Random Matrix Analysis of Protein Families - Kumari R. (2023)
Proteins are vital for almost all biochemical and cellular processes. Although there is an enormous growth in the protein sequence data, the statistical characterization, structure and function of many of these sequences are still unknown. The statistical and spectral analysis of the Pearson correlation matrices between positions based on physiochemical properties of amino acids of seven protein families is performed and compared with the random Wishart matrix model results. A detailed analysis shows that the protein families significantly diverge from the Marchenko-Pastur distribution with many eigenvalues (outliers) outside the Wishart lower and upper bound. It is shown that level spacing distribution of protein families is similar to the Gaussian orthogonal ensemble. Further, the number variance varies as log of the system size indicating the presence of long range correlations within the protein families.