Computational identification of natural peptides based on analysis of molecular evolution.

Motivation: Many secretory peptides are synthesized as inactive precursors that must undergo post-translational processing to become biologically active peptides. Attempts to predict natural peptides are limited by the low performance of proteolytic site predictors and by the high combinatorial complexity of pairing such sites. To overcome these limitations, we analyzed the site-wise evolutionary mutation rates of peptide hormone precursors, calculated using the Rate4Site algorithm. Results: Our analysis revealed that within their precursors, peptide residues are significantly more conserved than the pro-peptide residues. This disparity enables the prediction of peptides with a precision of similar to 60% at a recall of 40% [receiver-operating characteristic curve (ROC) AUC 0.79]. Subsequently, combining the Rate4Site score with additional features and training a Random Forest classifier enable the prediction of natural peptides hidden within secreted human proteins at a precision of similar to 90% at a recall of 50% (ROC AUC 0.96). The high performance of our method allows it to be applied to full secretomes and to predict naturally occurring active peptides. Our prediction on Homo sapiens revealed several putative peptides in the human secretome that are currently unannotated. Furthermore, the unique expression of some of these peptides implies a potential hormone function, including peptides that are highly expressed in endocrine glands.