Predicting Protein Subcellular

Abstract        The biological functions of a protein are closely related to its attributes in a cell. With the rapid
accumulation of newly found protein sequence data in databanks, it is highly desirable to develop an auto-
mated method for predicting the subcellular location of proteins. The establishment of such a predictor will
expedite the functional determination of newly found proteins and the process of prioritizing genes and
proteins identified by genomic efforts as potential molecular targets for drug design. The traditional algo-
rithms for predicting these attributes were based solely on 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 in protein sequences is extremely large, posing a formidable
difficulty for realizing this goal. To deal with such difficulty, a well-developed tool in digital signal processing
named digital Fourier transform (DFT) [1] was introduced. After being translated to a digital signal according
to the hydrophobicity of each amino acid, a protein was analyzed by DFT within the frequency domain. A set
of frequency spectrum parameters, thus obtained, were regarded as the factors to represent the sequence
order effect. A significant improvement in prediction quality was observed by incorporating the frequency
spectrum parameters with the conventional amino acid composition. One of the crucial merits of this ap-
proach is that many existing tools in mathematics and engineering can be easily applied in the predicting
process. It is anticipated that digital signal processing may serve as a useful vehicle for many other protein
science areas.