Computational approaches to therapeutic peptide discovery

Area of Science:

• Biotechnology
• Computational Biology
• Drug Discovery

Background:

• Therapeutic peptide development traditionally relies on serendipitous discovery of natural peptides.
• Natural peptide discovery is limited by proteolytic cleavage from precursor proteins, hindering genomic insights.
• De novo design offers a rational approach, particularly for peptides mimicking helical protein segments.

Purpose of the Study:

• To review systematic approaches for identifying natural therapeutic peptides.
• To discuss de novo peptide design strategies, emphasizing helical structures.
• To highlight the development of computational tools for rational peptide design.

Main Methods:

• In silico prediction of natural peptides (peptidome) using machine learning.
• Application of computational biology tools to identify peptides with specific biological activities (e.g., GPCR activation, immune modulation).
• Development of a computational tool to identify intramolecular helix-helix interactions for de novo design.

Main Results:

• A two-step process for systematic natural peptide identification has been developed.
• Rational design of peptides based on helical segments has yielded anti-cancer, anti-angiogenic, and anti-inflammatory activities.
• Computational tools enable targeted identification and design of therapeutic peptides.

Conclusions:

• Systematic discovery and de novo design represent powerful strategies for therapeutic peptide development.
• Computational approaches accelerate the identification and design of peptides with desired biological functions.
• Helical peptide segments are promising scaffolds for developing novel therapeutics.