Neuropeptides (NPs) play critical roles in synaptic signaling in various systems. NPs act as hormone, modulator, neurotransmitter and cytokine to regulate broad functions. NPs share the common characteristic that produced from a longer NP precursor (NPP). With the drastic growth of unknown protein sequences generated in the post-genomic age, it is highly desired to develop computational methods for rapidly and effectively identifying NPPs.

The NeuroPP tool was bulit based on optimized combined compositon which was integrated from amino acid, dipeptide and tripeptide composition. Evaluated with independent datasets, the predictor showed good performance that achieved an accuracy of 88.65% with AUC of 0.95 to identify NPPs, indicating that it preforms splendidly in recognition of NPPs.