Abstract
Computer Simulations have been employed by applying techniques of classical molecular dynamics simulations to explore the hydrogen bonding structure and dynamics in aqueous environment of cholecystokinin-8, in the presence of two different concentrations of ethanol (EtOH) and 2,2,2-trifluoroethanol (TFE). Different site–site and centre-of-mass radial distribution functions have been presented here to give an idea of the various microscopic interactions between different species in solution. It is observed that EtOH solution facilitates hydrogen bonding of CCK8 with its aqueous milieu, whereas TFE prefers to envelope the peptide and protects it from water due to its tendency of clustering and encouraging segregation of water. Significant decrease of methionine CH3-groups hydrophobic solvation is noted with increasing alcohol concentration. The structural relaxation lifetimes of peptide–water hydrogen bonds are observed to be lengthened with EtOH concentration in the solution while highest lifetimes are seen for CCK8–TFE hydrogen bonds. Stronger water–ethanol hydrogen bonds may cause slower translational motion of water molecules in concentrated ethanol than in TFE solution. Conformational clustering analysis shows higher number of similar compact structures of CCK8 in TFE solution relative to aqueous/EtOH solution.
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References
Sansom, C.E., Smith, C.A.: Computer applications in the biomolecular sciences. Part 1: Molecular modelling. Biochem. Mol. Biol. Educ. 26(2), 103–110 (1998)
Mitra, S., Hayashi, Y.: Bioinformatics with soft computing. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 36(5), 616–635 (2006)
Belda, I., Llorà, X., Giralt, E.: Evolutionary algorithms and de novo peptide design. Soft Comput. Fusion Found. Methodol. Appl. 10(4), 295–304 (2006)
Fersht, A.R.: From the first protein structures to our current knowledge of protein folding: delights and scepticisms. Nat. Rev. Mol. Cell Biol. 9(8), 650 (2008)
Feher, M., Schmidt, J.M.: Fuzzy clustering as a means of selecting representative conformers and molecular alignments. J. Chem. Inf. Comput. Sci. 43(3), 810–818 (2003)
Gordon, H.L., Somorjai, R.L.: Fuzzy cluster analysis of molecular dynamics trajectories. Proteins Struct. Funct. Bioinf. 14(2), 249–264 (1992)
Ghosh, R., Roy, S., Bagchi, B.: Solvent sensitivity of protein unfolding: dynamical study of chicken villin headpiece subdomain in water ethanol binary mixture. J. Phys. Chem. B 117(49), 15625–15638 (2013)
Buck, M.: Trifluoroethanol and colleagues: cosolvents come of age. Recent studies with peptides and proteins. Q. Rev. Biophys. 31(3), 297–355 (1998)
Fioroni, M., Diaz, M., Burger, K., Berger, S.: Solvation phenomena of a tetrapeptide in water/trifluoroethanol and water/ethanol mixtures: a diffusion NMR, intermolecular NOE, and molecular dynamics study. J. Am. Chem. Soc. 124(26), 7737–7744 (2002)
Kulkosky, P.J.: Effect of cholecystokinin octapeptide on ethanol intake in the rat. Alcohol. 1(2), 125–128 (1984)
Kulkosky, P.J., Sanchez, M.R., Glazner, G.W.: Cholecystokinin octapeptide: effect on the ethogram of ethanol consumption and blood ethanol levels in the rat. Physiol. Psychol. 14(2), 23–30 (1986)
Toth, P., Shaw, C., Perlanski, E., Grupp, L.: Cholecystokinin octapeptide reduces ethanol intake in food-and water-sated rats. Pharmacol. Biochem. Behav. 35(2), 493–495 (1990)
Evangelista, S., Maggi, C.: Protection induced by cholecystokinin-8 (CCK-8) in ethanol-induced gastric lesions is mediated via vagal capsaicin-sensitive fibres and CCKA receptors. Br. J. Pharmacol. 102(1), 119–122 (1991)
Rapaport, D.C., Blumberg, R.L., McKay, S.R., Christian, W.: The art of molecular dynamics simulation. Comput. Phys. 10(5), 456–456 (1996)
Frenkel, D., Smith, B.: Understanding Molecular Simulation: From Algorithms to Applications. Academic Press, New York (1996)
Nucleic Acids Flexibility (n.d.). http://mmb.irbbarcelona.org/NAFlex/help.php?id=md. Accessed 22 Nov 2017
Pronk, S., Páll, S., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., Shirts, M.R., Smith, J.C., Kasson, P.M., van der Spoel, D.: GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 29(7), 845–854 (2013)
Oostenbrink, C., Villa, A., Mark, A.E., Van Gunsteren, W.F.: A biomolecular force field based on the free enthalpy of hydration and solvation: the GROMOS force-field parameter sets 53A5 and 53A6. J. Comput. Chem. 25(13), 1656–1676 (2004)
Pellegrini, M., Mierke, D.F.: Molecular complex of cholecystokinin-8 and N-terminus of the cholecystokinin A receptor by NMR spectroscopy. Biochemistry (Mosc.) 38(45), 14775–14783 (1999)
Jorgensen, W.L., Maxwell, D.S., Tirado-Rives, J.: Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J. Am. Chem. Soc. 118(45), 11225–11236 (1996)
Berendsen, H.J.C., Grigera, J.R., Straatsma, T.P.: The missing term in effective pair potentials. J. Phys. Chem. 91(24), 6269–6271 (1987)
Khattab, I.S., Bandarkar, F., Fakhree, M.A.A., Jouyban, A.: Density, viscosity, and surface tension of water + ethanol mixtures from 293 to 323 K. Korean J. Chem. Eng. 29(6), 812–817 (2012)
Harris, K.R., Newitt, P.J.: Self-diffusion of water at low temperatures and high pressure. J. Chem. Eng. Data 42(2), 346–348 (1997)
Hess, B., Bekker, H., Berendsen, H.J., Fraaije, V.: LINCS: a linear constraint solver for molecular simulations. J. Comput. Chem. 18(12), 1463–1472 (1997)
Miyamoto, S., Kollman, P.A.: SETTLE: an analytical version of the SHAKE and RATTLE algorithm for rigid water models. J. Comput. Chem. 13(8), 952–962 (1992)
Bussi, G., Donadio, D., Parrinello, M.: Canonical sampling through velocity rescaling. J. Chem. Phys. 126(1), 014101 (2007)
Berendsen, H.J., van Postma, J., van Gunsteren, W.F., DiNola, A., Haak, J.R.: Molecular dynamics with coupling to an external bath. J. Chem. Phys. 81(8), 3684–3690 (1984)
Cuendet, M.A., van Gunsteren, W.F.: On the calculation of velocity-dependent properties in molecular dynamics simulations using the leapfrog integration algorithm. J. Chem. Phys. 127(18), 184102 (2007)
Darden, T., York, D., Pedersen, L.: Particle mesh Ewald: an N · log(N) method for Ewald sums in large systems. J. Chem. Phys. 98(12), 10089–10092 (1993)
Matsugami, M., Yamamoto, R., Kumai, T., Tanaka, M., Umecky, T., Takamuku, T.: Hydrogen bonding in ethanol–water and trifluoroethanol–water mixtures studied by NMR and molecular dynamics simulation. J. Mol. Liq. 217, 3–11 (2016)
Rajan, R., Balaram, P.: A model for the interaction of trifluoroethanol with peptides and proteins. Chem. Biol. Drug Des. 48(4), 328–336 (1996)
Pikkarainen, L.: Excess enthalpies of (2,2,2-trifluoroethanol+2-butanone or N-methylacetamide or N,N-dimethylacetamide). J. Chem. Thermodyn. 20(4), 481–484 (1988)
Pikkarainen, L.: Excess enthalpies of binary solvent mixtures of N-methylacetamide with aliphatic alcohols. J. Solut. Chem. 16(2), 125–132 (1987)
Chand, A., Chowdhuri, S.: Behaviour of aqueous N-methylacetamide solution in presence of ethanol and 2,2,2 tri-fluoroethanol: hydrogen bonding structure and dynamics. J. Mol. Liq. 224, 1370–1379 (2016)
Murto, J.: Hydroxide and alkoxide ions in alkanol-water mixtures. Acta Chem. Scand. 18(5), 1029–1042 (1964)
Rao, C., Goldman, G., Balasubramanian, A.: Structural and solvent effects on the n → π*(′ U ← ′ A) transitions in aliphatic carbonyl derivatives: evidence for hyperconjugation in the electronically excited states of molecules. Can. J. Chem. 38(12), 2508–2513 (1960)
Loomis, R.E., Lee, P.C., Tseng, C.C.: Conformational analysis of the cholecystokinin C-terminal octapeptide: a nuclear magnetic resonance and computer-simulation approach. Biochim. Biophys. Acta BBA-Protein Struct. Mol. Enzymol. 911(2), 168–179 (1987)
Pattanayak, S.K., Chowdhuri, S.: Effect of water on solvation structure and dynamics of ions in the peptide bond environment: importance of hydrogen bonding and dynamics of the solvents. J. Phys. Chem. B. 115(45), 13241–13252 (2011)
Pattanayak, S.K., Chettiyankandy, P., Chowdhuri, S.: Effects of co-solutes on the hydrogen bonding structure and dynamics in aqueous N-methylacetamide solution: a molecular dynamics simulations study. Mol. Phys. 112(22), 2906–2919 (2014)
Chowdhuri, S., Chandra, A.: Dynamics of halide ion-water hydrogen bonds in aqueous solutions: dependence on ion size and temperature. J. Phys. Chem. B. 110(19), 9674–9680 (2006)
Rapaport, D.: Hydrogen bonds in water: network organization and lifetimes. Mol Phys. 50(5)s, 1151–1162 (1983)
Luzar, A., Chandler, D.: Hydrogen bond kinetics in liquid water. Nat. Lond. 379(6560), 55–57 (1996)
Luzar, A.: Resolving the hydrogen bond dynamics conundrum. J. Chem. Phys. 113(23), 10663–10675 (2000)
van der Spoel, D., van Maaren, P.J., Larsson, P., Tîmneanu, N.: Thermodynamics of hydrogen bonding in hydrophilic and hydrophobic media. J. Phys. Chem. B. 110(9), 4393–4398 (2006)
Keller, B., Daura, X., van Gunsteren, W.F.: Comparing geometric and kinetic cluster algorithms for molecular simulation data. J. Chem. Phys. 132(7), 02B610 (2010)
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Authors are grateful to the Indian Institute of Technology, Bhubaneswar, for infrastructural support and Council of Scientific and Industrial Research (CSIR), Government of India, for research fellowship.
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Chand, A., Chettiyankandy, P., Chowdhuri, S. (2019). Application of Computer Simulation in Exploring Influence of Alcohol on Aqueous Milieu of a Gut-Brain Octapeptide, Cholecystokinin-8. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 817. Springer, Singapore. https://doi.org/10.1007/978-981-13-1595-4_3
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