Skip to main content

Advertisement

SpringerLink
Log in

Combined molecular docking and QSAR study of fused heterocyclic herbicide inhibitors of D1 protein in photosystem II of plants

  • Original Article
  • Published:
Molecular Diversity Aims and scope Submit manuscript

Abstract

Cinnoline, pyridine, pyrimidine, and triazine herbicides were found be inhibitors of the D1 protein in photosystem II (D1 PSII) electron transport of plants. The photosystem II inhibitory activity of these herbicides, expressed by experimental \(\hbox {pIC}_{50}\) values, was modeled by a docking and quantitative structure-activity relationships study. A conformer ensemble for each of the herbicide structure was generated using the MMFF94s force field. These conformers were further employed in a docking approach, which provided new information about the rational “active conformations” and various interaction patterns of the herbicide derivatives with D1 PSII. The most “active conformers” from the docking study were used to calculate structural descriptors, which were further related to the inhibitory experimental \(\hbox {pIC}_{50}\) values by multiple linear regression (MLR). The dataset was divided into training and test sets according to the partition around medoids approach, taking 27% of the compounds from the entire series for the test set. Variable selection was performed using the genetic algorithm, and several criteria were checked for model performance. WHIM and GETAWAY geometrical descriptors (position of substituents and moieties in the molecular space) were found to contribute to the herbicidal activity. The derived MLR model is statistically significant, shows very good stability and was used to predict the herbicidal activity of new derivatives having cinnoline, indeno[1.2-c]cinnoline-ll-one, triazolo[1,5-a] pyridine, imidazo[1,2-a]pyridine, triazine and triazolo[1,5-a] pyrimidine scaffolds whose experimental inhibitory activity against D1 PSII had not been determined up to now.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Troyer JR (2001) In the beginning: the multiple discovery of the first hormone herbicides. Weed Sci 49:290–297. doi:10.1614/0043-1745(2001)049[0290:ITBTMD]2.0.CO;2

  2. Liu YX, Zhao HP, Wang ZW, Li YH, Song HB, Wang QM (2013) The discovery of 3-(1-aminoethylidene)quinoline-2, \(4(1H,3H)\)-dione derivatives as novel PSII electron transport inhibitors. Mol Divers 17:701–710. doi:10.1007/s11030-013-9466-6

    Article  CAS  PubMed  Google Scholar 

  3. Dayan FE, de Zaccaro ML (2012) Chlorophyll fluorescence as a marker for herbicide mechanisms of action. Pestic Biochem Phys 102:189–197. doi:10.1016/j.pestbp.2012.01.005

    Article  CAS  Google Scholar 

  4. Zhang C, Chang S, Tian X, Tian Y (2014) 3D-QSAR and docking modeling study of 1,3,5-triazine derivatives as PSII electron transport inhibitor. Asian J Chem 26:264–268. doi:10.14233/ajchem.2014.15883

    CAS  Google Scholar 

  5. Pfister K, Arntzen CJ (1979) The mode of action of photosystem II-specific inhibitors in herbicide-resistant weed biotypes. Z Naturforsch 34c:996–1009. doi:10.1515/znc-1979-1123

    CAS  Google Scholar 

  6. Boussac A, Sugiura M, Rappaport F (2011) Probing the quinone binding site of photosystem II from Thermosynechococcus elongatus containing either PsbA1 or PsbA3 as the D1 protein through the binding characteristics of herbicides. BBA Bioenerg 1807:119–129. doi:10.1016/j.bbabio.2010.10.004

    Article  CAS  Google Scholar 

  7. Broser M, Glöckner C, Gabdulkhakov A, Guskov A, Buchta J, Kern J, Müh F, Dau H, Saenger W, Zouni A (2011) Structural basis of cyanobacterial photosystem II inhibition by the herbicide terbutryn. J Biol Chem 286:15964–15972. doi:10.1074/jbc.M110.215970

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Michel H, Epp O, Deisenhofer J (1986) Pigment protein interactions in the photosynthetic reaction centre from Rhodopseudomonas viridis. EMBO J 5:2445–2451

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Speck-Planche A, Kleandrova VV, Rojas-Vargas JA (2011) QSAR model toward the rational design of new agrochemical fungicides with a defined resistance risk using substructural descriptors. Mol Divers 15:901–909. doi:10.1007/s11030-011-9320-7

    Article  CAS  PubMed  Google Scholar 

  10. Gandy MN, Corral MG, Mylne JS, Stubbs KA (2015) An interactive database to explore herbicide physicochemical properties. Org Biomol Chem 13:5586–5590. doi:10.1039/C5OB00469A

    Article  CAS  PubMed  Google Scholar 

  11. Avram S, Funar-Timofei S, Borota A, Chennamaneni SR, Manchala AK, Muresan S (2014) Quantitative estimation of pesticide-likeness for agrochemical discovery. J Cheminform 6:42. doi:10.1186/s13321-014-0042-6

    Article  PubMed  PubMed Central  Google Scholar 

  12. Shimizu R, Iwamura H, Fujita T (1988) Quantitative structure–activity relationships of photosystem II inhibitory anilides and triazines. Topological aspects of their binding to the active site. J Agric Food Chem 36:1276–1283. doi:10.1021/jf00084a038

    Article  CAS  Google Scholar 

  13. Mitsutake KI, Iwamura H, Shimizu R, Fujita T (1986) Quantitative structure–activity relationship of photosystem II inhibitors in chloroplasts and its link to herbicidal action. J Agric Food Chem 34:725–732. doi:10.1021/jf00070a034

    Article  CAS  Google Scholar 

  14. Karacan MS, Yakan C, Yakan M, Karacan N, Zharmukhamedov SK, Shitov A, Los DA, Klimov VV, Allakhverdiev SI (2012) Quantitative structure-activity relationship analysis of perfluoroiso-propyldinitrobenzene derivatives known as photosystem II electron transfer inhibitors. BBA Bioenerg 1817:1229–1236. doi:10.1016/j.bbabio.2012.01.010

    Article  CAS  Google Scholar 

  15. Egner U, Gerbling KP, Hoyer GA, Kriiger G, Wegnerb P (1996) Design of inhibitors of photosystem II using a model of the D1 protein. Pestic Sci 47:145–158. doi:10.1002/(SICI)1096-9063(199606)47:2

    Article  CAS  Google Scholar 

  16. Sketch M (2015) Version 15.2.9. is software of Chemaxon. http://www.chemaxon.com

  17. OMEGA (2013) Version 2.5.1.4 is software of OpenEye Scientific Software., Santa Fe., NM. http://www.eyesopen.com

  18. Hawkins PCD, Skillman AG, Warren GL, Ellingson BA, Stahl MT (2010) Conformer generation with OMEGA: algorithm and validation using high quality structures from the protein databank and cambridge structural database. J Chem Inf Model 50:572–584. doi:10.1021/ci100031x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Hawkins PCD, Nicholls A (2012) Conformer generation with OMEGA: learning from the data set and the analysis of failures. J Chem Inf Model 52:2919–2936. doi:10.1021/ci300314k

    Article  CAS  PubMed  Google Scholar 

  20. Halgren TA (1999) MMFF VI. MMFF94s option for energy minimization studies. J Comput Chem 20:720–729. doi:10.1002/(SICI)1096-987X(199905)

    Article  CAS  Google Scholar 

  21. Di Tommaso P, Moretti S, Xenarios I, Orobitg M, Montanyola A, Chang JM, Taly JF, Notredame C (2011) T-Coffee: a web server for the multiple sequence alignment of protein and RNA sequences using structural information and homology extension. Nucleic Acids Res 39(Web Server issue):W13–W17. doi:10.1093/nar/gkr245

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. FRED Receptor module (2009) Version 2.2.5 OpenEye Scientific Software Inc., Santa Fe NM, USA. http://www.eyesopen.com

  23. FRED module (2012) Version 3.0.1 OpenEye Scientific Software Inc. Santa Fe NM, USA. www.eyesopen.com

  24. McGann M (2011) FRED pose prediction and virtual screening accuracy. J Chem Inf Model 51:578–596. doi:10.1021/ci100436p

    Article  CAS  PubMed  Google Scholar 

  25. McGann MR, Almond HR, Nicholls A, Grant JA, Brown FK (2003) Gaussian docking functions. Biopolymers 68:76–90. doi:10.1002/bip.10207

    Article  CAS  PubMed  Google Scholar 

  26. Dragon Professional (2007) Version 5.5, Talete S.R.L., Milano, Italy. http://www.talete.mi.it/products/dragon_description.htm

  27. Instant JChem (2012) Version 5.10.0, is software of Chemaxon. http://www.chemaxon.com

  28. Wold S, Dunn WJ III (1983) Multivariate quantitative structure activity relationships (QSAR): conditions for their applicability. J Chem Inf Comput Sci 23:6–13. doi:10.1021/ci00037a002

    Article  CAS  Google Scholar 

  29. Chirico N, Papa E, Kovarich S, Cassani S, Gramatica P (2012) QSARINS, software for QSAR MLR model development and validation. QSAR Res. Unit in Environ. Chem. and Ecotox., DiSTA, University of Insubria, Varese, Italy. http://www.qsar.it

  30. Gramatica P, Chirico N, Papa E, Cassani S, Kovarich S (2013) QSARINS: a new software for the development, analysis, and validation of QSAR MLR models. J Comput Chem 34:2121–2132. doi:10.1002/jcc.23361

    Article  CAS  Google Scholar 

  31. Kaufman L, Rousseeuw PJ (1990) Finding groups in data: an introduction to cluster analysis. Wiley, New York

    Book  Google Scholar 

  32. R Development Core Team (2010) ISBN 3-900051-07-0. www.r-project.org

  33. Goodarzi M, Deshpande S, Murugesan V, Katti SB, Prabhakar YS (2009) Is feature selection essential for ANN modeling? QSAR Comb Sci 28:1487–1499. doi:10.1002/qsar.200960074

    Article  CAS  Google Scholar 

  34. Chirico N, Gramatica P (2011) Real external predictivity of QSAR models: how to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient. J Chem Inf Model 51:2320–2335. doi:10.1021/ci200211n

    Article  CAS  PubMed  Google Scholar 

  35. Chirico N, Gramatica P (2012) Real external predictivity of QSAR models. Part 2. New intercomparable thresholds for different validation criteria and the need for scatter plot inspection. J Chem Inf Model 52:2044–2058. doi:10.1021/ci300084j

    Article  CAS  PubMed  Google Scholar 

  36. Roy PP, Paul S, Mitra I, Roy K (2009) On two novel parameters for validation of predictive QSAR models. Molecules 14:1660–1701. doi:10.3390/molecules14051660

    Article  CAS  PubMed  Google Scholar 

  37. Shi LM, Fang H, Tong W, Wu J, Perkins R, Blair RM, Branham WS, Dial SL, Moland CL, Sheehan DM (2001) QSAR models using a large diverse set of estrogens. J Chem Inf Model 41:186–195. doi:10.1021/ci000066d

    CAS  Google Scholar 

  38. Schüürmann G, Ebert RU, Chen J, WangB Kuhne R (2008) External validation and prediction employing the predictive squared correlation coefficient test set activity mean versus training set activity mean. J Chem Inf Model 48:2140–2145. doi:10.1021/ci800253u

    Article  PubMed  Google Scholar 

  39. Consonni V, Ballabio D, Todeschini R (2009) Comments on the definition of the Q2 parameter for QSAR validation. J Chem Inf Model 49:1669–1678. doi:10.1021/ci900115y

    Article  CAS  PubMed  Google Scholar 

  40. Roy K, Mitra I (2012) On the use of the metric \(\text{ r }_{{\rm m}}^2\) as an effective tool for validation of QSAR models in computational drug design and predictive toxicology. Mini Rev Med Chem 12:491–504. doi:10.2174/138955712800493861

    Article  CAS  PubMed  Google Scholar 

  41. Chatterjee S, Price B (1991) Regression analysis by example, 2nd edn. John Wiley & Sons, New York

    Google Scholar 

  42. Frank IE, Todeschini R (1994) The data analysis handbook. Elsevier, Amsterdam

    Google Scholar 

  43. Eriksson L, Johansson E, Kettaneh-Wold N, Wold S (2001) Multi and megavariate data analysis: principles and applications. Umetrics AB, Umea, pp 92–97

    Google Scholar 

  44. Keller HR, Massart DL, Brans JP (1991) Multicriteria decision making: a case study. Chemom Int Lab Syst 11:175–189. doi:10.1016/0169-7439(91)80064-W

    Article  CAS  Google Scholar 

  45. Gramatica P (2013) Computational toxicology methods in molecular biology. In: Reisfeld B, Mayeno AN (eds) On the development and validation of QSAR models, vol 930. Springer, Berlin, pp 499–526

    Google Scholar 

  46. Egner U, Hoyer GA, Saenger W (1993) Modeling and energy minimization studies on the herbicide binding protein (D1) in photosystem II of plants. Biochim Biophys Acta 1142:106–114. doi:10.1016/0005-2728(93)90091-S

    Article  CAS  Google Scholar 

  47. Gohlke H, Hendlich M, Klebe G (2000) Knowledge-based scoring function to predict protein–ligand interactions. J Mol Biol 295:337–356. doi:10.1006/jmbi.1999.3371

    Article  CAS  PubMed  Google Scholar 

  48. Sobolev V, Edelman M (1995) Modeling the quinone-B binding site of the photosystem-II reaction center using notions of complementarity and contact-surface between atoms. Proteins 21:214–225. doi:10.1002/prot.340210304

    Article  CAS  PubMed  Google Scholar 

  49. Takahashi R, Hasegawa K, Takano A, Noguchi T (2010) Structures and binding sites of phenolic herbicides in the Q(B) pocket of photosystem II. Biochem 49:5445–5454. doi:10.1021/bi100639q

    Article  CAS  Google Scholar 

  50. Lambreva MD, Russo D, Polticelli F, Scognamiglio V, Antonacci A, Zobnina V, Campi G, Rea G (2014) Structure/function/dynamics of photosystem II plastoquinone binding sites. Curr Protein Pept Sci 15:285–295. doi:10.2174/1389203715666140327104802

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Petrouleas V, Crofts AR (2005) The iron-quinone acceptor complex. In: Wydrzynski TJ, Satoh K (eds) Advances in photosynthesis and respiration, Vol. 22, Photosystem II: the light-driven water: plastoquinone oxidoreductase, Part III, Chapter 8, Springer, Dordrecht, pp. 177–206. doi:10.1007/1-4020-4254-X_9

  52. Mackay SP, O’Malley PJ (1993) Molecular modelling of the interactions between optically active triazine herbicides and photosystem II. Z Naturforsch 48c:474–481

    Google Scholar 

  53. STATISTICA (2005) Version 7.1, Tulsa, StatSoft Inc, OK, USA

  54. Todeschini R, Bettiol C, Giurin G, Gramatica P, Miana P, Argese E (1996) Modeling and prediction by using WHIM descriptors in QSAR studies: submitochondrial particles (SMP) as toxicity blosensors of chlorophenols. Chemosphere 33:71–79. doi:10.1016/0045-6535(96)00153-1

    Article  CAS  Google Scholar 

  55. Consonni V, Todeschini R, Pavan M (2002) Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 1. Theory of the Novel 3D molecular descriptors. J Chem Inf Comput Sci 42:682–692. doi:10.1021/ci015504a

  56. Lewis KA, Tzilivakis J, Warner D, Green A (2016) An international database for pesticide risk assessments and management. Hum Ecol Risk Assess Int J 22:1050–1064. doi:10.1080/10807039.2015.1133242

    Article  CAS  Google Scholar 

  57. Gramatica P, Cassani S, Roy PP, Kovarich S, Yap CW, Papa E E (2012) QSAR modeling is not “Push a Button and Find a Correlation”: a case study of toxicity of (benzo-)triazoles on algae. Mol Inf 31:817–835. doi:10.1002/minf.201200075

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was financially supported by the Project No. 1.1/2016 of the Institute of Chemistry of Romanian Academy, Timişoara. Access to the Chemaxon Ltd., OpenEye Ltd., Accelerys Software Inc. (for the Discovery Studio Visualiser version 2.5 program, used for the preparation of Figs. 1, 2, 3, 4, 5, 8) and Prof. Paola Gramatica from the University of Insubria (Varese, Italy) software are greatly acknowledged by the authors.

Author information

Authors and Affiliations

  1. Institute of Chemistry Timisoara of the Romanian Academy, B-dul Mihai Viteazul 24, 300223, Timisoara, Romania

    Simona Funar-Timofei, Ana Borota & Luminita Crisan

Authors
  1. Simona Funar-Timofei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ana Borota
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luminita Crisan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Luminita Crisan.

Additional information

Dedicated to the 150th anniversary of the Romanian Academy.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Funar-Timofei, S., Borota, A. & Crisan, L. Combined molecular docking and QSAR study of fused heterocyclic herbicide inhibitors of D1 protein in photosystem II of plants. Mol Divers 21, 437–454 (2017). https://doi.org/10.1007/s11030-017-9735-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11030-017-9735-x

Keywords

Search

Navigation