domains are stable and compact units of the structure that can fold independent of the rest of the protein and perform a specific function. A domain usually contains a hydrophobic core and a protein is usually formed by the combination of two or several domains. There are many methods that decompose a protein structure into domains [@alexandrov:2003; @Guo:2003; @holm:1996; @SKundu:2004; @murzin:1995; @orengo:1997; @veretnik:2007]. Some of them are done manually through structure visualization. One of them is the structural classification of proteins (SCOP) database, where data are largely manually classified into protein structural domains based on similarities of their structures and amino acid sequences. With the surge of protein structure data, efficient and robust computational algorithms are developed. They have demonstrated a high level of consistency and robustness in the process of partitioning a structure into domains. Graph theory is also used in RNA structure analysis, particularly in RNA motif representation and RNA classification[@Gan:2004rag; @Fera:2004rag; @Kim2004:candidates]. Spectral graph theory is widely used for clustering. The essential idea is to study and explore graphs through the eigenvalues and eigenvectors of matrices naturally associated with these graphs.

Molecular nonlinear dynamics (MND) models can be naturally derived from biomolecular graph models [@KLXia:2014b]. Essentially, each 