Titre :

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

Auteur(s) :

Abdo, Ammar [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Hodeidah University, Hodeidah, Yemen
Ghaleb, Eissa [Auteur]
Computer Science Department, Aden Community College
Alajmi, Naser [Auteur]
Saad Al‐Abdullah Academy for Security Sciences, Al Kuwait, Kuwait
Pupin, Maude [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]

Titre de la revue :

JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN

Pagination :

1147-1156

Éditeur :

Springer Verlag

Date de publication :

2020-11

ISSN :

0920-654X

Mot(s)-clé(s) en anglais :

Nonribosomal peptides
Target prediction
Drug discovery
Molecular fingerprints
Ligand-based virtual screening
Natural products

Discipline(s) HAL :

Informatique [cs]/Bio-informatique [q-bio.QM]
Chimie/Chemo-informatique
Statistiques [stat]/Machine Learning [stat.ML]

Résumé en anglais : [en]

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 ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL