Background
To predict the subcellular localization of proteins and other attributes based on their sequence, two kinds of models are generally used to represent protein samples: (1) the sequential model, and (2) the non-sequential model or discrete model.
The most typical sequential representation for a protein sample is its entire amino acid (AA) sequence, which can contain its most complete information. This is an obvious advantage of the sequential model. To get the desired results, the sequence-similarity-search-based tools are usually utilized to conduct the prediction. However, this kind of approach failed to work when a query protein did not have significant homology to the attribute-known proteins. Thus, various discrete models were proposed.
The simplest discrete model is using the amino acid composition (AAC) to represent protein samples, as formulated as follows. Given a protein sequence P with amino acid residues, i.e.,
where R1 represents the 1st residue of the protein P, R2 the 2nd residue, and so forth, according to the amino acic composition (AAC) model, the protein P of Eq.1 can be expressed by
where are the normalized occurrence frequencies of the 20 native amino acids in P, and T the transposing operator. Accordingly, the amino acid composition of a protein can be easily derived once the protein sequencing information is known.
Owing to its simplicity, the amino acid composition (AAC) model was widely used in many earlier statistical methods for predicting protein attributes. However, all the sequence-order information would be lost by using the AA composition to represent a protein. This is its main shortcoming.
Read more about this topic: Pseudo Amino Acid Composition
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