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Journal articles on the topic 'Rapid resonance assignment of proteins'

Author: Grafiati

Published: 6 September 2023

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1

Fredriksson, Jonas, Wolfgang Bermel, and Martin Billeter. "Complete protein assignment from sets of spectra recorded overnight." Journal of Biomolecular NMR 73, no. 1-2 (February 15, 2019): 59–70. http://dx.doi.org/10.1007/s10858-019-00226-8.

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Abstract A flexible and scalable approach for protein NMR is introduced that builds on rapid data collection via projection spectroscopy and analysis of the spectral input data via joint decomposition. Input data may originate from various types of spectra, depending on the ultimate goal: these may result from experiments based on triple-resonance pulse sequences, or on TOCSY or NOESY sequences, or mixtures thereof. Flexible refers to the free choice of spectra for the joint decompositions depending on the purpose: assignments, structure, dynamics, interactions. Scalable means that the approach is open to the addition of similar or different experiments, e.g. larger proteins may require a wider selection of triple-resonance based experiments. Central to the proposed approach is the mutual support among the different spectra during the spectral analysis: for example, sparser triple-resonance spectra may help decomposing (separating) spin systems in a TOCSY or identifying unique NOEs. In the example presented, backbone plus side chain assignments of ubiquitin were obtained from the combination of either two or three of the following projection experiments: a 4D HCCCONH, a 4D HNCACO and a 3D HNCACB. In all cases, TOCSY data (4D HCCCONH) proved crucial not only for the side chain assignments, but also for the sequential assignment. Even when total recording time was reduced to about 10 h, nearly complete assignments were obtained, with very few missing assignments and even fewer differences to a reference.

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2

Rout, Ashok K., Ravi P. Barnwal, Geetika Agarwal, and Kandala V. R. Chary. "Root-mean-square-deviation-based rapid backbone resonance assignments in proteins." Magnetic Resonance in Chemistry 48, no. 10 (August 27, 2010): 793–97. http://dx.doi.org/10.1002/mrc.2664.

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3

Sukumaran, Sujeesh, Shahid A. Malik, Shankararama Sharma R., Kousik Chandra, and Hanudatta S. Atreya. "Rapid NMR assignments of intrinsically disordered proteins using two-dimensional13C-detection based experiments." Chemical Communications 55, no. 54 (2019): 7820–23. http://dx.doi.org/10.1039/c9cc03530c.

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4

Chatterjee, Amarnath, Neel S. Bhavesh, Sanjay C. Panchal, and Ramakrishna V. Hosur. "A novel protocol based on HN(C)N for rapid resonance assignment in (15N, 13C) labeled proteins: implications to structural genomics." Biochemical and Biophysical Research Communications 293, no. 1 (April 2002): 427–32. http://dx.doi.org/10.1016/s0006-291x(02)00240-1.

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5

Kostic, Milka, Susan Sondej Pochapsky, and Thomas C. Pochapsky. "Rapid Recycle13C‘,15N and13C,13C‘ Heteronuclear and Homonuclear Multiple Quantum Coherence Detection for Resonance Assignments in Paramagnetic Proteins: Example of Ni2+-Containing Acireductone Dioxygenase." Journal of the American Chemical Society 124, no. 31 (August 2002): 9054–55. http://dx.doi.org/10.1021/ja0268480.

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6

Vendrell, J., F. X. Avilés, M. Vilanova, C. H. Turner, and C. Crane-Robinson. "1H-n.m.r. studies of the isolated activation segment from pig procarboxypeptidase A." Biochemical Journal 267, no. 1 (April 1, 1990): 213–20. http://dx.doi.org/10.1042/bj2670213.

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The isolated activation segment (asA) from pig pancreatic procarboxypeptidase A was studied by 1H-n.m.r. spectroscopy over a wide range of solution conditions. Isolated asA shows many characteristics of compactly folded globular proteins, such as the observation of perturbed positions for resonances from methyl groups, alpha-carbon atoms, histidine residues and the tyrosine residue. The single tyrosine residue (Tyr-70) exhibits a very high pKa, and both histidine and tyrosine residues show slow chemical modification (deuteration and iodination). In contrast, asA shows rapid NH exchange. Analysis of the spectra by pH titration and nuclear Overhauser effects revealed several residue interactions. Quantitative analysis of deuterium and tritium exchange allowed the assignment of the histidine C-2-H resonances to their respective residues in the sequence. His-66, the closest to the sites of proteolytic attack in the proenzyme, is shown to be the most accessible to solvent in procarboxypeptidase A. It was also shown that asA is thermally very stable [‘melting’ temperature (Tm) 88 degrees C] and requires a high urea concentration for denaturation (6.25 M, at pH 7.5). Evidence is presented for some degree of conformational flexibility in the premelting range, a feature that could be ascribed to the preponderance of helical secondary structure and to the lack of disulphide bridges. The free solution structure of asA is probably unchanged when it binds to carboxypeptidase A.

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7

Kumar, Dinesh, and Ramakrishna V. Hosur. "hNCOcanH pulse sequence and a robust protocol for rapid and unambiguous assignment of backbone (1 HN , 15 N and 13 C′) resonances in 15 N/13 C-labeled proteins." Magnetic Resonance in Chemistry 49, no. 9 (August 5, 2011): 575–83. http://dx.doi.org/10.1002/mrc.2787.

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8

Fiorito, Francesco, Sebastian Hiller, Gerhard Wider, and Kurt Wüthrich. "Automated Resonance Assignment of Proteins: 6 DAPSY-NMR." Journal of Biomolecular NMR 35, no. 1 (May 2006): 27–37. http://dx.doi.org/10.1007/s10858-006-0030-x.

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9

Higman, Victoria A. "Solid-state MAS NMR resonance assignment methods for proteins." Progress in Nuclear Magnetic Resonance Spectroscopy 106-107 (June 2018): 37–65. http://dx.doi.org/10.1016/j.pnmrs.2018.04.002.

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10

Crippen, Gordon M., Aikaterini Rousaki, Matthew Revington, Yongbo Zhang, and Erik R. P. Zuiderweg. "SAGA: rapid automatic mainchain NMR assignment for large proteins." Journal of Biomolecular NMR 46, no. 4 (March 16, 2010): 281–98. http://dx.doi.org/10.1007/s10858-010-9403-2.

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11

Fox, Daniel A., and Linda Columbus. "Solution NMR resonance assignment strategies for β-barrel membrane proteins." Protein Science 22, no. 8 (June 27, 2013): 1133–40. http://dx.doi.org/10.1002/pro.2291.

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12

Trbovic, Nikola, Christian Klammt, Alexander Koglin, Frank Löhr, Frank Bernhard, and Volker Dötsch. "Efficient Strategy for the Rapid Backbone Assignment of Membrane Proteins." Journal of the American Chemical Society 127, no. 39 (October 2005): 13504–5. http://dx.doi.org/10.1021/ja0540270.

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13

ALIPANAHI, BABAK, XIN GAO, EMRE KARAKOC, SHUAI CHENG Li, FRANK BALBACH, GUANGYU FENG, LOGAN DONALDSON, and MING LI. "ERROR TOLERANT NMR BACKBONE RESONANCE ASSIGNMENT AND AUTOMATED STRUCTURE GENERATION." Journal of Bioinformatics and Computational Biology 09, no. 01 (February 2011): 15–41. http://dx.doi.org/10.1142/s0219720011005276.

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Error tolerant backbone resonance assignment is the cornerstone of the NMR structure determination process. Although a variety of assignment approaches have been developed, none works sufficiently well on noisy fully automatically picked peaks to enable the subsequent automatic structure determination steps. We have designed an integer linear programming (ILP) based assignment system (IPASS) that has enabled fully automatic protein structure determination for four test proteins. IPASS employs probabilistic spin system typing based on chemical shifts and secondary structure predictions. Furthermore, IPASS extracts connectivity information from the inter-residue information and the (automatically picked) 15N-edited NOESY peaks which are then used to fix reliable fragments. When applied to automatically picked peaks for real proteins, IPASS achieves an average precision and recall of 82% and 63%, respectively. In contrast, the next best method, MARS, achieves an average precision and recall of 77% and 36%, respectively. The assignments generated by IPASS are then fed into our protein structure calculation system, FALCON-NMR, to determine the 3D structures without human intervention. The final models have backbone RMSDs of 1.25Å, 0.88Å, 1.49Å, and 0.67Å to the reference native structures for proteins TM1112, CASKIN, VRAR, and HACS1, respectively. The web server is publicly available at .

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14

Pannetier, Nicolas, Klaartje Houben, Laurence Blanchard, and Dominique Marion. "Optimized 3D-NMR sampling for resonance assignment of partially unfolded proteins." Journal of Magnetic Resonance 186, no. 1 (May 2007): 142–49. http://dx.doi.org/10.1016/j.jmr.2007.01.013.

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15

Leopold, M. F., Jeffrey L. Urbauer, and A. Joshua Wand. "Resonance assignment strategies for the analysis of nmr spectra of proteins." Molecular Biotechnology 2, no. 1 (August 1994): 61–93. http://dx.doi.org/10.1007/bf02789290.

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16

Piai, Alessandro, Leonardo Gonnelli, Isabella C. Felli, Roberta Pierattelli, Krzysztof Kazimierczuk, Katarzyna Grudziąż, Wiktor Koźmiński, and Anna Zawadzka-Kazimierczuk. "Amino acid recognition for automatic resonance assignment of intrinsically disordered proteins." Journal of Biomolecular NMR 64, no. 3 (February 18, 2016): 239–53. http://dx.doi.org/10.1007/s10858-016-0024-2.

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17

Li, Kuo-Bin, and B. C. Sanctuary. "Automated Resonance Assignment of Proteins Using Heteronuclear 3D NMR. 2. Side Chain and Sequence-Specific Assignment." Journal of Chemical Information and Computer Sciences 37, no. 3 (May 1997): 467–77. http://dx.doi.org/10.1021/ci960372k.

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18

Knox, Robert W., George J. Lu, Stanley J. Opella, and Alexander A. Nevzorov. "A Resonance Assignment Method for Oriented-Sample Solid-State NMR of Proteins." Journal of the American Chemical Society 132, no. 24 (June 23, 2010): 8255–57. http://dx.doi.org/10.1021/ja102932n.

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19

Gossert, Alvar D., Sebastian Hiller, and César Fernández. "Automated NMR Resonance Assignment of Large Proteins for Protein−Ligand Interaction Studies." Journal of the American Chemical Society 133, no. 2 (January 19, 2011): 210–13. http://dx.doi.org/10.1021/ja108383x.

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20

Wei, Qingtao, Jiajing Chen, Juan Mi, Jiahai Zhang, Ke Ruan, and Jihui Wu. "NMR Backbone Assignment of Large Proteins by Using13Cα-Only Triple-Resonance Experiments." Chemistry - A European Journal 22, no. 28 (June 8, 2016): 9556–64. http://dx.doi.org/10.1002/chem.201601871.

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21

Karjalainen, Mikael, Helena Tossavainen, Maarit Hellman, and Perttu Permi. "HACANCOi: a new Hα-detected experiment for backbone resonance assignment of intrinsically disordered proteins." Journal of Biomolecular NMR 74, no. 12 (October 28, 2020): 741–52. http://dx.doi.org/10.1007/s10858-020-00347-5.

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AbstractUnidirectional coherence transfer is highly efficient in intrinsically disordered proteins (IDPs). Their elevated ps-ns timescale dynamics ensures long transverse (T2) relaxation times allowing sophisticated coherence transfer pathway selection in comparison to folded proteins. 1Hα-detection ensures non-susceptibility to chemical exchange with the solvent and enables chemical shift assignment of consecutive proline residues, typically abundant in IDPs. However, many IDPs undergo a disorder-to-order transition upon interaction with their target protein, which leads to the loss of the favorable relaxation properties. Long coherence transfer routes now result in prohibitively large decrease in sensitivity. We introduce a novel 4D 1Hα-detected experiment HACANCOi, together with its 3D implementation, which warrant high sensitivity for the assignment of proline-rich regions in IDPs in complex with a globular protein. The experiment correlates 1Hαi, 13Cαi, 15Ni and $$^{13} C^{\prime}_{i}$$ 13 C i ′ spins by transferring the magnetization concomitantly from 13Cαi to 15Ni and $$^{13} C^{\prime}_{i}$$ 13 C i ′ . The B1 domain of protein G (GB1), and the enteropathogenic E.coli EspF in complex with human SNX9 SH3, serve as model systems to demonstrate the attainable sensitivity and successful sequential assignment.

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22

Barbet-Massin, Emeline, Andrew J. Pell, Joren S. Retel, Loren B. Andreas, Kristaps Jaudzems, W. Trent Franks, Andrew J. Nieuwkoop, et al. "Rapid Proton-Detected NMR Assignment for Proteins with Fast Magic Angle Spinning." Journal of the American Chemical Society 136, no. 35 (August 18, 2014): 12489–97. http://dx.doi.org/10.1021/ja507382j.

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23

Morris, Howard R., and Piero Pucci. "A new method for rapid assignment of S-S bridges in proteins." Biochemical and Biophysical Research Communications 126, no. 3 (February 1985): 1122–28. http://dx.doi.org/10.1016/0006-291x(85)90302-x.

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24

Lin, Guohui, Dong Xu, Zhi-Zhong Chen, Tao Jiang, Jianjun Wen, and Ying Xu. "Computational Assignment of Protein Backbone NMR Peaks by Efficient Bounding and Filtering." Journal of Bioinformatics and Computational Biology 01, no. 02 (July 2003): 387–409. http://dx.doi.org/10.1142/s0219720003000083.

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NMR resonance assignment is one of the key steps in solving an NMR protein structure. The assignment process links resonance peaks to individual residues of the target protein sequence, providing the prerequisite for establishing intra- and inter-residue spatial relationships between atoms. The assignment process is tedious and time-consuming, which could take many weeks. Though there exist a number of computer programs to assist the assignment process, many NMR labs are still doing the assignments manually to ensure quality. This paper presents a new computational method based on the combination of a suite of algorithms for automating the assignment process, particularly the process of backbone resonance peak assignment. We formulate the assignment problem as a constrained weighted bipartite matching problem. While the problem, in the most general situation, is NP-hard, we present an efficient solution based on a branch-and-bound algorithm with effective bounding techniques using two recently introduced approximation algorithms. We also devise a greedy filtering algorithm for reducing the search space. Our experimental results on 70 instances of (pseudo) real NMR data derived from 14 proteins demonstrate that the new solution runs much faster than a recently introduced (exhaustive) two-layer algorithm and recovers more correct peak assignments than the two-layer algorithm. Our result demonstrates that integrating different algorithms can achieve a good tradeoff between backbone assignment accuracy and computation time.

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25

Hiller, Sebastian, Christian Wasmer, Gerhard Wider, and Kurt Wüthrich. "Sequence-Specific Resonance Assignment of Soluble Nonglobular Proteins by 7D APSY-NMR Spectroscopy." Journal of the American Chemical Society 129, no. 35 (September 2007): 10823–28. http://dx.doi.org/10.1021/ja072564+.

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26

Cutting, Brian, André Strauss, Gabriele Fendrich, Paul W. Manley, and Wolfgang Jahnke. "NMR resonance assignment of selectively labeled proteins by the use of paramagnetic ligands." Journal of Biomolecular NMR 30, no. 2 (October 2004): 205–10. http://dx.doi.org/10.1023/b:jnmr.0000048947.28598.ea.

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27

Schubert, Mario, Michael Kolbe, Brigitte Kessler, Dieter Oesterhelt, and Peter Schmieder. "Heteronuclear Multidimensional NMR Spectroscopy of Solubilized Membrane Proteins: Resonance Assignment of Native Bacteriorhodopsin." ChemBioChem 3, no. 10 (October 4, 2002): 1019–23. http://dx.doi.org/10.1002/1439-7633(20021004)3:10<1019::aid-cbic1019>3.0.co;2-c.

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28

Solyom, Zsofia, Melanie Schwarten, Leonhard Geist, Robert Konrat, Dieter Willbold, and Bernhard Brutscher. "BEST-TROSY experiments for time-efficient sequential resonance assignment of large disordered proteins." Journal of Biomolecular NMR 55, no. 4 (February 24, 2013): 311–21. http://dx.doi.org/10.1007/s10858-013-9715-0.

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29

Schmidt, Elena, and Peter Güntert. "Reliability of exclusively NOESY-based automated resonance assignment and structure determination of proteins." Journal of Biomolecular NMR 57, no. 2 (September 15, 2013): 193–204. http://dx.doi.org/10.1007/s10858-013-9779-x.

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30

Shcherbakov, Alexander A., Matthias Roos, Byungsu Kwon, and Mei Hong. "Two-dimensional 19F–13C correlation NMR for 19F resonance assignment of fluorinated proteins." Journal of Biomolecular NMR 74, no. 2-3 (February 22, 2020): 193–204. http://dx.doi.org/10.1007/s10858-020-00306-0.

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31

Kumar, Dinesh, Subhradip Paul, and Ramakrishna V. Hosur. "BEST-HNN and 2D-(HN)NH experiments for rapid backbone assignment in proteins." Journal of Magnetic Resonance 204, no. 1 (May 2010): 111–17. http://dx.doi.org/10.1016/j.jmr.2010.02.013.

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32

Averseng, Olivier, Agnès Hagège, Frédéric Taran, and Claude Vidaud. "Surface Plasmon Resonance for Rapid Screening of Uranyl Affine Proteins." Analytical Chemistry 82, no. 23 (December 2010): 9797–802. http://dx.doi.org/10.1021/ac102578y.

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33

LI, K. B., and B. C. SANCTUARY. "ChemInform Abstract: Automated Resonance Assignment of Proteins Using Heteronuclear 3D NMR. Part 2. Side Chain and Sequence-Specific Assignment." ChemInform 28, no. 37 (August 3, 2010): no. http://dx.doi.org/10.1002/chin.199737284.

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34

Romero, Javier A., Paulina Putko, Mateusz Urbańczyk, Krzysztof Kazimierczuk, and Anna Zawadzka-Kazimierczuk. "Linear discriminant analysis reveals hidden patterns in NMR chemical shifts of intrinsically disordered proteins." PLOS Computational Biology 18, no. 10 (October 6, 2022): e1010258. http://dx.doi.org/10.1371/journal.pcbi.1010258.

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NMR spectroscopy is key in the study of intrinsically disordered proteins (IDPs). Yet, even the first step in such an analysis—the assignment of observed resonances to particular nuclei—is often problematic due to low peak dispersion in the spectra of IDPs. We show that the assignment process can be aided by finding “hidden” chemical shift patterns specific to the amino acid residue types. We find such patterns in the training data from the Biological Magnetic Resonance Bank using linear discriminant analysis, and then use them to classify spin systems in an α-synuclein sample prepared by us. We describe two situations in which the procedure can greatly facilitate the analysis of NMR spectra. The first involves the mapping of spin systems chains onto the protein sequence, which is part of the assignment procedure—a prerequisite for any NMR-based protein analysis. In the second, the method supports assignment transfer between similar samples. We conducted experiments to demonstrate these cases, and both times the majority of spin systems could be unambiguously assigned to the correct residue types.

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35

Song, Sunho, Sanford A. Asher, Samuel Krimm, and Jagdeesh Bandekar. "Assignment of a new conformation-sensitive UV resonance Raman band in peptides and proteins." Journal of the American Chemical Society 110, no. 25 (December 1988): 8547–48. http://dx.doi.org/10.1021/ja00233a042.

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36

Salzmann, M., K. Pervushin, G. Wider, H. Senn, and K. Wuthrich. "TROSY in triple-resonance experiments: New perspectives for sequential NMR assignment of large proteins." Proceedings of the National Academy of Sciences 95, no. 23 (November 10, 1998): 13585–90. http://dx.doi.org/10.1073/pnas.95.23.13585.

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37

Feuerstein, Sophie, Michael J. Plevin, Dieter Willbold, and Bernhard Brutscher. "iHADAMAC: A complementary tool for sequential resonance assignment of globular and highly disordered proteins." Journal of Magnetic Resonance 214 (January 2012): 329–34. http://dx.doi.org/10.1016/j.jmr.2011.10.019.

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38

Williams, Robert V., Monique J. Rogals, Alexander Eletsky, Chin Huang, Laura C. Morris, Kelley W. Moremen, and James H. Prestegard. "AssignSLP_GUI, a software tool exploiting AI for NMR resonance assignment of sparsely labeled proteins." Journal of Magnetic Resonance 345 (December 2022): 107336. http://dx.doi.org/10.1016/j.jmr.2022.107336.

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39

Löhr, Frank, and Heinz Rüterjans. "A new triple-resonance experiment for the sequential assignment of backbone resonances in proteins." Journal of Biomolecular NMR 6, no. 2 (September 1995): 189–97. http://dx.doi.org/10.1007/bf00211783.

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40

Medvedeva, Svetlana, Jean-Pierre Simorre, Bernhard Brutscher, Françoise Guerlesquin, and Dominique Marion. "Extensive1H NMR resonance assignment of proteins using natural abundance gradient-enhanced13C−1H correlation spectroscopy." FEBS Letters 333, no. 3 (November 1, 1993): 251–56. http://dx.doi.org/10.1016/0014-5793(93)80664-g.

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41

Löhr, Frank, and Heinz Rüterjans. "Novel Pulse Sequences for the Resonance Assignment of Aromatic Side Chains in13C-Labeled Proteins." Journal of Magnetic Resonance, Series B 112, no. 3 (September 1996): 259–68. http://dx.doi.org/10.1006/jmrb.1996.0140.

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42

Plevin, Michael J., Olivier Hamelin, Jérôme Boisbouvier, and Pierre Gans. "A simple biosynthetic method for stereospecific resonance assignment of prochiral methyl groups in proteins." Journal of Biomolecular NMR 49, no. 2 (February 2011): 61–67. http://dx.doi.org/10.1007/s10858-010-9463-3.

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43

Iuga, Adriana, Michael Spoerner, Christian Ader, Eike Brunner, and Hans Robert Kalbitzer. "Rapid assignment of solution 31P NMR spectra of large proteins by solid-state spectroscopy." Biochemical and Biophysical Research Communications 346, no. 1 (July 2006): 301–5. http://dx.doi.org/10.1016/j.bbrc.2006.05.116.

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44

Boyko, Kristina V., Erin A. Rosenkranz, Derrick M. Smith, Heather L. Miears, Melissa Oueld es cheikh, Micah Z. Lund, Jeffery C. Young, et al. "Sortase-mediated segmental labeling: A method for segmental assignment of intrinsically disordered regions in proteins." PLOS ONE 16, no. 10 (October 28, 2021): e0258531. http://dx.doi.org/10.1371/journal.pone.0258531.

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A significant number of proteins possess sizable intrinsically disordered regions (IDRs). Due to the dynamic nature of IDRs, NMR spectroscopy is often the tool of choice for characterizing these segments. However, the application of NMR to IDRs is often hindered by their instability, spectral overlap and resonance assignment difficulties. Notably, these challenges increase considerably with the size of the IDR. In response to these issues, here we report the use of sortase-mediated ligation (SML) for segmental isotopic labeling of IDR-containing samples. Specifically, we have developed a ligation strategy involving a key segment of the large IDR and adjacent folded headpiece domain comprising the C-terminus of A. thaliana villin 4 (AtVLN4). This procedure significantly reduces the complexity of NMR spectra and enables group identification of signals arising from the labeled IDR fragment, a process we refer to as segmental assignment. The validity of our segmental assignment approach is corroborated by backbone residue-specific assignment of the IDR using a minimal set of standard heteronuclear NMR methods. Using segmental assignment, we further demonstrate that the IDR region adjacent to the headpiece exhibits nonuniform spectral alterations in response to temperature. Subsequent residue-specific characterization revealed two segments within the IDR that responded to temperature in markedly different ways. Overall, this study represents an important step toward the selective labeling and probing of target segments within much larger IDR contexts. Additionally, the approach described offers significant savings in NMR recording time, a valuable advantage for the study of unstable IDRs, their binding interfaces, and functional mechanisms.

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45

Jang, Richard, Xin Gao, and Ming Li. "Towards Fully Automated Structure-Based NMR Resonance Assignment of15N-Labeled Proteins From Automatically Picked Peaks." Journal of Computational Biology 18, no. 3 (March 2011): 347–63. http://dx.doi.org/10.1089/cmb.2010.0251.

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46

Zawadzka-Kazimierczuk, Anna, Krzysztof Kazimierczuk, and Wiktor Koźmiński. "A set of 4D NMR experiments of enhanced resolution for easy resonance assignment in proteins." Journal of Magnetic Resonance 202, no. 1 (January 2010): 109–16. http://dx.doi.org/10.1016/j.jmr.2009.10.006.

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47

Wen, Jie, Jihui Wu, and Pei Zhou. "Sparsely sampled high-resolution 4-D experiments for efficient backbone resonance assignment of disordered proteins." Journal of Magnetic Resonance 209, no. 1 (March 2011): 94–100. http://dx.doi.org/10.1016/j.jmr.2010.12.012.

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48

Boucher, Wayne, Ernest D. Laue, Sharon Campbell-Burk, and Peter J. Domaille. "Four-dimensional heteronuclear triple resonance NMR methods for the assignment of backbone nuclei in proteins." Journal of the American Chemical Society 114, no. 6 (March 1992): 2262–64. http://dx.doi.org/10.1021/ja00032a053.

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49

Stratmann, Dirk, Carine van Heijenoort, and Eric Guittet. "NOEnet–Use of NOE networks for NMR resonance assignment of proteins with known 3D structure." Bioinformatics 25, no. 4 (December 12, 2008): 474–81. http://dx.doi.org/10.1093/bioinformatics/btn638.

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50

McIntosh, Lawrence P., and Frederick W. Dahlquist. "Biosynthetic Incorporation of15N and13C for Assignment and Interpretation of Nuclear Magnetic Resonance Spectra of Proteins." Quarterly Reviews of Biophysics 23, no. 1 (February 1990): 1–38. http://dx.doi.org/10.1017/s0033583500005400.

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The use of isotopic substitution is a time-honoured method for simplifying the nuclear magnetic resonance spectra of biological macromolecules. For example, the biosynthetic incorporation of a heteronucleus such as15N or13C into a specific amino acid residue in a protein followed by direct observation of the15N or13C NMR spectrum could provide a means to specifically observe a given amino acid type in that protein. By observation of the chemical shift or relaxation properties as a function of pH, ligand concentration, etc. a number of important conclusions concerning the pKavalues of specific residues, the affinity of the protein for various ligands, or dynamic properties of the protein can be deduced. (See Henryet al.1986a,b; 1987 for an elegant modern example). In such situations, direct observation of the heteronucleus is a powerful means to observe environmental changes (Niuet al.1979) but often these measurements are not readily interpretable in terms of alterations of protein structure. Although proton-proton dipolar interactions (NOEs) typically provide the richest source of such structural information, these interactions are not monitored in most experiments which directly observe the heteronucleus.

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