Convergence of sampling in protein simulations.

By Berk Hess. Physical review. E, Statistical, nonlinear, and soft matter physics, 2002

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Unsupervised Learning Methods for Molecular Simulation Data (2021, background): “95,96 − 100 Aside from sampling concerns, the strong assumption of the existence of a linear manifold which correctly captures the important modes of variation of a molecular system can easily fall short, giving rise to systematic errors in analysis and predictions.”
Principal Component Analysis: A Method for Determining the Essential Dynamics of Proteins (2014, background): “It has been noted that when MD trajectories insufficiently sample conformational space the top few PCs resemble cosine functions with periods equal to half the mode number, which is what occurs for a random diffusion process [47].”
Principal component analysis for protein folding dynamics (2008, background): “However, the identification of all conformational states is difficult if the subspace is formed by more than two PCs, thereby requiring a high-dimensional energy landscape.”