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      Mechanism of the allosteric activation of the ClpP protease machinery by substrates and active-site inhibitors

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          Abstract

          We decipher how an allosteric inhibitor activates the ClpP protease machinery by binding to the catalytic site.

          Abstract

          Coordinated conformational transitions in oligomeric enzymatic complexes modulate function in response to substrates and play a crucial role in enzyme inhibition and activation. Caseinolytic protease (ClpP) is a tetradecameric complex, which has emerged as a drug target against multiple pathogenic bacteria. Activation of different ClpPs by inhibitors has been independently reported from drug development efforts, but no rationale for inhibitor-induced activation has been hitherto proposed. Using an integrated approach that includes x-ray crystallography, solid- and solution-state nuclear magnetic resonance, molecular dynamics simulations, and isothermal titration calorimetry, we show that the proteasome inhibitor bortezomib binds to the ClpP active-site serine, mimicking a peptide substrate, and induces a concerted allosteric activation of the complex. The bortezomib-activated conformation also exhibits a higher affinity for its cognate unfoldase ClpX. We propose a universal allosteric mechanism, where substrate binding to a single subunit locks ClpP into an active conformation optimized for chaperone association and protein processive degradation.

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          Linking crystallographic model and data quality.

          In macromolecular x-ray crystallography, refinement R values measure the agreement between observed and calculated data. Analogously, R(merge) values reporting on the agreement between multiple measurements of a given reflection are used to assess data quality. Here, we show that despite their widespread use, R(merge) values are poorly suited for determining the high-resolution limit and that current standard protocols discard much useful data. We introduce a statistic that estimates the correlation of an observed data set with the underlying (not measurable) true signal; this quantity, CC*, provides a single statistically valid guide for deciding which data are useful. CC* also can be used to assess model and data quality on the same scale, and this reveals when data quality is limiting model improvement.
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            Prediction of hydrodynamic and other solution properties of rigid proteins from atomic- and residue-level models.

            Here we extend the ability to predict hydrodynamic coefficients and other solution properties of rigid macromolecular structures from atomic-level structures, implemented in the computer program HYDROPRO, to models with lower, residue-level resolution. Whereas in the former case there is one bead per nonhydrogen atom, the latter contains one bead per amino acid (or nucleotide) residue, thus allowing calculations when atomic resolution is not available or coarse-grained models are preferred. We parameterized the effective hydrodynamic radius of the elements in the atomic- and residue-level models using a very large set of experimental data for translational and rotational coefficients (intrinsic viscosity and radius of gyration) for >50 proteins. We also extended the calculations to very large proteins and macromolecular complexes, such as the whole 70S ribosome. We show that with proper parameterization, the two levels of resolution yield similar and rather good agreement with experimental data. The new version of HYDROPRO, in addition to considering various computational and modeling schemes, is far more efficient computationally and can be handled with the use of a graphical interface. Copyright © 2011 Biophysical Society. Published by Elsevier Inc. All rights reserved.
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              Cross-correlated relaxation enhanced 1H[bond]13C NMR spectroscopy of methyl groups in very high molecular weight proteins and protein complexes.

              A comparison of HSQC and HMQC pulse schemes for recording (1)H[bond](13)C correlation maps of protonated methyl groups in highly deuterated proteins is presented. It is shown that HMQC correlation maps can be as much as a factor of 3 more sensitive than their HSQC counterparts and that the sensitivity gains result from a TROSY effect that involves cancellation of intra-methyl dipolar relaxation interactions. (1)H[bond](13)C correlation spectra are recorded on U-[(15)N,(2)H], Ile delta 1-[(13)C,(1)H] samples of (i) malate synthase G, a 723 residue protein, at 37 and 5 degrees C, and of (ii) the protease ClpP, comprising 14 identical subunits, each with 193 residues (305 kDa), at 5 degrees C. The high quality of HMQC spectra obtained in short measuring times strongly suggests that methyl groups will be useful probes of structure and dynamics in supramolecular complexes.
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                Author and article information

                Journal
                Sci Adv
                Sci Adv
                SciAdv
                advances
                Science Advances
                American Association for the Advancement of Science
                2375-2548
                September 2019
                04 September 2019
                : 5
                : 9
                : eaaw3818
                Affiliations
                [1 ]Institut de Biologie Structurale, Université Grenoble Alpes, CEA, CNRS, IBS, 71 Avenue des Martyrs, F-38044 Grenoble, France.
                [2 ]LPCT, UMR 7019 Université de Lorraine CNRS, Vandoeuvre-les-Nancy F-54500, France.
                [3 ]Laboratoire International Associé CNRS and University of Illinois at Urbana−Champaign, Vandoeuvre-les-Nancy F-54506, France.
                [4 ]Department of Physics, University of Illinois at Urbana-Champaign, 1110 West Green Street, Urbana, IL 61801, USA.
                [5 ]Institute of Biocomputation and Physics of Complex Systems (BIFI), Joint Units IQFR-CSIC-BIFI and GBsC-CSIC-BIFI, and Department of Biochemistry and Molecular and Cell Biology, Universidad de Zaragoza, 50018 Zaragoza, Spain.
                [6 ]Aragon Institute for Health Research (IIS Aragon), 50009 Zaragoza, Spain.
                [7 ]Biomedical Research Networking Centre for Liver and Digestive Diseases (CIBERehd), Madrid, Spain.
                [8 ]Aragon Health Sciences Institute (IACS), 50009 Zaragoza, Spain.
                [9 ]Fundacion ARAID, Government of Aragon, 50018 Zaragoza, Spain.
                [10 ]Departamento de Biomedicina, Faculdade de Medicina da Universidade do Porto, Porto, Portugal.
                [11 ]i3S, Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal.
                Author notes
                [* ]Corresponding author. Email: hfraga@ 123456med.up.pt (H.F.); paul.schanda@ 123456ibs.fr (P.S.)
                Author information
                http://orcid.org/0000-0002-8436-9467
                http://orcid.org/0000-0002-9122-1698
                http://orcid.org/0000-0001-8076-6222
                http://orcid.org/0000-0002-1908-3921
                http://orcid.org/0000-0002-9350-7606
                http://orcid.org/0000-0001-7677-4086
                Article
                aaw3818
                10.1126/sciadv.aaw3818
                6726451
                31517045
                a4039e37-2816-47a2-906e-e7a165d11840
                Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

                This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

                History
                : 18 December 2018
                : 02 August 2019
                Funding
                Funded by: doi http://dx.doi.org/10.13039/100011199, FP7 Ideas: European Research Council;
                Award ID: StG-2012-311318
                Categories
                Research Article
                Research Articles
                SciAdv r-articles
                Biochemistry
                Structural Biology
                Biochemistry
                Custom metadata
                Eunice Diego

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