A comprehensive dataset of therapeutic peptides on multi-function property and structure information

Area of Science:

• Biochemistry
• Pharmacology
• Bioinformatics

Background:

• Therapeutic peptides offer targeted treatment options.
• Understanding peptide sequence-structure-function is crucial for drug development.
• A comprehensive, curated dataset is needed to advance research.

Purpose of the Study:

• To present a comprehensive dataset of experimentally validated therapeutic peptides.
• To provide annotated structural and functional information for these peptides.
• To facilitate computational tool development for peptide discovery.

Main Methods:

• Data compilation from experimental validation.
• Annotation of peptide structure and function.
• Categorization of peptides into 47 functional groups.

Main Results:

• A dataset of 58,583 therapeutic peptides was created.
• 21,130 peptides were identified as multi-functional.
• 54,722 peptides have associated structural annotation.
• Peptides are classified into 47 functional categories.

Conclusions:

• The dataset serves as a valuable resource for therapeutic peptide research.
• It supports the development of computational tools for drug discovery.
• Facilitates deeper exploration of sequence-structure-function relationships in therapeutic peptides.