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.