Pseudo amino acid composition, or PseAAC, in
molecular biology
Molecular biology is the branch of biology that seeks to understand the molecular basis of biological activity in and between cells, including biomolecular synthesis, modification, mechanisms, and interactions. The study of chemical and physi ...
, was originally introduced by
Kuo-Chen Chou in 2001 to represent
protein
Proteins are large biomolecules and macromolecules that comprise one or more long chains of amino acid residues. Proteins perform a vast array of functions within organisms, including catalysing metabolic reactions, DNA replication, res ...
samples for improving
protein subcellular localization prediction
Protein subcellular localization prediction (or just protein localization prediction) involves the prediction of where a protein resides in a cell (biology), cell, its subcellular localization.
In general, prediction tools take as input informatio ...
and
membrane protein
Membrane proteins are common proteins that are part of, or interact with, biological membranes. Membrane proteins fall into several broad categories depending on their location. Integral membrane proteins are a permanent part of a cell membrane ...
type prediction.
Like the vanilla amino acid composition (AAC) method, it characterizes the protein mainly using a matrix of amino-acid frequencies, which helps with dealing with proteins without significant sequential homology to other proteins. Compared to AAC, additional information are also included in the matrix to represent some local features, such as correlation between residues of a certain distance.
When dealing the cases of PseAAC, the
Chou's invariance theorem has been often used.
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
Amino acids are organic compounds that contain both amino and carboxylic acid functional groups. Although hundreds of amino acids exist in nature, by far the most important are the alpha-amino acids, which comprise proteins. Only 22 alpha a ...
(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.
Given a protein sequence P with
amino acid residues, i.e.,
*
where R
1 represents the 1st residue of the protein P, R
2 the 2nd residue, and so forth. This is the representation of the protein under the sequential model.
However, this kind of approach fails when a query protein does not have significant homology to the known protein(s). Thus, various discrete models were proposed that do not rely on sequence-order. 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
*
where
are the normalized occurrence frequencies of the 20 native amino acids in P, and T the transposing operator. The AAC of a protein is trivially derived with the
protein primary structure known like given in Eq.1; it is also possible by hydrolysis without knowing the exact sequence, and such a step in fact is often
a prerequisite for protein sequencing.
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 is lost. This is its main shortcoming.
Concept
To avoid completely losing the sequence-order information, the concept of PseAAC (pseudo amino acid composition) was proposed.
In contrast with the conventional amino acid composition (AAC) that contains 20 components with each reflecting the occurrence frequency for one of the 20 native amino acids in a protein, the PseAAC contains a set of greater than 20 discrete factors, where the first 20 represent the components of its conventional
amino acid
Amino acids are organic compounds that contain both amino and carboxylic acid functional groups. Although hundreds of amino acids exist in nature, by far the most important are the alpha-amino acids, which comprise proteins. Only 22 alpha a ...
composition while the additional factors incorporate some sequence-order information via various pseudo components.
The additional factors are a series of rank-different correlation factors along a protein chain, but they can also be any combinations of other factors so long as they can reflect some sorts of sequence-order effects one way or the other. Therefore, the essence of PseAAC is that on one hand it covers the AA composition, but on the other hand it contains the information beyond the AA composition and hence can better reflect the feature of a protein sequence through a discrete model.
Meanwhile, various modes to formulate the PseAAC vector have also been developed, as summarized in a 2009 review article.
Algorithm
According to the PseAAC model, the protein P of Eq.1 can be formulated as
*
where the (
) components are given by
*
where
is the weight factor, and
the
-th tier correlation factor that reflects the sequence order correlation between all the
-th most contiguous residues as formulated by
*
with
*
where
is the
-th function of the amino acid
, and
the total number of the functions considered. For example, in the original paper by Chou,
[ , and are respectively the hydrophobicity value, hydrophilicity value, and side chain mass of amino acid ; while , and the corresponding values for the amino acid . Therefore, the total number of functions considered there is . It can be seen from Eq.3 that the first 20 components, i.e. are associated with the conventional AA composition of protein, while the remaining components are the correlation factors that reflect the 1st tier, 2nd tier, …, and the -th tier sequence order correlation patterns (Figure 1).
It is through these additional factors that some important sequence-order effects are incorporated.
in Eq.3 is a parameter of integer and that choosing a different integer for will lead to a dimension-different PseAA composition.]
Using Eq.6 is just one of the many modes for deriving the correlation factors in PseAAC or its components. The others, such as the physicochemical distance mode and amphiphilic pattern mode, can also be used to derive different types of PseAAC, as summarized in a 2009 review article.[ In 2011, the formulation of PseAAC (Eq.3) was extended to a form of the general PseAAC as given by:]
*
where the subscript is an integer, and its value and the components will depend on how to extract the desired information from the amino acid sequence of P in Eq.1.
The general PseAAC can be used to reflect any desired features according to the targets of research, including those core features such as functional domain
Domain may refer to:
Mathematics
*Domain of a function, the set of input values for which the (total) function is defined
**Domain of definition of a partial function
**Natural domain of a partial function
**Domain of holomorphy of a function
* Do ...
, sequential evolution
Evolution is change in the heritable characteristics of biological populations over successive generations. These characteristics are the expressions of genes, which are passed on from parent to offspring during reproduction. Variation ...
, and gene ontology
The Gene Ontology (GO) is a major bioinformatics initiative to unify the representation of gene and gene product attributes across all species. More specifically, the project aims to: 1) maintain and develop its controlled vocabulary of gene and ge ...
to improve the prediction quality for the subcellular localization of proteins. as well as their many other important attributes.
References
External links
PseAAC web server
{{Portal bar, Biology, Technology, Science
Amino acids
Bioinformatics algorithms