Mathematical Modeling and Optimization Methods for De Novo Protein Design - Systems Biology: Networks, Models, and Applications - page 60

15. Emberly, E. G., R. Mukhopadhyay, C. Tang and N. S. Wingreen. Flexibility

of a-helices: results of a statistical analysis of database protein structures.
Journal of Molecular Biology , 327:229-37, 2003.

16. Emberly, E. G., R. Mukhopadhyay, C. Tang and N. S. Wingreen. Flexibility

of b-sheets: principal component analysis of database protein structures.
Proteins: Structure, Function, and Genetics , 55:91-8, 2004.

17. Su, A. and S. L. Mayo. Coupling backbone flexibility and amino acid

sequence selection in protein design. Protein Science , 6:1701-7, 1997.

18. Mooers, B. H. M., D. Datta, W. A. Baase, E. S. Zollars, S. L. Mayo and

B. W. Matthews. Repacking the core of T4 lysozyme by automated design.
Journal of Molecular Biology , 332:741-56, 2003.

19. Kuhlman, B. and D. Baker. Native protein sequences are close to optimal

for their structures. Proceedings of the National Academy of Sciences USA ,
97:10383-8, 2000.

20. Desjarlais, J. R. and T. M. Handel. Side chain and backbone flexibility in

protein core design. Journal of Molecular Biology , 290:305-18, 1999.

21. Kraemer-Pecore, C. M., J. T. Lecomte and J. R. Desjarlais. A de novo

redesign of the WW domain. Protein Science , 12:2194-205, 2003.

22. Farinas, E. and L. Regan. The de novo design of a rubredoxin-like Fe site.

Protein Science , 7:1939-46, 1998.

23. Harbury, P. B., J. J. Plecs, B. Tidor, T. Alber and P. S. Kim. High-resolution

protein design with backbone freedom. Science , 282:1462-7, 1998.

24. Klepeis, J. L., C. A. Floudas, D. Morikis, C. G. Tsokos, E. Argyropoulos,

L. Spruce and J. D. Lambris. Integrated computational and experimental
approach for lead optimization and design of compstatin variants with
improved activity. Journal of the American Chemical Society, 125:8422-3, 2003.

25. Klepeis, J. L., C. A. Floudas, D. Morikis, C. G. Tsokos and J. D. Lambris.

Design of peptide analogs with improved activity using a novel de novo
protein design approach. Industrial and Engineering Chemistry Research ,
43:3817, 2004.

26. Lee, C. Predicting protein mutant energetics by self-consistent ensemble

optimization. Journal of Molecular Biology , 236:918-39, 1994.

27. Desmet, J., M. De Maeyer, B. Hazes and I. Lasters. The dead-end elimina-

tion theorem and its use in side-chain positioning. Nature , 356:539-42, 1992.

28. Koehl, P. and M. Delarue. Application of a self-consistent mean field

theory to predict protein side-chains conformation and estimate their con-
formational entropy. Journal of Molecular Biology , 239:249-75, 1994.

29. Koehl, P. and M. Delarue. A self consistent mean field approach to simul-

taneouos gap closure and side-chain positioning in homology modeling.
Nature Structural Biology , 2:163-70, 1995.

30. Koehl, P. and M. Delarue. Mean-field minimization methods for biological

macromolecules. Current Opinion in Structural Biology , 6:222-6, 1996.

31. Pierce, N. A., J. A. Spriet, J. Desmet and S. L. Mayo. Conformational splitting:

a more powerful criterion for dead-end elimination. Journal of
Computational Chemistry , 21:999-1009, 2000.

32. Voigt, C. A., D. B. Gordon and S. L. Mayo. Trading accuracy for speed: a

quantitative comparison of search algorithms in protein sequence design.
Journal of Molecular Biology , 299:789-803, 2000.