Automatic Determination of Protein Backbone Resonance Assignments from Triple Resonance Nuclear Magnetic Resonance Data

Moseley, Hunter N. B.; Monleón, Daniel; Montelione, Gaetano T.

doi:10.1016/s0076-6879(01)39311-4

Cited by 162 publications

(161 citation statements)

References 25 publications

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“…The expected increase in precision (9, 10) was registered; use of (5,3)D HCC-CH leads to a reduction of ⌬⍀( 1 H) and ⌬⍀( 13 C) by factors of 2.1 Ϯ 0.3 and 3.6 Ϯ 0.7, respectively. These numbers are in agreement with previous statistical considerations (9) and imply that automated assignment protocols (11,20) can be used, respectively, with Ϸ2-to 4-fold reduced matching tolerances. (Table 1) 13 CЈ) (for peaks labeled ''3'').…”

Section: Assignment Of Peripheral Aliphaticsupporting

confidence: 90%

G-matrix Fourier transform NMR spectroscopy for complete protein resonance assignment

Atreya

Szyperski

2004

Proc. Natl. Acad. Sci. U.S.A.

124

131

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A G-matrix Fourier transform (GFT) NMR spectroscopy-based strategy for resonance assignment of proteins is described. Each of the GFT NMR experiments presented here rapidly affords four-, five-, or six-dimensional spectral information in combination with precise measurements of chemical shifts. The resulting high information content enables one to obtain nearly complete assignments by using only four NMR experiments. For the backbone amide proton detected ''out-and-back'' experiments, data collection was further accelerated up to Ϸ2.5-fold by use of longitudinal 1 H relaxation optimization. The GFT NMR experiments were acquired for three proteins with molecular masses ranging from 8.6 to 17 kDa, demonstrating that the proposed strategy is of key interest for automated resonance assignment in structural genomics.

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Section: Assignment Of Peripheral Aliphaticsupporting

confidence: 90%

G-matrix Fourier transform NMR spectroscopy for complete protein resonance assignment

Atreya

Szyperski

2004

Proc. Natl. Acad. Sci. U.S.A.

124

131

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“…Earlier approaches to automated resonance assignment have generally required a high number of specialized NMR experiments [7]. The acquisition of these data is time consuming, and often not feasible, especially for larger molecules where dynamic limitations of the molecules hinder spectral acquisition.…”

Section: Introductionmentioning

confidence: 99%

AutoLink: Automated sequential resonance assignment of biopolymers from NMR data by relative-hypothesis-prioritization-based simulated logic

Masse

Keller

2005

Journal of Magnetic Resonance

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We have developed a new computer algorithm for determining the backbone resonance assignments for biopolymers. The approach we have taken, relative hypothesis prioritization, is implemented as a Lua program interfaced to the recently developed computer-aided resonance assignment (CARA) program. Our program can work with virtually any spectrum type, and is especially good with NOESY data. The results of the program are displayed in an easy-to-read, color-coded, graphic representation, allowing users to assess the quality of the results in minutes. Here we report the application of the program to two RNA recognition motifs of Apobec-1 Complementation Factor. The assignment of these domains demonstrates AutoLinkÕs ability to deliver accurate resonance assignments from very minimal data and with minimal user intervention.

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“…There are a large number of automated and semi-automated approaches that have been published or described including AUTOASSIGN [95], GARANT [96], Smartnotebook [97], PASTA [98], CAMRA [99] and others [93]. Some are restricted to using homonuclear 1 H spectra and so are critically dependent of having very good quality TOCSY and NOESY data.…”

Section: Innovations In Spectral Assignment and Validationmentioning

confidence: 99%

NMR Spectroscopy and Protein Structure Determination: Applications to Drug Discovery and Development

Wishart

2005

CPB

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Recent technological advances in NMR methods and instrumentation are having a significant impact in structural biology. These innovations are also impacting pharmaceutical biotechnology as it is now possible to use NMR spectroscopy to rapidly characterize a growing number of prospective protein drugs and protein drug targets. This review provides a general summary of how solution-state NMR can be used to determine protein structures. It also focuses on exploring how advances in solution state NMR are changing the way in which protein structures can be determined and protein-ligand interactions can be characterized. Recent innovations in protein sample preparation, in instrumentation and data collection, in spectral assignment and in structure generation are highlighted. The impact of solution-state NMR on pharmaceutical biotechnology is also discussed, with a special emphasis on describing how NMR has been used to study a number of pharmaceutically important proteins and how NMR is currently being used to rapidly screen and to map the binding sites of small molecules to a range of protein targets.

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Automatic Determination of Protein Backbone Resonance Assignments from Triple Resonance Nuclear Magnetic Resonance Data

Cited by 162 publications

References 25 publications

G-matrix Fourier transform NMR spectroscopy for complete protein resonance assignment

G-matrix Fourier transform NMR spectroscopy for complete protein resonance assignment

AutoLink: Automated sequential resonance assignment of biopolymers from NMR data by relative-hypothesis-prioritization-based simulated logic

NMR Spectroscopy and Protein Structure Determination: Applications to Drug Discovery and Development

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