AutoStructure Theory - Revision history

Yphuang at 21:00, 18 December 2009

2009-12-18T21:00:55Z

← Older revision		Revision as of 21:00, 18 December 2009
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	AutoStructure <ref>Huang, Y.J., Tejero, R., Powers, R., and Montelione, G.T., A topology-constrained distance network algorithm for protein structure determination from NOESY data. Proteins, 2006. 62(3): p. 587-603.</ref> is an automated NOESY assignment engine, which uses a distinct bottom-up topology-constrained approach for iterative NOE interpretation and structure determination. AutoStructure first builds an initial chain fold based on intraresidue and sequential NOESY data, together with characteristic NOE		AutoStructure <ref>Huang, Y.J., Tejero, R., Powers, R., and Montelione, G.T., A topology-constrained distance network algorithm for protein structure determination from NOESY data. Proteins, 2006. 62(3): p. 587-603.</ref> is an automated NOESY assignment engine, which uses a distinct bottom-up topology-constrained approach for iterative NOE interpretation and structure determination. AutoStructure first builds an initial chain fold based on intraresidue and sequential NOESY data, together with characteristic NOE patterns of secondary structures, including helical medium-range NOE interactions and interstrand b-sheet NOE interactions, and unambiguous long-range NOE interactions, based on chemical shift matching and NOESY spectral symmetry considerations. NOESY cross peaks that cannot be uniquely assigned using these methods are not used in the initial structure calculations. Once initial structures are generated and validated, additional NOESY cross peaks are iteratively assigned using the intermediate 3D structures and contact maps, together with knowledge of high-order topology constraints of alpha-helix and beta-sheet packing geometries. This protocol, in principle, resembles the method that an expert would utilize in manually solving a protein structure by NMR.
	patterns of secondary structures, including helical medium-range NOE interactions and interstrand b-sheet NOE interactions, and unambiguous long-range NOE interactions, based on chemical shift matching and NOESY spectral symmetry considerations. NOESY cross peaks that cannot be uniquely assigned using these methods are not used in the initial structure calculations. Once initial structures are generated and validated, additional NOESY cross peaks are iteratively assigned using the intermediate 3D structures and contact maps, together with knowledge of high-order topology constraints of alpha-helix and beta-sheet packing geometries. This protocol, in principle, resembles the method that an expert would utilize in manually
	solving a protein structure by NMR.

	The input data for AutoStructure are: (i) resonance assignment table, (ii) 2D, 3D, and/or 4D NOESY peak lists, (iii) list of scalar coupling, RDC and slow amide exchange data. AutoStructure generates distance constraint lists and utilizes the programs DYANA/CYANA, Xplor for 3D structure generation on a Linux-based computer cluster. Fig. 1 shows AutoStructure results for three different human protein NMR test data sets: FGF-2, IL-13 and MMP-1, ranging in size from 113 to 169 amino-acid residues. The mean coordinate differences between structures determined by AutoStructure and by manual analysis (0.5 to 0.8 Å for backbone atoms of ordered residues) demonstrate good accuracy of these automated methods.		The input data for AutoStructure are: (i) resonance assignment table, (ii) 2D, 3D, and/or 4D NOESY peak lists, (iii) list of scalar coupling, RDC and slow amide exchange data. AutoStructure generates distance constraint lists and utilizes the programs DYANA/CYANA, Xplor for 3D structure generation on a Linux-based computer cluster. Fig. 1 shows AutoStructure results for three different human protein NMR test data sets: FGF-2, IL-13 and MMP-1, ranging in size from 113 to 169 amino-acid residues. The mean coordinate differences between structures determined by AutoStructure and by manual analysis (0.5 to 0.8 Å for backbone atoms of ordered residues) demonstrate good accuracy of these automated methods.
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			<br>

			<br>

	AutoStructure's ‘bottom up’ strategy is quite different from the “top down” strategies used by the alternative programs CANDID and ARIA, which rely on “ambiguous constraints”.  For NOESY spectra with poor signal-to-noise ratios, such automatically assigned ‘ambiguous constraint” sets may not include any true NOESY assignments, and result in small distortions of the protein structure which maybe avoided by the “bottom up” approach of AutoStructure. CANDID/CYANA also uses a ‘network anchoring” approach similar to, but less comprehensive than, the topology-constrained approach used by AutoStructure. For these reasons, some users may prefer to use both AutoStructure and CANDID/CYANA or ARIA in parallel to assess potential errors in automated NOESY cross peak assignments		AutoStructure's ‘bottom up’ strategy is quite different from the “top down” strategies used by the alternative programs CANDID and ARIA, which rely on “ambiguous constraints”.  For NOESY spectra with poor signal-to-noise ratios, such automatically assigned ‘ambiguous constraint” sets may not include any true NOESY assignments, and result in small distortions of the protein structure which maybe avoided by the “bottom up” approach of AutoStructure. CANDID/CYANA also uses a ‘network anchoring” approach similar to, but less comprehensive than, the topology-constrained approach used by AutoStructure. For these reasons, some users may prefer to use both AutoStructure and CANDID/CYANA or ARIA in parallel to assess potential errors in automated NOESY cross peak assignments

	<br> <references />		<br> <references />

Yphuang at 21:00, 18 December 2009

2009-12-18T21:00:27Z

← Older revision		Revision as of 21:00, 18 December 2009
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	~~<span style="font-size: 11pt; font-family: Arial;">~~AutoStructure ~~</span>~~<ref>Huang, Y.J., Tejero, R., Powers, R., and Montelione, G.T., A topology-constrained distance network algorithm for protein structure determination from NOESY data. Proteins, 2006. 62(3): p. 587-603.</ref~~><span style="font-size: 11pt; font-family: Arial;"~~> is an automated NOESY assignment engine, which uses a distinct bottom-up topology-constrained approach for iterative NOE interpretation and structure determination. AutoStructure first builds an initial chain fold based on intraresidue and sequential NOESY data, together with characteristic NOE		AutoStructure <ref>Huang, Y.J., Tejero, R., Powers, R., and Montelione, G.T., A topology-constrained distance network algorithm for protein structure determination from NOESY data. Proteins, 2006. 62(3): p. 587-603.</ref> is an automated NOESY assignment engine, which uses a distinct bottom-up topology-constrained approach for iterative NOE interpretation and structure determination. AutoStructure first builds an initial chain fold based on intraresidue and sequential NOESY data, together with characteristic NOE
	patterns of secondary structures, including helical medium-range NOE interactions and interstrand b-sheet NOE interactions, and unambiguous long-range NOE interactions, based on chemical shift matching and NOESY spectral symmetry considerations. NOESY cross peaks that cannot be uniquely assigned using these methods are not used in the initial structure calculations. Once initial structures are generated and validated, additional NOESY cross peaks are iteratively assigned using the intermediate 3D structures and contact maps, together with knowledge of high-order topology constraints of alpha-helix and beta-sheet packing geometries. This protocol, in principle, resembles the method that an expert would utilize in manually		patterns of secondary structures, including helical medium-range NOE interactions and interstrand b-sheet NOE interactions, and unambiguous long-range NOE interactions, based on chemical shift matching and NOESY spectral symmetry considerations. NOESY cross peaks that cannot be uniquely assigned using these methods are not used in the initial structure calculations. Once initial structures are generated and validated, additional NOESY cross peaks are iteratively assigned using the intermediate 3D structures and contact maps, together with knowledge of high-order topology constraints of alpha-helix and beta-sheet packing geometries. This protocol, in principle, resembles the method that an expert would utilize in manually
	solving a protein structure by NMR.		solving a protein structure by NMR.

	The input data for AutoStructure are: (i) resonance assignment table, (ii) 2D, 3D, and/or 4D NOESY peak lists, (iii) list of scalar coupling, RDC and slow amide exchange data. AutoStructure generates distance constraint lists and utilizes the programs DYANA/CYANA, Xplor for 3D structure generation on a Linux-based computer cluster. Fig. 1 shows AutoStructure results for three different human protein NMR		The input data for AutoStructure are: (i) resonance assignment table, (ii) 2D, 3D, and/or 4D NOESY peak lists, (iii) list of scalar coupling, RDC and slow amide exchange data. AutoStructure generates distance constraint lists and utilizes the programs DYANA/CYANA, Xplor for 3D structure generation on a Linux-based computer cluster. Fig. 1 shows AutoStructure results for three different human protein NMR test data sets: FGF-2, IL-13 and MMP-1, ranging in size from 113 to 169 amino-acid residues. The mean coordinate differences between structures determined by AutoStructure and by manual analysis (0.5 to 0.8 Å for backbone atoms of ordered residues) demonstrate good accuracy of these automated methods.
	test data sets: FGF-2, IL-13 and MMP-1, ranging in size from 113 to 169
	amino-acid residues. The mean coordinate differences between structures
	determined by AutoStructure and by manual analysis (0.5 to 0.8 Å for backbone
	atoms of ordered residues) demonstrate good accuracy of these automated
	methods.

	[[Image:AS.jpg\|left\|307x229px\|Ribbon diagrams of representative structures of FGF-2, MMP-1 and IL-13 proteins used for the validation of the AutoStructure process]]		[[Image:AS.jpg\|left\|307x229px\|Ribbon diagrams of representative structures of FGF-2, MMP-1 and IL-13 proteins used for the validation of the AutoStructure process]]

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			<br>

	~~AutoStructure's ‘bottom up’ strategy is quite different from the “top down”~~
	~~strategies used by the alternative programs CANDID and ARIA, which rely on~~
	“ambiguous constraints”.  For NOESY spectra with poor signal-to-noise ratios, such automatically assigned ‘ambiguous constraint” sets may not include any true NOESY assignments, and result in small distortions of the protein structure which maybe avoided by the “bottom up” approach of AutoStructure. CANDID/CYANA also uses a ‘network anchoring” approach similar to, but less comprehensive than, the topology-constrained approach used by AutoStructure. For these reasons, some users may prefer to use both AutoStructure and CANDID/CYANA or ARIA in parallel to assess potential errors in automated NOESY cross peak assignments


	<references />


			AutoStructure's ‘bottom up’ strategy is quite different from the “top down” strategies used by the alternative programs CANDID and ARIA, which rely on “ambiguous constraints”.  For NOESY spectra with poor signal-to-noise ratios, such automatically assigned ‘ambiguous constraint” sets may not include any true NOESY assignments, and result in small distortions of the protein structure which maybe avoided by the “bottom up” approach of AutoStructure. CANDID/CYANA also uses a ‘network anchoring” approach similar to, but less comprehensive than, the topology-constrained approach used by AutoStructure. For these reasons, some users may prefer to use both AutoStructure and CANDID/CYANA or ARIA in parallel to assess potential errors in automated NOESY cross peak assignments

			<br> <references />

Yphuang at 20:59, 18 December 2009

2009-12-18T20:59:31Z

← Older revision		Revision as of 20:59, 18 December 2009
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	patterns of secondary structures, including helical medium-range NOE interactions and interstrand b-sheet NOE interactions, and unambiguous long-range NOE interactions, based on chemical shift matching and NOESY spectral symmetry considerations. NOESY cross peaks that cannot be uniquely assigned using these methods are not used in the initial structure calculations. Once initial structures are generated and validated, additional NOESY cross peaks are iteratively assigned using the intermediate 3D structures and contact maps, together with knowledge of high-order topology constraints of alpha-helix and beta-sheet packing geometries. This protocol, in principle, resembles the method that an expert would utilize in manually		patterns of secondary structures, including helical medium-range NOE interactions and interstrand b-sheet NOE interactions, and unambiguous long-range NOE interactions, based on chemical shift matching and NOESY spectral symmetry considerations. NOESY cross peaks that cannot be uniquely assigned using these methods are not used in the initial structure calculations. Once initial structures are generated and validated, additional NOESY cross peaks are iteratively assigned using the intermediate 3D structures and contact maps, together with knowledge of high-order topology constraints of alpha-helix and beta-sheet packing geometries. This protocol, in principle, resembles the method that an expert would utilize in manually
	solving a protein structure by NMR.		solving a protein structure by NMR.


	~~<br>~~

	The input data for AutoStructure are: (i) resonance assignment		The input data for AutoStructure are: (i) resonance assignment table, (ii) 2D, 3D, and/or 4D NOESY peak lists, (iii) list of scalar coupling, RDC and slow amide exchange data. AutoStructure generates distance constraint lists and utilizes the programs DYANA/CYANA, Xplor for 3D structure generation on a Linux-based computer cluster. Fig. 1 shows AutoStructure results for three different human protein NMR
	table, (ii) 2D, 3D, and/or 4D NOESY peak lists, (iii) list of scalar
	coupling, RDC and slow amide exchange data. AutoStructure generates
	distance constraint lists and utilizes the programs DYANA/CYANA, Xplor~~ </span><span>~~for 3D structure
	generation on a Linux-based computer cluster. ~~</span><span>~~Fig. 1 shows AutoStructure results for three different human protein NMR
	test data sets: FGF-2, IL-13 and MMP-1, ranging in size from 113 to 169		test data sets: FGF-2, IL-13 and MMP-1, ranging in size from 113 to 169
	amino-acid residues. The mean coordinate differences between structures		amino-acid residues. The mean coordinate differences between structures
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	methods.		methods.

	~~<span>~~[[Image:AS.jpg\|left\|307x229px\|Ribbon diagrams of representative structures of FGF-2, MMP-1 and IL-13 proteins used for the validation of the AutoStructure process]]~~</span>~~		[[Image:AS.jpg\|left\|307x229px\|Ribbon diagrams of representative structures of FGF-2, MMP-1 and IL-13 proteins used for the validation of the AutoStructure process]]

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	~~<br>~~

	~~<br>~~		AutoStructure's ‘bottom up’ strategy is quite different from the “top down”

	~~<br>~~

	~~<span style="font-size: 12pt; font-family: Arial;">~~AutoStructure's ‘bottom up’ strategy is quite different from the “top down”
	strategies used by the alternative programs CANDID and ARIA, which rely on		strategies used by the alternative programs CANDID and ARIA, which rely on
	“ambiguous constraints”.  For NOESY spectra with poor signal-to-noise ratios, such automatically assigned ‘ambiguous constraint” sets may not include any true NOESY assignments, and result in small distortions of the protein structure which maybe avoided by the “bottom up” approach of AutoStructure~~<!--[if supportFields]><![endif]-->  </span>~~CANDID/CYANA also uses a ‘network anchoring” approach similar to, but less comprehensive than, the topology-constrained approach used by AutoStructure. For these reasons, some users may prefer to use both AutoStructure and CANDID/CYANA or ARIA in parallel to assess potential errors in automated NOESY cross peak assignments		“ambiguous constraints”.  For NOESY spectra with poor signal-to-noise ratios, such automatically assigned ‘ambiguous constraint” sets may not include any true NOESY assignments, and result in small distortions of the protein structure which maybe avoided by the “bottom up” approach of AutoStructure. CANDID/CYANA also uses a ‘network anchoring” approach similar to, but less comprehensive than, the topology-constrained approach used by AutoStructure. For these reasons, some users may prefer to use both AutoStructure and CANDID/CYANA or ARIA in parallel to assess potential errors in automated NOESY cross peak assignments



	<references />		<references />

Yphuang at 20:57, 18 December 2009

2009-12-18T20:57:53Z

@@ Line 1: / Line 1: @@
 <span style="font-size: 11pt; font-family: Arial;">AutoStructure&nbsp;</span><ref>Huang, Y.J., Tejero, R., Powers, R., and Montelione, G.T., A topology-constrained distance network algorithm for protein structure determination from NOESY data. Proteins, 2006. 62(3): p. 587-603.</ref><span style="font-size: 11pt; font-family: Arial;"> is an automated NOESY assignment engine, which&nbsp;uses a distinct bottom-up topology-constrained approach for iterative NOE interpretation and structure determination. AutoStructure first builds an initial chain fold based on intraresidue and sequential NOESY data, together with characteristic NOE
 patterns of secondary structures, including helical medium-range NOE interactions and interstrand b-sheet NOE interactions, and unambiguous long-range NOE interactions, based on chemical shift matching and NOESY spectral symmetry considerations. NOESY cross peaks that cannot be uniquely assigned using these methods are not used in the initial structure calculations.&nbsp;Once initial structures are generated and validated, additional NOESY cross peaks are iteratively assigned using the intermediate 3D structures and contact maps, together with knowledge of high-order topology constraints of alpha-helix and beta-sheet packing geometries. This protocol, in principle, resembles the method that an expert would utilize in manually
-solving a protein structure by NMR.<br>
+solving a protein structure by NMR.
 <br>
-<span>The input data for AutoStructure are: (i) resonance assignment
+The input data for AutoStructure are: (i) resonance assignment
 table, (ii) 2D, 3D, and/or 4D NOESY peak lists, (iii) list of scalar
 coupling, RDC and slow amide exchange data. AutoStructure generates
@@ Line 14: / Line 15: @@
 determined by AutoStructure and by manual analysis (0.5 to 0.8 Å for backbone
 atoms of ordered residues) demonstrate good accuracy of these automated
-methods.</span>
+methods.
 <span>[[Image:AS.jpg|left|307x229px|Ribbon diagrams of representative structures of FGF-2, MMP-1 and IL-13 proteins used for the validation of the AutoStructure process]]</span>
@@ Line 35: / Line 36: @@
 “ambiguous constraints”.&nbsp; For NOESY spectra with poor signal-to-noise ratios, such automatically assigned ‘ambiguous constraint” sets may not include any true NOESY assignments, and result in small distortions of the protein structure which maybe avoided by the “bottom up” approach of AutoStructure<!--[if supportFields]><![endif]-->&nbsp; </span>CANDID/CYANA also uses a ‘network anchoring” approach similar to, but less comprehensive than, the topology-constrained approach used by AutoStructure. For these reasons, some users may prefer to use both AutoStructure and CANDID/CYANA or ARIA in parallel to assess potential errors in automated NOESY cross peak assignments
 <references />

Yphuang at 20:56, 18 December 2009

2009-12-18T20:56:44Z

← Older revision		Revision as of 20:56, 18 December 2009
Line 1:		Line 1:
	<span style="font-size: 11pt; font-family: Arial;">AutoStructure </span><ref>Huang, Y.J., Tejero, R., Powers, R., and Montelione, G.T., A topology-constrained distance network algorithm for protein structure determination from NOESY data. Proteins, 2006. 62(3): p. 587-603.</ref><span style="font-size: 11pt; font-family: Arial;"> is an automated NOESY assignment engine, which ~~<span style="font-family: sans-serif; font-size: 13px;" class="Apple-style-span"><span lang="FR" style="font-size: 11pt; font-family: Arial;">~~uses a distinct bottom-up~~</span>~~ topology-constrained approach for iterative NOE interpretation and structure determination. ~~<span style="font-size: 11pt; font-family: Arial;">~~AutoStructure first builds an initial chain fold based on		<span style="font-size: 11pt; font-family: Arial;">AutoStructure </span><ref>Huang, Y.J., Tejero, R., Powers, R., and Montelione, G.T., A topology-constrained distance network algorithm for protein structure determination from NOESY data. Proteins, 2006. 62(3): p. 587-603.</ref><span style="font-size: 11pt; font-family: Arial;"> is an automated NOESY assignment engine, which uses a distinct bottom-up topology-constrained approach for iterative NOE interpretation and structure determination. AutoStructure first builds an initial chain fold based on intraresidue and sequential NOESY data, together with characteristic NOE
	intraresidue and sequential NOESY data, together with characteristic NOE		patterns of secondary structures, including helical medium-range NOE interactions and interstrand b-sheet NOE interactions, and unambiguous long-range NOE interactions, based on chemical shift matching and NOESY spectral symmetry considerations. NOESY cross peaks that cannot be uniquely assigned using these methods are not used in the initial structure calculations. Once initial structures are generated and validated, additional NOESY cross peaks are iteratively assigned using the intermediate 3D structures and contact maps, together with knowledge of high-order topology constraints of alpha-helix and beta-sheet packing geometries. This protocol, in principle, resembles the method that an expert would utilize in manually
	patterns of secondary structures, including helical medium-range NOE		solving a protein structure by NMR.<br>
	interactions and interstrand ~~</span><span style="font-size: 11pt; font-family: Symbol;">~~b~~</span><span style="font-size: 11pt; font-family: Arial;">~~-sheet NOE interactions, and unambiguous long-range
	NOE interactions, based on chemical shift matching and NOESY spectral symmetry
	considerations. NOESY cross peaks that cannot be uniquely assigned using these
	methods are not used in the initial structure calculations.~~<span style="">~~  ~~</span>~~Once initial structures are generated and validated, additional NOESY cross peaks are iteratively assigned using the intermediate 3D structures and contact maps, together with knowledge of high-order topology constraints of alpha-helix and beta-sheet packing geometries~~</span><span style="font-size: 11pt; font-family: Arial;">~~. This protocol, in
	principle, resembles the method that an expert would utilize in manually
	solving a protein structure by NMR.~~<span style=""> </span></span></span></span>~~<br>

	<br>		<br>
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	<br>		<br>

	<span style="font-size: ~~11pt~~; font-family: Arial;">AutoStructure's ‘bottom up’ strategy is quite different from the “top down”		<span style="font-size: 12pt; font-family: Arial;">AutoStructure's ‘bottom up’ strategy is quite different from the “top down”
	strategies used by the alternative programs CANDID and ARIA, which rely on		strategies used by the alternative programs CANDID and ARIA, which rely on
	“ambiguous constraints”.~~<span style="">~~  ~~</span>~~For NOESY spectra with poor signal-to-noise ratios, such automatically assigned ‘ambiguous constraint” sets may not include any true NOESY assignments, and result in small distortions of the protein structure which maybe avoided by the “bottom up” approach of AutoStructure~~</span>~~<!--[if supportFields]~~><span~~		“ambiguous constraints”.  For NOESY spectra with poor signal-to-noise ratios, such automatically assigned ‘ambiguous constraint” sets may not include any true NOESY assignments, and result in small distortions of the protein structure which maybe avoided by the “bottom up” approach of AutoStructure<!--[if supportFields]><![endif]-->  </span>CANDID/CYANA also uses a ‘network anchoring” approach similar to, but less comprehensive than, the topology-constrained approach used by AutoStructure. For these reasons, some users may prefer to use both AutoStructure and CANDID/CYANA or ARIA in parallel to assess potential errors in automated NOESY cross peak assignments
	~~style='font-size:11.0pt;mso-bidi-font-size:12.0pt;font-family:Arial'><span~~
	~~style='mso-element:field-end'></span></span~~><![endif]--~~><span style="font-size: 11pt; font-family: Arial;">.<span style=""~~>  </span>CANDID/CYANA also uses a ‘network anchoring” approach similar to, but less comprehensive than, the topology-constrained approach used by AutoStructure~~</span><!--[if supportFields]><span~~		ADDIN EN.CITE
	~~style='font-size:11.0pt;mso-bidi-font-size:12.0pt;font-family:Arial'><span~~
	~~style='mso-element:field-end'></span></span><![endif]--><span style="font-size: 11pt; font-family: Arial;">~~.~~<span style="">  </span>~~For these reasons, some users may prefer to use both AutoStructure and CANDID/CYANA or ARIA in parallel to assess potential errors in automated NOESY cross peak assignments ~~</span><!--[if supportFields]><span~~
	~~style='font-size:11.0pt;mso-bidi-font-size:12.0pt;font-family:Arial'><span~~
	~~style='mso-element:field-begin'></span><span style="mso-spacerun:~~
	~~yes"> </span>~~ADDIN EN.CITE
	<EndNote><Cite><Author>Liu</Author><Year>2005</Year><RecNum>193</RecNum><record><rec-number>193</rec-number><foreign-keys><key		<EndNote><Cite><Author>Liu</Author><Year>2005</Year><RecNum>193</RecNum><record><rec-number>193</rec-number><foreign-keys><key
	app="EN"		app="EN"
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	<br>		<br>

	~~Calibration~~



	<references />		<references />

Yphuang at 15:28, 18 December 2009

2009-12-18T15:28:03Z

← Older revision		Revision as of 15:28, 18 December 2009
Line 1:		Line 1:
	<span style="font-size: 11pt; font-family: Arial;">AutoStructure </span><ref>Huang, Y.J., Tejero, R., Powers, R., and Montelione, G.T., A topology-constrained distance network algorithm for protein structure determination from NOESY data. Proteins, 2006. 62(3): p. 587-603.</ref><span style="font-size: 11pt; font-family: Arial;"> is an automated NOESY assignment engine, which <span ~~class="Apple-style-span"~~ style="font-family: sans-serif; font-size: 13px;"><span lang="FR" style="font-size: 11pt; font-family: Arial;">uses a distinct bottom-up</span> topology-constrained approach for iterative NOE interpretation and structure determination. <span style="font-size: 11pt; font-family: Arial;">AutoStructure first builds an initial chain fold based on		<span style="font-size: 11pt; font-family: Arial;">AutoStructure </span><ref>Huang, Y.J., Tejero, R., Powers, R., and Montelione, G.T., A topology-constrained distance network algorithm for protein structure determination from NOESY data. Proteins, 2006. 62(3): p. 587-603.</ref><span style="font-size: 11pt; font-family: Arial;"> is an automated NOESY assignment engine, which <span style="font-family: sans-serif; font-size: 13px;" class="Apple-style-span"><span lang="FR" style="font-size: 11pt; font-family: Arial;">uses a distinct bottom-up</span> topology-constrained approach for iterative NOE interpretation and structure determination. <span style="font-size: 11pt; font-family: Arial;">AutoStructure first builds an initial chain fold based on
	intraresidue and sequential NOESY data, together with characteristic NOE		intraresidue and sequential NOESY data, together with characteristic NOE
	patterns of secondary structures, including helical medium-range NOE		patterns of secondary structures, including helical medium-range NOE
Line 83:		Line 83:

	<br>		<br>

			Calibration



	<references />		<references />

Yphuang at 15:14, 18 December 2009

2009-12-18T15:14:58Z

← Older revision		Revision as of 15:14, 18 December 2009
Line 1:		Line 1:
	<span style="font-size: 11pt; font-family: Arial;">AutoStructure </span><ref>Huang, Y.J., ~~Swapna, G.V., Rajan, P.K., Ke, H., Xia~~, B., ~~Shukla~~, ~~K., Inouye, M~~., and Montelione, G.T., ~~Solution NMR structure of ribosome-binding factor~~ A ~~(RbfA), a cold~~-~~shock adaptation~~ protein from ~~Escherichia coli~~. ~~J Mol Biol~~, ~~2003~~. ~~327~~(2): p. ~~521~~-36.</ref><span style="font-size: 11pt; font-family: Arial;"> is an automated NOESY assignment engine, which <span style="font-family: sans-serif; font-size: 13px;~~" class="Apple-style-span~~"><span lang="FR" style="font-size: 11pt; font-family: Arial;">uses a distinct bottom-up</span> topology-constrained approach for iterative NOE interpretation and structure determination. <span style="font-size: 11pt; font-family: Arial;">AutoStructure first builds an initial chain fold based on		<span style="font-size: 11pt; font-family: Arial;">AutoStructure </span><ref>Huang, Y.J., Tejero, R., Powers, R., and Montelione, G.T., A topology-constrained distance network algorithm for protein structure determination from NOESY data. Proteins, 2006. 62(3): p. 587-603.</ref><span style="font-size: 11pt; font-family: Arial;"> is an automated NOESY assignment engine, which <span class="Apple-style-span" style="font-family: sans-serif; font-size: 13px;"><span lang="FR" style="font-size: 11pt; font-family: Arial;">uses a distinct bottom-up</span> topology-constrained approach for iterative NOE interpretation and structure determination. <span style="font-size: 11pt; font-family: Arial;">AutoStructure first builds an initial chain fold based on
	intraresidue and sequential NOESY data, together with characteristic NOE		intraresidue and sequential NOESY data, together with characteristic NOE
	patterns of secondary structures, including helical medium-range NOE		patterns of secondary structures, including helical medium-range NOE

Yphuang at 15:13, 18 December 2009

2009-12-18T15:13:55Z

← Older revision		Revision as of 15:13, 18 December 2009
Line 1:		Line 1:
	<span style="font-size: 11pt; font-family: Arial;">AutoStructure </span><ref>Huang, Y.J., Swapna, G.V., Rajan, P.K., Ke, H., Xia, B., Shukla, K., Inouye, M., and Montelione, G.T., Solution NMR structure of ribosome-binding factor A (RbfA), a cold-shock adaptation protein from Escherichia coli. J Mol Biol, 2003. 327(2): p. 521-36.</ref><span style="font-size: 11pt; font-family: Arial;"> is an automated NOESY assignment engine, which <span ~~class="Apple-style-span"~~ style="font-family: sans-serif; font-size: 13px;"><span lang="FR" style="font-size: 11pt; font-family: Arial;">uses a distinct bottom-up</span> topology-constrained approach for iterative NOE interpretation and structure determination. <span style="font-size: 11pt; font-family: Arial;">AutoStructure first builds an initial chain fold based on		<span style="font-size: 11pt; font-family: Arial;">AutoStructure </span><ref>Huang, Y.J., Swapna, G.V., Rajan, P.K., Ke, H., Xia, B., Shukla, K., Inouye, M., and Montelione, G.T., Solution NMR structure of ribosome-binding factor A (RbfA), a cold-shock adaptation protein from Escherichia coli. J Mol Biol, 2003. 327(2): p. 521-36.</ref><span style="font-size: 11pt; font-family: Arial;"> is an automated NOESY assignment engine, which <span style="font-family: sans-serif; font-size: 13px;" class="Apple-style-span"><span lang="FR" style="font-size: 11pt; font-family: Arial;">uses a distinct bottom-up</span> topology-constrained approach for iterative NOE interpretation and structure determination. <span style="font-size: 11pt; font-family: Arial;">AutoStructure first builds an initial chain fold based on
	intraresidue and sequential NOESY data, together with characteristic NOE		intraresidue and sequential NOESY data, together with characteristic NOE
	patterns of secondary structures, including helical medium-range NOE		patterns of secondary structures, including helical medium-range NOE
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	methods are not used in the initial structure calculations.<span style="">  </span>Once initial structures are generated and validated, additional NOESY cross peaks are iteratively assigned using the intermediate 3D structures and contact maps, together with knowledge of high-order topology constraints of alpha-helix and beta-sheet packing geometries</span><span style="font-size: 11pt; font-family: Arial;">. This protocol, in		methods are not used in the initial structure calculations.<span style="">  </span>Once initial structures are generated and validated, additional NOESY cross peaks are iteratively assigned using the intermediate 3D structures and contact maps, together with knowledge of high-order topology constraints of alpha-helix and beta-sheet packing geometries</span><span style="font-size: 11pt; font-family: Arial;">. This protocol, in
	principle, resembles the method that an expert would utilize in manually		principle, resembles the method that an expert would utilize in manually
	solving a protein structure by NMR.<span style=""> </span></span></span></span><~~span /~~>		solving a protein structure by NMR.<span style=""> </span></span></span></span><br>

			<br>

			<span>The input data for AutoStructure are: (i) resonance assignment
	~~<span />~~<span>The input data for AutoStructure are: (i) resonance assignment
	table, (ii) 2D, 3D, and/or 4D NOESY peak lists, (iii) list of scalar		table, (ii) 2D, 3D, and/or 4D NOESY peak lists, (iii) list of scalar
	coupling, RDC and slow amide exchange data. AutoStructure generates		coupling, RDC and slow amide exchange data. AutoStructure generates
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	style='mso-element:field-separator'></span></span><![endif]--><span style="font-size: 11pt; font-family: Arial;">. </span><br><!--EndFragment-->		style='mso-element:field-separator'></span></span><![endif]--><span style="font-size: 11pt; font-family: Arial;">. </span><br><!--EndFragment-->

	<~~span style="font-size: 11pt; font-family: Arial;" /~~>		<br>

	<references />		<references />

Yphuang at 15:13, 18 December 2009

2009-12-18T15:13:27Z

← Older revision		Revision as of 15:13, 18 December 2009
Line 1:		Line 1:
	<span style="font-size: 11pt; font-family: Arial;">AutoStructure </span><ref>Huang, Y.J., Swapna, G.V., Rajan, P.K., Ke, H., Xia, B., Shukla, K., Inouye, M., and Montelione, G.T., Solution NMR structure of ribosome-binding factor A (RbfA), a cold-shock adaptation protein from Escherichia coli. J Mol Biol, 2003. 327(2): p. 521-36.</ref><span style="font-size: 11pt; font-family: Arial;"> is an automated NOESY assignment engine, which <span style="font-family: sans-serif; font-size: 13px;~~" class="Apple-style-span~~"><span lang="FR" style="font-size: 11pt; font-family: Arial;">uses a distinct bottom-up</span> topology-constrained approach for iterative NOE interpretation and structure determination. <span style="font-size: 11pt; font-family: Arial;">AutoStructure first builds an initial chain fold based on		<span style="font-size: 11pt; font-family: Arial;">AutoStructure </span><ref>Huang, Y.J., Swapna, G.V., Rajan, P.K., Ke, H., Xia, B., Shukla, K., Inouye, M., and Montelione, G.T., Solution NMR structure of ribosome-binding factor A (RbfA), a cold-shock adaptation protein from Escherichia coli. J Mol Biol, 2003. 327(2): p. 521-36.</ref><span style="font-size: 11pt; font-family: Arial;"> is an automated NOESY assignment engine, which <span class="Apple-style-span" style="font-family: sans-serif; font-size: 13px;"><span lang="FR" style="font-size: 11pt; font-family: Arial;">uses a distinct bottom-up</span> topology-constrained approach for iterative NOE interpretation and structure determination. <span style="font-size: 11pt; font-family: Arial;">AutoStructure first builds an initial chain fold based on
	intraresidue and sequential NOESY data, together with characteristic NOE		intraresidue and sequential NOESY data, together with characteristic NOE
	patterns of secondary structures, including helical medium-range NOE		patterns of secondary structures, including helical medium-range NOE
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	methods are not used in the initial structure calculations.<span style="">  </span>Once initial structures are generated and validated, additional NOESY cross peaks are iteratively assigned using the intermediate 3D structures and contact maps, together with knowledge of high-order topology constraints of alpha-helix and beta-sheet packing geometries</span><span style="font-size: 11pt; font-family: Arial;">. This protocol, in		methods are not used in the initial structure calculations.<span style="">  </span>Once initial structures are generated and validated, additional NOESY cross peaks are iteratively assigned using the intermediate 3D structures and contact maps, together with knowledge of high-order topology constraints of alpha-helix and beta-sheet packing geometries</span><span style="font-size: 11pt; font-family: Arial;">. This protocol, in
	principle, resembles the method that an expert would utilize in manually		principle, resembles the method that an expert would utilize in manually
	solving a protein structure by NMR.<span style=""> </span></span></span></span><span>The input data for AutoStructure are: (i) resonance assignment		solving a protein structure by NMR.<span style=""> </span></span></span></span><span />



			<span /><span>The input data for AutoStructure are: (i) resonance assignment
	table, (ii) 2D, 3D, and/or 4D NOESY peak lists, (iii) list of scalar		table, (ii) 2D, 3D, and/or 4D NOESY peak lists, (iii) list of scalar
	coupling, RDC and slow amide exchange data. AutoStructure generates		coupling, RDC and slow amide exchange data. AutoStructure generates
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	<br>		<br>

	~~<br>~~		<span style="font-size: 11pt; font-family: Arial;">AutoStructure's ‘bottom up’ strategy is quite different from the “top down”

	<span style="font-size: 11pt; font-family: Arial;">~~This~~ ‘bottom up’ strategy is quite different from the “top down”
	strategies used by the alternative programs CANDID and ARIA, which rely on		strategies used by the alternative programs CANDID and ARIA, which rely on
	“ambiguous constraints”.<span style="">  </span>For NOESY spectra with poor signal-to-noise ratios, such automatically assigned ‘ambiguous constraint” sets may not include any true NOESY assignments, and result in small distortions of the protein structure which maybe avoided by the “bottom up” approach of AutoStructure</span><!--[if supportFields]><span		“ambiguous constraints”.<span style="">  </span>For NOESY spectra with poor signal-to-noise ratios, such automatically assigned ‘ambiguous constraint” sets may not include any true NOESY assignments, and result in small distortions of the protein structure which maybe avoided by the “bottom up” approach of AutoStructure</span><!--[if supportFields]><span
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	</url></related-urls></urls></record></Cite></EndNote><span		</url></related-urls></urls></record></Cite></EndNote><span
	style='mso-element:field-separator'></span></span><![endif]--><span style="font-size: 11pt; font-family: Arial;">. </span><br><!--EndFragment-->		style='mso-element:field-separator'></span></span><![endif]--><span style="font-size: 11pt; font-family: Arial;">. </span><br><!--EndFragment-->

			<span style="font-size: 11pt; font-family: Arial;" />

	<references />		<references />

Yphuang at 15:12, 18 December 2009

2009-12-18T15:12:16Z

← Older revision		Revision as of 15:12, 18 December 2009
Line 1:		Line 1:
	<span style="font-size: 11pt; font-family: Arial;">AutoStructure </span><ref>Huang, Y.J., Swapna, G.V., Rajan, P.K., Ke, H., Xia, B., Shukla, K., Inouye, M., and Montelione, G.T., Solution NMR structure of ribosome-binding factor A (RbfA), a cold-shock adaptation protein from Escherichia coli. J Mol Biol, 2003. 327(2): p. 521-36.</ref><span style="font-size: 11pt; font-family: Arial;"> is an automated NOESY assignment engine, which <span ~~class="Apple-style-span"~~ style="font-family: sans-serif; font-size: 13px;"><span lang="FR" style="font-size: 11pt; font-family: Arial;">uses a distinct bottom-up</span> topology-constrained approach for iterative NOE interpretation and structure determination. <span style="font-size: 11pt; font-family: Arial;">AutoStructure first builds an initial chain fold based on		<span style="font-size: 11pt; font-family: Arial;">AutoStructure </span><ref>Huang, Y.J., Swapna, G.V., Rajan, P.K., Ke, H., Xia, B., Shukla, K., Inouye, M., and Montelione, G.T., Solution NMR structure of ribosome-binding factor A (RbfA), a cold-shock adaptation protein from Escherichia coli. J Mol Biol, 2003. 327(2): p. 521-36.</ref><span style="font-size: 11pt; font-family: Arial;"> is an automated NOESY assignment engine, which <span style="font-family: sans-serif; font-size: 13px;" class="Apple-style-span"><span lang="FR" style="font-size: 11pt; font-family: Arial;">uses a distinct bottom-up</span> topology-constrained approach for iterative NOE interpretation and structure determination. <span style="font-size: 11pt; font-family: Arial;">AutoStructure first builds an initial chain fold based on
	intraresidue and sequential NOESY data, together with characteristic NOE		intraresidue and sequential NOESY data, together with characteristic NOE
	patterns of secondary structures, including helical medium-range NOE		patterns of secondary structures, including helical medium-range NOE
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	26</date></pub-dates></dates><accession-num>16027363</accession-num><urls><related-urls><url>http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=16027363		26</date></pub-dates></dates><accession-num>16027363</accession-num><urls><related-urls><url>http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&amp;db=PubMed&amp;dopt=Citation&amp;list_uids=16027363
	</url></related-urls></urls></record></Cite></EndNote><span		</url></related-urls></urls></record></Cite></EndNote><span
	style='mso-element:field-separator'></span></span><![endif]--><span style="font-size: 11pt; font-family: Arial;">. </span><br>		style='mso-element:field-separator'></span></span><![endif]--><span style="font-size: 11pt; font-family: Arial;">. </span><br><!--EndFragment-->
	~~<span /~~><!--EndFragment-->
	<references />		<references />