Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-25T15:42:09.545Z Has data issue: false hasContentIssue false

Selectivity of Polypeptide Binding to Nanoscale Substrates

Published online by Cambridge University Press:  01 February 2011

Steven R. Lustig
Affiliation:
Central Research & Development E.I., du Pont de Nemours & Co., Inc. Experimental Station, Route 141 Wilmington, DE 19880-0356, U.S.A.
Anand Jagota
Affiliation:
Central Research & Development E.I., du Pont de Nemours & Co., Inc. Experimental Station, Route 141 Wilmington, DE 19880-0356, U.S.A.
Get access

Abstract

We present new computational methodology for designing polymers, such as polypeptides and polyelectrolytes, which can selectively recognize nanostructured substrates. The methodology applies to polymers which might be used to: control placement and assembly for electronic devices, template structure during materials synthesis, as well as add new biological and chemical functionality to surfaces. Optimization of the polymer configurational sequence permits enhancement of both binding energy on and binding selectivity between one or more atomistic surfaces. A novel Continuous Rotational Isomeric State (CRIS) method permits continuous backbone torsion sampling and is seen to be critical in binding optimization problems where chain flexibility is important. We illustrate selective polypeptide binding between either analytic, uniformly charged surfaces or atomistic GaAs(100), GaAs(110) and GaAs(111) surfaces. Computational results compare very favorably with prior experimental phage display observations [S.R. Whaley et al., Nature, 405, 665 (2000)] for GaAs substrates. Further investigation indicates that chain flexibility is important to exhibit selective binding between surfaces of similar charge density. Such chains begin with sequences which repel the surfaces, continue with sequences that attract the surface and end with sequences that neither attract nor repel strongly.

Type
Research Article
Copyright
Copyright © Materials Research Society 2002

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Huse, W.D., Sastry, L., Iverson, S.A., Kang, A.S., Alting-Mees, M., Burton, D.R., Benkovic, S.J. and Lerner, R.A., Science, 246, 12751281 (1989).Google Scholar
2. Marks, J.D., Hoogboom, H.R., Bonnert, T.P., McCafferty, J., Griffiths, A.D. and Winter, G., J. Mol. Biol. 222, 581597 (1991).Google Scholar
3. Scott, J.K. and Smith, G.P., Science, 249, 386390 (1990).Google Scholar
4. Cwirla, S.E., Peters, E.A., Barrett, R.W. and Dower, W.J., Proc. Natl. Acad. Sci. USA, 87, 63786382 (1990).Google Scholar
5. Devlin, J.J., Panganiban, L.C., Devlin, P.E., Science, 249, 404406 (1990).Google Scholar
6. Lam, K.S., Salmon, S.E., Hersh, E.M., Hruby, V.J., Kazmierski, W.M. and Knapp, R.J., Nature (London), 354, 8284 (1991).Google Scholar
7. Brown, S., Proc. Natl. Acad. Sci. USA, 89, 86518655 (1992).Google Scholar
8. Brown, S., Nature Biotechnol. 15, 269272 (1997).Google Scholar
9. Whaley, S.R., English, D.S., Hu, E.L., Barbara, P.F. and Belcher, A.M., Nature, 405, 665668 (2000).Google Scholar
10. Flory, P.J., Statistical Mechanics of Chain Molecules (Interscience, New York, 1969).Google Scholar
11. Mattice, W.L. and Suter, U.W., Conformational Theory of Large Molecules; The Rotational Isomeric State Model in Macromolecular Systems (Wiley, New York, 1994).Google Scholar
12. Rehahn, M., Mattice, W.L. and Suter, U.W., “Rotational Isomeric State Models in Macromolecular Systems,” Advances in Polymer Science, 131/132, (Springer, New York, 1997).Google Scholar
13. Maple, J. R., Hwang, M.-J., Stockfisch, T. P., Dinur, U., Waldman, M., Ewig, C. S., and Hagler, A. T., J. Comput. Chem. 15, 162182 (1994)Google Scholar
14. Sun, H., J. Comput. Chem. 15, 752757 (1994)Google Scholar
15. At the time of writing this manuscript, the COMPASS force field is a licensed product of Accelrys Inc., 9685 Scranton Road, San Diego, CA 92121 (http://www.accelrys.com). See for example: Sun, H. and Rigby, D., Spectrochim. Acta, 53A, 1301 (1997); H. Sun, J. Phys. Chem. B, 102, 7338-7364 (1998); H. Sun, P. Ren and J. R. Fried, Comput. Theor. Polym. Sci, 8, 229. (1998).Google Scholar
16. Binder, K. and Heermann, D.W., “Monte Carlo Simulation in Statistical Physics”, 2nd corrected ed., Solid-State Sciences, 80, (Springer-Verlag, New York, 1992).Google Scholar
17. See for example: Kong, C.Y. and Muthukumar, M., J. Chem. Phys. 109, 1552–1527 (1998); Y.-H. Lee and B.J. Berne, J. Phys. Chem., 104, 86-95 (2000); S. Santos, U.W. Suter, M Muller and J. Nievergelt, J. Chem. Phys. 114, 9772-9779 (2001); P. Chodanowski and S. Stoll, Macromolecules, 34, 2320-2328 (2001).Google Scholar
18. Muthukumar, M, Proc. Nat. Acad. Sci. USA, 96, 1169011692 (1999).Google Scholar