MOTIVATION: To represent various aspects of receptors effectively, we developed the receptor database (RDB), using an object-oriented database management system ACEDB and the Internet/WWW technology. RESULTS: RDB was constructed so that the system collects data items such as attributes of proteins from distributed data sources of the Internet, and so that it provides various viewing tools effectively, depending on different types of receptor data. Such sources include standard international biological databases such as the up-to-date database of PIR, Swiss Prot, PDB, GenBank and GDB. Application to the endocrine disruptor problem is presented. AVAILABILITY: RDB is available through the Internet at http://impact.nihs.go.jp/RDB.html.

Using the neural network algorithm with back-propagation training procedure, we analysed the zinc finger DNA binding protein sequences. We incorporated the characteristic patterns around the zinc finger motifs TFIIIA type (Cys-X2-5-Cys-X12-13-His-X2-5-His) and the steroid hormone receptor type (Cys-X2-5-Cys-X12-15-Cys-X2-5-Cys-X15-16-Cys-X4-5-Cys-X8-10- Cys-X2-3-Cys) in the neural network algorithm. The patterns used in the neural network were the amino acid pattern, the electric charge and polarity pattern, the side-chain chemical property and subproperty patterns, the hydrophobicity and hydrophilicity patterns and the secondary structure propensity pattern. Two consecutive patterns were also considered. Each pattern was incorporated in the single layer perceptron algorithm and the combinations of patterns were considered in the two-layer perceptron algorithm. As for the TFIIIA type zinc finger DNA binding motifs, the prediction results of the two-layer perceptron algorithm reached up to 96.9% discrimination, and the prediction results of the discriminant analysis using the combination of several characters reached up to 97.0%. As for the steroid hormone receptor type zinc finger, the prediction results of neural network algorithm and the discriminant analyses reached up to 96.0%.

We have previously developed a general method based on the statistical technique of discriminant analysis to predict splice junctions in eukaryotic mRNA sequences [Nakata, K., Kanehisa, M. and DeLisi, C. (1985) Nucleic Acids Res., 13, 5327-5340]. In order to evaluate further applicability of this method, we now analyze the promoter region of Escherichia coli sequences. The attributes used for discrimination include the accuracy of consensus sequence patterns measured by the perceptron algorithm, the thermal stability map, the base composition and the Calladine-Dickerson rules for helical twist angle, roll angle, torsion angle and propeller twist angle. When applied to selected E. coli sequences in the GenBank database, the method correctly identifies 75% of the true promoter regions.