NDLI: A new fingerprint to predict nonribosomal peptides activity

Content Provider	Springer Nature Link
Author	Abdo, Ammar Caboche, Ségolène Leclère, Valérie Jacques, Philippe Pupin, Maude
Copyright Year	2012
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 fingerprint, built from monomer composition, allows an effective screening and prediction of biological activities of NRPs database.
Starting Page	1187
Ending Page	1194
Page Count	8
File Format	PDF
ISSN	0920654X
Journal	Journal of Computer-Aided Molecular Design
Volume Number	26
Issue Number	10
e-ISSN	15734951
Language	English
Publisher	Springer Netherlands
Publisher Date	2012-09-29
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Nonribosomal peptides Target Prediction Similarity searching Drug discovery Computer Applications in Chemistry Physical Chemistry Animal Anatomy / Morphology / Histology
Content Type	Text
Resource Type	Article
Subject	Drug Discovery Physical and Theoretical Chemistry Computer Science Applications

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