Mining protein sequences for motifs.
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.
Narasimhan
Giri
G
Department of Mathematical Sciences, The University of Memphis, Memphis, TN 38152, USA. giri@cs.fiu.edu
Bu
Changsong
C
Gao
Yuan
Y
Wang
Xuning
X
Xu
Ning
N
Mathee
Kalai
K
D000465:Algorithms; D020816:Amino Acid Motifs; D051777:AraC Transcription Factor; D001426:Bacterial Proteins / Q000737:chemistry; D019077:Helix-Turn-Helix Motifs; D018398:Homeodomain Proteins / Q000737:chemistry; D009154:Mutation; D011506:Proteins / Q000737:chemistry*; D012097:Repressor Proteins / Q000737:chemistry; D012808:Sigma Factor / Q000737:chemistry; D012985:Software Design; D014157:Transcription Factors / Q000737:chemistry
D051777:AraC Transcription Factor; D001426:Bacterial Proteins; D018398:Homeodomain Proteins; D011506:Proteins; D012097:Repressor Proteins; D012808:Sigma Factor; D014157:Transcription Factors; C073470:LysR protein, Bacteria