Analysis of E.coli promoter structures using neural networks.
Backpropagation neural network is trained to identify E.coli promoters of all spacing classes (15 to 21). A three module approach is employed wherein the first neural net module predicts the consensus boxes, the second module aligns the promoters to a length of 65 bases and the third neural net module predicts the entire sequence of 65 bases taking care of the possible interdependencies between the bases in the promoters. The networks were trained with 106 promoters and random sequences which were 60% AT rich and tested on 126 promoters (Bacterial, Mutant and Phage promoters). The network was 98% successful in promoter recognition and 90.2% successful in non-promoter recognition when tested on 5000 randomly generated sequences. The network was further trained with 11 mutated non-promoters and 8 mutated promoters of the p22ant promoter. The testing set with 7 mutated promoters and 13 mutated non-promoters of p22ant were identified. The network was upgraded using total 1665 data of promoters and non-promoters to identify any promoter sequences in the gene sequences. The network identified the locations of P1, P2 and P3 promoters in the pBR322 plasmid. A search for the start codon, Ribosomal Binding Site and the stop codon by a string search procedure has also been added to find the possible promoters that can yield protein products. The network was also successfully tested on a synthetic plasmid pWM528.
Astra Research Centre India, Bangalore.
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Mahadevan
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Ghosh
D001483:Base Sequence; D004269:DNA, Bacterial; D004926:Escherichia coli / Q000235:genetics*; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D010957:Plasmids / Q000235:genetics; D017354:Point Mutation; D011401:Promoter Regions, Genetic
D004269:DNA, Bacterial