BackgroundMolecular docking simulation is the Rational Drug Design (RDD) step that investigates the affinity between protein receptors and ligands. Typically, molecular docking algorithms consider receptors as rigid bodies. Receptors are, however, intrinsically flexible in the cellular environment. The use of a time series of receptor conformations is an approach to explore its flexibility in molecular docking computer simulations, but it is extensively time-consuming. Hence, selection of the most promising conformations can accelerate docking experiments and, consequently, the RDD efforts.ResultsWe previously docked four ligands (NADH, TCL, PIF and ETH) to 3,100 conformations of the InhA receptor from M. tuberculosis. Based on the receptor residues-ligand distances we preprocessed all docking results to generate appropriate input to mine data. Data preprocessing was done by calculating the shortest interatomic distances between the ligand and the receptor’s residues for each docking result. They were the predictive attributes. The target attribute was the estimated free-energy of binding (FEB) value calculated by the AutodDock3.0.5 software. The mining inputs were submitted to the M5P model tree algorithm. It resulted in short and understandable trees. On the basis of the correlation values, for NADH, TCL and PIF we obtained more than 95% correlation while for ETH, only about 60%. Post processing the generated model trees for each of its linear models (LMs), we calculated the average FEB for their associated instances. From these values we considered a LM as representative if its average FEB was smaller than or equal the average FEB of the test set. The instances in the selected LMs were considered the most promising snapshots. It totalized 1,521, 1,780, 2,085 and 902 snapshots, for NADH, TCL, PIF and ETH respectively.ConclusionsBy post processing the generated model trees we were able to propose a criterion of selection of linear models which, in turn, is capable of selecting a set of promising receptor conformations. As future work we intend to go further and use these results to elaborate a strategy to preprocess the receptors 3-D spatial conformation in order to predict FEB values. Besides, we intend to select other compounds, among the million catalogued, that may be promising as new drug candidates for our particular protein receptor target.

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.

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Knowledge discovery in databases has become an integral part of practically every aspect of bioinformatics research, which usually produces, and has to process, very large amounts of data. Rational drug design is one of the current scientific areas that has greatly benefited from bioinformatics, particularly a step, which analyzes receptor-ligand interactions via molecular docking simulations. An important challenge is the inclusion of the receptor flexibility since they can become computationally very demanding. We have represented this explicit flexibility as a series of different conformations derived from a molecular dynamics simulation trajectory of the receptor. This model has been termed as the fully flexible receptor (FFR) model. In our studies, the receptor is the enzyme InhA from Mycobacterium tuberculosis, which is the major drug target for the treatment of tuberculosis. The FFR model of InhA (named FFR InhA) was docked to four ligands, namely, nicotinamide adenine dinucleotide, pentacyano(isoniazid)ferrate II, triclosan, and ethionamide, thus, generating very large amounts of data, which needs to be mined to produce useful knowledge to help accelerate drug discovery and development. Very little work has been done in this area. In this article, we review our work on the application of classification decision trees, regression model tree, and association rules using properly preprocessed data of the FFR molecular docking results, and show how they can provide an improved understanding of the FFR InhA-ligand behavior. Furthermore, we explain how data mining techniques can support the acceleration of molecular docking simulations of FFR models.

In a Rational Drug Design (RDD) one important step is the receptor-ligand interaction evaluation through molecular docking simulations. How it is a way impossible to test all available compounds for a target receptor, there is a need to select the most promising. One possible approach for such selection is to consider characteristics like a set of molecular properties called molecular descriptors. Aiming at describing these characteristics, we introduce a Data Warehouse (DW) model that integrates molecular descriptors from different public databases of compounds, as well as relates them with Virtual Screening (VS) experiments data. With the proposed DW we are able to produce proper data sets for classification mining experiments. We performed a case study with a VS considering as receptor the HIV-1 Protease receptor and 76 compounds. The data sets produced from our DW are composed by 7 molecular descriptors as the predictive attributes, and as a target attribute the discretized Free Energy of Binding (FEB) value between the ligands and the target receptor. By performing C4.5 algorithm over the generated data sets, we got decision-trees models that indicates which molecular descriptors and their respective values are relevant to influence on good FEB results.