Literature DB >> 10756481

A CoMFA analysis with conformational propensity: an attempt to analyze the SAR of a set of molecules with different conformational flexibility using a 3D-QSAR method.

K Gohda1, I Mori, D Ohta, T Kikuchi.   

Abstract

CoMFA analysis, a widely used 3D-QSAR method, has limitations to handle a set of SAR data containing diverse conformational flexibility since it does not explicitly include the conformational entropic effects into the analysis. Here, we present an attempt to incorporate the conformational entropy effects of a molecule into a 3D-QSAR analysis. Our attempt is based on the assumption that the conformational entropic loss of a ligand upon making a ligand-receptor complex is small if the ligand in an unbound state has a conformational propensity to adopt an active conformation in a complex state. For a QSAR analysis, this assumption was interpreted as follows: a potent ligand should have a higher conformational propensity to adopt an 'active-conformation'-like structure in an unbound state than an inactive one. The conformational propensity value was defined as the populational ratio, Nactive/Nstable, of the number of energetically stable conformers, Nstable, to the number of 'active-conformation'-like structures, Nactive. The latter number was calculated by counting the number of conformers that satisfied the structural parameters deduced from the active conformation. A set of SAR data of imidazoleglycerol phosphate dehydratase inhibitors containing 20 molecules with different conformational flexibility was used as a training set for developing a 3D structure-activity relationship by a CoMFA analysis with the conformational propensity value. This resulted in a cross-validated squared correlation coefficient of the CoMFA model with the conformational propensity value (Rcross2 = 0.640) higher than that of the standard CoMFA model (Rcross2 = 0.431). Then we evaluated the quality of the CoMFA models by predicting the inhibitory activity for a new molecule.

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Year:  2000        PMID: 10756481     DOI: 10.1023/a:1008193217627

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  15 in total

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