In silico 3D-modeling and Molecular dynamic analysis ofTGFBI and its variants:
The nucleotide sequence of TGFBI protein was retrieved from NCBI
(ID), and the structure was modeled through comparative modeling using
the Robetta server (Song et al. 2013).The structure of full-lengthTGFBI was modeled using the coordinates of
PDBID:5NV6(Garcia-Castellanos et al. 2017). For further refinement, the
model was subjected to molecular dynamics (MD) simulation for 200ns. The
refined structure was validated using MolProbablity Server(Williams et
al. 2018). The last frame of the 200ns simulation was used as a template
for modeling the mutant forms of TGFBI, including A323E, A323V, and 649
frameshift. The modeled mutants were then simulated for 200ns each in
order to understand the impact of variations on the structure and
dynamics of the TGFBI protein.
MD simulations of the modeled proteins were done employing GROMACS
software version 2021.5(Van Der Spoel et al. 2005). CharmM36 forcefield
was used for generating the topology, and the system was solvated using
the TIP3P water model(Vanommeslaeghe et al. 2010). Neutralization of the
system was done by the addition of the appropriate number of sodium
ions. Equilibration of the system was then performed under successive
NVT and NPT ensembles for 500ps each. The production simulation was run
using a leap-frog dynamics integrator and a step size of 2fs with the
consideration of periodic boundary conditions in all three dimensions.
The trajectory was analyzed. Principal component (PC) analysis was
performed to understand the global motions of the TGFBI and the three
mutant forms. The covariance matrix was generated from the Cα atom
coordinates and diagonalized to obtain the eigenvectors. Since the first
two eigenvectors described the major proportion of the trace of the
covariance matrix, the trajectory was projected along the first two
principal components. Free energy landscape was further generated using
the 2-dimensional projection of the trajectory along PC1 and PC2.