Automated protein structure calculation from NMR data

Williamson, Michael P.; Craven, C. Jeremy

doi:10.1007/s10858-008-9295-6

Cited by 57 publications

(43 citation statements)

References 84 publications

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“…A ccurate and fast validation of protein structures constitutes a long-standing problem in NMR spectroscopy (1)(2)(3). Investigators have proposed a plethora of methods to determine the accuracy and reliability of protein structures in recent years (4)(5)(6)(7)(8).…”

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confidence: 99%

Quantum-mechanics-derived ¹³ C ^α chemical shift server ( Che Shift) for protein structure validation

Vila

Arnautova

Martin

et al. 2009

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

101

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A server (CheShift) has been developed to predict 13 C ␣ chemical shifts of protein structures. It is based on the generation of 696,916 conformations as a function of the , , , 1 and 2 torsional angles for all 20 naturally occurring amino acids. Their 13 C ␣ chemical shifts were computed at the DFT level of theory with a small basis set and extrapolated, with an empirically-determined linear regression formula, to reproduce the values obtained with a larger basis set. Analysis of the accuracy and sensitivity of the CheShift predictions, in terms of both the correlation coefficient R and the conformational-averaged rmsd between the observed and predicted 13 C ␣ chemical shifts, was carried out for 3 sets of conformations: (i) 36 x-ray-derived protein structures solved at 2.3 Å or better resolution, for which sets of 13 C ␣ chemical shifts were available; (ii) 15 pairs of x-ray and NMR-derived sets of protein conformations; and (iii) a set of decoys for 3 proteins showing an rmsd with respect to the x-ray structure from which they were derived of up to 3 Å. Comparative analysis carried out with 4 popular servers, namely SHIFTS, SHIFTX, SPARTA, and PROSHIFT, for these 3 sets of conformations demonstrated that CheShift is the most sensitive server with which to detect subtle differences between protein models and, hence, to validate protein structures determined by either x-ray or NMR methods, if the observed 13 C ␣ chemical shifts are available. CheShift is available as a web server. chemical shifts prediction ͉ DFT calculations ͉ validation server A ccurate and fast validation of protein structures constitutes a long-standing problem in NMR spectroscopy (1-3). Investigators have proposed a plethora of methods to determine the accuracy and reliability of protein structures in recent years (4-8). Despite this progress, there is a growing need for more sophisticated, physics-based and fast structure-validation methods (1, 2, 7). With these goals in mind, we recently proposed a new, physics-based solution of this important problem (9), viz., a methodology that makes use of observed and computed 13 C ␣ chemical shifts (at the DFT level of theory) for an accurate validation of protein structures in solution (9) and in a crystal (10). Assessment of the ability of computed 13 C ␣ chemical shifts to reproduce observed values for a single or an ensemble of structures in solution and in a crystal was accomplished by using the conformationally-averaged root-mean-square-deviation (ca-rmsd) as a scoring function (9). While computationally intensive, this methodology has several advantages: (i) it makes use of the 13 C ␣ chemical shifts, not shielding, that are ubiquitous to proteins; (ii) it can be computed accurately from the , , and torsional angles; (iii) there is no need for a priori knowledge of the oligomeric state of the protein; and (iv) no knowledgebased information or additional NMR data are required.However, the primary and the most serious limitation of the method is the computational cost of such calculations, whi...

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mentioning

confidence: 99%

Quantum-mechanics-derived ¹³ C ^α chemical shift server ( Che Shift) for protein structure validation

Vila

Arnautova

Martin

et al. 2009

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

101

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“…Improving the stereochemical quality Williamson and Craven (2009) have described the effectiveness of explicit solvent refinement of NMR structures and suggest that it should be a standard procedure. For protein structures that have regions of high mobility/uncertainty due to few or no NOE observations, we have successfully employed a hybrid protocol from XPLOR-NIH that incorporates thin-layer water refinement (Linge et al, 2003) and a multidimensional torsion angle database (Kuszewski et al, 1996(Kuszewski et al, , 1997.…”

Section: Alipanahi Et Almentioning

confidence: 99%

Determining Protein Structures from NOESY Distance Constraints by Semidefinite Programming

Alipanahi

Krislock

Ghodsi

et al. 2013

Journal of Computational Biology

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Contemporary practical methods for protein nuclear magnetic resonance (NMR) structure determination use molecular dynamics coupled with a simulated annealing schedule. The objective of these methods is to minimize the error of deviating from the nuclear overhauser effect (NOE) distance constraints. However, the corresponding objective function is highly nonconvex and, consequently, difficult to optimize. Euclidean distance matrix (EDM) methods based on semidefinite programming (SDP) provide a natural framework for these problems. However, the high complexity of SDP solvers and the often noisy distance constraints provide major challenges to this approach. The main contribution of this article is a new SDP formulation for the EDM approach that overcomes these two difficulties. We model the protein as a set of intersecting two-and three-dimensional cliques. Then, we adapt and extend a technique called semidefinite facial reduction to reduce the SDP problem size to approximately one quarter of the size of the original problem. The reduced SDP problem can be solved approximately 100 times faster, and it is also more resistant to numerical problems from erroneous and inexact distance bounds.

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“…Simulated annealing by torsion angle dynamics became the standard method to calculate NMR protein structures. A recent survey ( Table 1 in [30]) revealed that the structure calculation programs that are cited most often in the NMR protein structures deposited to the Protein Data Bank [5] When the basic problem of NMR protein structure calculation was solved satisfactorily by these programs, interest turned towards automating the most time-consuming part of NMR spectral analysis, namely the assignment of multidimensional NOESY spectra for the collection of conformational restraints. Because of the extensive degeneracy of the chemical shifts this task is cumbersome and error-prone if done manually.…”

Section: Historical Developmentmentioning

confidence: 99%

“…Network anchoring reduced the initial ambiguity of NOESY cross-peak assignments by inducing self-consistency with the network of other assigned NOEs, and constraint combination minimised the impact of erroneous distance restraints on the resulting structure. At present, the combination of the automated assignment of NOESY cross-peaks and the structure calculation with CYANA or ARIA have become the standard approach to protein structure analysis by NMR [30]. Alternative approaches for the automated assignment of NOESY cross-peaks were implemented in the AutoStructure [33], PASD [43], and KNOWNOE [44] algorithms, and in a Bayesian approach [45].…”

Section: Historical Developmentmentioning

confidence: 99%

Calculation of Structures from NMR Restraints

Güntert

2011

Protein NMR Spectroscopy: Practical Techniques and Applications

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When the NMR method for protein structure determination was introduced in the early 1980s the new approach met with enthusiasm amongst NMR spectroscopists, as well as scepticism and disbelief by structural biologists until the simultaneous but independent determinations of the three-dimensional structure of the protein tendamistat by X-ray crystallography [1] and NMR spectroscopy [2,3] yielded virtually identical results [4]. Since that time, NMR has become a firmly established method for determining the threedimensional structures of proteins. More than 7800 structures in the Protein Data Bank [5] of March 2009 have been determined by NMR ( Figure 5.1). This remarkable achievement would not have been possible without the development of sophisticated computational methods to compute three-dimensional protein structures from NMR-derived conformational restraints, and by increasingly automated approaches for analysing multidimensional NMR spectra.NMR structure calculations can be performed in several ways that differ essentially by the extent to which the analysis of the spectra is automated. In a basic structure calculation, all spectra are analysed by the spectroscopist who also interprets the data and provides the structure calculation program with geometric restraints in the form of allowed interatomic distance ranges, ranges of allowed torsion angle values, and Protein NMR Spectroscopy: Practical Techniques and Applications, First Edition. Edited

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Automated protein structure calculation from NMR data

Cited by 57 publications

References 84 publications

Quantum-mechanics-derived ¹³ C ^α chemical shift server ( Che Shift) for protein structure validation

Quantum-mechanics-derived ¹³ C ^α chemical shift server ( Che Shift) for protein structure validation

Determining Protein Structures from NOESY Distance Constraints by Semidefinite Programming

Calculation of Structures from NMR Restraints

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Automated protein structure calculation from NMR data

Cited by 57 publications

References 84 publications

Quantum-mechanics-derived 13 C α chemical shift server ( Che Shift) for protein structure validation

Quantum-mechanics-derived 13 C α chemical shift server ( Che Shift) for protein structure validation

Determining Protein Structures from NOESY Distance Constraints by Semidefinite Programming

Calculation of Structures from NMR Restraints

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Quantum-mechanics-derived ¹³ C ^α chemical shift server ( Che Shift) for protein structure validation

Quantum-mechanics-derived ¹³ C ^α chemical shift server ( Che Shift) for protein structure validation