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Abstract
In-cell structural biology aims at extracting structural information about proteins or nucleic acids in their native, cellular environment. This emerging field holds great promise and is already providing new facts and outlooks of interest at both fundamental and applied levels. NMR spectroscopy has important contributions on this stage: It brings information on a broad variety of nuclei at the atomic scale, which ensures its great versatility and uniqueness. Here, we detail the methods, the fundamental knowledge, and the applications in biomedical engineering related to in-cell structural biology by NMR. We finally propose a brief overview of the main other techniques in the field (EPR, smFRET, cryo-ET, etc.) to draw some advisable developments for in-cell NMR. In the era of large-scale screenings and deep learning, both accurate and qualitative experimental evidence are as essential as ever to understand the interior life of cells. In-cell structural biology by NMR spectroscopy can generate such a knowledge, and it does so at the atomic scale. This review is meant to deliver comprehensive but accessible information, with advanced technical details and reflections on the methods, the nature of the results, and the future of the field.
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Affiliation(s)
- Francois-Xavier Theillet
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
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Ikeya T, Hanashima T, Hosoya S, Shimazaki M, Ikeda S, Mishima M, Güntert P, Ito Y. Improved in-cell structure determination of proteins at near-physiological concentration. Sci Rep 2016; 6:38312. [PMID: 27910948 PMCID: PMC5133543 DOI: 10.1038/srep38312] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2016] [Accepted: 11/07/2016] [Indexed: 11/29/2022] Open
Abstract
Investigating three-dimensional (3D) structures of proteins in living cells by in-cell nuclear magnetic resonance (NMR) spectroscopy opens an avenue towards understanding the structural basis of their functions and physical properties under physiological conditions inside cells. In-cell NMR provides data at atomic resolution non-invasively, and has been used to detect protein-protein interactions, thermodynamics of protein stability, the behavior of intrinsically disordered proteins, etc. in cells. However, so far only a single de novo 3D protein structure could be determined based on data derived only from in-cell NMR. Here we introduce methods that enable in-cell NMR protein structure determination for a larger number of proteins at concentrations that approach physiological ones. The new methods comprise (1) advances in the processing of non-uniformly sampled NMR data, which reduces the measurement time for the intrinsically short-lived in-cell NMR samples, (2) automatic chemical shift assignment for obtaining an optimal resonance assignment, and (3) structure refinement with Bayesian inference, which makes it possible to calculate accurate 3D protein structures from sparse data sets of conformational restraints. As an example application we determined the structure of the B1 domain of protein G at about 250 μM concentration in living E. coli cells.
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Affiliation(s)
- Teppei Ikeya
- Department of Chemistry, Graduate School of Science and Engineering, Tokyo Metropolitan University, Tokyo, 192-0397, Japan.,CREST/Japan Science and Technology Agency (JST), 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan
| | - Tomomi Hanashima
- Department of Chemistry, Graduate School of Science and Engineering, Tokyo Metropolitan University, Tokyo, 192-0397, Japan
| | - Saori Hosoya
- Department of Chemistry, Graduate School of Science and Engineering, Tokyo Metropolitan University, Tokyo, 192-0397, Japan
| | - Manato Shimazaki
- Department of Chemistry, Graduate School of Science and Engineering, Tokyo Metropolitan University, Tokyo, 192-0397, Japan
| | - Shiro Ikeda
- The Institute of Statistical Mathematics, 10-3 Midori-cho, Tachikawa, Tokyo, 190-8562, Japan
| | - Masaki Mishima
- Department of Chemistry, Graduate School of Science and Engineering, Tokyo Metropolitan University, Tokyo, 192-0397, Japan.,CREST/Japan Science and Technology Agency (JST), 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan
| | - Peter Güntert
- Department of Chemistry, Graduate School of Science and Engineering, Tokyo Metropolitan University, Tokyo, 192-0397, Japan.,CREST/Japan Science and Technology Agency (JST), 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan.,Institute of Biophysical Chemistry, Center for Biomolecular Magnetic Resonance, Goethe University Frankfurt, 60438, Frankfurt am Main, Germany.,Laboratory of Physical Chemistry, ETH Zürich, 8093, Zurich, Switzerland
| | - Yutaka Ito
- Department of Chemistry, Graduate School of Science and Engineering, Tokyo Metropolitan University, Tokyo, 192-0397, Japan.,CREST/Japan Science and Technology Agency (JST), 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan
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3
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Wu Q, Coggins BE, Zhou P. Unbiased measurements of reconstruction fidelity of sparsely sampled magnetic resonance spectra. Nat Commun 2016; 7:12281. [PMID: 27459896 PMCID: PMC4974455 DOI: 10.1038/ncomms12281] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Accepted: 06/17/2016] [Indexed: 12/21/2022] Open
Abstract
The application of sparse-sampling techniques to NMR data acquisition would benefit from reliable quality measurements for reconstructed spectra. We introduce a pair of noise-normalized measurements, and , for differentiating inadequate modelling from overfitting. While and can be used jointly for methods that do not enforce exact agreement between the back-calculated time domain and the original sparse data, the cross-validation measure is applicable to all reconstruction algorithms. We show that the fidelity of reconstruction is sensitive to changes in and that model overfitting results in elevated and reduced spectral quality.
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Affiliation(s)
- Qinglin Wu
- Department of Biochemistry, Duke University Medical Center, Durham, North Carolina 27710, USA
| | - Brian E Coggins
- Department of Biochemistry, Duke University Medical Center, Durham, North Carolina 27710, USA
| | - Pei Zhou
- Department of Biochemistry, Duke University Medical Center, Durham, North Carolina 27710, USA
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Didenko T, Proudfoot A, Dutta SK, Serrano P, Wüthrich K. Non-Uniform Sampling and J-UNIO Automation for Efficient Protein NMR Structure Determination. Chemistry 2015; 21:12363-9. [PMID: 26227870 PMCID: PMC4576834 DOI: 10.1002/chem.201502544] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2015] [Indexed: 11/10/2022]
Abstract
High-resolution structure determination of small proteins in solution is one of the big assets of NMR spectroscopy in structural biology. Improvements in the efficiency of NMR structure determination by advances in NMR experiments and automation of data handling therefore attracts continued interest. Here, non-uniform sampling (NUS) of 3D heteronuclear-resolved [(1)H,(1)H]-NOESY data yielded two- to three-fold savings of instrument time for structure determinations of soluble proteins. With the 152-residue protein NP_372339.1 from Staphylococcus aureus and the 71-residue protein NP_346341.1 from Streptococcus pneumonia we show that high-quality structures can be obtained with NUS NMR data, which are equally well amenable to robust automated analysis as the corresponding uniformly sampled data.
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Affiliation(s)
- Tatiana Didenko
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037 (USA) http://www.jcsg.org
- Joint Center for Structural Genomics, La Jolla, CA 92037 (USA), Fax: (+1) 858-784-8014
- GPCR-Network, 3430 S. Vermont Ave., TRF 105, Los Angeles, CA 90089-3301 (USA), Fax: (+1) 858-784-8014 http://gpcr.usc.edu
| | - Andrew Proudfoot
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037 (USA) http://www.jcsg.org
- Joint Center for Structural Genomics, La Jolla, CA 92037 (USA), Fax: (+1) 858-784-8014
| | - Samit Kumar Dutta
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037 (USA) http://www.jcsg.org
- Joint Center for Structural Genomics, La Jolla, CA 92037 (USA), Fax: (+1) 858-784-8014
| | - Pedro Serrano
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037 (USA) http://www.jcsg.org
- Joint Center for Structural Genomics, La Jolla, CA 92037 (USA), Fax: (+1) 858-784-8014
| | - Kurt Wüthrich
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037 (USA) http://www.jcsg.org. , ,
- Joint Center for Structural Genomics, La Jolla, CA 92037 (USA), Fax: (+1) 858-784-8014. , ,
- GPCR-Network, 3430 S. Vermont Ave., TRF 105, Los Angeles, CA 90089-3301 (USA), Fax: (+1) 858-784-8014 http://gpcr.usc.edu. , ,
- Skaggs Institute for Chemical Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037 (USA), Fax: (+1) 858-784-8014. , ,
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