A new fingerprint to predict nonribosomal peptides activity.

Bacteria/enzymology; Databases, Pharmaceutical; Databases, Protein; Drug Discovery/methods; Fungi/enzymology; Peptide Synthases/metabolism; Peptides/chemistry/metabolism/pharmacology

Abstract :

[en] 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.

Disciplines :

Microbiology

Author, co-author :

Abdo, Ammar

Caboche, Segolene

Leclere, Valerie

Jacques, Philippe ; Université de Liège - ULiège > Chimie et bio-industries > Bio-industries

Pupin, Maude

Language :

English

Title :

A new fingerprint to predict nonribosomal peptides activity.

Publication date :

2012

Journal title :

Journal of Computer-Aided Molecular Design

ISSN :

0920-654X

eISSN :

1573-4951

Publisher :

Kluwer Academic Publishers, Netherlands

Volume :

Issue :

Pages :

1187-94

Peer reviewed :

Peer Reviewed verified by ORBi

Available on ORBi :

since 21 October 2014

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Bibliography

Newman DJ, Cragg GM, Snader KM (2000) The influence of natural products upon drug discovery. Nat Prod Rep 17:215-234. doi:10.1039/A902202C
Balunas MJ, Kinghorn AD (2005) Drug discovery from medicinal plants. Life Sci 78:431-441. doi:10.1016/j.lfs.2005.09.012
Koehn FE, Carter GT (2005) The evolving role of natural products in drug discovery. Nat Rev Drug Discov 4:206-220. doi: 10.1038/nrd1657
Newman DJ, Cragg GM, Snader KM (2003) Natural products as sources of new drugs over the period 1981-2002. J Nat Prod 66:1022-1037. doi:10.1021/np030096l
Paterson I, Anderson EA (2005) The renaissance of natural products as drug candidates. Science 310:451-453. doi:10.1126/ science.1116364
Entzeroth M, Chapelain B, Guilbert J, Hamon V (2000) High throughput drug profiling. J Assoc Lab Autom 5:69-71. doi: 10.1016/s1535-5535(04)00085-1
Merino A, Bronowska AK, Jackson DB, Cahill DJ (2010) Drug profiling: knowing where it hits. Drug Discov Today 15:749-756. doi:10.1016/j.drudis.2010. 06.006
Marahiel MA (2009) Working outside the protein-synthesis rules: insights into non-ribosomal peptide synthesis. J Pept Sci 15:799-807. doi:10.1002/psc.1183
Stachelhaus T, Mootz HD, Marahiel MA (1999) The specificityconferring code of adenylation domains in nonribosomal peptide synthetases. Chem Biol 6:493-505. doi:10.1016/S1074-5521 (99)80082-9
Rottig M, Medema MH, Blin K, Weber T, Rausch C, Kohlbacher O (2011) NRPSpredictor2 - a web server for predicting NRPS adenylation domain specificity. Nucleic Acids Res 39:362-367. doi:10.1093/nar/gkr323
Rausch C, Weber T, Kohlbacher O, Wohlleben W, Huson DH (2005) Specificity prediction of adenylation domains in nonribosomal peptide synthetases (NRPS) using transductive support vector machines (TSVMs). Nucleic Acids Res 33:5799-5808. doi:10.1093/nar/gki885
Challis GL, Ravel J, Townsend CA (2000) Predictive, structurebased model of amino acid recognition by nonribosomal peptide synthetase adenylation domains. Chem Biol 7:211-224. doi: 10.1016/S1074-5521(00)00091-0
Medema MH, Blin K, Cimermancic P, de Jager V, Zakrzewski P, Fischbach MA, Weber T, Takano E, Breitling R (2011) anti- SMASH: rapid identification, annotation and analysis of secondary metabolite biosynthesis gene clusters in bacterial and fungal genome sequences. Nucleic Acids Res 39(2):W339- W346. doi:10.1093/nar/gkr466
Caboche S, Pupin M, Leclere V, Fontaine A, Jacques P, Kucherov G (2008) NORINE: a database of nonribosomal peptides. Nucleic Acids Res 36:D326-D331. doi:10.1093/nar/gkm792
Bajorath J (2008) Computational analysis of ligand relationships within target families. Curr Opin Chem Biol 12:352-358. doi: 10.1016/j.cbpa.2008.01.044
Ekins S, Mestres J, Testa B (2007) In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br J Pharmacol 152:9-20. doi:10.1038/sj.bjp.0707305
Caboche S, Leclere V, Pupin M, Kucherov G, Jacques P (2010) Diversity of monomers in nonribosomal peptides: towards the prediction of origin and biological activity. J Bacteriol 192:5143-5150. doi:10.1128/jb.00315-10
Abdo A, Chen B, Mueller C, Salim N, Willett P (2010) Ligandbased virtual screening using bayesian networks. J Chem Inf Model 50:1012-1020. doi:10.1021/ci100090p
Abdo A, Salim N (2011) New fragment weighting scheme for the bayesian inference network in ligand-based virtual screening. J Chem Inf Model 51:25-32. doi:10.1021/ci100232h
Abdo A, Salim N, Ahmed A (2011) Implementing relevance feedback in ligand-based virtual screening using bayesian inference network. J Biomol Screen 16:1081-1088. doi:10.1177/ 1087057111416658
Arif S, Holliday J, Willett P (2009) Analysis and use of fragmentoccurrence data in similarity-based virtual screening. J Comput Aided Mol Des 23:655-668. doi:10.1007/s10822-009-9285-0
Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explor Newsl 11:10-18. doi:10.1145/1656274.1656278
Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco
John GH, Langley P (1995) Estimating continuous distributions in Bayesian classifiers. In: Paper presented at the proceedings of the eleventh conference on uncertainty in artificial intelligence, Montre'al, Qué, Canada
Fan R-E, Chang K-W, Hsieh C-J, Wang X-R, Lin C-J (2008) LIBLINEAR: a library for large linear classification. J Mach Learn Res 9:1871-1874
Keerthi SS, Shevade SK, Bhattacharyya C, Murthy KRK (2001) Improvements to Platt's SMO algorithm for SVM classifier design. Neural Comput 13:637-649. doi:10.1162/08997660 1300014493
Swets J (1988) Measuring the accuracy of diagnostic systems. Science 240:1285-1293. doi:10.1126/science.3287615
Triballeau N, Acher F, Brabet I, Pin J-P, Bertrand H-O (2005) Virtual screening workflow development guided by the ''Receiver Operating Characteristic'' curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4. J Med Chem 48(7):2534-2547. doi:10.1021/ jm049092j
Challis GL, Ravel J (2000) Coelichelin, a new peptide siderophore encoded by the Streptomyces coelicolor genome: structure prediction from the sequence of its non-ribosomal peptide synthetase. FEMS Microbiol Lett 187:111-114. doi:10.1111/j.1574-6968.2000.tb09145.x
Pradeille N, Zerbe O, Mohle K, Linden A, Heimgartner H (2005) The first total synthesis of the Peptaibol Hypomurocin A1 and its conformation analysis: an application of the 'Azirine/Oxazolone Method'. Chem Biodivers 2:1127-1152. doi:10.1002/cbdv. 200590084
Gross H, Stockwell VO, Henkels MD, Nowak-Thompson B, Loper JE, Gerwick WH (2007) The genomisotopic approach: a systematic method to isolate products of orphan biosynthetic gene clusters. Chem Biol 14:53-63. doi:10.1016/j.chembiol. 2006. 11.007
Matthijs S, Laus G, Meyer J-M, Abbaspour-Tehrani K, Schafer M, Budzikiewicz H, Cornelis P (2009) Siderophore-mediated iron acquisition in the entomopathogenic bacterium Pseudomonas entomophila L48 and its close relative Pseudomonas putida KT2440. Biometals 22:951-964. doi:10.1007/s10534-009-9247-y
Wiest A, Grzegorski D, Xu B-W, Goulard C, Rebuffat S, Ebbole DJ, Bodo B, Kenerley C (2002) Identification of peptaibols from Trichoderma virens and cloning of a peptaibol synthetase. J Biol Chem 277:20862-20868. doi:10.1074/jbc.M201654200