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Fascaplysin (Figure 1) is a sponge-derived red pigment that exhibits a wide range of biological activity among which are antibacterial, antifungal, antiviral and anticancer properties [1]. Recently it was reported that fascaplysin serves as selective μ–opioid receptor (MOR) agonist with a signaling profile that resembles endorphins [2]. It makes fascaplysin a perspective compound for development of novel analgesics with reduced side effects.

fascaplysin,opioid receptor
Figure 1. Molecular structure of fascapysin 1

In this research molecular docking analysis of an array of fascaplysin derivatives was executed against activated MOR (PDB-ID: 5C1M [3]) to find out the most promising compounds for further synthesis and investigation. Previously we utilized docking simulations to the study of interaction of fascaplysin derivatives with the active sites of acetylcholinesterase [4] and cyclin-dependent kinase 4 (CDK4) [5].

Fascaplysin docking simulations resulted in several conformations differing in localization in the active site of MOR. To reveal what conformations define MOR agonist activity docking procedure was performed against homology model of activated d-opioid receptor (DOR). It is known that fascaplysin is inactive against DOR [2]. Superposition of results of docking with DOR and MOR allowed to exclude matching conformations and identify interactions that are crucial for MOR selective agonist activity (Figure 2).

fascaplysin,opioid receptor
Figure 2. Superposition of fascaplysin conformations in MOR (light grey) and DOR (black) active sites

Nevertheless, fascaplysin ability to arrest the cell cycle via CDK4 inhibition makes it impossible to use the alkaloid in pain treatment. This problem can be solved by substitution of indole-NH that is crucial for CDK4 inhibition activity with N-methyl.
The docking simulations were executed with Autodock4 software according to user manual [6]. Homology model of activated DOR was built based on the crystal structure of activated MOR [3] using SWISS-MODEL server [7]. We studied the interaction of 160 fascaplysin and N-methyl-fascaplysin derivatives with various substituents in position 9 (Table 1) with the binding site of activated MOR model.

Table 1. Fascaplysin derivatives

fascaplysin,opioid receptor R1=
NO2, Halogen, OH, N, Ph;
CH2OH, COOH, COOEt, C(O)Me, C(O)Et, C(O)NH2;
OC(O)CH2(Me)Et, OC(O)CH2i-Pr,
NH(CH2)nCOOH; n=1-3;
R2=H, Me.
fascaplysin,opioid receptor R1=
Bu, (CH2)nt-Bu, CH(Me)CH2(Me), CH(Me)i-Pr,
(CH2)nMe; n=1-3;
Naphthyl, BzNHBz;
R2=H, Me.
fascaplysin,opioid receptor R1=H, Me;
R2, R3=NO2, Halogen, Me, OH, N+ – substituent position 1-4.
fascaplysin,opioid receptor R1=H, Me;
R2= Me, Et, Ph;
NO2, Halogen, Me, Et, OH;
OMe, OEt, NHMe, NHEt, CH2NHMe, C(O)Me;, NHC(O)Me – substituent position 1-4.
fascaplysin,opioid receptor fascaplysin,opioid receptor R1=H, Me;
R2, R3=NO2, Halogen, Me, OH – substituent position 1-4.
fascaplysin,opioid receptor fascaplysin,opioid receptor
fascaplysin,opioid receptor fascaplysin,opioid receptor

Most fascaplysin derivatives interact with amino acids of the binding site that are critical for MOR agonist activity. Replacement of indole-NH with N-methyl shifts the ligand from the pocket of active conformations. However, insertion of substituent in position 9 of N-methyl-fascaplysin makes them bind to receptor according to native fascaplysin (Figure 3).

fascaplysin,opioid receptor
Figure 3. Orientation of fascaplysin (black), N-methyl-fascaplysin (light-grey) and N-methyl-fascaplysin derivative with substituent in position 9 (grey) in the active site

The energies of binding of most derivatives surpassed the same parameter for fascaplysin in absolute value what makes the studied compounds perspective for further research. The most energetically favorable derivative is compound 2 (Figure 4) with energy value minus 15,92 kkal/mol. We have chosen compound 2 for subsequent synthesis and exploration taking into account the score of binding energy and ligand orientation in the active site.

fascaplysin,opioid receptor
Figure 4. Molecular structure of compound 2

This work represents the molecular modeling study of fascaplysin derivatives as new promising selective MOR agonists. To conclude, we defined structural fragments that enhance the binding affinity of fascaplysin derivatives to MOR what makes them perspective for development of analgesics with reduced liability for dependence and tolerance. One fascaplysin derivative was chosen for synthesis and further comprehensive research.


  1. B. Bharate, S., Manda, S., Mupparapu, N., Battini, N., & A. Vishwakarma, R. (2012). Chemistry and Biology of Fascaplysin, a Potent Marine-Derived CDK-4 Inhibitor. MRMC, 12(7), 650-664. http://dx.doi.org/10.2174/138955712800626719
  2. Johnson, T., Milan-Lobo, L., Che, T., Ferwerda, M., Lambu, E., & McIntosh, N. et al. (2016). Identification of the First Marine-Derived Opioid Receptor “Balanced” Agonist with a Signaling Profile That Resembles the Endorphins. ACS Chemical Neuroscience, 8(3), 473-485. http://dx.doi.org/10.1021/acschemneuro.6b00167
  3. Huang, W., Manglik, A., Venkatakrishnan, A., Laeremans, T., Feinberg, E., & Sanborn, A. et al. (2015). Structural insights into µ-opioid receptor activation. Nature, 524(7565), 315-321. http://dx.doi.org/10.1038/nature14886
  4. Пак, М. А. Изучение взаимодействия производных фаскаплизина с активным сайтом ацетилхолинэстеразы методом молекулярного моделирования / М. А. Пак, М. Е. Жидков // Региональная научно-практическая конференция студентов, аспирантов и молодых учёных по естественным наукам, Владивосток 15-30 апреля 2016 г.: тез. докл. – Владивосток, 2016. – С. 467-469. https://www.dvfu.ru/schools/school_of_natural_sciences/sciences/the-conference/new-page.php
  5. Pak, M. A. Molecular docking study of fascaplysin derivatives interaction with CDK4 / M. A. Pak, M. E. Zhidkov, V.B. Kolycheva // 3rd Fefu SNS Students, Master’s Degree Students and Postgraduate Students Scientific-Practical Conference in English, Vladivostok 25 April – 08 May 2016: тез. докл. – Владивосток, 2016. – С. 78-81. https://www.dvfu.ru/schools/school_of_natural_sciences/sciences/the-conference/
  6. Morris, G., Huey, R., Lindstrom, W., Sanner, M., Belew, R., Goodsell, D., & Olson, A. (2009). AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem., 30(16), 2785-2791. http://dx.doi.org/10.1002/jcc.21256
  7. Biasini, M., Bienert, S., Waterhouse, A., Arnold, K., Studer, G., & Schmidt, T. et al. (2014). SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Research, 42(W1), W252-W258. http://dx.doi.org/10.1093/nar/gku340

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