In molecular biology, pseudo amino acid composition (PseACC) is a method introduced by Kuo-Chen Chou to convert the protein sequence into a numerical vector for enhancing pattern recognition techniques, such as during discrimination between classes of proteins based on their sequences (e.g. between membrane proteins, transmembrane proteins, cytosolic proteins, and other types). This method represented an advance beyond using the immediate amino acid composition (AAC). Instead, the protein is characterized into a matrix of amino-acid frequencies. This matrix incorporates not only amino acid composition, but can also incorporate information from local features of the protein sequence. Due to the success and widespread application of the PseACC method, it was extended to address sequence-order effects in nucleotide compositions, giving rise to a comparative method called PseKNC.
Sequential and discrete models Two kinds of models are usually used to represent protein samples: the sequential and the discrete (or non-sequential) models. The most elementary sequential model is to use the entire amino acid sequence, as expressed by:
P = [ R 1 R 2 R 3 R 4 R 5 R 6 R 7 ⋯ R L ] (1) {\displaystyle \mathbf {P} ={\begin{bmatrix}\mathrm {R} _{1}\mathrm {R} _{2}\mathrm {R} _{3}\mathrm {R} _{4}\mathrm {R} _{5}\mathrm {R} _{6}\mathrm {R} _{7}\cdots \mathrm {R} _{L}\end{bmatrix}}\qquad {\text{(1)}}}
where, P represents the amino acid sequence, L {\displaystyle L} is the number of amino acid residues, R1 is the first residue of the protein P, R2 is the second residue, and so forth. The problem with this approach was that in some sequence-similarity-search-based tools, the query protein often lacked significant homology (or sequence similarity) with any other known protein in the database. To resolve this problem, discrete models for representing protein samples were proposed. The simplest discrete model is using the amino acid composition (AAC) to represent protein samples. Under the AAC model, the protein P of Eq.1 can also be expressed by
P = [ f 1 f 2 ⋯ f 20 ] T (2) {\displaystyle \mathbf {P} ={\begin{bmatrix}f_{1}&f_{2}&\cdots &f_{20}\end{bmatrix}}^{\mathbf {T} }\qquad {\text{(2)}}}
where f u ( u = 1 , 2 , ⋯ , 20 ) {\displaystyle \,f_{u}\,(u=1,2,\cdots ,20)} are the normalized occurrence frequencies of the 20 native amino acids in P, and T is the transposing operator.
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