Computational approaches for identifying neuropeptides: A comprehensive review.

Area of Science:

• Biochemistry and Neuroscience
• Computational Biology

Background:

• Neuropeptides (NPs) are vital signaling molecules regulating neuronal, endocrine, and immune functions.
• NPs are implicated in various diseases, presenting therapeutic opportunities.
• Traditional NP identification methods are laborious and expensive.

Purpose of the Study:

• To review the application of machine learning (ML) in neuropeptide research.
• To explore ML's role in predicting NP sequences, cleavage sites, and precursors.
• To provide an overview of NP databases and computational tools.

Main Methods:

• Literature review of computational methods, specifically machine learning (ML).
• Focus on ML techniques for NP identification and characterization.
• Analysis of existing NP databases and specialized bioinformatics tools.

Main Results:

• Machine learning offers efficient, accurate, and cost-effective NP identification.
• ML models can predict critical NP features like sequences and precursors.
• Numerous databases and tools support computational NP research.

Conclusions:

• ML is a powerful approach for advancing neuropeptide research.
• Computational methods significantly enhance the study of NPs in health and disease.
• Integration of ML tools is essential for future NP discovery and therapeutic development.