Markov random fields reveal an N-terminal double beta-propeller motif as part of a bacterial hybrid two-component sensor system.
The recent explosion in newly sequenced bacterial genomes is outpacing the capacity of researchers to try to assign functional annotation to all the new proteins. Hence, computational methods that can help predict structural motifs provide increasingly important clues in helping to determine how these proteins might function. We introduce a Markov Random Field approach tailored for recognizing proteins that fold into mainly beta-structural motifs, and apply it to build recognizers for the beta-propeller shapes. As an application, we identify a potential class of hybrid two-component sensor proteins, that we predict contain a double-propeller domain.
Tufts University, Medford, MA 02155, USA.
Matt
M
Menke
Bonnie
B
Berger
Lenore
L
Cowen
D001426:Bacterial Proteins / Q000737:chemistry*; D000071677:Histidine Kinase; D008390:Markov Chains; D011487:Protein Conformation; D011494:Protein Kinases / Q000737:chemistry
D001426:Bacterial Proteins; D011494:Protein Kinases; D000071677:Histidine Kinase