Prediction of neuropeptide prohormone cleavages with application to RFamides

Genomic information is becoming available for an ever-wider range of animals with the genes for several well-characterized peptide families, such as the RFamides, detected in a surprisingly diverse set of these animals. While bioinformatic tools allow the prediction of the RFamide-related prohormones from genetic information, it is more difficult to accurately predict the final processed peptides because of the large number of processing steps required to convert a prohormone into mature bioactive peptides. Several statistical-based methods for predicting basic site cleavages in prohormones are described, and their ability to predict the basic site cleavages in a variety of RFamide-related peptides from vertebrates and invertebrates is reported. Specifically, the cleavages in the invertebrate FMRFamides, and the vertebrate NPFFa, RFRPa, and PrRPa peptide families are modeled. The three models compared here are based on known cleavage motifs, a logistic regression, and artificial neural networks. Improvements in the accuracy and precision of the cleavage estimates will lead to increased utilization of these models for predicting bioactive neuropeptides before experimental verification is available.