Biomedical Engineering Reference
In-Depth Information
[62] J. Moult, A decade of CASP: Progress, bottlenecks and prognosis in protein structure
prediction, Curr. Opin. Struct Biol. , 15, 285-289 (2005).
[63] A. Sali and T. L. Blundell, Comparative protein modelling by satisfaction of spatial
restraints, J. Mol. Biol. , 234, 779-815 (1993).
[64] A. Fiser and A. Sali, Modeller: Generation and refinement of homology-based
protein structure models, Methods in Enzymology , 374, 461-491 (2003).
[65] A. Fiser and A. Sali, Modloop: Automated modeling of loops in protein structures,
Bioinformatics , 19, 2500-2501 (2003).
[66] A. Godzik, Fold recognition methods, Methods of Biochemical Analysis , 44,
525-546 (2003).
[67] R. T. Miller, D. T. Jones and J. M. Thornton, Protein fold recognition by
sequence threading: Tools and assessment techniques, FASEB Journal , 10, 171-
178 (1996).
[68] R. Day, D. A. Beck, R. S. Armen and V. Daggett, A consensus view of fold space:
Combining SCOP, CATH, and the DALI domain dictionary, Protein Sci. , 12,
2150-2160 (2003).
[69] Y. Zhang and J. Skolnick, The protein structure prediction problem could be solved
using the current PDB library, PNAS , 102, 1029-1034 (2005).
[70] R. Casadio, P. Fariselli, P. L. Martelli and G. Tasco, Thinking the impossible: How to
solve the protein folding problem with and without homologous structures and more,
Methods in Molecular Biology , 350, 305-320 (2007).
[71] H. Zhou and Y. Zhou, Fold recognition by combining sequence profiles derived from
evolution and from depth-dependent structural alignment of fragments, Proteins ,
58, 321-328 (2005).
[72] Y. Zhang, A. K. Arakaki and J. Skolnick, TASSER: An automated method for the
prediction of protein tertiary structures in CASP6, Proteins , 61 Suppl. 7, 91-98 (2005).
[73] Y. An and R. A. Friesner, A novel fold recognition method using composite predicted
secondary structures, Proteins , 48, 352-366 (2002).
[74] S. Wu, J. Skolnick and Y. Zhang, Ab initio modeling of small proteins by iterative
tasser simulations, BMC Biology , 5, 17 (2007).
[75] S. Wu and Y. Zhang, Lomets: A local meta-threading-server for protein structure
prediction, Nucleic Acids Res. , 35, 3375-3382 (2007).
[76] L. Rychlewski and D. Fischer, Livebench-8: The large-scale, continuous assessment
of automated protein structure prediction, Protein Sci. , 14, 240-245 (2005).
[77] C. A. Rohl, C. E. M. Strauss, K. M. S. Misura and D. Baker, Protein structure
prediction using Rosetta, Methods in Enzymology , 383, 66-93 (2004).
[78] Z. Li and H. A. Scheraga, Monte Carlo-minimization approach to the multiple-
minima problem in protein folding, PNAS , 84, 6611-6615 (1987).
[79] C. A. Rohl, C. E. Strauss, D. Chivian and D. Baker, Modeling structurally variable
regions in homologous proteins with Rosetta, Proteins , 55, 656-677 (2004).
[80] C. Bystroff and D. Baker, Blind predictions of local protein structure in CASP2
targets using the i-sites library, Proteins , Suppl. 1, 167-171 (1997).
[81] K. T. Simons, R. Bonneau, I. Ruczinski and D. Baker, Ab initio protein
structure prediction of CASP III targets using Rosetta, Proteins , Suppl. 3, 171-176
(1999).
[82] R. Bonneau, J. Tsai, I. Ruczinski, D. Chivian, C. Rohl, C. E. Strauss and D. Baker,
Rosetta in CASP4: Progress in ab initio protein structure prediction, Proteins , Suppl.
5, 119-126 (2001).
Search WWH ::




Custom Search