The resonant recognition model (RRM) predicts amino acid residues in highly conserved regions of the hormone prolactin (PRL).
The resonant recognition model (RRM) is a model which treats the protein sequence as a discrete signal. It has been shown previously that certain periodicities (frequencies) in this signal characterise protein biological function. The RRM was employed to determine the characteristic frequencies of the hormone prolactin (PRL), and to identify amino acids ('hot spots') mostly contributing to these frequencies and thus proposed to mostly contribute to the biological function. The predicted 'hot spot' amino acids, Phe-19, Ser-26, Ser-33, Phe-37, Phe-40, Gly-47, Gly-49, Phe-50, Ser-61, Gly-129, Arg-176, Arg-177, Cys-191 and Arg-192 are found in the highly conserved amino-terminal and C-terminus regions of PRL. Our predictions agree with previous experimentally tested residues by site-direct mutagenesis and photoaffinity labelling.
Hejase de Trad
C
C
Department of Electrical and Computer Systems Engineering, Monash University, Clayton, VIC, Australia.
Fang
Q
Q
Cosic
I
I
D000465:Algorithms; D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry*; D017124:Conserved Sequence; D006801:Humans; D008954:Models, Biological; D008958:Models, Molecular; D011388:Prolactin / Q000737:chemistry*; D011498:Protein Precursors / Q000737:chemistry; D012815:Signal Processing, Computer-Assisted
D000596:Amino Acids; D011498:Protein Precursors; D011388:Prolactin