Automatic cDNA Classi cation System for Mouse
Genome Project
Yoshifumi Fukunishi 12

fukunisi@rtc.riken.go.jp

Hideaki Konno 12

hkonno@rtc.riken.go.jp

Yoshihide Hayashizaki 1
yosihide@rtc.riken.go.jp

1 Genome Science Lab, RIKEN Life Science Tsukuba Center
3-1-1 Koyadai, Tsukuba, Ibaraki 305, Japan
2 CREST, Japan Science and Technology Corporation (JST)1 Introduction
Classi cation of cDNA/protein sequence data is necessary for analysis and further application ofsequence data, i.e. understanding of pathway of proteins in a cell and signal transduction. Since rapid
increase of the number of cDNA/protein sequences is expected, the procedure of the classi cation must
be systematic and automated to avoid human eye inspection. In this study, the sequence data areclassi ed based on two terms; subcellular location and function. Again, false positive/false negative
results are always expected in the use of prediction method, and the human inspection is necessary
to avoid this problem. We proposed a method to reduce the false positive for PROSITE motif searchby using a structural information.

2 Method
2.1 Classi cation by subcellular location
The classi cation is done by two methods; one is a database search and another is an analysis of signal
peptide. The rst step is a FASTA homology search [5] based on the data les in which the entry
proteins were classi ed by subcellular location. The data les for nuclear consists of the sequencesof 2353 nuclear localized proteins, that for cytoplasm consists of the sequences of 1101 cytoplasmic
proteins, etc. The same proteins from di erent species are removed from the data les to reduce theredundancy of proteins. The second step is the searching signal peptide and the checking physicalproperty of peptide sequence in question (Fig. 1). PSORT II [4] and GCG are used to check the signal
peptide, and TopPred [3] is used to check the existence of the transmembrane domain. The nal result
is given by the linear combination of the scores calculated by these programs, and the parameters forweighting were optimized to give the best prediction result for 275 known proteins.

2.2 Classi cation by function: PROSITE motif search
PROSITE [1] is one of the most useful database to nd the functional domain in protein, and thenumber of entries is more than 1300. Some rare motifs consist of 20 amino acids while the mostfrequently found motif consists of only 2 amino acids. We will nd false positive PROSITE motifs
every 10 amino acids in a protein in question, even if the protein includes only a few real motifs.
One method to avoid the false positive motifs is using a subset of PROSITE which consists of onlyrare motifs. This method always ignores some motifs of small number of amino acids. We assume
that the motif can function only in the suitable structural environment. The ATP-binding P-loop
motif can bind an ATP only when the secondary structure of ATP-binding site is loop and the site
is accessible for solvent molecules. We prepared a data le of secondary structure and accessibility321

of amino acid for each entries of PROSITE. After the PROSITE motif search, GOR4 [2] is used to
predict the secondary structure of query protein. If the secondary structure of the predicted region of
the query protein matches the secondary structure of the motif in PROSITE database, we can expectthat the motif is functional (Fig. 2). Otherwise the motif is false positive.

Acknowledgements
This study has been supported by Special Coordination Funds and a Research Grant for the Genome
Exploration Research Project from the Science and Technology Agency of the Japanese Government,
CREST (Core Research for Evolutional Science and Technology) of Japan Science and Technology
Corporation (JST), and a Grant-in-Aid for Scienti c Research on Priority Areas and Human Genome
Program from the Ministry of Education and Culture, Japan to Y.H.

References[1] Bairoch, A., Bucher, P., and Hofmann, K., The PROSITE database, its status in 1997, Nucleic
Acids Res., 24:217{221, 1997.
[2] Garnier, J., Gibrat, J.F., and Robson, B., Analysis of the accuracy and implications of simple
methods for predicting the secondary structure of globular proteins, J. Mol. Biol., 120:97{120,1978.
[3] Heijne, G., Membrane Protein Structure Prediction, Hydrophobicity Analysis and the Positiveinside Rule, J. Mol. Biol., 225:487{494, 1992.
[4] Horton, P. and Nakai, K., Better Prediction of Protein Cellular Localization Sites with the k
Nearest Neighbors Classi er, Intelligent Systems for Molecular Biology, 5:147{152, 1997.
[5] Pearson, W.R. and Lipman, D.J., Improved tools for biological sequence analysis, Pro. Natl.
Acad. Sci. USA, 85:2444{2448, 1988.

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Figure 1: Automatic classi cation by protein subcellular location.

Figure 2: Schematic representation of functional-motif selection method. PS00c is chosen as a functional motif while PS00a and PS00b are false positive.

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