Principal Component Analysis and representative structures

The last 600ns of five independent molecular dynamics simulation runs were merged into one concatenated trajectory for each system. Two merged trajectories were further created based on the concatenated trajectories: one contains the WT Gα_i1 and Gα_i2 trajectories, and the other contains all four trajectories. Principal component analysis was performed on each merged trajectory using the gmx covar module of GROMACS with covariance matrix of C alpha atoms of all residues. The first two principal components (PC1 and PC2) of every system were extracted using gmx anaeig module of GROMACS and imported into Python as a data-frame using the Pandas package. Kernel density estimation maps were generated using Python Seaborn package (version 0.9.0) and plotted using Python Matplotlib package.