Abstract
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.
Highlights
Molecular docking simulation is the Rational Drug Design (RDD) step that investigates the affinity between protein receptors and ligands
It is very important to consider the receptor flexibility during molecular docking and, during RDD [6] because frequently the receptor can modify its shape upon ligand binding, moulding itself to be complementary to its ligand, increasing favourable contacts and reducing adverse interactions, minimizing the total free energy of binding (FEB) [7]
Adding full flexibility to the receptor, as we propose in this work, this time would be extraordinarily much bigger, turning virtual screening with this type of receptor flexibility model literally impossible and an efficient RDD process impractical
Summary
Molecular docking simulation is the Rational Drug Design (RDD) step that investigates the affinity between protein receptors and ligands. In-silico based RDD is a four-step cycle that combines structural information and computational efforts [4] based on a detailed understanding of the target protein (or receptor) and ligand interactions. In this sense, molecular docking algorithms are applied to evaluate and find the best ligand position and conformation inside the receptor binding site. The majority of molecular docking algorithms consider only the ligand as flexible while the receptor remains rigid since it has far more atoms and has a much greater number of degrees of freedom. It is very important to consider the receptor flexibility during molecular docking and, during RDD [6] because frequently the receptor can modify its shape upon ligand binding, moulding itself to be complementary to its ligand, increasing favourable contacts and reducing adverse interactions, minimizing the total free energy of binding (FEB) [7]
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