On-the-fly rotamer pair energy evaluation in protein design

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4983 LNBI, Issue , 2008, Pages 343-354

  Leaver Fay, Andrew ^a   Snoeyink, Jack ^b   Kuhlman, Brian ^a  

^a UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

^b UNIVERSITY OF NORTH CAROLINA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ENERGY EVALUATIONS; ENERGY VALUES; INTERNATIONAL SYMPOSIUM; MOLECULAR MODELLING; ON THE FLY; PROTEIN DESIGN; ROSETTA; ROTAMER; RUNNING TIME; SIMULATED ANNEALING ALGORITHMS;

ANNEALING; BIOCOMMUNICATIONS; BIOINFORMATICS; INFORMATION SCIENCE; MACHINE DESIGN;

SIMULATED ANNEALING;

EID: 49949112974     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-79450-9_32     Document Type: Conference Paper

References (33)

1. Dunbrack Jr., R.L., Karplus, M.: Backbone dependant rotamer library for proteins: Application to side chain prediction. Journal of Molecular Biology 230, 543-574 (1993)
2. Lovell, S.C., Word, J.M., Richardson, J.S., Richardson, D.C.: The penultimate rotamer library. Proteins: Structure Function and Genetics 40, 389-408 (2000)
3. Looger, L.L., Dwyer, M.A., Smith, J.J., Hellinga, H.W.: Computational design of receptor and sensor proteins with novel functions. Nature 423, 185-190 (2003)
4. Kuhlman, B., O'Neill, J.W., Kim, D.E., Zhang, K.Y., Baker, D.: Accurate computer-based design of a new backbone conformation in the second turn of protein L. Journal of Molecular Biology 315, 471-477 (2002)
5. Pierce, N., Winfree, E.: Protein design is NP-hard. Protein Engineering 15, 779-782 (2002)
6. Desmet, J., Maeyer, M.D., Hazes, B., Lasters, I.: The dead-end elimination theorem and its use in protein side-chain positioning. Nature 356, 539-541 (1992)
7. Lasters, I., Desmet, J.: The fuzzy-ended elimination theorem: Correctly implementing the side chain placement algorithm based on the dead-end elimination theorem. Protein Engineering 6, 717-722 (1993)
8. Goldstein, R.F.: Efficient rotamer elimination applied to protein side-chains and related spin glasses. Biophysical Journal 66, 1335-1340 (1994)
9. Gordon, D.B., Mayo, S.L.: Radical performance enhancements for combinatorial optimization algorithms based on the dead-end elimination theorem. Journal of Computational Chemistry 19, 1505-1514 (1998)
10. Looger, L.L., Hellinga, H.W.: Generalized dead-end elimination algorithms make large-scale protein side-chain structure prediction tractable: Implications for protein design and structural genomics. Journal of Molecular Biology 307(1), 429-445 (2001)
11. Gordon, D., Mayo, S.: Branch-and-terminate: A combinatorial optimization algorithm for protein design. Structure Fold Des 7, 1089-1098 (1999)
12. Canutescu, A.A., Shelenkov, A.A., Dunbrack Jr., R.: A graph-theory algorithm for rapid protein side-chain prediction. Protein Science 12, 2001-2014 (2003)
13. Leaver-Fay, A., Liu, Y., Snoeyink, J.: Faster placement of hydrogen atoms in protein structures by dynamic programming. In: 6th Workshop on Algorithm Engineering and Experiments (ALENEX 2004) (2004)
14. Leaver-Fay, A., Kuhlman, B., Snoeyink, J.: An adaptive dynamic programming algorithm for the side chain placement problem. In: Pacific Symposium on Biocomputing, The Big Island, HI, pp. 17-28. World Scientific, Singapore (2005)
15. Koehl, P., Delarue, M.: Application of a self-consistent mean field theory to predict protein side-chains conformation and estimate their conformational entropy. J. Mol. Biol. 239(2), 249-275 (1994)
16. Desjarlais, J., Handle, T.: De novo design of hydrophobic cores of proteins. Protein Science 4, 2006-2018 (1995)
17. Holm, L., Sander, C.: Fast and simple Monte Carlo algorithm for side chain optimization in proteins: Application to model building by homology. Proteins 14(2), 213-223 (1992)
18. Hellinga, H., Richards, F.: Optimal sequence selection in proteins of known structure by simulated evolution. Proceedings of the National Academy of Sciences, USA 91, 5803-5807 (1994)
19. Kuhlman, B., Baker, D.: Native protein sequences are close to optimal for their structures. Proceedings of the National Academy of Sciences, USA 97, 10383-10388 (2000)
20. Desmet, J., Spriet, J., Lasters, I.: Fast and accurate side-chain topology and energy refinement (FASTER) as a new method for protein structure optimization. Proteins 48, 31-43 (2002)
21. Allen, B.D., Mayo, S.L.: Dramatic performance enhancements for the faster optimization algorithm. Journal of Computational Chemistry 27, 1071-1075 (2006)
22. Xu, J.: A tree-decompositon based approach to protein structure prediction. In: Miyano, S., Mesirov, J., Kasif, S., Istrail, S., Pevzner, P.A., Waterman, M. (eds.) RECOMB 2005. LNCS (LNBI), vol. 3500, pp. 423-439. Springer, Heidelberg (2005)
23. Simons, K.T., Bonneau, R., Ruczinski, I., Baker, D.: Ab initio protein structure prediction of CASP III targets using ROSETTA. Proteins: Structure Function and Genetics 37, 171-176 (1999)
24. Bradley, P., Chivian, D., Meiler, J., Misura, K., Rohl, C., Schief, W., Wedemeyer, W., Schueler-Furman, O., Murphy, P., Strauss, J.S.C., Baker, D.: Rosetta predictions in CASP5: Successes, failures, and prospects for complete automation. Proteins: Structure Function and Genetics 53, 457-468 (2003)
25. Kuhlman, B., Dantas, G., Ireton, G., Varani, G., Stoddard, B., Baker, D.: Design of a novel globular protein fold with atomic-level accuracy. Science 302, 1364-1368 (2003)
26. Dantas, G., Kuhlman, B., Callender, D., Wong, M., Baker, D.: A large scale test of computational protein design: Folding and stability of nine completely redesigned globular proteins. Journal of Molecular Biology 332, 449-460 (2003)
27. Ashworth, J., Havranek, J., Duarte, C., Sussman, D., Monnat, R.J., Monnat, R.J., BL, B.S., Baker, D.: Computational redesign of endonuclease dna binding and cleavage specificity. Nature 441, 656-659 (2006)
28. Sood, V., Baker, D.: Recapitulation and design of protein binding peptide structures and sequences. Journal of Molecular Biology 357, 917-927 (2006)
29. Rohl, C., Strauss, C., Misura, K., Baker, D.: Protein structure prediction using rosetta. Methods in Enzymology 383, 66-93 (2004)
30. Lazaridis, T., Karplus, M.: Effective energy function for proteins in solution. Proteins: Structure Function and Genetics 35, 133-152 (1999)
31. Kortemme, T., Morozov, A.V., Baker, D.: An orientation-dependent hydrogen bonding potential improves prediction of specificity and structure for proteins and protein-protein complexes. Journal of Molecular Biology 326, 1239-1259 (2003)
32. Simons, K., Ruczinski, I., Kooperberg, C., Fox, B., Bystroff, C., Bystroff, C., D., D.B.: Improved recognition of native-like protein structures using a combination of sequence-dependent and sequence-independent features of proteins. Proteins: Structure Function and Genetics 34, 82-95 (1999)
33. Leaver-Fay, A., Kuhlman, B., Snoeyink, J.: Rotamer-pair energy calculations using a trie data structure. In: Casadio, R., Myers, G. (eds.) WABI 2005. LNCS (LNBI), vol. 3692, pp. 500-511. Springer, Heidelberg (2005)