Abstract
The three-dimensional conformation of Met-enkephalin, corresponding to the lowest minimum of the empirical potential energy function ECEPP/2 (empirical conformational energy program for peptides), has been determined using a new algorithm, viz. the Electrostatically Driven Monte Carlo Method. This methodology assumes that a polypeptide or protein molecule is driven toward the native structure by the combined action of electrostatic interactions and stochastic conformational changes associated with thermal movements. These features are included in the algorithm that produces a Monte Carlo search in the conformational hyperspace of the polypeptide, using electrostatic predictions and a random sampling technique to locate low-energy conformations. In addition, we have incorporated an alternative mechanism that allows the structure to escape from some conformational regions representing metastable local energy minima and even from regions of the conformational space with great stability. In 33 test calculations on Met-enkephalin, starting from arbitrary or completely random conformations, the structure corresponding to the global energy minimum was found inall the cases analyzed, with a relatively small search of the conformational space. Some of these starting conformations wereright orleft-handed α-helices, characterized by good electrostatic interactions involving their backbone peptide dipoles; nevertheless, the procedure was able to convert such locally stable structures to the global-minimum conformation.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abe, H., Braun, W., Noguti, T., and Gō, N. (1984).Computers in Chem. 8, 239–247.
Billeter M., Havel, T. F., and Wüthrich, K. (1987).J. Comput. Chem. 8, 132–141.
Brant, D. A., and Flory, P. J. (1965).J. Am. Chem. Soc. 87, 2791–2800.
Chou, K. C., Némethy G., and Scheraga, H. A. (1983).J. Phys. Chem. 87, 2869–2881.
Gay, D. M. (1983).ACM Trans. Math. Software 9, 503–524.
Gibson, K. D., and Scheraga, H. A. (1988). inStructure and Expression Vol. I:From Proteins to Ribosomes (M. H. Sarma and R. H. Sarma, eds.), (Adenine Press, Guilderland, New York) pp. 67–94.
Gilson, M. K., and Honig, B. H. (1988).Proteins 3, 32–52.
Hol, W. G. J., Van Duijnen, P. T., and Berendsen, H. J. C. (1978).Nature (Lond.) 273, 443–446.
Hol, W. G. J., Halie, L. M., and Sander, C. (1981).Nature (Lond.) 294, 532–536.
Isogai, Y., Némethy, G., and Scheraga, H. A. (1977).Proc. Nat. Acad. Sci. USA 74, 414–418.
Levitt, M., and Chothia, C. (1976).Nature (Lond.) 261, 552–558.
Li, Z., and Scheraga, H. A. (1987).Proc. Natl. Acad. Sci. USA 84, 6611–6615.
Li, Z., and Scheraga, H. A. (1988).J. Molec. Structure (Theochem) 179, 333–352.
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E. (1953).J. Chem. Phys. 21, 1087–1092.
Momany, F. A., McGuire, R. F., Burgess, A. W., and Scheraga, H. A. (1975).J. Phys. Chem. 79, 2361–2381.
Nakamura, H., and Nishida, S. (1987).J. Phys. Soc. Jpn. 56, 1609–1622.
Némethy, G., Pottle, M. S., and Scheraga, H. A. (1983).J. Phys. Chem. 87, 1883–1887.
Noguti, T., and Gō N. (1983).J. Phys. Soc. Jpn. 52, 3685–3690.
Paine, G. H., and Scheraga, H. A. (1985).Biopolymers 24, 1391–1436.
Paine, G. H., and Scheraga, H. A. (1986).Biopolymers 25, 1547–1563.
Paine, G. H., and Scheraga, H. A. (1987).Biopolymers 26, 1125–1162.
Piela, L., and Scheraga, H. A. (1987).Biopolymers 26, S33-S58.
Purisima, E. O., and Scheraga, H. A. (1987).J. Mol. Biol. 196, 697–709.
Rashin, A. A., and Namboordiri, K. (1987).J. Phys. Chem. 91, 6003–6012.
Ripoll, D., and Scheraga, H. A. (1988).Biopolymers 27, 1283–1303.
Russell, S. T., and Warshel, A. (1985).J. Mol. Biol. 185, 389–404.
Scheraga, H. A. (1988a). inComputer-Assisted Modeling of Receptor-Ligand Interactions: Theoretical Aspects and Applications to Drug Design (Rein, R., and Golombek, A., eds.), Liss, New York, pp. 3–18.
Scheraga, H. A. (1988b). inBiological and Artificial Intelligence Systems, Clementi, E., and Chin, S. eds. (ESCOM Science, Leiden), pp. 1–14.
Silverman, D. N., and Scheraga, H. A. (1972).Arch. Biochem. Biophys. 153, 449–456.
Sippl, M. J., Némethy, G., and Scheraga, H. A. (1984).J. Phys. Chem. 88, 6231–6233.
Vásquez, M., and Scheraga, H. A. (1985).Biopolymers 24, 1437–1447.
Wada, A. (1976).Adv. Biophys. 9, 1–63.
Warshel, A., and Russell, S. T. (1984).Q. Rev. Biophys. 17, 283–422.
Warwicker, J., and Watson, H. C. (1982).J. Mol. Biol. 157, 671–679.
Zauhar, R. J., and Morgan, R. S. (1985).J. Mol. Biol. 186, 815–820.
Zauhar, R. J., and Morgan, R. S. (1988).J. Comp. Chem. 9, 171–187.
Author information
Authors and Affiliations
Additional information
On leave from the National University of San Luis, Faculty of Sciences and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Instituto de Matemática Aplicada, San Luis, Ejército de los Andes 950, 5700 San Luis, Argentina.
Rights and permissions
About this article
Cite this article
Ripoll, D.R., Scheraga, H.A. The multiple-minima problem in the conformational analysis of polypeptides. III. An Electrostatically Driven Monte Carlo Method: Tests on enkephalin. J Protein Chem 8, 263–287 (1989). https://doi.org/10.1007/BF01024949
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/BF01024949