Conserved Dynamic Mechanism of Allosteric Response to L-arg in Divergent Bacterial Arginine Repressors

. 2020 May 10 ; 25 (9) : . [epub] 20200510

Jazyk angličtina Země Švýcarsko Médium electronic

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid32397647

Grantová podpora
13-21053S Grantová Agentura České Republiky
DBI13-58737 National Science Foundation
DBI16-59726 National Science Foundation
APVV-16-0600 Agentúra na Podporu Výskumu a Vývoja
LM2015055 Ministerstvo Školství, Mládeže a Tělovýchovy
LM2010005 Ministerstvo Školství, Mládeže a Tělovýchovy
2018 MoA between the Institute of Microbiology, Czech Academy of Sciences, and the College of Biomedical Sciences, Larkin University.

Hexameric arginine repressor, ArgR, is the feedback regulator of bacterial L-arginine regulons, and sensor of L-arg that controls transcription of genes for its synthesis and catabolism. Although ArgR function, as well as its secondary, tertiary, and quaternary structures, is essentially the same in E. coli and B. subtilis, the two proteins differ significantly in sequence, including residues implicated in the response to L-arg. Molecular dynamics simulations are used here to evaluate the behavior of intact B. subtilis ArgR with and without L-arg, and are compared with prior MD results for a domain fragment of E. coli ArgR. Relative to its crystal structure, B. subtilis ArgR in absence of L-arg undergoes a large-scale rotational shift of its trimeric subassemblies that is very similar to that observed in the E. coli protein, but the residues driving rotation have distinct secondary and tertiary structural locations, and a key residue that drives rotation in E. coli is missing in B. subtilis. The similarity of trimer rotation despite different driving residues suggests that a rotational shift between trimers is integral to ArgR function. This conclusion is supported by phylogenetic analysis of distant ArgR homologs reported here that indicates at least three major groups characterized by distinct sequence motifs but predicted to undergo a common rotational transition. The dynamic consequences of L-arg binding for transcriptional activation of intact ArgR are evaluated here for the first time in two-microsecond simulations of B. subtilis ArgR. L-arg binding to intact B. subtilis ArgR causes a significant further shift in the angle of rotation between trimers that causes the N-terminal DNA-binding domains lose their interactions with the C-terminal domains, and is likely the first step toward adopting DNA-binding-competent conformations. The results aid interpretation of crystal structures of ArgR and ArgR-DNA complexes.

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Maas W.K., Clark A.J. Studies on the mechanism of repression of arginine biosynthesis in Escherichia coli. II. Dominance of repressibility in diploids. J. Mol. Biol. 1964;8:365–370. doi: 10.1016/S0022-2836(64)80200-X. PubMed DOI

Maas W.K. The arginine repressor of Escherichia coli. Microbiol. Rev. 1994;58:631–640. doi: 10.1128/MMBR.58.4.631-640.1994. PubMed DOI PMC

Van Duyne G.D., Ghosh G., Maas W.K., Sigler P.B. Structure of the oligomerization and L-arginine binding domain of the arginine repressor of Escherichia coli. J. Mol. Biol. 1996;256:377–391. doi: 10.1006/jmbi.1996.0093. PubMed DOI

Ni J., Sakanyan V., Charlier D., Glansdorff N., Van Duyne G.D. Structure of the arginine repressor from Bacillus stearothermophilus. Nat. Struct. Biol. 1999;6:427–432. PubMed

Dennis C.A., Glykos N.M., Parsons M.R., Phillips S.E.V. The structure of AhrC, the arginine repressor/activator protein from Bacillus subtilis. Acta. Cryst. D. 2002;58:421–430. doi: 10.1107/S0907444901021692. PubMed DOI

Smith M.C., Czaplewski L., North A.K., Baumberg S., Stockley P.G. Sequences required for regulation of arginine biosynthesis promoters are conserved between Bacillus subtilis and Escherichia coli. Mol. Microbiol. 1989;3:23–28. doi: 10.1111/j.1365-2958.1989.tb00099.x. PubMed DOI

Cherney L.T., Cherney M.M., Garen C.R., Lu G.J., James M.N. Structure of the C-domain of the arginine repressor protein from Mycobacterium tuberculosis. Acta. Cryst. D. 2008;64:950–956. doi: 10.1107/S0907444908021513. PubMed DOI PMC

Tian G., Maas W.K. Mutational analysis of the arginine repressor of E. coli. Mol. Microbiol. 1994;13:599–608. doi: 10.1111/j.1365-2958.1994.tb00454.x. PubMed DOI

Sunnerhagen M.S., Nilges M., Otting G., Carey J. Solution structure of the DNA-binding domain and model for the complex of multifunctional, hexameric arginine repressor with DNA. Nat. Str. Biol. 1997;4:819–825. doi: 10.1038/nsb1097-819. PubMed DOI

Garnett J.A., Baumberg S., Stockley P.G., Phillips S.E. Structure of the C-terminal effector-binding domain of AhrC bound to its corepressor L-arginine. Acta. Cryst. F. 2007;63:918–921. doi: 10.1107/S1744309107049391. PubMed DOI PMC

Cherney L.T., Cherney M.M., Garen C.R., James M.N. The structure of the arginine repressor from Mycobacterium tuberculosis bound with its DNA operator and co-repressor, L-arginine. J. Mol. Biol. 2009;388:85–97. doi: 10.1016/j.jmb.2009.02.053. PubMed DOI

Cherney L.T., Cherney M.M., Garen C.R., James M.N. Crystal structure of the intermediate complex of the arginine repressor from Mycobacterium tuberculosis bound with its DNA operator reveals detailed mechanism of arginine repression. J. Mol. Biol. 2010;399:240–254. doi: 10.1016/j.jmb.2010.03.065. PubMed DOI

Strawn R., Melichercik M., Green M., Stockner T., Carey J., Ettrich R. Symmetric allosteric mechanism of hexameric Escherichia coli arginine repressor exploits competition between L-arginine ligands and resident arginine residues. PloS Comput. Biol. 2010;6:e1000801. doi: 10.1371/journal.pcbi.1000801. PubMed DOI PMC

Pandey S.K., Reha D., Zayats V., Melichercik M., Carey J., Ettrich R. Binding-competent states for L-arginine in E. coli arginine repressor apoprotein. J. Mol. Model. 2014;20:2330. doi: 10.1007/s00894-014-2330-5. PubMed DOI

Dion M., Charlier D., Wang H., Gigot D., Savchenko A., Hallet J.N., Glansdorff N., Sakanyan V. The highly thermostable arginine repressor from Bacillus stearothermophilus: Gene cloning and repressor-operator interactions. Mol. Microbiol. 1997;25:385–398. doi: 10.1046/j.1365-2958.1997.4781845.x. PubMed DOI

Chen S.H., Merican A.F., Sherratt D.J. DNA binding of E. coli arginine repressor mutants altered in oligomeric state. Mol. Microbiol. 1997;24:1143–1156. doi: 10.1046/j.1365-2958.1997.4301791.x. PubMed DOI

Miller C.M., Baumberg S., Stockley P.G. Operator interactions by the Bacillus subtilis arginine repressor/activator, AhrC: Novel positioning and DNA mediated assembly of a transcriptional activator at catabolic sites. Mol. Microbiol. 1997;26:37–48. doi: 10.1046/j.1365-2958.1997.5441907.x. PubMed DOI

Holtham C.A.M., Jumel K., Miller C.M., Harding S.E., Baumberg S., Stockley P.G. Probing activation of the prokaryotic arginine transcriptional regulator using chimeric proteins. J. Mol. Biol. 1999;289:707–727. doi: 10.1006/jmbi.1999.2790. PubMed DOI

Storbakk N., Oxender D.L., Raafat El-Gewely M. Intragenic complementation between E. coli trp repressors with different defects in the tryptophan binding pocket. Gene. 1992;117:23–29. doi: 10.1016/0378-1119(92)90485-8. PubMed DOI

Komeiji Y., Fujita I., Honda N., Tsutsui M., Tamura T., Yamato I. Glycine 85 of the trp-repressor of E. coli is important in forming the hydrophobic tryptophan binding pocket: Experimental and computational approaches. Prot. Eng. 1994;7:1239–1247. doi: 10.1093/protein/7.10.1239. PubMed DOI

Grandori R., Lavoie T.A., Pflumm M., Tian G., Niersbach H., Maas W.K., Fairman R., Carey J. The DNA-binding domain of the hexameric arginine repressor. J. Mol. Biol. 1995;254:150–162. doi: 10.1006/jmbi.1995.0607. PubMed DOI

Szwajkajzer D., Dai L., Fukayama J.W., Abramczyk B., Fairman R., Carey J. Quantitative analysis of DNA binding by E. coli arginine repressor. J. Mol. Biol. 2001;312:949–962. doi: 10.1006/jmbi.2001.4941. PubMed DOI

Otwinowski Z., Schevitz R.W., Zhang R.-G., Lawson C.L., Joachimiak A., Marmorstein R.Q., Luisi B.F., Sigler P.B. Crystal structure of trp repressor/operator complex at atomic resolution. Nature. 1988;335:321–329. doi: 10.1038/335321a0. PubMed DOI

Lawson C.L., Carey J. Tandem binding in crystals of a trp repressor/operator half-site complex. Nature. 1993;366:178–182. doi: 10.1038/366178a0. PubMed DOI

Jin L., Xue W.-F., Fukayama J.W., Yetter J., Pickering M., Carey J. Asymmetric allosteric activation of the symmetric ArgR hexamer. J. Mol. Biol. 2005;346:43–56. doi: 10.1016/j.jmb.2004.11.031. PubMed DOI

Szwajkajzer D., Carey J. Molecular and biological constraints on ligand-binding affinity and specificity. Biopolymers. 1997;44:181–198. doi: 10.1002/(SICI)1097-0282(1997)44:2<181::AID-BIP5>3.0.CO;2-R. PubMed DOI

Huerta-Cepas J., Serra F., Bork P. ETE 3: Reconstruction, analysis, and visualization of phylogenomic data. Mol. Biol. Evol. 2016;33:1635–1638. doi: 10.1093/molbev/msw046. PubMed DOI PMC

Sievers F., Wilm A., Dineen D.G., Gibson T.J., Karplus K., Li W., Lopez R., McWilliam H., Remmert M., Söding J., et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 2011;7:539–544. doi: 10.1038/msb.2011.75. PubMed DOI PMC

Konagurthu A.S., Whisstock J.C., Stuckey P.J., Lesk A.M. MUSTANG: A multiple structural alignment algorithm. Proteins. 2006;64:559–574. doi: 10.1002/prot.20921. PubMed DOI

Krieger E., Koraimann G., Vriend G. Increasing the precision of comparative models with YASARA NOVA: A self-parameterizing force field. Proteins. 2002;47:393–402. doi: 10.1002/prot.10104. PubMed DOI

Mahoney M.W. A five-site model for liquid water and the reproduction of the density anomaly by rigid, nonpolarizable potential functions. J. Chem. Phy. 2000;112:8910–8922. doi: 10.1063/1.481505. DOI

Bayly C.I., Cieplak P., Cornell W., Kollman P.A. A well-behaved electrostatic potential based method using charge restraints for deriving atomic charges: The RESP model. J. Phys. Chem. 1993;97:10269–10280. doi: 10.1021/j100142a004. DOI

Frisch M.J., Trucks G.W., Schlegel H.B., Scuseria G.E., Robb M.A., Cheeseman J.R., Scalmani G., Barone V., Petersson G.A., Nakatsuji H., et al. Gaussian 09, Revision B.01. Gaussian, Inc.; Wallingford, CT, USA: 2010.

Horn A.H.C. A consistent force field parameter set for zwitterionic amino acid residues. J. Mol. Model. 2014;20:2478. doi: 10.1007/s00894-014-2478-z. PubMed DOI

Pall S., Abraham M.J., Kutzner C., Hess B., Lindahl E. Tackling exascale software challenges in molecular dynamics simulations with GROMACS. In: Markidis S., Laure E., editors. Solving Software Challenges for Exascale. Volume 8759. Springer International Publishing; Stockholm, Sweden: 2015. pp. 3–27.

Abraham M.J., Murtola T., Schulz R., Pall S., Smith J.C., Hess B., Lindahl E. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX. 2015;1:19–25. doi: 10.1016/j.softx.2015.06.001. DOI

Wang J., Cieplak P., Kollmann P.A. How well does a restrained electrostatic potential (RESP) model perform in calculating conformational energies of organic and biological molecules? J. Comput. Chem. 2000;21:1049–1074. doi: 10.1002/1096-987X(200009)21:12<1049::AID-JCC3>3.0.CO;2-F. DOI

Hornak V., Abel R., Okur A., Strockbine B., Raitberg A., Simmerling C. Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins. 2006;65:712–725. doi: 10.1002/prot.21123. PubMed DOI PMC

Lindorff-Larsen K., Piana S., Palmo K., Maragakis P., Klepeis J.L., Dror R.O., Shaw D.E. Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins. 2010;78:1950–1958. doi: 10.1002/prot.22711. PubMed DOI PMC

Essman U., Perela L., Berkowitz M.L., Darden T., Lee H., Pedersen L.G. A smooth particle mesh Ewald method. J. Chem. Phys. 1995;103:8577–8592. doi: 10.1063/1.470117. DOI

Bussi G., Donadio D., Parrinello M. Canonical sampling through velocity rescaling. J. Chem. Phys. 2007;126:014101. doi: 10.1063/1.2408420. PubMed DOI

Berendsen H.J.C., Postma J.P.M., DiNola A., Haak J.R. Molecular dynamics with coupling to an external bath. J. Chem. Phys. 1984;81:3684–3690. doi: 10.1063/1.448118. DOI

Grace. [(accessed on 31 October 2019)]; Available online: http://plasma-gate.weizmann.ac.il/Grace/

Humphrey W., Dalke A., Schulten K. VMD—Visual molecular dynamics. J. Mol. Graph. 1996;14:33–38. doi: 10.1016/0263-7855(96)00018-5. PubMed DOI

Karplus M., Kushick J. Method for estimating the configurational entropy of macromolecules. Macromolecules. 1981;14:325–332. doi: 10.1021/ma50003a019. DOI

Schlitter J. Estimation of absolute and relative entropies of macromolecules using the covariance matrix. Chem. Phys. Lett. 1993;215:617–621. doi: 10.1016/0009-2614(93)89366-P. DOI

Andricioaei I., Karplus M. On the calculation of entropy from covariance matrices of the atomic fluctuations. J. Chem. Phys. 2001;115:6289–6292. doi: 10.1063/1.1401821. DOI

Hsin J., Arkhipov A., Yin Y., Stone J.E., Schulten K. Using VMD: An introductory tutorial. Curr. Protoc. Bioinf. 2008;24:5.7.1–5.7.48. doi: 10.1002/0471250953.bi0507s24. PubMed DOI PMC

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