Prediction of protein cellular attributes using pseudo‐amino acid composition

Abstract

The cellular attributes of a protein, such as which compartment of a cell it belongs to and how it is associated with the lipid bilayer of an organelle, are closely correlated with its biological functions. The success of human genome project and the rapid increase in the number of protein sequences entering into data bank have stimulated a challenging frontier: How to develop a fast and accurate method to predict the cellular attributes of a protein based on its amino acid sequence? The existing algorithms for predicting these attributes were all based on the amino acid composition in which no sequence order effect was taken into account. To improve the prediction quality, it is necessary to incorporate such an effect. However, the number of possible patterns for protein sequences is extremely large, which has posed a formidable difficulty for realizing this goal. To deal with such a difficulty, the pseudo-amino acid composition is introduced. It is a combination of a set of discrete sequence correlation factors and the 20 components of the conventional amino acid composition. A remarkable improvement in prediction quality has been observed by using the pseudo-amino acid composition. The success rates of prediction thus obtained are so far the highest for the same classification schemes and same data sets. It has not escaped from our notice that the concept of pseudo-amino acid composition as well as its mathematical framework and biochemical implication may also have a notable impact on improving the prediction quality of other protein features.

Improved Peptide Elution Time Prediction for Reversed-Phase Liquid Chromatography-MS by Incorporating Peptide Sequence Information

“…As a result, we used the "quasi-sequence-order" approach, first introduced by Chou 72,73 to predict protein sub-cellular locations and attributes. The idea is to assume that the sequence order effect of L amino acids with the form a 1 a 2 a 3 a 4 a 5 ⋯a L , can be approximately reflected through the following set of sequence-order-coupling factors:…”

Using Functional Domain Composition and Support Vector Machines for Prediction of Protein Subcellular Location

“…Compared with the amino acid composition (14 -16) and the pseudo amino acid composition (12), the entire protein sequence contains of course the most complete information. Unfortunately, if using the entire sequence of a protein as its representation to formulate the statistical prediction algorithm, one would face the difficulty of dealing with almost an infinity of sample patterns, as elaborated by Chou (12). Accordingly, to formulate a feasible statistical prediction algorithm, a protein must be expressed in terms of a set of discrete numbers.…”

“…The introduction of the pseudo amino acid composition is a pioneer effort in this regard that has no doubt made one important step forward for such a goal. The pseudo amino acid composition consists of 20 ϩ discrete numbers, where the first 20 numbers are the same as those in the amino acid composition and the remaining numbers represent different ranks of sequencecorrelation factors (12). In this paper, we would like to introduce a * The costs of publication of this article were defrayed in part by the payment of page charges.…”

“…Actually, many efforts have been made trying to develop some computational methods for quickly predicting the subcellular locations of proteins (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13). It is instructive to point out that, of these algorithms, most are based on the amino acid composition alone without including any sequence-order effects, and some (9,12,13) are based on the pseudo amino acid composition that incorporated partial sequence-order effects. To further improve the prediction quality, a logical and key step would be to find an effective way to incorporate the sequence-order effects.…”

Integration of Multi-Feature Fusion and PLS-DA in Protein Secondary Structure Prediction

“…The usually adopted feature representation of protein includes Amino Acid Composition (AAC) [5], polypeptide composition, functional domain composition [6], physicochemical features, PSI-BLAST profiles [7] and function annotation information [8]. Obviously, making full use of available protein feature information can effectively improve the prediction of protein structures.…”