Investigation of the proteins folding rates and their properties of amino acid networks

Abstract The mechanism of protein folding is an important problem in molecular biology. It is usually thought that protein folding is a complex system process related to the entire molecule. In this article, we have investigated 78 structures of folding proteins in native state, from complex networks perspective, to understand the role of topological parameters in proteins folding kinetics. The 31 parameters were calculated based on the amino acid networks of the folding proteins. The relationship between those parameters and protein folding rates has been systematically analyzed. Our results show that the significant parameters between two-state and multi-state folding proteins correlate well with the folding rates of proteins. It is also found that classifying the proteins into different classes can improve the correlation coefficient from 0.926 to 0.983 between the parameters and folding rates of two- and multi-state proteins, respectively. Genetic Algorithms–Multiple Linear Regression (GA–MLR) was adopted to select the best subset parameters from the whole 31 parameters to construct the MLR model to avoid over-fitting. Our methods show a correlation coefficient of 0.921 for the all folding proteins based on the classification of the folding proteins. The results indicate that the general topological parameters of the amino acids networks of the folding proteins can effectively represent the structural and functional properties, such as the rates of folding.