Mining Protein Sequences for Motifs

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.

Mining minimal distinguishing subsequence patterns with gap constraints

“…[10]). Such patterns are related to MDSs, but are far more general and thus much more difficult to mine.…”

Training Set Design for Pattern Discovery With Applications to Protein Motif Detection

“…GYM is a HTH motif detection algorithm based on a supervised pattern discovery approach and developed by Narasimhan et al [12]. GYM was successfully trained to detect HTH motifs.…”

“…GYM was successfully trained to detect HTH motifs. Experiments with over 1000 proteins have estimated its false negative rate (i.e., sensitivity) to be about 2% and its false positive rate (i.e., accuracy) to be about 7% [10,12]. As with any supervised pattern discovery method, GYM works in two phases: a pattern discovery (or pattern mining) phase and a motif detection phase.…”

“…Pattern discovery techniques have been successfully used to tackle motif detection problems in protein sequences [2,3,7,12,14,[16][17][18]. Both supervised and unsupervised techniques have been developed for this problem [12,16].…”

“…Both supervised and unsupervised techniques have been developed for this problem [12,16]. The supervised pattern discovery approach, as used in motif detection, strives to find statistically significant patterns that are present in known examples of the motif and that are known to be absent in sequences that are not examples of the same motif.…”