Generic placeholder image

Letters in Drug Design & Discovery

Editor-in-Chief

ISSN (Print): 1570-1808
ISSN (Online): 1875-628X

Research Article

Molecular Dynamics Studies on COX-2 Protein-tyrosine Analogue Complex and Ligand-based Computational Analysis of Halo-substituted Tyrosine Analogues

Author(s): Ayarivan Puratchikody*, Appavoo Umamaheswari, Navabshan Irfan and Dharmarajan Sriram

Volume 16, Issue 11, 2019

Page: [1211 - 1232] Pages: 22

DOI: 10.2174/1570180815666180627123445

Price: $65

Abstract

Background: The quest for new drug entities and novel structural fragments with applications in therapeutic areas is always at the core of medicinal chemistry.

Methods: As part of our efforts to develop novel selective cyclooxygenase-2 (COX-2) inhibitors containing tyrosine scaffold. The objective of this study was to identify potent COX-2 inhibitors by dynamic simulation, pharmacophore and 3D-QSAR methodologies. Dynamics simulation was performed for COX-2/tyrosine derivatives complex to characterise structure validation and binding stability. Certainly, Arg120 and Tyr355 residue of COX-2 protein formed a constant interaction with tyrosine inhibitor throughout the dynamic simulation phase. A four-point pharmacophore with one hydrogen bond acceptor, two hydrophobic and one aromatic ring was developed using the HypoGen algorithm. The generated, statistically significant pharmacophore model, Hypo 1 with a correlation coefficient of r2, 0.941, root mean square deviation, 1.15 and total cost value of 96.85.

Results: The QSAR results exhibited good internal (r2, 0.992) and external predictions (r2pred, 0.814). The results of this study concluded the COX-2 docked complex was stable and interactive like experimental protein structure. Also, it offered vital chemical features with geometric constraints responsible for the inhibition of the selective COX-2 enzyme by tyrosine derivatives.

Conclusion: In principle, this work offers significant structural understandings to design and develop novel COX-2 inhibitors.

Keywords: COX-2 inhibitors, tyrosine derivatives, dynamic simulation, pharmacophore, 3D QSAR, anti-inflammatory.

Graphical Abstract

[1]
Ricciotti, E.; Fitzgerald, G.A. Prostaglandins and inflammation. Arterioscler. Thromb. Vasc. Biol., 2011, 31(5), 986-1000.
[http://dx.doi.org/10.1161/ATVBAHA.110.207449]
[2]
Bhosale, U.A.; Quraishi, N.; Yegnanarayan, R.; Devasthale, D. A Comparative study to evaluate the cardiovascular risk of selective and nonselective cyclooxygenase inhibitors (COX-Is) in arthritic patients. J. Basic Clin. Physiol. Pharmacol., 2015, 26(1), 73-79.
[http://dx.doi.org/10.1515/jbcpp-2014-0005]
[3]
Goldstein, J.; Cryer, B. Gastrointestinal injury associated with NSAID Use: A case study and review of risk factors and preventative strategies. Drug Healthc. Patient Saf., 2015, 7, 31.
[http://dx.doi.org/10.2147/DHPS.S71976]
[4]
Ahmetaj-Shala, B.; Kirkby, N.S.; Knowles, R.; Al’Yamani, M.; Mazi, S.; Wang, Z.; Tucker, A.T.; Mackenzie, L.; Armstrong, P.C.J.; Nüsing, R.M. Evidence that links loss of cyclooxygenase-2 with increased asymmetric dimethylarginine novel explanation of cardiovascular side effects associated with anti-inflammatory drugs. Circulation, 2015, 131(7), 633-642.
[http://dx.doi.org/10.1161/CIRCULATIONAHA.114.011591]
[5]
Botting, R.M. Cyclooxygenase: Past, present and future. Tribute to John R. Vane (1927-2004). J. Therm. Biol. Pergamon, 2006, 31, 208-219.
[http://dx.doi.org/10.1016/j.jtherbio.2005.11.008]
[6]
Liu, B.; Luo, W.; Zhang, Y.; Li, H.; Zhu, N.; Huang, D.; Zhou, Y. Role of cyclooxygenase-1-mediated prostacyclin synthesis in endothelium-dependent vasoconstrictor activity of porcine interlobular renal arteries. AJP Ren. Physiol., 2012, 302(9), F1133-F1140.
[http://dx.doi.org/10.1152/ajprenal.00604.2011]
[7]
Singh, S.K.; Saibaba, V.; Ravikumar, V.; Rudrawar, S.V.; Daga, P.; Rao, C.S.; Akhila, V.; Hegde, P.; Rao, Y.K. Synthesis and biological evaluation of 2,3-diarylpyrazines and quinoxalines as selective COX-2 inhibitors. Bioorg. Med. Chem., 2004, 12(8), 1881-1893.
[http://dx.doi.org/10.1016/j.bmc.2004.01.033]
[8]
Penning, T.D.; Talley, J.J.; Bertenshaw, S.R.; Carter, J.S.; Collins, P.W.; Docter, S.; Graneto, M.J.; Lee, L.F.; Malecha, J.W.; Miyashiro, J.M. Synthesis and biological evaluation of the 1,5-diarylpyrazole class of cyclooxygenase-2 inhibitors: Identification of 4-[5-(4-methylphenyl)- 3(trifluoromethyl)-1h-pyrazol-1-yl]benzenesulfonamide (Sc-58635, Celecoxib). J. Med. Chem., 1997, 40(9), 1347-1365.
[http://dx.doi.org/10.1021/jm960803q]
[9]
Zarghi, A.; Arfaei, S. Selective COX-2 Inhibitors: A Review of Their Structure-Activity Relationships. Iran. J. Pharm. Res., 2011, 10, 655-683.
[10]
Chaudhary, P. Sharma, P.K.; Sharma, A.; Varshney. Recent advances in pharmacological activity of benzothiazole derivatives. J. Recent Advances in Pharmacological Activity of Benzothiazole Derivatives., 2010, 2(4), 5-11.
[11]
Bali, A.; Ohri, R.; Deb, P.K. Synthesis, evaluation and docking studies on 3-alkoxy-4-methanesulfonamide acetophenone derivatives as non ulcerogenic anti-inflammatory agents. Eur. J. Med. Chem., 2012, 49, 397-405.
[http://dx.doi.org/10.1016/j.ejmech.2012.01.018]
[12]
Taranalli, A.D.; Thimmaiah, N.V.; Srinivas, S.; Saravanan, E. Anti-inflammatory, analgesic and anti-ulcer activity of certain thiazolidinones. Asian J. Pharm. Clin. Res., 2009, 2(4), 79-83.
[13]
Sallam, A.A.; Ramasahayam, S.; Meyer, S.A.; El Sayed, K.A. Design, synthesis, and biological evaluation of dibromo-tyrosine analogues inspired by marine natural products as inhibitors of human prostate cancer proliferation, invasion, and migration. Bioorg. Med. Chem., 2010, 18(21), 7446-7457.
[http://dx.doi.org/10.1016/j.bmc.2010.08.057]
[14]
Puratchikody, A.; Sriram, D.; Umamaheswari, A.; Irfan, N. 3-D structural interactions and quantitative structural toxicity studies of tyrosine derivatives intended for safe potent inflammation treatment. Chem. Cent. J., 2016, 10(1), 24.
[http://dx.doi.org/10.1186/s13065-016-0169-9]
[15]
[16]
Vila-Viçosa, D.; Teixeira, V.H.; Santos, H.A.F.; Baptista, A.M.; Machuqueiro, M. Treatment of ionic strength in bio-molecular simulations of charged lipid bilayers. J. Chem. Theory Comput., 2014, 10(12), 5483-5492.
[http://dx.doi.org/10.1021/ct500680q]
[17]
Padarthi, P.K.; Chandramohan, V.; Jayaraj, R.L. Chalcones as effective antimicrobials - a comparative in silico approach. Int. J. Chem. Pharm. Sci., 2012, 3(4), 67-74.
[18]
Gajula, M.; Kumar, A.; Ijaq, J. Protocol for molecular dynamics simulations of proteins. Bio Protoc., 2016, 6(23), 1-11.
[http://dx.doi.org/10.21769/BioProtoc.2051]
[19]
Hospital, A.; Goñi, J.R.; Orozco, M.; Gelpí, J.L. Molecular dynamics simulations: Advances and applications. Adv. Appl. Bioinform. Chem., 2015, 8, 37-47.
[20]
Di Pierro, M.; Elber, R.; Leimkuhler, B. A stochastic algorithm for the isobaric-isothermal ensemble with ewald summations for all long range forces. J. Chem. Theory Comput., 2015, 11(12), 5624-5637.
[http://dx.doi.org/10.1021/acs.jctc.5b00648]
[21]
Bhatiya, R.; Vaidya, A.; Kashaw, S.K.; Jain, A.K.; Agrawal, R.K. QSAR analysis of furanone derivatives as potential COX-2 inhibitors: kNN MFA approach. J. Saudi Chem. Soc., 2014, 18(6), 977-984.
[http://dx.doi.org/10.1016/j.jscs.2011.12.002]
[22]
Prasanna, S.; Manivannan, E.; Chaturvedi, S.C. QSAR studies on structurally similar 2-(4-methanesulfonylphenyl)pyran-4-ones as selective cox-2 inhibitors: A hansch approach. Bioorg. Med. Chem. Lett., 2005, 15(2), 313-320.
[http://dx.doi.org/10.1016/j.bmcl.2004.10.077]
[23]
Michaux, C.; de Leval, X.; Julémont, F.; Dogné, J.M.; Pirotte, B.; Durant, F. Structure-based pharmacophore of cox-2 selective inhibitors and identification of original lead compounds from 3D database searching method. Eur. J. Med. Chem., 2006, 41(12), 1446-1455.
[http://dx.doi.org/10.1016/j.ejmech.2006.07.017]
[24]
Eleftheriou, P.; Geronikaki, A.; Hadjipavlou-Litina, D.; Vicini, P.; Filz, O.; Filimonov, D.; Poroikov, V.; Chaudhaery, S.S.; Roy, K.K.; Saxena, A.K. Fragment-based design, docking, synthesis, biological evaluation and structure-activity relationships of 2-benzo/benzisothiazolimino-5-aryliden-4- thi-azolidinones as cycloxygenase/lipoxygenase inhibitors. Eur. J. Med. Chem., 2012, 47(1), 111-124.
[http://dx.doi.org/10.1016/j.ejmech.2011.10.029]
[25]
Singh, S.K.; Saibaba, V.; Rao, K.S.; Reddy, P.G.; Daga, P.R.; Rajjak, S.A.; Misra, P.; Rao, Y.K. Synthesis and SAR/3D-QSAR studies on the COX-2 inhibitory activity of 1,5-diarylpyrazoles to validate the modified pharmacophore. Eur. J. Med. Chem., 2005, 40(10), 977-990.
[http://dx.doi.org/10.1016/j.ejmech.2005.03.016]
[26]
Lokwani, D.K.; Mokale, S.N.; Shinde, D.B. 3D qsar studies based in silico screening of 4,5,6-triphenyl-1,2,3,4- tet-rahydropyrimidine analogs for anti-inflammatory activity. Eur. J. Med. Chem., 2014, 73, 233-242.
[http://dx.doi.org/10.1016/j.ejmech.2013.10.083]
[27]
Unsal-Tan, O.; Ozadali, K.; Piskin, K.; Balkan, A. Molecular modeling, synthesis and screening of some new 4-thiazolidinone derivatives with promising selective COX-2 inhibitory activity. Eur. J. Med. Chem., 2012, 57, 59-64.
[http://dx.doi.org/10.1016/j.ejmech.2012.08.046]
[28]
Brooks, B.R.; Brooks, C.L.; Mackerell, A.D.; Nilsson, L.; Petrella, R.J.; Roux, B.; Won, Y.; Archontis, G.; Bartels, C.; Boresch, S.; Caflisch, A.; Caves, L.; Cui, Q.; Dinner, A.R.; Feig, M.; Fischer, S.; Gao, J.; Hodoscek, M. Im, W.; Kuczera, K.; Lazaridis, T.; Ma, J.; Ovchinnikov, V.; Paci, E.; Pastor, R. W.; Post, C. B.; Pu, J. Z.; Schaefer, M.; Tidor, B.; Venable, R. M.; Woodcock, H. L.; Wu, X.; Yang, W.; York, D. M.; Kar-plus, M.; York, D. M.; Karplus, M. CHARMM: The biomolecular simulation program. J. Comput. Chem., 2009, 30(10), 1545-1614.
[http://dx.doi.org/10.1002/jcc.21287]
[29]
Niu, M.; Dong, F.; Tang, S.; Fida, G.; Qin, J.; Qiu, J.; Liu, K.; Gao, W.; Gu, Y. Pharmacophore modeling and virtual screening for the discovery of new type 4 cAMP phosphodiesterase (PDE4) inhibitors. PLoS One, 2013, 8(12)e82360
[http://dx.doi.org/10.1371/journal.pone.0082360]
[30]
Wei, D.G.; Yang, G.F.; Wan, J.; Zhan, C.G. Binding Model Construction of Antifungal 2-Aryl-4-Chromanones Using CoMFA, CoMSIA, and QSAR Analyses. J. Agric. Food Chem., 2005, 53(5), 1604-1611.
[http://dx.doi.org/10.1021/jf048313r]
[31]
Ben Wagner, A. SciFinder Scholar 2006: An empirical analysis of research topic query processing. J. Chem. Inform. Model. Am. Chem. Soc., 2006, 46, 767-774.
[32]
Wang, Y.; Bolton, E.; Dracheva, S.; Karapetyan, K.; Shoe-maker, B.A.; Suzek, T.O.; Wang, J.; Xiao, J.; Zhang, J.; Bry-ant, S.H. An overview of the pubchem bioassay resource. Nucleic Acids Res., 2009, 38(Suppl. 1), D255-D266.
[http://dx.doi.org/10.1093/nar/gkp965]
[33]
Liu, M.; Sun, Z.; Hu, W. Three-Dimensional Pharmacophore Screening for Fentanyl Derivatives. Neural Regen. Res., 2012, 7(18), 1398-1405.
[34]
Katritzky, A.R.; Kuanar, M.; Slavov, S.; Hall, C.D.; Karel-son, M.; Kahn, I.; Dobchev, D.A. quantitative correlation of physical and chemical properties with chemical structure: Utility for prediction. Chem. Rev., 2010, 110(10), 5714-5789.
[http://dx.doi.org/10.1021/cr900238d]
[35]
Alajmi, A.; Wright, J. Selecting the most efficient genetic algorithm sets in solving unconstrained building optimization problem. Int. J. Sustain. Built Environ., 2014, 3(1), 18-26.
[http://dx.doi.org/10.1016/j.ijsbe.2014.07.003]
[36]
Wagh, N.K.; Deokar, H.S.; Juvale, D.C.; Kadam, S.S.; Kul-karni, V.M. 3D-QSAR of histone deacetylase inhibitors as anticancer agents by genetic function approximation. Indian J. Biochem. Biophys., 2006, 43(6), 360-371.
[37]
Berhanu, W.M.; Pillai, G.G.; Oliferenko, A.A.; Katritzky, A.R. Quantitative structure-activity/property relationships: The ubiquitous links between cause and effect. ChemPlusChem, 2012, 77(7), 507-517.
[http://dx.doi.org/10.1002/cplu.201200038]
[38]
Liu, P.; Long, W. Current mathematical methods used in QSAR/QSPR studies. Int. J. Mol. Sci., 2009, 10(5), 1978-1998.
[http://dx.doi.org/10.3390/ijms10051978]
[39]
Awad, M.K.; El-Bastawissy, E.A.; Atlam, F.M. QSAR studies for the computational prediction of HMG-CoA reductase inhibitors by genetic function approximation technique. Can. J. Chem., 2013, 91(4), 263-274.
[http://dx.doi.org/10.1139/cjc-2012-0379]
[40]
Ujashkumar, A.; Shah, N.K.; Wagh, H.S.; Deokar, S.S.; Kadam, V.M. 3D-QSAR of biphenyl analogues as anti-inflammatory agents by genetic function approximation (GFA). Int. J. Pharma Bio Sci., 2010, 9(4), 512-522.
[41]
Cramer, R.D.; Bunce, J.D.; Patterson, D.E.; Frank, I.E. Crossvalidation, bootstrapping, and partial least squares compared with multiple regression in conventional QSAR studies. Quant. Struct. Relationships, 1988, 7(1), 18-25.
[http://dx.doi.org/10.1002/qsar.19880070105]
[42]
Gunamalai, L.; Jaynthy, C. Studies on molecular dynamics simulation and solvent stability analysis of collagen mimetic peptide GFO with cyclodextrin? An in silico analysis for tissue engineering. Biomed. Res., 2015.
[43]
Basconi, J.E.; Shirts, M.R. Effects of temperature control algorithms on transport properties and kinetics in molecular dynamics simulations. J. Chem. Theory Comput., 2013, 9(7), 2887-2899.
[http://dx.doi.org/10.1021/ct400109a]
[44]
Leach, A.R. Ligand-based approaches: Core molecular modeling. Comprehensive Medicinal Chemistry II; Elsevier, 2007, pp. 87-118.
[http://dx.doi.org/10.1016/B0-08-045044-X/00246-7]
[45]
Dhanjal, J.K.; Sreenidhi, A.K.; Bafna, K.; Katiyar, S.P.; Goyal, S.; Grover, A.; Sundar, D. Computational structure-based de novo design of hypothetical inhibitors against the anti-inflammatory target COX-2. PLoS One, 2015, 10(8)e0134691
[http://dx.doi.org/10.1371/journal.pone.0134691]
[46]
Mohammad-Aghaie, D.; Zehra, J. Docking and Molecular Dynamics Simulation Studies of Interactions between Cyclooxygenases Enzymes and Celecoxib drug. 12th Iran Biophys. Chem. Conf., 2013.
[47]
Furse, K.E.; Pratt, D.A.; Porter, N.A.; Lybrand, T.P. Molecular dynamics simulations of arachidonic acid complexes with COX-1 and COX-2: insights into equilibrium behavior. Biochemistry, 2006, 45(10), 3189-3205.
[http://dx.doi.org/10.1021/bi052337p]
[48]
Lindahl, E.R. Molecular dynamics simulations. Methods Mol. Biol., 2008, 443, 3-23.
[http://dx.doi.org/10.1007/978-1-59745-177-2_1]
[49]
Padariya, M.; Kalathiya, U. Structure-based design and evaluation of novel N-phenyl-1h-indol-2-amine derivatives for fat mass and obesity-associated (FTO) protein inhibition. Comput. Biol. Chem., 2016, 64, 414-425.
[http://dx.doi.org/10.1016/j.compbiolchem.2016.09.008]
[50]
Kufareva, I.; Abagyan, R. Methods of protein structure comparison. Methods Mol. Biol., 2012, 857, 231-257.
[http://dx.doi.org/10.1007/978-1-61779-588-6_10]
[51]
Lobanov, M.I.; Bogatyreva, N.S.; Galzitskaia, O.V. Radius of gyration is an indicator of compactness of protein structure. Mol. Biol. (Mosk.), 2008, 42(4), 701-706.
[52]
Sharma, B.K.; Singh, P.; Pilania, P.; Shekhawat, M.; Prabhakar, Y.S. QSAR of 2-(4-methylsulphonylphenyl)pyrimidine derivatives as cyclooxy-genase-2 inhibitors: Simple structural fragments as potential modulators of activity. J. Enzyme Inhib. Med. Chem., 2012, 27(2), 249-260.
[http://dx.doi.org/10.3109/14756366.2011.587414]
[53]
Podlogar, B.L.; Ferguson, D.M. Qsar and CoMFA: A perspective on the practical application to drug discovery. Drug Des. Discov., 2000, 17(1), 4-12.
[54]
Press, I.; Proof, C. Corrected proof corrosion inhibition of mild steel by some sulfur containing compounds. Artificial neural network modeling. J. Mater. Environ. Sci., 2014, 5(4), 1288-1297.
[55]
Kandakatla, N.; Ramakrishnan, G. Ligand based pharmacophore modeling and virtual screening studies to design novel HDAC2 inhibitors. Adv. Bioinforma., 2014, 2014812148
[http://dx.doi.org/10.1155/2014/812148]
[56]
Madan, K.; Verma, A.N.; Paliwal, S.K.; Yadav, D.; Sharma, S. Sharma. Pharmacophore modeling and database mining to identify novel lead compounds active against the disease stage of trypanosomiasis in the central nervous system. M. Int. J. Nutr. Pharmacol. Neurol. Dis., 2018, 8(1), 16.
[57]
Ravindra Kumar, C.; Raghuram Rao, A.; Ram Kishor, A. Pharmacophore modeling and QSAR analysis of novel β-carboline derivatives as antitumor agents. Lett. Drug Des. Discov., 2017, 10(7), 572-584.
[58]
Zhang, H.; Xiang, M.L.; Liang, J.Y.; Zeng, T.; Zhang, X.N.; Zhang, J.; Yang, S.Y. Combination of pharmacophore hypothesis, genetic function approximation model, and molecular docking to identify novel inhibitors of S6K1. Mol. Divers., 2013, 17(4), 767-772.
[http://dx.doi.org/10.1007/s11030-013-9473-7]
[59]
Roy, K.; Kar, S.; Das, R.N. A Primer on QSAR/QSPR Modeling; Springer brief in molecular sciences, Springer international publishing: Cham, Switzerland, 2015, p. 121.
[60]
Fei, J.; Zhou, L.; Liu, T.; Tang, X-Y. Pharmacophore modeling, virtual screening, and molecular docking studies for discovery of novel Akt2 inhibitors. Int. J. Med. Sci., 2013, 10(3), 265-275.
[http://dx.doi.org/10.7150/ijms.5344]
[61]
Sakkiah, S.; Lee, K.W. Pharmacophore-based virtual screening and density functional theory approach to identifying novel butyrylcholinesterase inhibitors. Acta Pharmacol. Sin., 2012, 33(7), 964-978.
[http://dx.doi.org/10.1038/aps.2012.21]
[62]
Sivakumar, P.M.; Kumar, V.; Seenivasan, S.P.; Mohana-priya, J.; Doble, M. Experimental and theoretical approaches to enhance anti tubercular activity of chalcones. WSEAS Trans. Biol. Biomed., 2010, 7(2), 51-61.
[63]
Lumbiny, B.J.; Hui, Z.; Islam, M.A. Antiaging, antioxidant flavonoids, synthesis, antimicrobial screening as well as 3D-QSAR COMFA models for the prediction of biological activity. J. Asiat. Soc. Bangladesh. Sci., 2013, 39(2), 191-199.
[64]
Damale, M.; Harke, S.; Kalam Khan, F.; Shinde, D.; Sangshet-ti, J. Recent advances in multidimensional QSAR (4D-6D): A critical review. Mini Rev. Med. Chem., 2014, 14(1), 35-55.
[65]
Hansch, C.; Coats, E. A‐chymotrypsin: A case study of substituent constants and regression analysis in enzymic structure-activity relationships. J. Pharm. Sci., 1970, 1, 731-743.
[66]
Song, Z.; Mansbach, R.A.; He, H.; Shih, K-C.; Baumgartner, R.; Zheng, N.; Ba, X.; Huang, Y.; Mani, D.; Liu, Y.; Lin, Y.; Nieh, M-P.; Ferguson, A.L.; Yin, L.; Cheng, J. Modulation of polypeptide conformation through donor-acceptor transformation of side-chain hydrogen bonding ligands. Nat. Commun., 2017, 8(1), 92.
[http://dx.doi.org/10.1038/s41467-017-00079-5]
[67]
Lewis, D.F.V. Molecular orbital calculations on solvents and other small molecules: correlation between electronic and molecular properties N, αMOL, Π*, and B. J. Comput. Chem., 1987, 8(8), 1084-1089.
[http://dx.doi.org/10.1002/jcc.540080803]
[68]
Rogers, D.; Hahn, M. Extended-connectivity fingerprints. J. Chem. Inf. Model., 2010, 50(5), 742-754.
[http://dx.doi.org/10.1021/ci100050t]
[69]
Neighbor, U.K.; Field, M.; Kj, S.; Achal, M. 3D QSAR analysis on isatin derivatives as carboxyl esterase inhibitors. J. Theor. Comput. Sci., 2015, 2(2), 1-12.
[70]
Karelson, M.; Lobanov, V.S.; Katritzky, A.R. Quantum-chemical descriptors in QSAR/QSPR studies. Chem. Rev., 1996, 96(3), 1027-1044.
[http://dx.doi.org/10.1021/cr950202r]
[71]
Niazi, S. Handbook of preformulation : Chemical, biological, and botanical drugs; Informa Healthcare; CRC Press, 2007.
[72]
Luque, F.J.; Dehez, F.; Chipot, C.; Orozco, M. Polarization effects in molecular interactions. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2011, 1(5), 844-854.
[http://dx.doi.org/10.1002/wcms.32]
[73]
Reeder, B.J.; Grey, M.; Silaghi-Dumitrescu, R-L.; Svistunen-ko, D.A.; Bülow, L.; Cooper, C.E.; Wilson, M.T. Tyrosine residues as redox cofactors in human hemoglobin: Implications for engineering nontoxic blood substitutes. J. Biol. Chem., 2008, 283(45), 30780-30787.
[http://dx.doi.org/10.1074/jbc.M804709200]
[74]
Brownell, L.V.; Robins, K.A.; Jeong, Y.; Lee, Y.; Lee, D.C. Highly systematic and efficient HOMO-LUMO energy gap control of thiophene-pyrazine-acenes. J. Phys. Chem. C, 2013, 117(48), 25236-25247.
[http://dx.doi.org/10.1021/jp407269p]
[75]
Ebrahim, H.; El Sayed, K. Discovery of novel antiangiogenic marine natural product scaffolds. Mar. Drugs, 2016, 14(3), 57.
[http://dx.doi.org/10.3390/md14030057]

© 2024 Bentham Science Publishers | Privacy Policy