Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based...

The drug discovery process is a rigorous, time-consuming, and expensive operation. The computational approach in drug discovery allows researchers to prioritize the most promising compounds for further testing, which would greatly reduce the required resources, leading to an increment of the overall efficiency in the drug discovery pipelines. Structure-based drug discovery is a common approach that requires the structural information of the target protein in a three-dimensional format. However, the current limitation of most computer-aided drug discovery strategies is their inability to introduce the flexibility and dynamics of the target protein structure during the ligand-protein docking simulation. While both induced fit docking and ensemble-based docking aim to address protein flexibility in the docking procedure, the latter can provide a more comprehensive view of dynamic protein behavior by incorporating multiple conformations throughout the simulation. In this report, we demonstrate and discuss the application of a technique called ensemble-based docking analysis that indirectly introduces the target protein structure's flexibility and dynamics in the molecular docking process. The protein and ligand selected for ensemble-based docking studies were lysozyme and Flovokawain B (FB), respectively. FB has been previously reported to have binding activity with lysozyme. A molecular dynamics (MD) simulation was performed on lysozyme in the presence of water, and the total energy, root-mean-square deviation (RMSD), and root-mean-square fluctuation (RMSF) were examined. Conformation clustering was generated based on several clustering cutoff values and was chosen for additional docking analysis with FB. Cluster no 2 gives the lowest binding energy at -29.37 kJ/mol. Molecular docking images were generated to anticipate the presence of binding forces. By incorporating the structural dynamics of the protein, the ensemble-based docking approach can better capture the range of possible binding scenarios, leading to more reliable predictions of binding outcomes.