Distance matrix-based approach to protein structure prediction

Abstract Much structural information is encoded in the internal distances; a distance matrix-based approach can be used to predict protein structure and dynamics, and for structural refinement. Our approach is based on the square distance matrix D = [rij 2] containing all square distances between residues in proteins. This distance matrix contains more information than the contact matrix C, that has ele-ments of either 0 or 1 depending on whether the distance rij is greater or less than a cutoff value rcutoff. We have per-formed spectral decomposition of the distance matrices D P kkvkvTk, in terms of eigenvalues kk and the corre-sponding eigenvectors vk and found that it contains at most five nonzero terms. A dominant eigenvector is proportional to r2—the square distance of points from the center o