Mining protein sequences for motifs.

Giri Narasimhan; Changsong Bu; Yuan Gao; Xuning Wang; Ning Xu; Kalai Mathee

doi:10.1089/106652702761034145

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Mining protein sequences for motifs.

Giri Narasimhan·Changsong Bu·Yuan Gao·Xuning Wang·Ning Xu·Kalai Mathee

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Abstract

We use methods from Data Mining and Knowledge Discovery to design an algorithm for detecting motifs in protein sequences. The algorithm assumes that a motif is constituted by the presence of a "good" combination of residues in appropriate locations of the motif. The algorithm attempts to compile such good combinations into a "pattern dictionary" by processing an aligned training set of protein sequences. The dictionary is subsequently used to detect motifs in new protein sequences. Statistical significance of the detection results are ensured by statistically determining the various parameters of the algorithm. Based on this approach, we have implemented a program called GYM. The Helix-Turn-Helix motif was used as a model system on which to test our program. The program was also extended to detect Homeodomain motifs. The detection results for the two motifs compare favorably with existing programs. In addition, the GYM program provides a lot of useful information about a given protein sequence.

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Algorithms Amino Acid Motifs AraC Transcription Factor Bacterial Proteins Helix-Turn-Helix Motifs Homeodomain Proteins Mutation Proteins Repressor Proteins Sigma Factor Software Design Transcription Factors Journal Article

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Giri Narasimhan, Changsong Bu, Yuan Gao, Xuning Wang, Ning Xu, Kalai Mathee. 2002. Mining protein sequences for motifs.. https://doi.org/10.1089/106652702761034145

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