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Planta Med 2018; 84(05): 304-310
DOI: 10.1055/s-0043-121992
Biological and Pharmacological Activity
Original Papers
Georg Thieme Verlag KG Stuttgart · New York

Structural Searching of Biosynthetic Enzymes to Predict Protein Targets of Natural Products

Noé Sturm
1   Laboratory of Therapeutic Innovation, Medalis Drug Discovery Center, University of Strasbourg, Illkirch, France
2   Griffith Institute for Drug Discovery, Griffith University, Brisbane, Australia
,
Ronald J. Quinn
2   Griffith Institute for Drug Discovery, Griffith University, Brisbane, Australia
,
Esther Kellenberger
1   Laboratory of Therapeutic Innovation, Medalis Drug Discovery Center, University of Strasbourg, Illkirch, France
› Institutsangaben
Weitere Informationen

Publikationsverlauf

received 06. Juli 2017
revised 18. Oktober 2017

accepted 20. Oktober 2017

Publikationsdatum:
03. November 2017 (online)

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Abstract

Recently, we have demonstrated that site comparison methodology using flavonoid biosynthetic enzymes as the query could automatically identify structural features common to different flavonoid-binding proteins, allowing for the identification of flavonoid targets such as protein kinases. With the aim of further validating the hypothesis that biosynthetic enzymes and therapeutic targets can contain a similar natural product imprint, we collected a set of 159 crystallographic structures representing 38 natural product biosynthetic enzymes by searching the Protein Databank. Each enzyme structure was used as a query to screen a repository of approximately 10 000 ligandable sites by active site similarity. We report a full analysis of the screening results and highlight three retrospective examples where the natural product validates the method, thereby revealing novel structural relationships between natural product biosynthetic enzymes and putative protein targets of the natural product. From a prospective perspective, our work provides a list of up to 64 potential novel targets for 25 well-characterized natural products.

Key words

structural similarity - protein binding site - biosynthetic enzymes - virtual screening - natural products

Supporting Information

 

  • References

  • 1 Newman DJ, Cragg GM. Natural products as sources of new drugs from 1981 to 2014. J Nat Prod 2016; 79: 629-661
    CrossrefPubMedGoogle Scholar
  • 2 Tajabadi FM, Campitelli MR, Quinn RJ. Scaffold flatness: reversing the trend. Springer Sci Rev 2013; 1: 141-151
    PubMedGoogle Scholar
  • 3 Romo D, Liu JO. Editorial: Strategies for cellular target identification of natural products. Nat Prod Rep 2016; 33: 592-594
    CrossrefPubMedGoogle Scholar
  • 4 Olğaç A, Orhan IE, Banoglu E. The potential role of in silico approaches to identify novel bioactive molecules from natural resources. Future Med Chem 2017; 9: 1663-1684
    CrossrefPubMedGoogle Scholar
  • 5 Ma ST, Feng CT, Dai GL, Song Y, Zhou GL, Zhang XL, Miao CG, Yu H, Ju WZ. In silico target fishing for the potential bioactive components contained in Huanglian Jiedu Tang (HLJDD) and elucidating molecular mechanisms for the treatment of sepsis. Chin J Nat Med 2015; 13: 30-40
    PubMedGoogle Scholar
  • 6 Gu J, Gui Y, Chen L, Yuan G, Lu HZ, Xu X. Use of natural products as chemical library for drug discovery and network pharmacology. PLoS One 2013; 8: e62839
    CrossrefPubMedGoogle Scholar
  • 7 Sturm N, Quinn RJ, Kellenberger E. Similarity between flavonoid biosynthetic enzymes and flavonoid protein targets captured by three-dimensional computing approach. Planta Med 2015; 81: 467-473
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 8 Desaphy J, Bret G, Rognan D, Kellenberger E. sc-PDB: a 3D-database of ligandable binding sites – 10 years on. Nucleic Acids Res 2015; 43: D399-D404
    CrossrefPubMedGoogle Scholar
  • 9 Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE. The protein Data Bank. Nucleic Acids Res 2000; 28: 235-242
    CrossrefPubMedGoogle Scholar
  • 10 Caspi R, Altman T, Billington R, Dreher K, Foerster H, Fulcher CA, Holland TA, Keseler IM, Kothari A, Kubo A, Krummenacker M, Latendresse M, Mueller LA, Ong Q, Paley S, Subhraveti P, Weaver DS, Weerasinghe D, Zhang P, Karp PD. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 2014; 42: D459-D471
    CrossrefPubMedGoogle Scholar
  • 11 Morgat A, Coissac E, Coudert E, Axelsen KB, Keller G, Bairoch A, Bridge A, Bougueleret L, Xenarios I, Viari A. UniPathway: a resource for the exploration and annotation of metabolic pathways. Nucleic Acids Res 2012; 40: D761-D769
    CrossrefPubMedGoogle Scholar
  • 12 The UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res 2015; 43: D204-D212
    CrossrefPubMedGoogle Scholar
  • 13 Furnham N, Holliday GL, de Beer TAP, Jacobsen JOB, Pearson WR, Thornton JM. The catalytic site atlas 2.0: cataloging catalytic sites and residues identified in enzymes. Nucleic Acids Res 2014; 42: D485-D489
    CrossrefPubMedGoogle Scholar
  • 14 Desaphy J, Azdimousa K, Kellenberger E, Rognan D. Comparison and druggability prediction of protein-ligand binding sites from pharmacophore-annotated cavity shapes. J Chem Inf Model 2012; 52: 2287-2299
    CrossrefPubMedGoogle Scholar
  • 15 Roach P, Clifton I, Hensgens C, Shibata N, Schofield C, Hajdu J, Baldwin J. Structure of isopenicillin N synthase complexed with substrate and the mechanism of penicillin formation. Nature 1997; 387: 827-830
    CrossrefPubMedGoogle Scholar
  • 16 Starks C, Back K, Chappell J, Noel J. Structural basis for cyclic terpene biosynthesis by tobacco 5-epi-aristolochene synthase. Science 1997; 277: 1815-1820
    CrossrefPubMedGoogle Scholar
  • 17 Gaulton A, Hersey A, Nowotka M, Bento AP, Chambers J, Mendez D, Mutowo P, Atkinson F, Bellis LJ, Cibrián-Uhalte E, Davies M, Dedman N, Karlsson A, Magariños MP, Overington JP, Papadatos G, Smit I, Leach AR. The ChEMBL database in 2017. Nucleic Acids Res 2017; 45: D945-D954
    CrossrefPubMedGoogle Scholar
  • 18 Law V, Knox C, Djoumbou Y, Jewison T, Guo AC, Liu Y, Maciejewski A, Arndt D, Wilson M, Neveu V, Tang A, Gabriel G, Ly C, Adamjee S, Dame ZT, Han B, Zhou Y, Wishart DS. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res 2014; 42: D1091-1097
    CrossrefPubMedGoogle Scholar
  • 19 Schalon C, Surgand JS, Kellenberger E, Rognan D. A simple and fuzzy method to align and compare druggable ligand-binding sites. Proteins Struct Funct Bioinforma 2008; 71: 1755-1778
    CrossrefPubMedGoogle Scholar
  • 20 Bush K, Jacoby GA. Updated functional classification of β-lactamases. Antimicrob Agents Chemother 2010; 54: 969-976
    CrossrefPubMedGoogle Scholar
  • 21 Sturm N, Rognan D, Quinn RJ, Kellenberger E. Comparing atom-based with residue-based descriptors in predicting binding site similarity: do backbone atoms matter?. Future Med Chem 2016; 8: 1871-1885
    CrossrefPubMedGoogle Scholar
  • 22 Hoffman AF, Lupica CR. Synaptic targets of Δ9-tetrahydrocannabinol in the central nervous system. Cold Spring Harb Perspect Med 2013; 3: a012237
    PubMedGoogle Scholar
  • 23 Moss DE, Peck PL, Salome R. Tetrahydrocannabinol and acetylcholinesterase. Pharmacol Biochem Behav 1978; 8: 763-765
    CrossrefPubMedGoogle Scholar
  • 24 Xie X, Meehan MJ, Xu W, Dorrestein PC, Tang Y. Acyltransferase mediated polyketide release from a fungal megasynthase. J Am Chem Soc 2009; 131: 8388-8389
    CrossrefPubMedGoogle Scholar
  • 25 Stender S, Nordestgaard B. [Cholesterol – when is preventive treatment with statin indicated?]. Ugeskr Laeger 2011; 173: 1747 author reply 1747
    PubMedGoogle Scholar
  • 26 Lin YC, Lin JH, Chou CW, Chang YF, Yeh SH, Chen CC. Statins increase p21 through inhibition of histone deacetylase activity and release of promoter-associated HDAC1/2. Cancer Res 2008; 68: 2375-2383
    CrossrefPubMedGoogle Scholar
  • 27 Shahbazian MD, Grunstein M. Functions of site-specific histone acetylation and deacetylation. Annu Rev Biochem 2007; 76: 75-100
    CrossrefPubMedGoogle Scholar
  • 28 Guidotti A, Auta J, Chen Y, Davis JM, Dong E, Gavin DP, Grayson DR, Matrisciano F, Pinna G, Satta R, Sharma RP, Tremolizzo L, Tueting P. Epigenetic GABAergic targets in schizophrenia and bipolar disorder. Neuropharmacology 2011; 60: 1007-1016
    CrossrefPubMedGoogle Scholar
  • 29 Chuang DM, Leng Y, Marinova Z, Kim HJ, Chiu CT. Multiple roles of HDAC inhibition in neurodegenerative conditions. Trends Neurosci 2009; 32: 591-601
    CrossrefPubMedGoogle Scholar
  • 30 Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE. UCSF Chimera – a visualization system for exploratory research and analysis. J Comput Chem 2004; 25: 1605-1612
    CrossrefPubMedGoogle Scholar
  • 31 Hastings J, de Matos P, Dekker A, Ennis M, Harsha B, Kale N, Muthukrishnan V, Owen G, Turner S, Williams M, Steinbeck C. The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013. Nucleic Acids Res 2013; 41: D456-D463
    PubMedGoogle Scholar
  • 32 Morgat A, Axelsen KB, Lombardot T, Alcántara R, Aimo L, Zerara M, Niknejad A, Belda E, Hyka-Nouspikel N, Coudert E, Redaschi N, Bougueleret L, Steinbeck C, Xenarios I, Bridge A. Updates in Rhea – a manually curated resource of biochemical reactions. Nucleic Acids Res 2015; 43: D459-D464
    CrossrefPubMedGoogle Scholar
  • 33 Velankar S, Dana JM, Jacobsen J, van Ginkel G, Gane PJ, Luo J, Oldfield TJ, OʼDonovan C, Martin MJ, Kleywegt GJ. SIFTS: Structure integration with function, taxonomy and sequences resource. Nucleic Acids Res 2013; 41: D483-D489
    PubMedGoogle Scholar
  • 34 Needleman SB, Wunsch CD. A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol 1970; 48: 443-453
    CrossrefPubMedGoogle Scholar