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

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

One of the core issues to the sequence-based methods is how to characterize protein sequences and harvest the fruits hidden in them. The most typical approach is using the amino acid composition (AAC) to formulate a protein sequence. Owing to its simplicity, the AAC model was widely applied in a number of earlier statistic-based methods. However, as pointed out in Ref [6] , if we denote by the counts of 20 standard amino acids in a protein sequence, then we can see that there are a total of different sequences/strings possessing the same AAC. The reason is that AAC model neglects the order relation among elements of a sequence. To overcome this drawback, the concept of pseudo amino acid composition (PseAAC, or Chou's PseAAC) was proposed [10] [11] [12] [13] [14] [15] [16] [17] [18] . The essence of PseAAC is that it not only covers AAC, but also contains additional order-correlated factors along a protein sequence. Another popular way for sequence analysis is to convert the protein primary sequence over 20 amino acids into a reduced one. The earliest and simplest reduction was the well-known HP model, in which 20 standard amino acids are divided into two types, hydrophobic (H) (or non-polar) and polar (P) (or hydrophilic). On the basis of the classic model, a detailed HP model was introduced by dividing the polar class into three subclasses: positive polar, uncharged polar and negative polar [19] . In addition, a few five-group classifications of amino acids were presented for practical purposes [20] [21] [22] [23] . By considering property-based triples, Li et al. [6] put forward a six-letter model of amino acids. Also based on three physical-chemical properties of amino acids, Yao et al. [24] mapped the 20 standard amino acids to eight vertices of a cube with the center of origin, and thus an eightgroup model of amino acids is obtained.