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ISSN 1672-9145 Acta Biochim Biophys Sin 2005, 37(2): 88–96 CN 31-1940/Q

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Predicting Protein Subcellular Location Using Digital Signal Processing

Yu-Xi PAN^1,2, Da-Wei LI^1,2, Yun DUAN^2,1, Zhi-Zhou ZHANG^1,2, Ming-Qing XU^1,2, Guo-Yin FENG^1,2, and Lin HE^2,3*

¹Bio-X Life Science Research Center, Shanghai Jiaotong University, Shanghai 200030, China;

²Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Science, Shanghai 200030, China;

³Neuropsychiatric & Human Genetics Group, Bio-X Center, Shanghai Jiaotong University, Shanghai 200030, China

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 automated 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 algorithms 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 approach 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.

Key words sequence order effect; digital signal processing; digital Fourier transform (DFT); frequency domain; covariance discriminant algorithm; bioinformatics; proteomics

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Received: September 29, 2004 Accepted: December 25, 2004

This work was supported by the grants from the Major State Basic Research Development Program of China (No. 001CB510301), the National High Technology Research and development Program of China (No. 2002AA223021), the National Natural Science Foundation of China, and Shanghai Municipal Commission for Science and Technology

*Corresponding author: Tel, 86-21-62822491; Fax, 86-21-62822491; E-mail, [email protected] & [email protected]