title: Computational identification of natural peptides based on analysis of molecular evolution.
abstract: BACKGROUND
  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 ∼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 ∼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.
  
  
  METHODS
  A pseudocode is available in the SUPPLEMENTARY INFORMATION.
  
  
  BACKGROUND
  doron.gerber@biu.ac.il or kliger@cgen.com
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: The Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, 52900 Ramat-Gan and Compugen Ltd., 69512 Tel Aviv, IsraelThe Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, 52900 Ramat-Gan and Compugen Ltd., 69512 Tel Aviv, Israel.
authors.forename: Amir
authors.initials: A
authors.lastname: Toporik
authors.affiliation: The Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, 52900 Ramat-Gan and Compugen Ltd., 69512 Tel Aviv, Israel.
authors.forename: Itamar
authors.initials: I
authors.lastname: Borukhov
authors.affiliation: The Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, 52900 Ramat-Gan and Compugen Ltd., 69512 Tel Aviv, Israel.
authors.forename: Avihay
authors.initials: A
authors.lastname: Apatoff
authors.affiliation: The Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, 52900 Ramat-Gan and Compugen Ltd., 69512 Tel Aviv, Israel.
authors.forename: Doron
authors.initials: D
authors.lastname: Gerber
authors.affiliation: The Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, 52900 Ramat-Gan and Compugen Ltd., 69512 Tel Aviv, Israel.
authors.forename: Yossef
authors.initials: Y
authors.lastname: Kliger
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D002116:Calcitonin / Q000737:chemistry; D019295:Computational Biology / Q000379:methods*; D017124:Conserved Sequence; D019143:Evolution, Molecular; D006801:Humans; D008969:Molecular Sequence Data; D059645:Mutation Rate; D036361:Peptide Hormones / Q000737:chemistry* / Q000378:metabolism; D012372:ROC Curve; D017421:Sequence Analysis
chemical_list: D036361:Peptide Hormones; D002116:Calcitonin