Analysis of ligand binding effect on enzyme dynamics using elastic network models

The objective of this thesis was to investigate the effect of ligand binding on global vibrational modes of an enzyme, triosphosphate isomerase (TIM). For this aim, mixed coarse-grained elastic network model ENM (MCG\_ENM) was used to observe the frequency shifts in the most collective modes due to the presence of the ligand. In MCG\_ENM, the ligand is taken in atomistic detail and the protein is in low-resolution. First analysis was performed on previous blind docking results, which comprise a total of 1496 poses of five benzothiazoles, three of which are inhibitors of catalytic activity, docked on three different TIM conformers. Several different binding sites/poses were detected including the tunnel region, catalytic site and others. The inhibitors bound to the previously identified tunnel region could be differentiated based on their impact on global vibrational modes of the enzyme. Later, six independent MD runs (three apo and three complex with inhibitor bt10) were analyzed by performing MCG\_ENM on a total of 54,000 MD snapshots of TIM. Through this computationally efficient technique, altered collective modes and positive shifts in eigenvalues were detected in the complex runs due to the constraining effect of inhibitor binding at the tunnel region. Lastly, a new computational technique was introduced for scanning protein side chains in terms of their constraining effect on ENM modes. In this methodology, the ligand binding effect in the vicinity of a specific residue is mimicked by adding extra nodes to its side chain atoms. ENM-based scanning of TIM also pinpointed to the tunnel region as a key binding site that can alter global dynamics of the enzyme. Scanning of other enzymes indicated marked constraining effect around the ligand positions observed in crystal structures.