Structure Calculation Using CS-DP ROSETTA - Revision history

Jma at 21:18, 6 January 2010

2010-01-06T21:18:17Z

← Older revision		Revision as of 21:18, 6 January 2010
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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~~.		[http://www.ncbi.nlm.nih.gov/pubmed/20000319?itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum&ordinalpos=1 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.'' ''132'', 202-207.]

	<br>		<br>

Jma at 21:16, 6 January 2010

2010-01-06T21:16:29Z

← Older revision		Revision as of 21:16, 6 January 2010
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	== '''Introduction''' ==		== '''Introduction''' ==

	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.		The CS-DP-Rosetta approach [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.

	<br>		<br>
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	in::file::psipred_ss2 t000_.psipred_ss2.gz -abinitio::fastrelax -score::weights		in::file::psipred_ss2 t000_.psipred_ss2.gz -abinitio::fastrelax -score::weights
	score13_env_hb -silent_gz		score13_env_hb -silent_gz
	</pre>		</pre>
	II. Second stage rebuild-and-refine		II. Second stage rebuild-and-refine
	<pre>aa t000 _ -relax -looprlx -nstruct 10 -fa_input -use_sspair -farlx -ex1 -ex2 -random_loop -		<pre>aa t000 _ -relax -looprlx -nstruct 10 -fa_input -use_sspair -farlx -ex1 -ex2 -random_loop -
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	idl_no_chain_break -loop_skip_rate 0.0 -loop_file t000.loop_file.gz -vary_omega -		idl_no_chain_break -loop_skip_rate 0.0 -loop_file t000.loop_file.gz -vary_omega -
	output_silent_gz -output_chi_silent -l s.list		output_silent_gz -output_chi_silent -l s.list
	</pre>		</pre>
	<br>		<br>

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	<br>		<br>

	~~-- JanetHuang/JimAramini - Nov 2009~~

Jma at 19:46, 27 November 2009

2009-11-27T19:46:05Z

@@ Line 13: / Line 13: @@
 **Side chain assignment:&nbsp; manual
 *3D <sup>13</sup>C- and <sup>15</sup>N-edited NOESY spectra<br>
 === Peak picking procedures  ===
@@ Line 25: / Line 23: @@
 *Refined NOESY peak lists are obtained by expert manual editing which involves artifact removal, picking of overlapped peaks and(or) picking starting from a script-based 3D-HcCH intra/sequential-residue NOE list that was manually extended to the long range unpicked resonances.
 === CS-DP-ROSETTA procedure<br>  ===
@@ Line 42: / Line 38: @@
 <pre>ti =  (CS-Rosetta all-atom energy)i + 1000*(1 – DP-score)i
 </pre>
 <br>V.&nbsp;&nbsp;&nbsp; Rank 1000 decoys based on t<sub>i</sub> , keep the first 20 lowest t<sub>i</sub> decoys for further model rebuild-and-refinement<br><br>'''Step 3: Model rebuilding and refinement for 20 lowest t<sub>i</sub> decoys'''<br>&nbsp;&nbsp;&nbsp; <br>I.&nbsp;&nbsp;&nbsp;&nbsp; Identifying flexible regions which have largest C-alpha deviations within the 20 lowest t<sub>i</sub> decoys&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; <br>II.&nbsp;&nbsp;&nbsp; Flexible regions are stochastically rebuilt by fragment insertion and CCD loop closure <br>III.&nbsp;&nbsp; Using physically realistic Rosetta force field to perform all-atom refinement for the whole structure<br>IV.&nbsp;&nbsp; The best 10 models with the lowest t<sub>i</sub> are saved as the final CS-DP-Rosetta models<br><br>
 <br>
@@ Line 66: / Line 65: @@
 <br>
 -- JanetHuang/JimAramini - Nov 2009

Jma at 19:42, 27 November 2009

2009-11-27T19:42:48Z

@@ Line 13: / Line 13: @@
 **Side chain assignment:&nbsp; manual
 *3D <sup>13</sup>C- and <sup>15</sup>N-edited NOESY spectra<br>
 === Peak picking procedures  ===
@@ Line 23: / Line 25: @@
 *Refined NOESY peak lists are obtained by expert manual editing which involves artifact removal, picking of overlapped peaks and(or) picking starting from a script-based 3D-HcCH intra/sequential-residue NOE list that was manually extended to the long range unpicked resonances.
 === CS-DP-ROSETTA procedure<br>  ===
@@ Line 38: / Line 42: @@
 <pre>ti =  (CS-Rosetta all-atom energy)i + 1000*(1 – DP-score)i
 </pre>
-<br>V.&nbsp;&nbsp;&nbsp; Rank 1000 decoys based on t<sub>i</sub> , keep the first 20 lowest t<sub>i</sub> decoys for further model rebuild-and-refinement<br><br>'''Step 3: Model rebuilding and refinement for 20 lowest t<sub>i</sub> decoys'''<br>&nbsp;&nbsp;&nbsp; <br>I.&nbsp;&nbsp;&nbsp;&nbsp; Identifying flexible regions which have largest C-alpha deviations within the 20 lowest t<sub>i</sub> decoys&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; <br>II.&nbsp;&nbsp;&nbsp; Flexible regions are stochastically rebuilt by fragment insertion and CCD loop closure <br>III.&nbsp;&nbsp; Using physically realistic Rosetta force field to perform all-atom refinement for the whole structure<br>IV.&nbsp;&nbsp; The best 10 models with the lowest t<sub>i</sub> are saved as the final CS-DP-Rosetta models<br><br><br><br>
+<br>V.&nbsp;&nbsp;&nbsp; Rank 1000 decoys based on t<sub>i</sub> , keep the first 20 lowest t<sub>i</sub> decoys for further model rebuild-and-refinement<br><br>'''Step 3: Model rebuilding and refinement for 20 lowest t<sub>i</sub> decoys'''<br>&nbsp;&nbsp;&nbsp; <br>I.&nbsp;&nbsp;&nbsp;&nbsp; Identifying flexible regions which have largest C-alpha deviations within the 20 lowest t<sub>i</sub> decoys&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp; <br>II.&nbsp;&nbsp;&nbsp; Flexible regions are stochastically rebuilt by fragment insertion and CCD loop closure <br>III.&nbsp;&nbsp; Using physically realistic Rosetta force field to perform all-atom refinement for the whole structure<br>IV.&nbsp;&nbsp; The best 10 models with the lowest t<sub>i</sub> are saved as the final CS-DP-Rosetta models<br><br>
 == '''References'''  ==

Jma at 19:28, 27 November 2009

2009-11-27T19:28:52Z

← Older revision		Revision as of 19:28, 27 November 2009
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	*unassigned raw or refined NOESY peak lists files (Xeasy format or Sparky format).<br>		*unassigned raw or refined NOESY peak lists files (Xeasy format or Sparky format).<br>

			<br> '''Step 1: Generating ~50000 CS-Rosetta decoys'''<br><br>I.    Generate 3-mer & 9-mer fragments library based on sequence and chemical shift information by Rosetta Fragments server or Rosetta software suite<br>II.    Use CS-Rosetta protocol to generate ~50,000 decoys, keep 1,000 decoys with lowest Rosetta energy<br><br>'''Step 2: Filter decoys by a linear combination of Rosetta energy and DP-Score'''<br>    <br>I.    Prepare sequence file and chemical shift file in BMRB format, and peak list files can be Sparky, Xeasy or any other table format. Use AutoStructure 2.2.1 GUI to make control file for RPF calculation. By default, tolerance for <sup>13</sup>C and <sup>15</sup>N is set to 0.5, and tolerance for the <sup>1</sup>H dimensions is set to 0.05.<br>Make sure sequence file, chemical shift file and all the peak list files in the project directory    <br>(1)  $Autostructure_install_path/bin/asgui.<br>(2)  Select  File->New Control File, a “Control File Display” window would appear.<br>(3)  In the “General Section” tab, input “Protein Name”, select “Sequence File”, “Chemical Shift File”, set “Iterative Analysis Cycles” to 1, then select tab “PeakList Section”, add peak list files one by one. Save the control file after all the peak lists have been added.    <br>    <br>II.    Calculate DP-Score for the 1000 decoys with lowest Rosetta energy using RPF module of AutoStructure 2.2.1. <sub></sub><br>Command:
	'''Step 1: Generating ~50000 CS-Rosetta decoys'''<br><br>I.    Generate 3-mer & 9-mer fragments library based on sequence and chemical shift information by Rosetta Fragments server or Rosetta software suite<br>II.    Use CS-Rosetta protocol to generate ~50,000 decoys, keep 1,000 decoys with lowest Rosetta energy<br><br>'''Step 2: Filter decoys by a linear combination of Rosetta energy and DP-Score'''<br>    <br>I.    Prepare sequence file and chemical shift file in BMRB format, and peak list files can be Sparky, Xeasy or any other table format. Use AutoStructure 2.2.1 GUI to make control file for RPF calculation. By default, tolerance for <sup>13</sup>C and <sup>15</sup>N is set to 0.5, and tolerance for the <sup>1</sup>H dimensions is set to 0.05.<br>Make sure sequence file, chemical shift file and all the peak list files in the project directory    <br>(1)  $Autostructure_install_path/bin/asgui.<br>(2)  Select  File->New Control File, a “Control File Display” window would appear.<br>(3)  In the “General Section” tab, input “Protein Name”, select “Sequence File”, “Chemical Shift File”, set “Iterative Analysis Cycles” to 1, then select tab “PeakList Section”, add peak list files one by one. Save the control file after all the peak lists have been added.    <br>    <br>II.    Calculate DP-Score for the 1000 decoys with lowest Rosetta energy using RPF module of AutoStructure 2.2.1. <sub></sub><br>Command:
	<pre>$Autostructure_install_path/bin/autostructure -c control_file –o rpf_output_path -q path_of_query_structure -s		<pre>$Autostructure_install_path/bin/autostructure -c control_file –o rpf_output_path -q path_of_query_structure -s
	</pre>		</pre>
	Extract DP-score from rpf_output_path/*.ovw file    <br><br>III.    Calculate DP-Score for the 1,000 decoys with lowest Rosetta energy using RPF module of AutoStructure 2.2.1     <br>IV.    Calculate target function for each decoy:		Extract DP-score from rpf_output_path/*.ovw file    <br><br>III.    Calculate DP-Score for the 1,000 decoys with lowest Rosetta energy using RPF module of AutoStructure 2.2.1     <br>IV.    Calculate target function for each decoy:
	<pre>~~t<sub>i</sub>~~ = CS-Rosetta all-atom energy~~<sub>~~i ~~</sub>~~+ 1000*(1 – DP-score~~<sub>~~i~~</sub>)~~		<pre>ti = (CS-Rosetta all-atom energy)i + 1000*(1 – DP-score)i
	</pre>		</pre>
	<br>V.    Rank 1000 decoys based on t<sub>i</sub> , keep the first 20 lowest t<sub>i</sub> decoys for further model rebuild-and-refinement<br><br>'''Step 3: Model rebuilding and refinement for 20 lowest t<sub>i</sub> decoys'''<br>    <br>I.     Identifying flexible regions which have largest C-alpha deviations within the 20 lowest t<sub>i</sub> decoys        <br>II.    Flexible regions are stochastically rebuilt by fragment insertion and CCD loop closure <br>III.   Using physically realistic Rosetta force field to perform all-atom refinement for the whole structure<br>IV.   The best 10 models with the lowest t<sub>i</sub> are saved as the final CS-DP-Rosetta models<br><br><br><br>		<br>V.    Rank 1000 decoys based on t<sub>i</sub> , keep the first 20 lowest t<sub>i</sub> decoys for further model rebuild-and-refinement<br><br>'''Step 3: Model rebuilding and refinement for 20 lowest t<sub>i</sub> decoys'''<br>    <br>I.     Identifying flexible regions which have largest C-alpha deviations within the 20 lowest t<sub>i</sub> decoys        <br>II.    Flexible regions are stochastically rebuilt by fragment insertion and CCD loop closure <br>III.   Using physically realistic Rosetta force field to perform all-atom refinement for the whole structure<br>IV.   The best 10 models with the lowest t<sub>i</sub> are saved as the final CS-DP-Rosetta models<br><br><br><br>

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	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.

			<br>

	-- JanetHuang/JimAramini - Nov 2009		-- JanetHuang/JimAramini - Nov 2009

Jma at 19:26, 27 November 2009

2009-11-27T19:26:54Z

@@ Line 5: / Line 5: @@
 <br>
-== '''Protocol'''  ==
+== '''Protocols'''  ==
 === Prerequisites  ===
@@ Line 13: / Line 13: @@
 **Side chain assignment:&nbsp; manual
 *3D <sup>13</sup>C- and <sup>15</sup>N-edited NOESY spectra<br>
 == '''References'''  ==
 .&nbsp; 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&nbsp;data.&nbsp; <span style="font-style: italic;">J</span>''. Am. Chem. Soc.'', in press.

Jma at 19:08, 27 November 2009

2009-11-27T19:08:30Z

← Older revision		Revision as of 19:08, 27 November 2009
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	=== Prerequisites ===		=== 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.		*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]].		**Backbone assignment:  using either [[AutoAssign\|AutoAssign]] or [[The PINE Server\|PINE]].
	*Side chain assignment:  manual		**Side chain assignment:  manual
	*3D <sup>13</sup>C- and <sup>15</sup>N-edited NOESY spectra<br>		*3D <sup>13</sup>C- and <sup>15</sup>N-edited NOESY spectra<br>





	== '''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.

Jma at 19:07, 27 November 2009

2009-11-27T19:07:16Z

← Older revision		Revision as of 19:07, 27 November 2009
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	== '''Introduction''' ==		== '''Introduction''' ==

	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.		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.

			<br>

			== '''Protocol''' ==

	== ~~'''Protocol'''~~ ==		=== Prerequisites ===

	~~=== Model generation with raw peak lists ===~~		*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.

Jma: Created page with '== '''Introduction''' == The CS-DP-Rosetta approach (Ref. 1) merges the ideas of model generation using CS-Rosetta with model filterin…'

2009-11-27T18:59:42Z

Created page with '== '''Introduction''' == The CS-DP-Rosetta approach (Ref. 1) merges the ideas of model generation using CS-Rosetta with model filterin…'

New page

== '''Introduction''' ==

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.

== '''Protocol''' ==

=== Model generation with raw peak lists ===

== '''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.