Non-linear quantitative structure-activity relationship for adenine derivatives as competitive inhibitors of adenosine deaminase

Department of Biophysics, Faculty of Science, Tarbiat Modares University, P.O. Box: 14115/175, Tehran, Iran.
Biochemical and Biophysical Research Communications (Impact Factor: 2.3). 01/2006; 338(2):1137-42. DOI: 10.1016/j.bbrc.2005.10.049
Source: PubMed


Logistic regression and artificial neural networks have been developed as two non-linear models to establish quantitative structure-activity relationships between structural descriptors and biochemical activity of adenosine based competitive inhibitors, toward adenosine deaminase. The training set included 24 compounds with known k(i) values. The models were trained to solve two-class problems. Unlike the previous work in which multiple linear regression was used, the highest of positive charge on the molecules was recognized to be in close relation with their inhibition activity, while the electric charge on atom N1 of adenosine was found to be a poor descriptor. Consequently, the previously developed equation was improved and the newly formed one could predict the class of 91.66% of compounds correctly. Also optimized 2-3-1 and 3-4-1 neural networks could increase this rate to 95.83%.

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    ABSTRACT: In this study we investigated the nature of the active site on the enzyme adenosine deaminase (ADA) in hopes of finding an analog structure to act as an inhibitor. Our research involved comparing the binding patterns of adenosine to several structural analogs by analyzing and comparing kinetic and spectroscopic data. We examined variations in positions 2 and 6 of the carbon ring to determine if either structural change may result in inhibition. 2-chloroadenosine was used to examine the 2-position, and similarly, 6- chloroadenosine and N-6-cyclohexyladenosine were used to consider the 6-position. The Vmax and KM values were established from data analysis with the Michaelis-Menton equation, and were used to compare adenosine and 6-chloroadenosine, which is observed to act as a substrate. The KM for adenosine was found to be 11.85 µM and the Vmax was determined to be 6.8x10-9 M/s. Both 2-chloroadenosine and N-6-cyclohexyladenosine were found not to bind at all, suggesting that structural alterations at either location would not yield inhibition. However, the possibility exists of smaller alterations at the 6 position leading to ADA inhibition.
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    ABSTRACT: Combinations of multiple linear regressions, genetic algorithms and artificial neural networks were utilized to develop models for seeking quantitative structure-activity relationships that correlate structural descriptors and inhibition activity of adenosine deaminase competitive inhibitors. Many quantitative descriptors were generated to express the physicochemical properties of 70 compounds with optimized structures in aqueous solution. Multiple linear regressions were used to linearly select different subsets of descriptors and develop linear models for prediction of log(k(i)). The best subset then fed artificial neural networks to develop nonlinear predictors. A committee of six hybrid models - that included genetic algorithm routines together with neural networks - was also utilized to nonlinearly select most efficient subsets of descriptors in a cross-validation procedure for nonlinear log(k(i)) prediction. The best prediction model was found to be an 8-3-1 artificial neural network which was fed by the most frequently selected descriptors among these subsets. This prediction model resulted in train set root mean sum square error (RMSE) of 0.84 log(k(i)) and prediction set RMSE of 0.85 log(k(i)) (both equivalent of 0.10 in normal range of log(k(i))) and correlation coefficient (r(2)) of 0.91.
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