Literature DB >> 10794685

Molecular determinants of MAO selectivity in a series of indolylmethylamine derivatives: biological activities, 3D-QSAR/CoMFA analysis, and computational simulation of ligand recognition.

J A Morón1, M Campillo, V Perez, M Unzeta, L Pardo.   

Abstract

A series of indolylmethylamine derivatives were assayed toward MAO-A and MAO-B inhibition. The K(i) values of these compounds are in the range from 0.8 to >10(6) nM for MAO-A or from 0.75 to 476000 nM for MAO-B. The most selective MAO-A or MAO-B inhibitors elicit a ratio of K(i) in the order of 1500 or 1000, respectively. Comparison of MAO-A and MAO-B CoMFA models showed that both the steric and electrostatic properties at the 5 position of the indole ring are determinant for MAO selectivity. Computational simulations of the complex between this part of the ligand and Phe-208 of MAO-A or Ile-199 of MAO-B, experimentally identified as responsible for substrate selectivity, allowed us to further characterize the nature of these enzyme-inhibitor interactions.

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Year:  2000        PMID: 10794685     DOI: 10.1021/jm991164x

Source DB:  PubMed          Journal:  J Med Chem        ISSN: 0022-2623            Impact factor:   7.446


  8 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.  Charge-transfer interactions in the inhibition of MAO-A by phenylisopropylamines--a QSAR study.

Authors:  Gabriel Vallejos; Marcos Caroli Rezende; Bruce K Cassels
Journal:  J Comput Aided Mol Des       Date:  2002-02       Impact factor: 3.686

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

Authors:  Alexander Golbraikh; Alexander Tropsha
Journal:  Mol Divers       Date:  2002       Impact factor: 2.943

4.  Rational selection of training and test sets for the development of validated QSAR models.

Authors:  Alexander Golbraikh; Min Shen; Zhiyan Xiao; Yun-De Xiao; Kuo-Hsiung Lee; Alexander Tropsha
Journal:  J Comput Aided Mol Des       Date:  2003 Feb-Apr       Impact factor: 3.686

Review 5.  Predicting monoamine oxidase inhibitory activity through ligand-based models.

Authors:  Santiago Vilar; Giulio Ferino; Elias Quezada; Lourdes Santana; Carol Friedman
Journal:  Curr Top Med Chem       Date:  2012       Impact factor: 3.295

6.  An MCASE approach to the search of a cure for Parkinson's Disease.

Authors:  Gilles Klopman; Aleksandr Sedykh
Journal:  BMC Pharmacol       Date:  2002-04-02

7.  3D-QSAR and in-silico Studies of Natural Products and Related Derivatives as Monoamine Oxidase Inhibitors.

Authors:  Priyanka Dhiman; Neelam Malik; Anurag Khatkar
Journal:  Curr Neuropharmacol       Date:  2018       Impact factor: 7.363

Review 8.  Monoamine Oxidase (MAO) as a Potential Target for Anticancer Drug Design and Development.

Authors:  Reem Aljanabi; Lina Alsous; Dima A Sabbah; Halise Inci Gul; Mustafa Gul; Sanaa K Bardaweel
Journal:  Molecules       Date:  2021-10-04       Impact factor: 4.411

  8 in total

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