This produced nine tables (ETH, TCL, and PIF against each of the InhA_wt, InhA_ I16T and InhA_I21V FFR models). 2. and InhA_I21V mutants in docking simulation with the inhibitors ETH, TCL, and PIF revealed significant differences in the way they interact as compared to the rigid, InhA crystal structure (PDB ID: 1ENY). In the latter, only up to five receptor residues interact with the three different ligands. Conversely, in the FFR models this number grows up to an astonishing 80 different residues. The comparison between the rigid crystal structure and the FFR models showed that the inclusion of explicit flexibility, despite the limitations of the FFR models employed Cefamandole nafate in this study, accounts in a substantial manner to the induced fit expected when a protein/receptor and ligand approach each other to interact in Rabbit polyclonal to BSG the most favourable manner. == Conclusions == Protein/receptor explicit flexibility, or FFR models, represented as an ensemble of MD simulation snapshots, can lead to a more realistic representation of the induced fit effect expected in the encounter and proper docking of receptors to Cefamandole nafate ligands. The FFR models of InhA explicitly characterizes the overall movements of the amino acid residues in helices, strands, loops, and turns, allowing the ligand to properly accommodate itself in the receptors binding site. Utilization of the intrinsic flexibility of Mtbs InhA enzyme and its mutants in virtual screening via molecular docking simulation may provide a novel platform to guide the rational or dynamical-structure-based drug design of novel inhibitors for Mtbs InhA. We have produced a short video sequence of each ligand (ETH, TCL and PIF) docked to the FFR models of InhA_wt. These videos are available athttp://www.inf.pucrs.br/~osmarns/LABIO/Videos_Cohen_et_al_19_07_2011.htm. == Background == Molecular docking simulation constitutes one of the main stages of rational or structure-based drug design [1]. It provides a prediction for a molecule binding to a protein in order to form a stable complex [2]. Knowledge of proper orientation can be used to predict the strength of association or binding affinity between two molecules. Initially, molecular docking was compared to the classic “key-lock theory of enzyme-substrate specificity postulated by Emil Fischer in 1894 (Reviewed by Koshland Jr., [3,4]). In this model, the three-dimensional (3-D) structure of both ligand and protein complement each other in the same way a key fits the corresponding lock [5]. However, since both protein and ligand are flexible molecules, the concept is no longer adequate as during the process of molecular docking both ligand and protein adjust their conformation in order to achieve the best protein-ligand fit. This type of conformational adjustment between the two molecules, or the induced fit theory, was first presented by Daniel E. Koshland Jr. in 1958 [3,4]. In order to make molecular docking simulations more realistic, an important issue is to treat both receptor and ligand as flexible structures instead of rigid bodies. In many methods the ligand, usually a small molecule with up to dozens of atoms, is treated as flexible, but the flexibility of the protein/receptor (for simplicity, herein protein and receptor are synonymous), depending on their complexity and size, which can reach dozens of thousands of atoms, is still treated in a more restricted manner. According to Cozzini et al. the challenge for drug discovery, as well as docking or virtual screening, is to model the plasticity of the receptor so that both structures can conformationally adapt to each other [6]. Therefore, it is well known in the literature that the recognition of the ligand by the receptor is a dynamic event, where both structures change their conformations to minimize the free energy of binding (FEB) Cefamandole nafate for their association [7]. Nevertheless, most methods of docking employ a single, rigid structure of the receptor. This happens for practical reasons. If we try to consider the explicit flexibility of receptor and ligand, the conformational space to be considered quickly becomes impractical [8,9], as the process would require an enormous computational effort. In addition, Totrov and Abagyan [10] state that the best docking algorithms today erroneously predict the position of ligand binding in 50 to 70% of the cases, when only one receptor conformation is considered. In biological systems, proteins express their functions in aqueous or semi-fluid environments. When in solution, proteins exist in a number of energetically different conformations, so that their structure is best described when all the different Cefamandole nafate states are represented [6]. A set of structures of a particular protein can be determined experimentally by X-ray crystallography or Nuclear Magnetic Resonance, through computational methods which includes Monte Carlo and molecular dynamics (MD) simulations [11]. Therefore.
