title: Random Matrix Theory in molecular dynamics analysis.
abstract: It is well known that, in some situations, principal component analysis (PCA) carried out on molecular dynamics data results in the appearance of cosine-shaped low index projections. Because this is reminiscent of the results obtained by performing PCA on a multidimensional Brownian dynamics, it has been suggested that short-time protein dynamics is essentially nothing more than a noisy signal. Here we use Random Matrix Theory to analyze a series of short-time molecular dynamics experiments which are specifically designed to be simulations with high cosine content. We use as a model system the protein apoCox17, a mitochondrial copper chaperone. Spectral analysis on correlation matrices allows to easily differentiate random correlations, simply deriving from the finite length of the process, from non-random signals reflecting the intrinsic system properties. Our results clearly show that protein dynamics is not really Brownian also in presence of the cosine-shaped low index projections on principal axes.
authors.affiliation: SMBNOS, University of Bari "Aldo Moro", Piazza G. Cesare, Policlinico, 70124 Bari, Italy. Electronic address: luigileonardo.palese@uniba.it.
authors.forename: Luigi Leonardo
authors.initials: LL
authors.lastname: Palese
mesh_terms: D001059:Apoproteins / Q000737:chemistry / Q000378:metabolism; D018832:Molecular Chaperones / Q000737:chemistry* / Q000378:metabolism; D056004:Molecular Dynamics Simulation; D025341:Principal Component Analysis; D017433:Protein Structure, Secondary
chemical_list: D001059:Apoproteins; D018832:Molecular Chaperones
keywords: ApoCox17; Brownian dynamics; Convergence; Molecular dynamics; Principal component analysis; Random Matrix Theory