Bioorganic & Medicinal Chemistry Letters 24 (2014) 835–838

Contents lists available at ScienceDirect

Bioorganic & Medicinal Chemistry Letters journal homepage: www.elsevier.com/locate/bmcl

Virtual screening and biochemical evaluation to identify new inhibitors of mammalian target of rapamycin (mTOR) Hwangseo Park a,⇑, Hyeonjeong Choe b, Sungwoo Hong b,⇑ a b

Department of Bioscience and Biotechnology, Sejong University, 98 Kunja-Dong, Kwangjin-Ku, Seoul 143-747, Republic of Korea Department of Chemistry, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 305-701, Republic of Korea

a r t i c l e

i n f o

Article history: Received 20 November 2013 Revised 17 December 2013 Accepted 19 December 2013 Available online 27 December 2013 Keywords: Virtual screening mTOR Docking Kinase inhibitor Anticancer agents

a b s t r a c t Mammalian target of rapamycin (mTOR) is a promising target for the development of anticancer medicines. Here, we report the first example for a successful application of the structure-based virtual screening to identify new mTOR inhibitors. Using the scoring function improved by implementing the ligand solvation effects on protein–ligand association, six novel mTOR inhibitors are found with IC50 values ranging from 8 to 60 lM. Because these new inhibitors are also computationally screened for having desirable physicochemical properties as a drug candidate, they deserve consideration for further development by structure–activity relationship studies to optimize the inhibitory and anticancer activities. Structural features relevant to the stabilization of the inhibitors in the ATP-binding site of mTOR are addressed in detail. Ó 2013 Elsevier Ltd. All rights reserved.

Mammalian target of rapamycin (mTOR) belongs to the phosphoinositide kinase-related kinase (PIKK) family, and occupies an important signaling node of PI3K/AKT/mTOR pathway. Although the normal activity of this pathway is indispensable for cell growth and proliferation,1 its aberrant activation can be responsible for the pathogenesis of various human cancers.2,3 Therefore, the inhibition of mTOR activity with small molecules has been considered a promising therapeutic strategy for the treatment of cancer. The clinical approval of its inhibitors such as rapamycin and related analogues (rapalogues) manifested the usefulness of mTOR as a target for the development of anticancer medicines.4 However, the efficacies of rapalogues have been limited due to the only partial inhibition of mTOR activity and the existence of a negative feedback loop.5,6 These prompted the discovery of new potent mTOR inhibitors with higher efficacy than rapalogues. Accordingly, a great deal of efforts have been devoted to the discovery of mTOR inhibitors with the aim to develop new anticancer medicine as recently reviewed in a comprehensive fashion.7,8 These scientific endeavors led to the discovery of potent mTOR inhibitors including pyrimidoaminotropane, GDC-0349, N-methylated imidazolo-pyrimidines, and CC214-2.9–12 Because the PI3K/ AKT/mTOR pathway may be resistant to the inhibitors due to the feedback activation,13 the discovery of dual PI3K/mTOR inhibitors has also been actively pursued.14–16 Complementary to various ⇑ Corresponding authors. Tel.: +82 2 3408 3766; fax: +82 2 3408 4334 (H.P.); tel.: +82 42 350 2811; fax: +82 42 350 2812 (S.H.). E-mail addresses: [email protected] (H. Park), [email protected] (S. Hong). 0960-894X/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.bmcl.2013.12.081

experimental findings, computational studies have also been carried out based on 3D-QSAR and molecular docking to address the structural and energetic features relevant to the potencies of known mTOR inhibitors.17 However, the lack of full-length structure of mTOR has limited the applicability of computational methods. Recently, three dimensional structure of mTOR was reported in complex with small-molecule inhibitors as well as in the resting form.18 The presence of structural information about the nature of the interactions between mTOR and its potent inhibitors allows for a rational design of new lead compounds for anticancer medicines. In the present study, we aim to identify new classes of mTOR inhibitors by means of a structure-based drug design protocol involving the virtual screening with docking simulations and in vitro enzyme assay. Virtual screening with docking simulation has not always been successful due to the inaccuracy in the scoring function. In particular, the binding affinity of a molecule with many polar atoms has often been overestimated due to the neglect of ligand solvation effects in the scoring function.19 Prior to the calculation of the binding free energies between mTOR and the putative ligands, therefore, we improve the scoring function by the implementation of an accurate solvation model. Docking simulations with the modified binding free energy function are expected to be useful for enriching the chemical library with molecules that are likely to have the inhibitory activity against mTOR. We prepared the receptor model from the X-ray crystal structure of mTOR in complex with the inhibitor Torin2 (PDB code: 4JSX)18 to perform the virtual screening with docking simulations to identify new mTOR inhibitors from a large chemical database.

836

H. Park et al. / Bioorg. Med. Chem. Lett. 24 (2014) 835–838

To obtain the all-atom model for mTOR, hydrogen atoms were added to each heavy atom of the target protein. A special attention was paid to assign the protonation states of the ionizable Asp, Glu, His, and Lys residues in the X-ray crystal structure of mTOR. The side chains of Asp and Glu residues were assumed to be neutral if one of their carboxylate oxygens pointed toward a hydrogenbond accepting group including the backbone aminocarbonyl oxygen at a distance within 3.5 Å, a generally accepted distance limit for a hydrogen bond of moderate strength.20 Similarly, the lysine side chains were assumed to be protonated unless the NZ atom was in proximity to a hydrogen-bond donating group. The same procedure was also applied to determine the protonation states of ND and NE atoms in His residues. The docking library for mTOR comprising about 260,000 compounds was constructed from the chemical database distributed by Interbioscreen (http://www.ibscreen.com) containing approximately 477,000 synthetic and natural compounds. Prior to the virtual screening with docking simulations, they were filtrated on the basis of Lipinski’s ‘Rule of Five’ with the ISIS/BASE program to adopt only the compounds with the physicochemical properties of potential drug candidates21 and without reactive functional group(s). To remove the structural redundancies in the docking library, the structurally similar compounds with Tanimoto coefficient larger than 0.8 were clustered into a single representative molecule. All of these pre-filtrated compounds were then processed with the CORINA program to generate the three dimensional atomic coordinates, followed by the assignment of Gasteiger-Marsilli atomic charges.22 We used the AutoDock program23 in the virtual screening of mTOR inhibitors. AMBER force field parameters were assigned to calculate van der Waals interactions and the internal energy of a ligand as implemented in the original AutoDock program. Docking simulations were then carried out to score and rank the compounds in the docking library according to the calculated binding affinities in the ATP-binding site of mTOR. In the actual docking simulation between mTOR and the compounds in the docking library, we used the binding free energy function constructed by combining the molecular hydration free energy function for ligands into the original AutoDock scoring function. This modified scoring function has the following form. Here, WvdW, Whbond, Welec, Wtor, and Wsol are the weighting factors of van der Waals, hydrogen bond, electrostatic interactions, torsional term, and desolvation energy of the putative inhibitors, respectively. rij represents the interatomic distance, and Aij, Bij, Cij, and Dij are related to the depth of the potential energy well and the equilibrium separations between the protein and ligand atoms. The hydrogen bond term has an additional weighting factor, E(t), representing the angle-dependent directionality. With respect to the distance-dependent dielectric constant (e(rij)), a sigmoidal function proposed by Mehler et al.24 was used in computing the interatomic electrostatic interactions between mTOR and the putative inhibitors. In the entropic term, Ntor is the number of all rotatable bonds in the ligand. In the desolvation term, Si and Vi are the solvation parameter and the fragmental volume of atom i,25 respectively, while Occimax stands for the maximum atomic occupancy. In the calculation of the solvation free energy term in Eq. 1, we used the atomic parameters developed by Choi et al. because they proved to be successful in predicting the solvation free energies of a variety of organic molecules.26 This modification of the scoring function seems to increase the accuracy in virtual screening of mTOR inhibitors because the underestimation of ligand solvation often leads to the overestimation of the binding affinity of a ligand with many polar atoms. Indeed, the superiority of the modified scoring function to the previous one was demonstrated in recent studies for virtual screening of kinase and phosphatase inhibitors.27,28

DGaq bind ¼ W v dW

XX A i¼1 j¼1

þW elec

XX i¼1 j¼1

ij r12 ij

   XX B C D  r6ij þ W hbond EðtÞ r12ij  r10ij

qi qj

eðr ij Þr ij

ij

i¼1 j¼1

þ W tor N tor þ W sol

ij

ij

X X  r2ij Si Occmax  V j e 2r2 i i¼1

!

j–i

ð1Þ

Docking simulation of a compound in the docking library started with the calculation of the three dimensional grids of interaction energy for all possible atom types. These uniquely defined potential grids for mTOR were then used in common for docking simulations of all compounds under consideration. As the center of the common grids in the ATP-binding site, we used the center of mass coordinates of Torin 2 in the original X-ray crystal structure of mTOR-Torin 2 complex. The calculated grid maps were of dimension 61  61  61 points with the spacing of 0.375 Å, yielding a receptor model that includes the atoms within 22.9 Å of the grid center. These grid maps were sufficient to cope with almost the entire part of the kinase domain of mTOR. Using this receptor model, docking simulations between mTOR and each compound in the docking library were carried out to select 200 top-scored compounds. Of 260,000 compounds screened with docking simulations in the ATP-binding site of mTOR, 200 top-scored compounds were selected as virtual hits. 186 of them were available from the compound supplier and were tested for having the inhibitory activity against mTOR at the concentration of 50 lM in a high-throughput binding assay (KINOMEscan, Ambit Biosciences).29 As a result, six compounds were found to have the percent of control (POC) value less than 60 for mTOR. Their IC50 values were then measured to quantify the inhibitory activities. The chemical structures and the inhibitory activities of the newly identified mTOR inhibitors are shown in Figure 1 and Table 1, respectively. None of these compounds has been reported as mTOR inhibitor so far neither in the literature nor in the patent. We note that 1, 2, 3, 4, 5, and 6 include furo[3,2-g]chromen-7-one, pyrazolo[3,4-d]pyrimidine, thiadiazolo[3,2-a]pyrimidin-7-one, phthalazine, pyrrolo[2,3-d]pyrimidine, and thieno[2,3-b]pyridine, respectively. These polycyclic aromatic groups seem to serve as a surrogate for the adenine moiety of ATP that establish the hydrogen bonds with backbone groups in the ATP binding site of mTOR. It is also a common structural feature that 1–6 have the nonpolar aromatic group at the end of molecular structures, which are expected to form the van der Waals contact with hydrophobic residues in the ATP-binding site. Due to the moderate inhibitory activities and low-molecular weights (330–385), 1–3 deserve consideration for further development by structure–activity relationship (SAR) studies to optimize the anticancer activity. To obtain structural insight into the inhibitory mechanisms of the identified inhibitors of mTOR, their binding modes in the ATP-binding site were investigated in a comparative fashion. Figure 2 shows the lowest-energy conformations of 1–6 in the ATP-binding site of mTOR calculated with the modified AutoDock program. The results for docking simulations of 1–6 are self-consistent in the sense that the inhibitors exhibit a similar binding mode with respect to the amino-acid residues around the ATP-binding site, activation and G loops. For example, the polar polycyclic aromatic rings of the inhibitors point toward the backbone groups of ATP-binding site while their hydrophobic groups are located in proximity to the activation and Gly loops. Although most kinase inhibitors bind in the ATP-binding pocket located between Nand C-lobes, some inhibitors are known to impair the kinase activity through the binding in the surface pocket.30 In order to examine the possibility of such an allosteric inhibition of mTOR by the identified inhibitors, docking simulations were carried out with the grid maps for the receptor model so as to include the entire part

H. Park et al. / Bioorg. Med. Chem. Lett. 24 (2014) 835–838

837

Figure 1. Chemical structures of the newly identified mTOR inhibitors.

Table 1 POCa and IC50 values (in lM) of 1–6 against mTOR together with their rankings in virtual screening Compounds

POC

IC50

Ranking

1 2 3 4 5 6

11 54 56 46 54 59

8.3 20.4 32.2 52.2 54.1 57.6

39 117 4 62 83 19

a Lower numbers of POC (percent of control) indicate stronger hits. Values show an average of duplicate measurements.

of the kinase domain. However, the binding configuration in which an inhibitor resides outside the ATP-binding site was not observed for 1–6. These results support the possibility that the newly iden-

Figure 2. Comparative view of the binding modes 1–6 in the ATP-binding site of mTOR. Carbon atoms of 1–6 are indicated in green, cyan, black, gray, pink, and violet, respectively. The positions of ATP-binding site, activation and Gly loops are also indicated.

tified inhibitors would impair the kinase activity of mTOR through the specific binding in the ATP-binding site. We now turn to the identification of the detailed interactions pertinent to the stabilization of the identified inhibitors in the ATP-binding site. The calculated binding mode of 1 in the ATPbinding site of mTOR is shown in Figure 3. We note that the ester group on furo[3,2-g]chromen-7-one ring of 1 receives a hydrogen bond from the backbone amidic nitrogen of Val2240. The hydrogen-bond interaction between the backbone amide group of Val2240 and the inhibitors was also observed in the X-ray crystal structures of mTOR in complex with the potent inhibitors.18 This consistency supports the reasonableness of the binding mode of 1 calculated with the modified scoring function. A stable hydrogen bond is also established between the terminal carboxylate group of 1 and the backbone amidic nitrogen of Thr2245, which should also be a significant binding force in the mTOR-1 complex. The two hydrogen bonds shown in Figure 3 seem to be the most significant binding forces to stabilize 1 in the ATP-binding site because no stronger interaction involving the positive and negative ions was found in the calculated mTOR-1 complex. The inhibitor 1 can be further stabilized in the ATP-binding site of mTOR by the hydrophobic interactions of its nonpolar groups with the side chains of Val2185, Leu2192, Tyr2225, Ile2237, Trp2239, Val2240, Met2345, and Ile2356. Judging from the overall structural features derived from docking simulations, the micromolar inhibitory activity of 1 can be attributed to the establishment of the multiple hydrogen

Figure 3. Calculated binding mode of 1 in the ATP-binding site of mTOR. Carbon atoms of the protein and the ligand are indicated in cyan and green, respectively. Each dotted line indicates a hydrogen bond.

838

H. Park et al. / Bioorg. Med. Chem. Lett. 24 (2014) 835–838

two inhibitors deserve consideration for further development by SAR studies to optimize the inhibitory and anticancer activities. Detailed binding mode analyses with docking simulations show that the inhibitors can be stabilized in the ATP-binding site of mTOR by the simultaneous establishment of multiple hydrogen bonds and van der Waals contacts. Acknowledgements This research was supported by National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF-2011-0022858, 2010-0022179, 0016436). Figure 4. Calculated binding mode of 2 in the ATP-binding site of mTOR. Carbon atoms of the protein and the ligand are indicated in cyan and green, respectively. Each dotted line indicates a hydrogen bond.

bonds and hydrophobic interactions in a simultaneous fashion in the ATP-binding site of mTOR. Considering the relatively low molecular weight (384), 1 is expected to serve as a good inhibitor scaffold from which much more potent inhibitors can be obtained by the substitutions of proper chemical groups to maximize the hydrogen-bond and hydrophobic interactions in the ATP-binding site. In this regard, the introduction of a small nonpolar group at the terminal naphthalene group seems to be a good choice for promoting the inhibitor potency because it resides near the small hydrophobic pocket comprising the side chains of Leu2192, Tyr2225, and Ile2237 in the calculated mTOR-1 complex. Figure 4 shows the lowest-energy binding mode of 2 in the ATPbinding site of mTOR. The binding mode of 2 is similar to that of 1 because its pyrazolo[3,4-d]pyrimidine and furan-2-carboxylic acid phenylamide moieties are directed to the backbone groups of ATPbinding site and the space between Gly and activation loops, respectively. In the calculated mTOR-2 complex, the inhibitor pyrimidine ring receives a hydrogen bond from the backbone amidic nitrogen of Val2240 as in the mTOR-1 complex. Actually, the capability to form the hydrogen bond with backbone groups of Val2240 seems to be necessary for the effective inhibition of mTOR because the similar hydrogen bonds are also observed in the calculated binding modes of 3–6 (data not shown here). An additional hydrogen bond is established in the mTOR-2 complex between the side-chain carboxylate ion of Asp2195 and the amide moiety connecting the furan and phenyl rings of 2, which would also play a significant role in stabilizing the inhibitor in the ATP-binding site. Hydrophobic interactions are also observed in the calculated mTOR-2 complex in the similar fashion to those in the mTOR-1 complex: its aromatic rings reside in close proximity to the side chains of Val2185, Leu2192, Tyr2225, Ile2237, Trp2239, Val2240, Met2345, Leu2354, and Ile2356. Judging from the binding mode shown in Figure 4, the substitution of a small nonpolar group in the central phenyl ring of 2 is likely to enhance the inhibitory activity due to the strengthening of hydrophobic interactions with the side chains of Leu2192, Tyr2225, and Ile2237. It is thus found to be a common feature in binding modes of the newly identified inhibitors that the multiple hydrogen bonds and hydrophobic interactions contribute to the stabilization of the inhibitors in the ATP-binding site in a cooperative fashion. In summary, we have identified six novel inhibitors of mTOR by applying a computer-aided drug design protocol involving the structure-based virtual screening with docking simulations under consideration of the effects of ligand solvation in the scoring function. These inhibitors are also computationally screened for having desirable physicochemical properties as a drug candidate and reveal a moderate activity with IC50 values ranging from 8 to 60 lM. In particular, 1 and 2 are found to be most potent and have relatively low-molecular weights (384 and 334). Therefore, these

References and notes 1. Guertin, D. A.; Sabatini, D. M. Cancer Cell 2007, 12, 9. 2. Cantley, L. C. Science 2002, 296, 1655. 3. Workman, P.; Clarke, P. A.; Raynaud, F. I.; van Montfort, R. L. M. Cancer Res. 2010, 70, 2146. 4. Benjamin, D.; Colombi, M.; Moroni, C.; Hall, M. N. Nat. Rev. Drug Disc. 2011, 10, 868. 5. Feldman, M. E.; Apsel, B.; Uotila, A.; Loewith, R.; Knight, Z. A.; Ruggero, D.; Shokat, K. M. PLoS Biol. 2009, 7, e38. 6. Wan, X.; Harkavy, B.; Shen, N.; Grohar, P.; Helman, L. J. Oncogene 2007, 26, 1932. 7. Sun, S.-Y. Cancer Lett. 2013, 340, 1. 8. Zaytseva, Y. Y.; Valentino, J. D.; Gulhati, P.; Evers, B. M. Cancer Lett. 2012, 319, 1. 9. Estrada, A. A.; Shore, D. G.; Blackwood, E.; Chen, Y.-H.; Deshmukh, G.; Ding, X.; DiPasquale, A. G.; Epler, J. A.; Friedman, L. S.; Koehler, M. F. T.; Liu, L.; Malek, S.; Nonomiya, J.; Ortwine, D. F.; Pei, Z.; Sideris, S.; St-Jean, F.; Trinh, L.; Truong, T.; Lyssikatos, J. P. J. Med. Chem. 2013, 56, 3090. 10. Pei, Z.; Blackwood, E.; Liu, L.; Malek, S.; Belvin, M.; Koehler, M. F. T.; Ortwine, D. F.; Chen, H.; Cohen, F.; Kenny, J. R.; Bergeron, P.; Lau, K.; Ly, C.; Zhao, X.; Estrada, A. A.; Truong, T.; Epler, J. A.; Nonomiya, J.; Trinh, L.; Sideris, S.; Lesnick, J.; Bao, L.; Vijapurkar, U.; Mukadam, S.; Tay, S.; Deshmukh, G.; Chen, Y.-H.; Ding, X.; Friedman, L. S.; Lyssikatos, J. P. ACS Med. Chem. Lett. 2013, 4, 103. 11. Lee, W.; Ortwine, D. F.; Bergeron, P.; Lau, K.; Lin, L.; Malek, S.; Nonomiya, J.; Pei, Z.; Robarge, K. D.; Schmidt, S.; Sideris, S.; Lyssikatos, J. P. Bioorg. Med. Chem. Lett. 2013, 23, 5097. 12. Mortensen, D. S.; Sapienza, J.; Lee, B. G. S.; Perrin-Ninkovic, S. M.; Harris, R.; Shevlin, G.; Parnes, J. S.; Whitefield, B.; Hickman, M.; Khambatta, G.; Bisonette, R. R.; Peng, S.; Gamez, J. C.; Leisten, J.; Narla, R. K.; Fultz, K. E.; Sankar, S. Bioorg. Med. Chem. Lett. 2013, 23, 1588. 13. Courtney, K. D.; Corcoran, R. B.; Engelman, J. A. J. Clin. Oncol. 2008, 28, 1075. 14. Cheng, H.; Li, C.; Bailey, S.; Baxi, S. M.; Goulet, L.; Guo, L.; Hoffman, J.; Jiang, Y.; Johnson, T. O.; Johnson, T. W.; Knighton, D. R.; Li, J.; Liu, K. K.-C.; Liu, Z.; Marx, M. A.; Walls, M.; Wells, P. A.; Yin, M.-J.; Zhu, J.; Zientek, M. ACS Med. Chem. Lett. 2013, 4, 91. 15. Stec, M. M.; Andrews, K. L.; Booker, S. K.; Caenepeel, S.; Freeman, D. J.; Jiang, J.; Liao, H.; McCarter, J.; Mullady, E. L.; Miguel, T. S.; Subramanian, R.; Tamayo, N.; Wang, L.; Yang, K.; Zalameda, L. P.; Zhang, N.; Hughes, P. E.; Norman, M. H. J. Med. Chem. 2011, 54, 5174. 16. Cheng, H.; Hoffman, J. E.; Le, P. T.; Pairish, M.; Kania, R.; Farrell, W.; Bagrodia, S.; Yuan, J.; Sun, S.; Zhang, E.; Xiang, C.; Dalvie, D.; Rahavendran, S. V. Bioorg. Med. Chem. Lett. 2013, 23, 2787. 17. Yang, W.; Shu, M.; Wanga, Y.; Wang, R.; Hua, Y.; Meng, L.; Lin, Z. J. Mol. Struct. 2013, 1054–1055, 107. 18. Yang, H.; Rudge, D. G.; Koos, J. D.; Vaidialingam, B.; Yang, H. J.; Pavletich, N. P. Nature 2013, 497, 217. 19. Shoichet, B. K.; Leach, A. R.; Kuntz, I. D. Proteins 1999, 34, 4. 20. Jeffrey, G. A. An Introduction to Hydrogen Bonding; Oxford University Press: Oxford, 1997. 21. Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J. Adv. Drug Delivery Rev. 1997, 23, 3. 22. Gasteiger, J.; Marsili, M. Tetrahedron 1980, 36, 3219. 23. Morris, G. M.; Goodsell, D. S.; Halliday, R. S.; Huey, R.; Hart, W. E.; Belew, R. K.; Olson, A. J. J. Comput. Chem. 1998, 19, 1639. 24. Mehler, E. L.; Solmajer, T. Protein Eng. 1991, 4, 903. 25. Stouten, P. F. W.; Frömmel, C.; Nakamura, H.; Sander, C. Mol. Simul. 1993, 10, 97. 26. Choi, H.; Kang, H.; Park, H. J. Cheminformatics 2013, 5, 8. 27. Park, H.; Hong, S.; Hong, S. J. Comput. Aided Mol. Des. 2012, 26, 983. 28. (a) Park, H.; Chi, O.; Kim, J.; Hong, S. J. Chem. Inf. Model. 2011, 51, 2986; (b) Park, H.; Hong, S.; Kim, J.; Hong, S. J. Am. Chem. Soc. 2013, 135, 8227. 29. Fabian, M. A.; Biggs, W. H., III; Treiber, D. K.; Atteridge, C. E.; Azimioara, M. D.; Bendetti, M. G.; Carter, T. A.; Ciceri, P.; Edeen, P. T.; Floyd, M.; Ford, J. M.; Galvin, M.; Gerlach, J. L.; Grotzfeld, R. M.; Herrgard, S.; Insko, D. E.; Insko, M. A.; Lai, A. G.; Lelias, J.-M.; Mehta, S. A.; Milanov, Z. V.; Velasco, A. M.; Wodicka, L. M.; Patel, H. K.; Zarrinkar, P. P.; Lockhart, D. J. Nat. Biotechnol. 2005, 23, 329. 30. Margulies, D.; Opatowsky, Y.; Fletcher, S.; Saraogi, I.; Tsou, L. K.; Saha, S.; Lax, I.; Schlessinger, J.; Hamilton, A. D. ChemBioChem 2009, 10, 1955.