Structure Calculation Using CS-DP ROSETTA: Difference between revisions
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== '''Introduction''' == | == '''Introduction''' == | ||
The CS-DP-Rosetta approach (Ref. 1) merges the ideas of model generation using [[ | The CS-DP-Rosetta approach (Ref. 1) merges the ideas of model generation using [[Structure Calculation Using CS-Rosetta|CS-Rosetta]] with model filtering by agreement to NOESY data via the DP-score from the [[RPF Analysis|RPF program]] to generate high accuracy protein structures. This hybrid approach uses both local backbone chemical shift data (CS-Rosetta) and unassigned NOESY data (DP-filtering) to direct Rosetta trajectories toward the native structure, producing more accurate models than CS-Rosetta alone. Given a raw (or refined) NOESY peak list and chemical shift (backbone and extensive sidechain) information, the DP-Score is used as a filter to effectively guide the trajectory of CS-Rosetta decoy generation, significantly reducing the search space. Since the NOESY peak list data are not directly included in structure calculation, CS-DP-Rosetta is much more robust with respect to the quality of these peak lists compared to methods which attempt to assign each NOESY peak to one or more specific interproton interactions. | ||
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== '''Protocol''' == | |||
== | === Prerequisites === | ||
*Complete <sup>1</sup>H, <sup>13</sup>C, and <sup>15</sup>N resonance assignments using either conventional triple resonance or GFT approaches. | |||
*Backbone assignment: using either [[AutoAssign|AutoAssign]] or [[The_PINE_Server|PINE]]. | |||
*Side chain assignment: manual | |||
*3D <sup>13</sup>C- and <sup>15</sup>N-edited NOESY spectra<br> | |||
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== '''References''' == | |||
== '''References''' == | |||
1. Raman, S., Huang, Y. J., Mao, B., Rossi, P., Aramini, J. M., Liu, G., Montelione, G. T., and Baker, D. (2010) Accurate automated protein NMR structure determination using unassigned NOESY data. <span style="font-style: italic;">J</span>''. Am. Chem. Soc.'', in press. | 1. Raman, S., Huang, Y. J., Mao, B., Rossi, P., Aramini, J. M., Liu, G., Montelione, G. T., and Baker, D. (2010) Accurate automated protein NMR structure determination using unassigned NOESY data. <span style="font-style: italic;">J</span>''. Am. Chem. Soc.'', in press. | ||
Revision as of 19:07, 27 November 2009
Introduction
The CS-DP-Rosetta approach (Ref. 1) merges the ideas of model generation using CS-Rosetta with model filtering by agreement to NOESY data via the DP-score from the RPF program to generate high accuracy protein structures. This hybrid approach uses both local backbone chemical shift data (CS-Rosetta) and unassigned NOESY data (DP-filtering) to direct Rosetta trajectories toward the native structure, producing more accurate models than CS-Rosetta alone. Given a raw (or refined) NOESY peak list and chemical shift (backbone and extensive sidechain) information, the DP-Score is used as a filter to effectively guide the trajectory of CS-Rosetta decoy generation, significantly reducing the search space. Since the NOESY peak list data are not directly included in structure calculation, CS-DP-Rosetta is much more robust with respect to the quality of these peak lists compared to methods which attempt to assign each NOESY peak to one or more specific interproton interactions.
Protocol
Prerequisites
- Complete 1H, 13C, and 15N resonance assignments using either conventional triple resonance or GFT approaches.
- Backbone assignment: using either AutoAssign or PINE.
- Side chain assignment: manual
- 3D 13C- and 15N-edited NOESY spectra
References
1. Raman, S., Huang, Y. J., Mao, B., Rossi, P., Aramini, J. M., Liu, G., Montelione, G. T., and Baker, D. (2010) Accurate automated protein NMR structure determination using unassigned NOESY data. J. Am. Chem. Soc., in press.