Emergence of fractal geometries in the evolution of a metabolic enzyme.


Journal

Nature
ISSN: 1476-4687
Titre abrégé: Nature
Pays: England
ID NLM: 0410462

Informations de publication

Date de publication:
10 Apr 2024
Historique:
received: 20 04 2023
accepted: 08 03 2024
medline: 11 4 2024
pubmed: 11 4 2024
entrez: 10 4 2024
Statut: aheadofprint

Résumé

Fractals are patterns that are self-similar across multiple length-scales

Identifiants

pubmed: 38600380
doi: 10.1038/s41586-024-07287-2
pii: 10.1038/s41586-024-07287-2
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2024. The Author(s).

Références

Mandelbrot, B. B. The Fractal Geometry of Nature. Vol. 1 (W. H. Freeman and Co., 1982).
Husain, A., Reddy, J., Bisht, D. & Sajid, M. Fractal dimension of coastline of Australia. Sci Rep. 11, 6304 (2021).
pubmed: 33737585 pmcid: 7973742 doi: 10.1038/s41598-021-85405-0
Du, J. X., Zhai, C. M. & Wang, Q. P. Recognition of plant leaf image based on fractal dimension features. Neurocomputing 116, 150–156 (2013).
doi: 10.1016/j.neucom.2012.03.028
Tarboton, D. G., Bras, R. L. & Rodriguez-Iturbe, I. The fractal nature of river networks. Water Resour. Res. 24, 1317–1322 (1988).
doi: 10.1029/WR024i008p01317
Shang, J. et al. Assembling molecular Sierpiński triangle fractals. Nat. Chem. 7, 389–393 (2015).
pubmed: 25901816 doi: 10.1038/nchem.2211
Wang, Y. et al. Construction and properties of Sierpiński triangular fractals on surfaces. ChemPhysChem 20, 2262–2270 (2019).
pubmed: 31291053 doi: 10.1002/cphc.201900258
Mo, Y., Chen, T., Dai, J., Wu, K. & Wang, D. On-surface synthesis of highly ordered covalent Sierpiński triangle fractals. J. Am. Chem. Soc. 141, 11378–11382 (2019).
pubmed: 31288514 doi: 10.1021/jacs.9b04815
Li, C. et al. Packing biomolecules into Sierpiński triangles with global organizational chirality. J. Am. Chem. Soc. 143, 14417–14421 (2021).
pubmed: 34387475 doi: 10.1021/jacs.1c05949
Li, C. et al. Construction of Sierpinski triangles up to the fifth order. J. Am. Chem. Soc. 139, 13749–13753 (2017).
pubmed: 28885024 doi: 10.1021/jacs.7b05720
Jiang, Z. et al. Assembling shape-persistent high-order Sierpiński triangular fractals. iScience 23, 101064 (2020).
pubmed: 32380420 pmcid: 7210427 doi: 10.1016/j.isci.2020.101064
Sarkar, R. et al. Sierpiński pyramids by molecular entanglement. J. Am. Chem. Soc. 142, 5526–5530 (2020).
pubmed: 32131597 doi: 10.1021/jacs.0c01168
Kempkes, S. N. et al. Design and characterization of electrons in a fractal geometry. Nat. Phys. 15, 127–131 (2019).
pubmed: 30886641 doi: 10.1038/s41567-018-0328-0
Azpeitia, E. et al. Cauliflower fractal forms arise from perturbations of floral gene networks. Science 373, 192–197 (2021).
pubmed: 34244409 doi: 10.1126/science.abg5999
Ahnert, S. E., Marsh, J. A., Hernandez, H., Robinson, C. V. & Teichmann, S. A. Principles of assembly reveal a periodic table of protein complexes. Science 350, aaa2245 (2015).
pubmed: 26659058 doi: 10.1126/science.aaa2245
Laniado, J. & Yeates, T. O. A complete rule set for designing symmetry combination materials from protein molecules. Proc. Natl Acad. Sci. USA 117, 31817–31823 (2020).
pubmed: 33239442 pmcid: 7749312 doi: 10.1073/pnas.2015183117
Sierpiński, W. Sur une courbe dont tout point est un point de ramification. C.R. Acad. Sci. Paris 160, 302–305 (1915).
Talatahari, S. & Azizi, M. Chaos game optimization: a novel metaheuristic algorithm. Artif. Intell. Rev. 54, 917–1004 (2021).
doi: 10.1007/s10462-020-09867-w
Nguyen, N. T. et al. Comparative analysis of folding and substrate binding sites between regulated hexameric type II citrate synthases and unregulated dimeric type I enzymes. Biochemistry 40, 13177–13187 (2001).
pubmed: 11683626 doi: 10.1021/bi010408o
Halley, J. M. et al. Uses and abuses of fractal methodology in ecology. Ecol. Lett. 7, 254–271 (2004).
doi: 10.1111/j.1461-0248.2004.00568.x
Gonzato, G., Mulargia, F. & Ciccotti, M. Measuring the fractal dimensions of ideal and actual objects: implications for application in geology and geophysics. Geophys. J. Int. 142, 108–116 (2000).
doi: 10.1046/j.1365-246x.2000.00133.x
Goodsell, D. S. & Olson, A. J. Structural symmetry and protein function. Annu. Rev. Biophys. Biomol. Struct. 29, 105–153 (2000).
pubmed: 10940245 doi: 10.1146/annurev.biophys.29.1.105
Baneyx, F. & Matthaei, J. F. Self-assembled two-dimensional protein arrays in bionanotechnology: from S-layers to designed lattices. Curr. Opin. Biotechnol. 28, 39–45 (2014).
pubmed: 24832073 doi: 10.1016/j.copbio.2013.11.001
James, L. C. & Tawfik, D. S. Conformational diversity and protein evolution—a 60-year-old hypothesis revisited. Trends Biochem. Sci 28, 361–368 (2003).
pubmed: 12878003 doi: 10.1016/S0968-0004(03)00135-X
Wiegand, G. & Remington, S. J. Citrate synthase: structure, control, and mechanism. Annu. Rev. Biophys. Biophys. Chem. 15, 97–117 (1986).
pubmed: 3013232 doi: 10.1146/annurev.bb.15.060186.000525
Kanamori, E., Kawaguchi, S.-I., Kuramitsu, S., Kouyama, T. & Murakami, M. Structural comparison between the open and closed forms of citrate synthase from Thermus thermophilus HB8. Biophys. Physicobiol. 12, 47–56 (2015).
pubmed: 27493854 pmcid: 4736845 doi: 10.2142/biophysico.12.0_47
Karpusas, M., Branchaud, B. & Remington, S. J. Proposed mechanism for the condensation reaction of citrate synthase: 1.9-Å structure of the ternary complex with oxaloacetate and carboxymethyl coenzyme A. Biochemistry 29, 2213–2219 (1990).
pubmed: 2337600 doi: 10.1021/bi00461a002
Mangan, N. M., Flamholz, A., Hood, R. D., Milo, R. & Savage, D. F. pH determines the energetic efficiency of the cyanobacterial CO
pubmed: 27551079 pmcid: 5018799 doi: 10.1073/pnas.1525145113
Welkie, D. G. et al. A hard day’s night: cyanobacteria in diel cycles. Trends Microbiol. 27, 231–242 (2019).
pubmed: 30527541 doi: 10.1016/j.tim.2018.11.002
Scholl, J., Dengler, L., Bader, L. & Forchhammer, K. Phosphoenolpyruvate carboxylase from the cyanobacterium Synechocystis sp. PCC 6803 is under global metabolic control by P
pubmed: 32274833 doi: 10.1111/mmi.14512
Broddrick, J. T. et al. Unique attributes of cyanobacterial metabolism revealed by improved genome-scale metabolic modeling and essential gene analysis. Proc. Natl Acad. Sci. USA 113, E8344–E8353 (2016).
pubmed: 27911809 pmcid: 5187688 doi: 10.1073/pnas.1613446113
Gallivan, J. P. & Dougherty, D. A. Cation–π interactions in structural biology. Proc. Natl Acad. Sci. USA 96, 9459–9464 (1999).
pubmed: 10449714 pmcid: 22230 doi: 10.1073/pnas.96.17.9459
Pillai, A. S. et al. Origin of complexity in haemoglobin evolution. Nature 581, 480–485 (2020).
pubmed: 32461643 pmcid: 8259614 doi: 10.1038/s41586-020-2292-y
Garcia-Seisdedos, H., Empereur-Mot, C., Elad, N. & Levy, E. D. Proteins evolve on the edge of supramolecular self-assembly. Nature 548, 244–247 (2017).
pubmed: 28783726 doi: 10.1038/nature23320
Grueninger, D. et al. Designed protein–protein association. Science 319, 206–209 (2008).
pubmed: 18187656 doi: 10.1126/science.1150421
East, N. J., Clifton, B. E., Jackson, C. J. & Kaczmarski, J. A. The role of oligomerization in the optimization of cyclohexadienyl dehydratase conformational dynamics and catalytic activity. Protein Sci. 31, e4510 (2022).
pubmed: 36382881 pmcid: 9703590 doi: 10.1002/pro.4510
Pillai, A. S., Hochberg, G. K. A. & Thornton, J. W. Simple mechanisms for the evolution of protein complexity. Protein Sci. 31, e4449 (2022).
pubmed: 36107026 pmcid: 9601886 doi: 10.1002/pro.4449
Gould, S. J. & Lewontin, R. C. The spandrels of San Marco and the Panglossian paradigm: a critique of the adaptationist programme. Proc. R. Soc. London. Ser. B Biol. Sci. 205, 581–598 (1979).
Yang, J., Zhang, Z., Zhang, X. A. & Luo, Q. A ligation-independent cloning method using nicking DNA endonuclease. Biotechniques 49, 817–821 (2010).
pubmed: 21091446 doi: 10.2144/000113520
Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).
pubmed: 15034147 pmcid: 390337 doi: 10.1093/nar/gkh340
Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).
pubmed: 24451623 pmcid: 3998144 doi: 10.1093/bioinformatics/btu033
Le, S. Q. & Gascuel, O. An improved general amino acid replacement matrix. Mol. Biol. Evol. 25, 1307–1320 (2008).
pubmed: 18367465 doi: 10.1093/molbev/msn067
Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).
pubmed: 20525638 doi: 10.1093/sysbio/syq010
Anisimova, M. & Gascuel, O. Approximate likelihood-ratio test for branches: a fast, accurate, and powerful alternative. Syst. Biol. 55, 539–552 (2006).
pubmed: 16785212 doi: 10.1080/10635150600755453
Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).
pubmed: 17483113 doi: 10.1093/molbev/msm088
Moore, K. R. et al. An expanded ribosomal phylogeny of cyanobacteria supports a deep placement of plastids. Front. Microbiol. 10, 1612 (2019).
pubmed: 31354692 pmcid: 6640209 doi: 10.3389/fmicb.2019.01612
Schirrmeister, B. E., Antonelli, A. & Bagheri, H. C. The origin of multicellularity in cyanobacteria. BMC Evol. Biol. 11, 45 (2011).
pubmed: 21320320 pmcid: 3271361 doi: 10.1186/1471-2148-11-45
Pinevich, A., Velichko, N. & Ivanikova, N. Cyanobacteria of the genus Prochlorothrix. Front. Microbiol. 3, 173 (2012).
pubmed: 22783229 pmcid: 3390582 doi: 10.3389/fmicb.2012.00173
Marty, M. T. et al. Bayesian deconvolution of mass and ion mobility spectra: from binary interactions to polydisperse ensembles. Anal. Chem. 87, 4370–4376 (2015).
pubmed: 25799115 pmcid: 4594776 doi: 10.1021/acs.analchem.5b00140
Ellman, G. L. Tissue sulfhydryl groups. Arch. Biochem. Biophys. 82, 70–77 (1959).
pubmed: 13650640 doi: 10.1016/0003-9861(59)90090-6
Srere, P. A., Brazil, H., Gonen, L. & Takahashi, M. The citrate condensing enzyme of pigeon breast muscle and moth flight muscle. Acta Chem. Scand. 17, 129–134 (1963).
doi: 10.3891/acta.chem.scand.17s-0129
Clerico, E. M., Ditty, J. L. & Golden, S. S. Specialized techniques for site-directed mutagenesis in cyanobacteria. Methods Mol. Biol. 362, 155–171 (2007).
pubmed: 17417008 doi: 10.1007/978-1-59745-257-1_11
Grant, T., Rohou, A. & Grigorieff, N. CisTEM, user-friendly software for single-particle image processing. eLife 7, e35383 (2018).
pubmed: 29513216 pmcid: 5854467 doi: 10.7554/eLife.35383
Kabsch, W. XDS. Acta Crystallogr. Sect. D Biol. Crystallogr. 66, 125–132 (2010).
doi: 10.1107/S0907444909047337
McCoy, A. J. et al. Phaser crystallographic software. J. Appl. Crystallogr. 40, 658–674 (2007).
pubmed: 19461840 pmcid: 2483472 doi: 10.1107/S0021889807021206
Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Acta Crystallogr. Sect. D Biol. Crystallogr. 60, 2126–2132 (2004).
doi: 10.1107/S0907444904019158
Adams, P. D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr. Sect. D Biol. Crystallogr. 66, 213–221 (2010).
doi: 10.1107/S0907444909052925
Mastronarde, D. N. Automated electron microscope tomography using robust prediction of specimen movements. J. Struct. Biol. 152, 36–51 (2005).
pubmed: 16182563 doi: 10.1016/j.jsb.2005.07.007
Punjani, A., Rubinstein, J. L., Fleet, D. J. & Brubaker, M. A. CryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat. Methods 14, 290–296 (2017).
pubmed: 28165473 doi: 10.1038/nmeth.4169
Bepler, T., Kelley, K., Noble, A. J. & Berger, B. Topaz-Denoise: general deep denoising models for cryoEM and cryoET. Nat. Commun. 11, 5208 (2020).
pubmed: 33060581 pmcid: 7567117 doi: 10.1038/s41467-020-18952-1
Biyani, N. et al. Focus: the interface between data collection and data processing in cryo-EM. J. Struct. Biol. 198, 124–133 (2017).
pubmed: 28344036 doi: 10.1016/j.jsb.2017.03.007
Zheng, S. Q. et al. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat. Methods 14, 331–332 (2017).
pubmed: 28250466 pmcid: 5494038 doi: 10.1038/nmeth.4193
Zhang, K. Gctf: real-time CTF determination and correction. J. Struct. Biol. 193, 1–12 (2016).
pubmed: 26592709 pmcid: 4711343 doi: 10.1016/j.jsb.2015.11.003
Wagner, T. et al. SPHIRE-crYOLO is a fast and accurate fully automated particle picker for cryo-EM. Commun. Biol. 2, 218 (2019).
pubmed: 31240256 pmcid: 6584505 doi: 10.1038/s42003-019-0437-z
Scheres, S. H. W. A Bayesian view on cryo-EM structure determination. J. Mol. Biol. 415, 406–418 (2012).
pubmed: 22100448 pmcid: 3314964 doi: 10.1016/j.jmb.2011.11.010
Scheres, S. H. W. Beam-induced motion correction for sub-megadalton cryo-EM particles. eLife 3, e03665 (2014).
pubmed: 25122622 pmcid: 4130160 doi: 10.7554/eLife.03665
Zi Tan, Y. et al. Addressing preferred specimen orientation in single-particle cryo-EM through tilting. Nat. Methods 14, 793–796 (2017).
doi: 10.1038/nmeth.4347
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
pubmed: 34265844 pmcid: 8371605 doi: 10.1038/s41586-021-03819-2
Kidmose, R. T. et al. Namdinator—automatic molecular dynamics flexible fitting of structural models into cryo-EM and crystallography experimental maps. IUCrJ 6, 526–531 (2019).
pubmed: 31316797 pmcid: 6608625 doi: 10.1107/S2052252519007619
Kieffer, J. et al. New data analysis for BioSAXS at the ESRF. J. Synchrotron Radiat. 29, 1318–1328 (2022).
pubmed: 36073892 pmcid: 9455220 doi: 10.1107/S1600577522007238
Tully, M. D., Tarbouriech, N., Rambo, R. P. & Hutin, S. Analysis of SEC–SAXS data via EFA deconvolution and Scatter. J. Vis. Exp. 167, e61578 (2021).
Hopkins, J. B., Gillilan, R. E. & Skou, S. BioXTAS RAW: improvements to a free open-source program for small-angle X-ray scattering data reduction and analysis. J. Appl. Crystallogr. 50, 1545–1553 (2017).
pubmed: 29021737 pmcid: 5627684 doi: 10.1107/S1600576717011438
Abraham, M. J. et al. GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25 (2015).
doi: 10.1016/j.softx.2015.06.001
Pagès, G. & Grudinin, S. AnAnaS: software for analytical analysis of symmetries in protein structures. Methods Mol. Biol. 2165, 245–257 (2020).
pubmed: 32621229 doi: 10.1007/978-1-0716-0708-4_14
Sendker, F. L. & Hochberg, G. K. A. Source data for “Emergence of fractal geometries in the evolution of a metabolic enzyme” V1. Edmond https://doi.org/10.17617/3.KNEQIR (2024).
Hasunuma, T., Matsuda, M., Kato, Y., Vavricka, C. J. & Kondo, A. Temperature enhanced succinate production concurrent with increased central metabolism turnover in the cyanobacterium Synechocystis sp. PCC 6803. Metab. Eng. 48, 109–120 (2018).
pubmed: 29847778 doi: 10.1016/j.ymben.2018.05.013

Auteurs

Franziska L Sendker (FL)

Evolutionary Biochemistry Group, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.

Yat Kei Lo (YK)

Center for Synthetic Microbiology (SYNMIKRO), Philipps-University Marburg, Marburg, Germany.

Thomas Heimerl (T)

Center for Synthetic Microbiology (SYNMIKRO), Philipps-University Marburg, Marburg, Germany.

Stefan Bohn (S)

Cryo-EM Platform and Institute of Structural Biology, Helmholtz Munich, Neuherberg, Germany.

Louise J Persson (LJ)

Department of Chemistry - BMC, Uppsala University, Uppsala, Sweden.

Christopher-Nils Mais (CN)

Department of Chemistry, Philipps-University Marburg, Marburg, Germany.

Wiktoria Sadowska (W)

Department of Chemistry, University of Oxford, Oxford, UK.
Kavli Institute for Nanoscience Discovery, Oxford, UK.

Nicole Paczia (N)

Department of Biochemistry and Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.

Eva Nußbaum (E)

Interfaculty Institute of Microbiology and Infection Medicine, Organismic Interactions Department, Cluster of Excellence 'Controlling Microbes to Fight Infections', Tübingen University, Tübingen, Germany.

María Del Carmen Sánchez Olmos (M)

MaxGENESYS Biofoundry, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.

Karl Forchhammer (K)

Interfaculty Institute of Microbiology and Infection Medicine, Organismic Interactions Department, Cluster of Excellence 'Controlling Microbes to Fight Infections', Tübingen University, Tübingen, Germany.

Daniel Schindler (D)

Center for Synthetic Microbiology (SYNMIKRO), Philipps-University Marburg, Marburg, Germany.
MaxGENESYS Biofoundry, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.

Tobias J Erb (TJ)

Center for Synthetic Microbiology (SYNMIKRO), Philipps-University Marburg, Marburg, Germany.
Department of Biochemistry and Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.
Department of Biology, Philipps-University Marburg, Marburg, Germany.

Justin L P Benesch (JLP)

Department of Chemistry, University of Oxford, Oxford, UK.
Kavli Institute for Nanoscience Discovery, Oxford, UK.

Erik G Marklund (EG)

Department of Chemistry - BMC, Uppsala University, Uppsala, Sweden.

Gert Bange (G)

Center for Synthetic Microbiology (SYNMIKRO), Philipps-University Marburg, Marburg, Germany.
Department of Chemistry, Philipps-University Marburg, Marburg, Germany.
Max Planck Fellow Group Molecular Physiology of Microbes, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.

Jan M Schuller (JM)

Center for Synthetic Microbiology (SYNMIKRO), Philipps-University Marburg, Marburg, Germany. jan.schuller@synmikro.uni-marburg.de.
Department of Chemistry, Philipps-University Marburg, Marburg, Germany. jan.schuller@synmikro.uni-marburg.de.

Georg K A Hochberg (GKA)

Evolutionary Biochemistry Group, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany. georg.hochberg@mpi-marburg.mpg.de.
Center for Synthetic Microbiology (SYNMIKRO), Philipps-University Marburg, Marburg, Germany. georg.hochberg@mpi-marburg.mpg.de.
Department of Chemistry, Philipps-University Marburg, Marburg, Germany. georg.hochberg@mpi-marburg.mpg.de.

Classifications MeSH