A new fingerprint to predict nonribosomal peptides activity

Area of Science:

• Biochemistry and Molecular Biology
• Drug Discovery
• Bioinformatics

Background:

• Nonribosomal peptides (NRPs) are natural products synthesized by enzymes called nonribosomal peptide synthetases.
• NRPs exhibit significant structural and biological diversity, forming the basis for various effective drugs.
• Identifying the biological activities of novel NRPs for drug development remains a significant challenge.

Purpose of the Study:

• To introduce a novel peptide fingerprint method, Monomer Composition Fingerprint (MCFP), for describing NRP structures.
• To evaluate the effectiveness of MCFP in predicting biological activities and screening NRP databases.
• To demonstrate MCFP's utility in accelerating the discovery of potential drug candidates from NRPs.

Main Methods:

• Development of a novel peptide fingerprint based on the monomer composition of nonribosomal peptides (NRPs).
• Application of the Monomer Composition Fingerprint (MCFP) method to represent NRP structures.
• Experimental validation using the Norine NRPs database to assess prediction accuracy and screening recall.

Main Results:

• MCFP achieved a high prediction accuracy exceeding 93% in experiments.
• MCFP demonstrated a high recall rate of over 82% when used for screening the NRPs database.
• The method provides a representative description of NRP structures from their monomer composition.

Conclusions:

• The developed monomer composition-based fingerprint (MCFP) is an effective tool for screening NRP databases.
• MCFP enables accurate prediction of biological activities for nonribosomal peptides.
• This approach facilitates the efficient discovery and development of novel NRP-based drugs.