From precursor to final peptides: a statistical sequence-based approach to predicting prohormone processing.
Predicting the final neuropeptide products from neuropeptides genes has been problematic because of the large number of enzymes responsible for their processing. The basic processing of 22 Aplysia californica prohormones representing 750 cleavage sites have been analyzed and statistically modeled using binary logistic regression analyses. Two models are presented that predict cleavage probabilities at basic residues based on prohormone sequence. The complex model has a correct classification rate of 97%, a sensitivity of 97%, and a specificity of 96% when tested on the Aplysia dataset.
Hummon
Amanda B
AB
Department of Chemistry and the Beckman Institute, University of Illinois, Urbana, Illinois 61801, USA.
Hummon
Norman P
NP
Corbin
Rebecca W
RW
Li
Lingjun
L
Vilim
Ferdinand S
FS
Weiss
Klaudiusz R
KR
Sweedler
Jonathan V
JV
D000595:Amino Acid Sequence; D000818:Animals; D001048:Aplysia / Q000378:metabolism*; D007447:Invertebrate Hormones / Q000378:metabolism*; D015233:Models, Statistical; D009479:Neuropeptides / Q000378:metabolism*; D011498:Protein Precursors / Q000378:metabolism*; D012044:Regression Analysis; D012680:Sensitivity and Specificity
D007447:Invertebrate Hormones; D009479:Neuropeptides; D011498:Protein Precursors