Monomer structure fingerprints: an extension of the monomer composition version for peptide databases
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Monomer structure fingerprints: an extension of the monomer composition version for peptide databases Ammar Abdo1,2 · Eissa Ghaleb3 · Naser K. A. Alajmi4 · Maude Pupin1 Received: 6 February 2020 / Accepted: 12 August 2020 © Springer Nature Switzerland AG 2020
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. Keywords Nonribosomal peptides · Target prediction · Drug discovery · Molecular fingerprints · Ligand-based virtual screening · Natural products
Introduction Natural products have been an important source of drugs for thousands of years [1]. They are produced by marine or terrestrial organisms (plants, vertebrates, invertebrates…) and microorganisms (fungi, bacteria, algae). Many studies in the literature discuss the importance of natural products in drug discovery [1–5]. Their importance is not only due Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10822-020-00336-8) contains supplementary material, which is available to authorized users. * Ammar Abdo [email protected] 1
Univ. Lille, CNRS, Centrale Lille, UMR 9189 - CRIStAL Centre de Recherche en Informatique Signal et Automatique de Lille, 59000 Lille, France
2
Computer Science Department, Hodeidah University, Hodeidah, Yemen
3
Computer Science Department, Aden Community College, Aden, Yemen
4
Saad Al‐Abdullah Academy for Security Sciences, Al Kuwait, Kuwait
to their capacity of being the source of many drugs on the market (e.g. morphine, cocaine, penicillin, taxols…) but also of playing a dominant role in the discovery of leads suitable for further modification during drug development. Discovering natural products requires specific steps, for example scientists need to identify which organisms produce interesting compounds and determine the conditions for production. The compounds produced must be extracted from cultured media or from natural environments, and then their chemical structures can finally be identified.
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