**3. Results and discussion**

*Homology Molecular Modeling - Perspectives and Applications*

visualizer [58] and Chimera software [59].

for the tubulin subunits and the TauR2.

better accuracy even for the larger systems [62].

**isotypes**

following Eq. (1),

β-bridge, bend, turn, coils, π-helix and 310-helices. DSSP is an algorithm developed by Kabsch and Sander to extract the secondary structural features based on atomic coordinates [56]. The overall stability of the structure is highly determined by the stable dynamics of these secondary structures and any significant changes in secondary structure attributes to the structural flexibility/fold as well as functional diversity of the protein. Hence, conformational changes in the secondary structure during MD simulation were analyzed using the DSSP programme [56]. The simulation movies over the entire trajectories were generated using the VMD software [57] and publication quality images were generated using the Biovia Discovery studio

**2.3 Calculations of contact surface area (CSA) for tubulin-TauR2 complexes**

**2.4 Binding affinity of tauR2 towards different neuronal specific tubulin** 

The biomolecular recognition pattern mainly depends on the binding ability of the interacting biomolecules. The binding affinity as well as the energy between the two interacting molecules can be calculated using various theoretical approaches like (i) Pathway methods such as Thermodynamic integration (TI) as well as Free energy perturbation (FEP) and (ii) End point methods such as Molecular Mechanics Poission-Boltzman Surface Area (MM/PBSA) and Molecular Mechanics Generalized Born Surface Area (MM/GBSA) [61]. In the present study, MM/PBSA approach was used to calculate relative binding energies of the simulated molecules. This MMPBSA approach is very popular, computationally less expensive, and has

Here, the binding affinity between different neuronal specific tubulin isotypes and TauR2 was estimated by performing relative binding energy calculation similar to earlier studies [63–65]. The stable trajectory observed in between 70 ns to 100 ns was chosen to perform the binding energy calculations for all the tubulin-TauR2 complexes. The 'g\_mmpbsa' tool v1.6 was used to perform binding energy calculation using MM/PBSA approach [66]. The parameters for the binding energy calculations were chosen from the earlier similar studies [52, 65, 67–69]. In the MMPBSA methods binding energy (ΔGbind) of tubulin and TauR2 was calculated by using the

Where, the ∆*Gtubulin TauR* <sup>−</sup> <sup>2</sup> , ∆*Gtubulin* and ∆*GTauR*2 represents the average free energies of the complex (tubulin-TauR2), receptor (tubulin) and ligand (TauR2), respectively. The calculation of the entropic contribution in binding energy is

∆ =∆ − ∆ +∆ *GG G G bind tubulin TauR* <sup>−</sup> 2 2 ( *tubulin TauR* ) (1)

Solvent Accessible Surface Area (SASA) is used to represent the degree of hydration of a biomolecule. SASA also be especially useful to quantify the stability of the biomolecular complexes in the aqueous medium. The C-terminal tail of the tubulin subunits is highly dynamic in nature and has no definite secondary structure, hence it affects the overall hydrophobic SASA. Therefore, interface of the MT (in this case tubulin trimer made up of β/α/β subunits) where TauR2 binds at the exterior surface has been selected for the calculating the precise CSA. The in-built gromacs tool *"gmx sasa*" [60] was used to calculate the SASA. In addition, SASA is also calculated

**74**

In this chapter we employ sequence analysis, homology modeling, MD simulations, and binding energy calculation to (i) gain structural insights to the detailed binding mode, (ii) study atomic level tubulin isotypes-tauR2 interactions and (iii) study relative binding affinity between neuronal specific tubulin isotypes and TauR2.
