A step-by-step tutorial to perform PCA with Gromacs MD trajectory
A step-by-step tutorial to perform PCA with Gromacs MD trajectory It is a common practice to perform principal component analysis to explore the transitions and dynamics of macromolecules simulations. There may exist multiple states in the free energy landscape, so it is important to extract the representative structures from the energy minima in the free energy surface. To generate such a free energy surface, we could define some collective variables (CVs). However, due to the high dimensionality of the simulation trajectory, it is not always straightforward to select several more important CVs. So the problem is how could we find one or two CVs which could describe the slow motions of the system? To this end, PCA-based dimension reduction and projections could partially solve the problem by transforming the original dataset and grasp the largest variance of the system. Although there has been a tool in Gromacs, which perform the PCA using g_covar and g_anaeig. Th