Dynamic Cross-Correlation Matrix (DCCM) and Collective Motion

The docked complexes were further subjected to the web-server DynOmics ENM to identify the residue cross-correlation matrix. The tool developed with the Elastic network models (ENM) that integrate Anisotropic Network Model (ANM) and GaussianNetwork Model (GNM) (Li et al., 2017). Time-correlated data was represented as a matrix between the protein atoms i and j (cij) in DCCM. Within the form of a map, typical fluctuations, and standardized correlations among residues are typically shown and represented with the following equation:

Cij = < ΔRi. ΔRj >

Cij (n) = < ΔRi. ΔRj >/[ < ΔRi. ΔRi > < ΔRj. ΔRj >]1/2

The range of Cij(n) varies in terms (−1, 1) and analyzes information on the cross-correlation between residue movements i and j (Rader et al., 2005). To determine how mutations affect the internal mechanics of protein conformations, the Bio3D module incorporated with the R studio was used to quantify residue-residue dynamic cross-correlation networks. The normal mode, network analysis, and correlation analysis were called with the function “nma(),” “can(),”and “dccm().” The role from these features is usually a cross-correlation matrix of residue-residue. The results were plotted by calling the functions “plot.dccm()” and “pymol.dccm()” (Grant et al., 2006; Scarabelli and Grant, 2013).