NeuroPID: A predictor for identifying neuropeptide precursors from metazoan proteomes

Dan Ofer, Michal Linial*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


Motivation: The evolution of multicellular organisms is associated with increasing variability of molecules governing behavioral and physiological states. This is often achieved by neuropeptides (NPs) that are produced in neurons from a longer protein, named neuropeptide precursor (NPP). The maturation of NPs occurs through a sequence of proteolytic cleavages. The difficulty in identifying NPPs is a consequence of their diversity and the lack of applicable sequence similarity among the short functionally related NPs.Results: Herein, we describe Neuropeptide Precursor Identifier (NeuroPID), a machine learning scheme that predicts metazoan NPPs. NeuroPID was trained on hundreds of identified NPPs from the UniProtKB database. Some 600 features were extracted from the primary sequences and processed using support vector machines (SVM) and ensemble decision tree classifiers. These features combined biophysical, chemical and informational-statistical properties of NPs and NPPs. Other features were guided by the defining characteristics of the dibasic cleavage sites motif. NeuroPID reached 89-94% accuracy and 90-93% precision in cross-validation blind tests against known NPPs (with an emphasis on Chordata and Arthropoda). NeuroPID also identified NPP-like proteins from extensively studied model organisms as well as from poorly annotated proteomes. We then focused on the most significant sets of features that contribute to the success of the classifiers. We propose that NPPs are attractive targets for investigating and modulating behavior, metabolism and homeostasis and that a rich repertoire of NPs remains to be identified.

Original languageAmerican English
Pages (from-to)931-940
Number of pages10
Issue number7
StatePublished - 1 Apr 2014

Bibliographical note

Funding Information:
Funding: The work was partially supported by PROSPECT, EU FRVII (201648) and the ISF grant 592/07.


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