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Monomer structure fingerprints: an extension of the monomer composition version for peptide databases

Ammar Abdo 1, 2 Eissa Ghaleb 3 Naser Alajmi 4 Maude Pupin 1
1 BONSAI - Bioinformatics and Sequence Analysis
Université de Lille, Sciences et Technologies, Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189, CNRS - Centre National de la Recherche Scientifique
Abstract : Previously a fingerprint based on monomer composition (MCFP) of nonribosomal peptides (NRPs) has been introduced. MCFP is a novel method for obtaining a representative description of NRP structures from their monomer composition in a fingerprint form. An effective screening and prediction of biological activities has been obtained from Norine NRPs database. In this paper, we present an extension of the MCFP fingerprint. This extension is based on adding few columns into the fingerprint; representing monomer clusters, 2D structures, peptide categories, and peptide diversity. All these data have been extracted from the NRP structure. Experiments with Norine NRPs database showed that the extended MCFP, that can be called Monomer Structure FingerPrint (MSFP) produced high prediction accuracy (> 95%) together with a high recall rate (86%) obtained when MSFP was used for prediction and similarity searching. From this study it appeared that MSFP mainly built from monomer composition can substantially be improved by adding more columns representing useful information about monomer composition and 2D structure of NRPs.
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Contributor : Maude Pupin <>
Submitted on : Friday, December 18, 2020 - 12:28:28 PM
Last modification on : Thursday, June 17, 2021 - 4:32:02 PM



Ammar Abdo, Eissa Ghaleb, Naser Alajmi, Maude Pupin. Monomer structure fingerprints: an extension of the monomer composition version for peptide databases. Journal of Computer-Aided Molecular Design, Springer Verlag, 2020, 34 (11), pp.1147-1156. ⟨10.1007/s10822-020-00336-8⟩. ⟨hal-03081813⟩



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