Mining flexible-receptor docking experiments to select promising protein receptor snapshots

• Published in: BMC genomics Vol. 11 Suppl 5; no. S5; p. S6
• Main Authors: Machado, Karina S, Winck, Ana T, Ruiz, Duncan D A, de Souza, Osmar Norberto
• Format: Journal Article
• Language: English
• Published: England BioMed Central 22.12.2010; BioMed Central Ltd
• Subjects: Algorithms; Bacterial Proteins - metabolism; Biology; Drug Design; Experiments; Ligands; Linear Models; Methods; Models, Molecular; Molecular Dynamics Simulation; Mycobacterium; Oxidoreductases - metabolism; Pharmaceutical industry; Proceedings; Protein Conformation; Proteins
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/1471-2164-11-S5-S6

Molecular 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. We 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. By 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.