3.10. Principal component and free energy landscape analysis
Numerous internal movements of the protein molecules are hard to comprehend. Principal component analysis (PCA) is a method for reducing the massive dimensions of a data set to the primary principal components, displaying the key variations that would represent the protein’s global motion with the crucial information. The PCs obtained during MD simulation are derived from the eigenvector values of the covariance matrix, each of which correlates to a variation in protein trajectories. PCA was employed to analyze the dynamics of protein-ligand complexes in order to better understand the influence of ligands binding on protein dynamics. It can be seen from Constructed Principal component analysis (PCA) for (A) PPARα-APHM13, (B) PPARα-Forasartan, (C) PPARα-Lanifibranor, (D) PPARγ-Raltitrexed, (E) PPARγ-Lanifibranor, (F) PPARδ-Iloprost, (G) PPARδ-Lifitegrast, (H) PPARδ-Lanifibranor.The free energy landscape (FEL) approach, which is based on PCA, provides a more accurate depiction of the protein conformational space in terms of energy and time. 