoline based pyruvate kinase isozyme M2 activators with antitumor activity.

Authors:  Can Chen; Ting Wang; Fengbo Wu; Wei Huang; Gu He; Liang Ouyang; Mingli Xiang; Cheng Peng; Qinglin Jiang
Journal:  Drug Des Devel Ther       Date:  2014-09-02       Impact factor: 4.162

8.  Discovery of High-Affinity Cannabinoid Receptors Ligands through a 3D-QSAR Ushered by Scaffold-Hopping Analysis.

Authors:  Giuseppe Floresta; Orapan Apirakkan; Antonio Rescifina; Vincenzo Abbate
Journal:  Molecules       Date:  2018-08-30       Impact factor: 4.411

9.  Identification of 3-((1-(Benzyl(2-hydroxy-2-phenylethyl)amino)-1-oxo-3-phenylpropan-2-yl)carbamoyl)pyrazine-2-carboxylic Acid as a Potential Inhibitor of Non-Nucleosidase Reverse Transcriptase Inhibitors through InSilico Ligand- and Structure-Based Approaches.

Authors:  Deepti Mathpal; Tahani M Almeleebia; Kholoud M Alshahrani; Mohammad Y Alshahrani; Irfan Ahmad; Mohammed Asiri; Mehnaz Kamal; Talha Jawaid; Swayam Prakash Srivastava; Mohd Saeed; Vishal M Balaramnavar
Journal:  Molecules       Date:  2021-08-30       Impact factor: 4.411

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.