Abstract
BackgroundThis paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance.ResultsThe empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application.ConclusionsWe conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor.
Highlights
This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis
Among all available methodologies to explicitly include the receptor flexibility in molecular docking simulations, a possible alternative is to select a series of different conformations derived from a molecular dynamics (MD) simulation of the target receptor [5]. We name this type of receptor representation fully flexible-receptor (FFR) model [6,7], and we investigate this methodology with target receptor InhA enzyme from Mycobacterium tuberculosis [8] (Mtb), which was modeled as a set of 3,100 snapshots derived from a 3.1 ns MD simulation trajectory [9]
We investigate the problem of Rational Drug Design (RDD) through flexible-receptor molecular docking simulations
Summary
This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important to correctly predict the free energy of binding, and to provide a comprehensible model that could be validated by a domain specialist. Biological macromolecules, like protein receptors, are intrinsically flexible in their cellular environment, considering that the receptor may modify its shape upon ligand binding, moulding itself to be complementary to its ligand This increases favorable contacts and reduces adverse interactions, which in turn minimizes the total FEB [4]
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