Protein Sequence Comparison and DNA-binding Protein Identification with Generalized PseAAC and Graphical Representation

Li, Chun; Zhao, Jialing; Wang, Changzhong; Yao, Yuhua

Motivated by the work mentioned above, we propose a generalized PseAAC which is grounded on a three-letter model and 2-D graphical representation of a protein sequence. We summarize the main work of this paper as follows: In section 2, we briefly introduce five datasets used in this study. In section 3, on the basis of two important physicochemical properties of amino acids, we cluster the 20 standard amino acids into three groups. By assigning to each group a representative symbol, we transform a protein sequence into a three-letter sequence. Then a 2-D graph without loops and multiple edges and its geometric line adjacency matrix are obtained. A sequence-derived feature vector of dimension (25+ ) is thus constructed to characterize a protein sequence. Our scheme is similar to, but obviously different from that of PseAAC. In section 4, we apply the presented feature vector to compare -globin proteins of 17 species and 72 spike proteins of coronaviruses respectively. Also, we develop a SVM (support vector machine) model using the generalized PseAAC to identify DNA-binding and non-binding proteins on three datasets. Experiment results show that the presented method outperforms the existing methods including DNAbinder [1] , DNA-Prot [2] , iDNA-Prot [3] and enDNA-Prot [4] . Finally, conclusions are given in section 5.