Abstract
The Distance Constraint Model (DCM) is an ensemble-based biophysical model that integrates thermodynamic and mechanical viewpoints of protein structure. The DCM outputs a large number of structural characterizations that collectively allow for Quantified Stability–Flexibility Relationships (QSFR) to be identified and compared across protein families. Using five metallo-β-lactamases (MBLs) as a representative set, we demonstrate how QSFR properties are both conserved and varied across protein families. Similar to our characterizations on other protein families, the backbone flexibility of the five MBLs are overall visually conserved, yet there are interesting specific quantitative differences. For example, the plasmid-encoded NDM-1 enzyme, which leads to a fast spreading drug-resistant version of Klebsiella pneumoniae, has several regions of significantly increased rigidity relative to the other four. In addition, the set of intramolecular couplings within NDM-1 are also atypical. While long-range couplings frequently vary significantly across protein families, NDM-1 is distinct because it has limited correlated flexibility, which is isolated within the active site S3/S4 and S11/H6 loops. These loops are flexibly correlated in the other members, suggesting it is important to function, but the others also have significant amounts of correlated flexibility throughout the rest of their structures.
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References
Feynman R (1963) The Feynman lectures on physics. Addison-Wesley Publishing Company, Reading, MA
Henzler-Wildman KA, Lei M, Thai V, Kerns SJ, Karplus M, Kern D (2007) A hierarchy of timescales in protein dynamics is linked to enzyme catalysis. Nature 450:913–916
Livesay DR (2010) Protein dynamics: dancing on an ever-changing free energy stage. Curr Opin Pharmacol 10:706–708
Jacobs DJ (2006) Predicting protein flexibility and stability using network rigidity: a new modeling paradigm. In: Pandalai SG (ed) Recent research developments in biophysics. Transworld Research Network, Trivandrum, India, pp 71–131
Jacobs DJ (2010) Ensemble-based methods for describing protein dynamics. Curr Opin Pharmacol 10:760–769
Jacobs DJ, Dallakyan S (2005) Elucidating protein thermodynamics from the three-dimensional structure of the native state using network rigidity. Biophys J 88:903–915
Livesay DR, Dallakyan S, Wood GG, Jacobs DJ (2004) A flexible approach for understanding protein stability. FEBS Lett 576:468–476
Jacobs DJ, Livesay DR, Hules J, Tasayco ML (2006) Elucidating quantitative stability/flexibility relationships within thioredoxin and its fragments using a distance constraint model. J Mol Biol 358:882–904
Jacobs DJ, Livesay DR, Mottonen JM, Vorov OK, Istomin AY, Verma D (2012) Ensemble properties of network rigidity reveal allosteric mechanisms. In: Fenton AW (ed) Methods in molecular biology. Springer, New York, pp 279–304
Livesay DR, Jacobs DJ (2006) Conserved quantitative stability/flexibility relationships (QSFR) in an orthologous RNase H pair. Proteins 62:130–143
Livesay DR, Huynh DH, Dallakyan S, Jacobs DJ (2008) Hydrogen bond networks determine emergent mechanical and thermodynamic properties across a protein family. Chem Cent J 2:17
Mottonen JM, Jacobs DJ, Livesay DR (2010) Allosteric response is both conserved and variable across three CheY orthologs. Biophys J 99:2245–2254
Mottonen JM, Xu M, Jacobs DJ, Livesay DR (2009) Unifying mechanical and thermodynamic descriptions across the thioredoxin protein family. Proteins 75:610–627
Verma D, Jacobs DJ, Livesay DR (2010) Predicting the melting point of human C-type lysozyme mutants. Curr Protein Pept Sci 11:562–572
Verma D, Jacobs DJ, Livesay DR (2012) Changes in lysozyme flexibility upon mutation are frequent, large and long-ranged. PLoS Comput Biol 8:e1002409
Savard PY, Gagne SM (2006) Backbone dynamics of TEM-1 determined by NMR: evidence for a highly ordered protein. Biochemistry 45:11414–11424
Verma D, Jacobs DJ, Livesay DR (2013) Variations within class-A β-lactamase physiochemical properties reflect evolutionary, but not antibiotic specificity, patterns. PLoS Comp Biol 9:e1003155
Cadag E, Vitalis E, Lennox KP, Zhou CL, Zemla AT (2012) Computational analysis of pathogen-borne metallo beta-lactamases reveals discriminating structural features between B1 types. BMC Res Notes 5:96
Sharma VK, Guleria R, Mehta V, Sood N, Singh SN (2010) NDM-1 resistance: Fleming’s predictions become true. Int J Appl Biol Pharm Technol 1:1244–1251
Dill KA (1997) Additivity principles in biochemistry. J Biol Chem 272:701–704
Mark AE, van Gunsteren WF (1994) Decomposition of the free energy of a system in terms of specific interactions. Implications for theoretical and experimental studies. J Mol Biol 240:167–176
Jacobs DJ, Dallakyan S, Wood GG, Heckathorne A (2003) Network rigidity at finite temperature: relationships between thermodynamic stability, the nonadditivity of entropy, and cooperativity in molecular systems. Phys Rev E Stat Nonlin Soft Matter Phys 68:061109
Jacobs DJ, Rader AJ, Kuhn LA, Thorpe MF (2001) Protein flexibility predictions using graph theory. Proteins 44:150–165
Jacobs DJ, Thorpe MF (1995) Generic rigidity percolation: the pebble game. Phys Rev Lett 75:4051–4054
Katoh N, Tanigawa S (2011) A proof of the molecular conjecture. Discrete Comput Geom 45:647–700
Vorov OK, Livesay DR, Jacobs DJ (2009) Helix/coil nucleation: a local response to global demands. Biophys J 97:3000–3009
Vorov OK, Livesay DR, Jacobs DJ (2011) Nonadditivity in conformational entropy upon molecular rigidification reveals a universal mechanism affecting folding cooperativity. Biophys J 100:1129–1138
Lassaux P, Traore DA, Loisel E, Favier A, Docquier JD, Sohier JS, Laurent C, Bebrone C, Frere JM, Ferrer JL, Galleni M (2011) Biochemical and structural characterization of the subclass B1 metallo-beta-lactamase VIM-4. Antimicrob Agents Chemother 55:1248–1255
Oelschlaeger P, Mayo SL (2005) Hydroxyl groups in the (beta)beta sandwich of metallo-beta-lactamases favor enzyme activity: a computational protein design study. J Mol Biol 350:395–401
Carfi A, Duee E, Galleni M, Frere JM, Dideberg O (1998) 1.85 A resolution structure of the zinc (II) beta-lactamase from Bacillus cereus. Acta Crystallogr D Biol Crystallogr 54:313–323
Concha NO, Janson CA, Rowling P, Pearson S, Cheever CA, Clarke BP, Lewis C, Galleni M, Frere JM, Payne DJ, Bateson JH, Abdel-Meguid SS (2000) Crystal structure of the IMP-1 metallo beta-lactamase from Pseudomonas aeruginosa and its complex with a mercaptocarboxylate inhibitor: binding determinants of a potent, broad-spectrum inhibitor. Biochemistry 39:4288–4298
Payne DJ, Hueso-Rodriguez JA, Boyd H, Concha NO, Janson CA, Gilpin M, Bateson JH, Cheever C, Niconovich NL, Pearson S, Rittenhouse S, Tew D, Diez E, Perez P, De La Fuente J, Rees M, Rivera-Sagredo A (2002) Identification of a series of tricyclic natural products as potent broad-spectrum inhibitors of metallo-beta-lactamases. Antimicrob Agents Chemother 46:1880–1886
Green VL, Verma A, Owens RJ, Phillips SE, Carr SB (2011) Structure of New Delhi metallo-beta-lactamase 1 (NDM-1). Acta Crystallogr Sect F Struct Biol Cryst Commun 67:1160–1164
Gordon JC, Myers JB, Folta T, Shoja V, Heath LS, Onufriev A (2005) H++: a server for estimating pKas and adding missing hydrogens to macromolecules. Nucleic Acids Res 33:W368–W371
Ponder JW, Case DA (2003) Force fields for protein simulations. Adv Protein Chem 66:27–85
Kim Y, Cunningham MA, Mire J, Tesar C, Sacchettini J, Joachimiak A (2013) NDM-1, the ultimate promiscuous enzyme: substrate recognition and catalytic mechanism. FASEB J 27(5):1917–1927. doi:10.1096/fj.12-224014
King DT, Worrall LJ, Gruninger R, Strynadka NCJ (2012) New Delhi metallo-beta-lactamase: structural insights into beta-lactam recognition and inhibition. J Am Chem Soc 134:11363–11365
Livesay DR, Kreth KE, Fodor AA (2012) A critical evaluation of correlated mutation algorithms and coevolution within allosteric mechanisms. In: Fenton AW (ed) Methods in molecular biology. Springer, New York, pp 385–398
Conigrave AD, Franks AH (2003) Allosteric activation of plasma membrane receptors—physiological implications and structural origins. Prog Biophys Mol Biol 81:219–240
Forman BM, Umesono K, Chen J, Evans RM (1995) Unique response pathways are established by allosteric interactions among nuclear hormone receptors. Cell 81:541–550
Fenton AW, Alontaga AY (2009) The impact of ions on allosteric functions in human liver pyruvate kinase. Methods Enzymol 466:83–107
Acknowledgments
This work has been partially supported by NIH grants R01 GM073082 (to D.J.J. and D.R.L.) and R15 GM101570 (to D.R.L.). Key to the distance constraint model is the use of graph-rigidity algorithms, claimed in US Patent 6,014,449, which has been assigned to the Board of Trustees Michigan State University. Used with permission.
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Brown, M.C., Verma, D., Russell, C., Jacobs, D.J., Livesay, D.R. (2014). A Case Study Comparing Quantitative Stability–Flexibility Relationships Across Five Metallo-β-Lactamases Highlighting Differences Within NDM-1. In: Livesay, D. (eds) Protein Dynamics. Methods in Molecular Biology, vol 1084. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-658-0_12
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DOI: https://doi.org/10.1007/978-1-62703-658-0_12
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