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Computer Simulations of Nonlinear Optical Chromophore Containing Polypeptides

Published online by Cambridge University Press:  15 February 2011

Ruth Pachter
Affiliation:
This work was done while the author held a National Research Council Research Associateship
Steven B. Fairchild
Affiliation:
Wright Laboratory, Materials Directorate, Wright-Patterson Air Force Base, Ohio 45433
James A. Lupo
Affiliation:
Wright Laboratory, Materials Directorate, Wright-Patterson Air Force Base, Ohio 45433
Brian S. Sennett
Affiliation:
Wright Laboratory, Materials Directorate, Wright-Patterson Air Force Base, Ohio 45433
Robert L. Crane
Affiliation:
Wright Laboratory, Materials Directorate, Wright-Patterson Air Force Base, Ohio 45433
W. Wade Adams
Affiliation:
Wright Laboratory, Materials Directorate, Wright-Patterson Air Force Base, Ohio 45433
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Abstract

In our continuing efforts towards the design of nonlinear (NLO) optical chromophore containing polypeptides we present an integrated computational approach, in which the design of biomolecular materials with defined secondary and tertiary structures is investigated by means of novel predictive tools, while the effects of the nonlinear optical chromophores are studied with molecular dynamics simulations. A neural network that was trained to predict the spatial proximity of Cα atoms that are less than a given threshold apart, is applied. The double-iterated Kalman filter (DIKF) technique is then employed with a constraints set that includes these pairwise atomic distances, and the distances and angles that define the structure as it is known from the protein's sequence. The results for test cases, particularly Crambin and genetically engineered Eglin-C, demonstrate that this integrated approach is useful for structure prediction at an intermediate resolution. Defined structural motifs of NLO chromophore containing polypeptides are investigated by using molecular dynamics techniques, particularly for the design of coil coiled amphiphatic biopolymers.

Type
Research Article
Copyright
Copyright © Materials Research Society 1994

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References

REFERENCES

1. Levine, B. F., Bethea, C. G., Wasserman, E., and Leenders, L., J. Chem. Phys. 68, 5042 (1978).CrossRefGoogle Scholar
2. Ishii, T., Wada, T., Garito, A., Sasabe, H., and Yamada, A., Mat. Res. Soc. Symp. Proc. 175, 129 (1990).Google Scholar
3. Cooper, T., Natarajan, L., Strasser, R., Pachter, R., and Crane, R., ACS Pol. Prep. 33, 1018 (1992).Google Scholar
4. Pachter, R., Cooper, T.M., Natarajan, L.V., Crane, R.L., and Adams, W.W., Biopolymers 32, 1129 (1992).Google Scholar
5. Holley, L. H. and Karplus, G., Proc. Natl. Acad. Sci. U.S.A., 86, 152 (1988) and references therein.CrossRefGoogle Scholar
6. Fairchild, S.B., Pachter, R., Perrin, R., Crane, R.L., and Adams, W.W., presented at the American Crystallographic Association, Pittsburgh PA, August, 1992.Google Scholar
7. Gelb, A., “Applied Optimal Estimation”, MIT Press, 1984.Google Scholar
8. Teeter, M.M., Proc. Nat. Acad. Sci. USA 81, 6014 (1984).CrossRefGoogle Scholar
9. Altman, R. B. and Jardetzky, O., Methods in Enzymology 177, 218 (1989).Google Scholar
10. Altman, R., Pachter, R., and Jardetzky, O., in “Protein Structure and Engineering”, (Jardetzky, O., ed.), Plenum Press, New York, pp. 79 (1989).CrossRefGoogle Scholar
11. Pachter, R., Altman, R., and Jardetzky, O., J. Magn. Reson. 89, 578 (1990).Google Scholar
12. Arrowsmith, C.H., Pachter, R., Altman, R., Iyer, S.B., and Jardetzky, O., Biochemistry 29, 6332 (1990).CrossRefGoogle Scholar
13. Altman, R., Arrowsmith, C.H., Pachter, R., and Jardetzky, O., “Computational Aspects of the Study of Biological Macromolecules by NMR Spectroscopy” (Hoch, J. C., ed.) pp. 375, Plenum Press, New York (1991).Google Scholar
14. Pachter, R., Altman, R., Czaplicki, J., and Jardetzky, O., J. Magn. Reson. 92, 648 (1991).Google Scholar
15. Arrowsmith, C.H., Pachter, R., Altman, R., and Jardetzky, O., FEBS Eur. J. Biochemistry 202, 53 (1991).CrossRefGoogle Scholar
16. Altman, R., Pachter, R., and Jardetzky, O., J. Appl. Magn. Reson. 4, 441 (1993).CrossRefGoogle Scholar
17. Kabsch, W. and Sanders, C., FEBS Letters, May 1983.Google Scholar
18. Wilcox, G., Poliac, M., and Liebman, M., Tetrahedron Computer Methodology 3, 196 (1990).CrossRefGoogle Scholar
19. NeuralWare, Inc. Penn Center West, Bldg. IV Pittsburgh, PA 15276.Google Scholar
20. Qian, N. and Sejnowski, T.J., J. Mol. Biology, 202, 868 (1988).CrossRefGoogle Scholar
21. Altman, R., Pachter, R., Carrara, C.A., and Jardetzky, O., , O. PROTEAN2: QCPE 10, 596 (2) (1990).Google Scholar
22. Pachter, R., programs: PROPARE, PROCON, PROCOPY, unpublished.Google Scholar
23. McPhalen, C.A. and James, M.N.G., Biochemistry 27, 6582 (1988).Google Scholar
24. Lupo, J. A., unpublished results.Google Scholar
25. Polygen Corporation (1992) Quanta Parameters: Release 3.2.Google Scholar
26. Sennett, B., manuscript in preparation.Google Scholar