A new fingerprint to predict nonribosomal peptides activity

Abstract Bacteria and fungi use a set of enzymes called nonribosomal peptide synthetases to provide a wide range of natural peptides displaying structural and biological diversity. So, nonribosomal peptides (NRPs) are the basis for some efficient drugs. While discovering new NRPs is very desirable, the process of identifying their biological activity to be used as drugs is a challenge. In this paper, we present a novel peptide fingerprint based on monomer composition (MCFP) of NRPs. MCFP is a novel method for obtaining a representative description of NRP structures from their monomer composition in fingerprint form. Experiments with Norine NRPs database and MCFP show high prediction accuracy ([93 %). Also a high recall rate ([82 %) is obtained when MCFP is used for screening NRPs database. From this study it appears that our fin-gerprint, built from monomer composition, allows an effective screening and prediction of biological activities of NRPs database