Single‐molecule enzymology à la Michaelis–Menten

Over the past 100 years, deterministic rate equations have been successfully used to infer enzyme‐catalysed reaction mechanisms and to estimate rate constants from reaction kinetics experiments conducted in vitro. In recent years, sophisticated experimental techniques have been developed that begin to allow the measurement of enzyme‐catalysed and other biopolymer‐mediated reactions inside single cells at the single‐molecule level. Time‐course data obtained using these methods are considerably noisy because molecule numbers within cells are typically quite small. As a consequence, the interpretation and analysis of single‐cell data requires stochastic methods, rather than deterministic rate equations. Here, we concisely review both experimental and theoretical techniques that enable single‐molecule analysis, with particular emphasis on the major developments in the field of theoretical stochastic enzyme kinetics, from its inception in the mid‐20th century to its modern‐day status. We discuss the differences between stochastic and deterministic rate equation models, how these depend on enzyme molecule numbers and substrate inflow into the reaction compartment, and how estimation of rate constants from single‐cell data is possible using recently developed stochastic approaches.

[1]  I. Tolic-Nørrelykke,et al.  Single-molecule imaging in vivo: the dancing building blocks of the cell. , 2013, Integrative biology : quantitative biosciences from nano to macro.

[2]  X. Fang,et al.  Single-molecule fluorescence imaging in living cells. , 2013, Annual review of physical chemistry.

[3]  Todd O Yeates,et al.  Bacterial microcompartment organelles: protein shell structure and evolution. , 2010, Annual review of biophysics.

[4]  H. W. Wiley LOIS GÉNÉRALES DE L'ACTION DES DIASTASES. , 1903 .

[5]  Jonas Ries,et al.  Fluorescence correlation spectroscopy , 2012, BioEssays : news and reviews in molecular, cellular and developmental biology.

[6]  Single Enzyme Pathways and Substrate Fluctuations , 2005 .

[7]  Ertugrul M. Ozbudak,et al.  Regulation of noise in the expression of a single gene , 2002, Nature Genetics.

[8]  D. Frishman,et al.  Protein abundance profiling of the Escherichia coli cytosol , 2008, BMC Genomics.

[9]  X. Xie,et al.  Living Cells as Test Tubes , 2006, Science.

[10]  Ramon Grima,et al.  Discreteness-induced concentration inversion in mesoscopic chemical systems , 2012, Nature Communications.

[11]  Adam P Arkin,et al.  Deviant effects in molecular reaction pathways , 2006, Nature Biotechnology.

[12]  Kevin Burrage,et al.  Stochastic approaches for modelling in vivo reactions , 2004, Comput. Biol. Chem..

[13]  Manuel A Palacios,et al.  Polymer nanofibre junctions of attolitre volume serve as zeptomole-scale chemical reactors. , 2009, Nature chemistry.

[14]  T. Kurtz The Relationship between Stochastic and Deterministic Models for Chemical Reactions , 1972 .

[15]  Zoran Konkoli,et al.  Biomimetic nanoscale reactors and networks. , 2004, Annual review of physical chemistry.

[16]  David A. Rand,et al.  Bayesian inference of biochemical kinetic parameters using the linear noise approximation , 2009, BMC Bioinformatics.

[17]  T. Yeates,et al.  Atomic-Level Models of the Bacterial Carboxysome Shell , 2008, Science.

[18]  D. Sherrington Stochastic Processes in Physics and Chemistry , 1983 .

[19]  P. Maini,et al.  A Century of Enzyme Kinetics: Reliability of the K M and v v max Estimates , 2003 .

[20]  H. Rigneault,et al.  Fluorescence correlation spectroscopy. , 2011, Methods in molecular biology.

[21]  M. Moore,et al.  New insights into the spliceosome by single molecule fluorescence microscopy. , 2011, Current opinion in chemical biology.

[22]  Darren J. Wilkinson,et al.  Bayesian inference for a discretely observed stochastic kinetic model , 2008, Stat. Comput..

[23]  Hong Qian,et al.  Dissipation, generalized free energy, and a self-consistent nonequilibrium thermodynamics of chemically driven open subsystems. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[24]  Wei Min,et al.  Single-molecule Michaelis-Menten equations. , 2005, The journal of physical chemistry. B.

[25]  P. Maini,et al.  Enzyme kinetics at high enzyme concentration , 2000, Bulletin of mathematical biology.

[26]  Dan S. Tawfik,et al.  The moderately efficient enzyme: evolutionary and physicochemical trends shaping enzyme parameters. , 2011, Biochemistry.

[27]  Elliot L Elson,et al.  Fluorescence correlation spectroscopy: past, present, future. , 2011, Biophysical journal.

[28]  D. Gillespie A rigorous derivation of the chemical master equation , 1992 .

[29]  M. Leake,et al.  Single molecule experimentation in biological physics: exploring the living component of soft condensed matter one molecule at a time , 2011, Journal of physics. Condensed matter : an Institute of Physics journal.

[30]  H. Gaub,et al.  Single-molecule mechanoenzymatics. , 2012, Annual review of biophysics.

[31]  B. JayasundaraJ.M.S. Lois générales de l'Action des Diastases , 1903, Nature.

[32]  Z. Qu,et al.  Roles of Protein Ubiquitination and Degradation Kinetics in Biological Oscillations , 2012, PloS one.

[33]  Anthony J. Manzo,et al.  Do-it-yourself guide: how to use the modern single-molecule toolkit , 2008, Nature Methods.

[34]  N. Walter,et al.  Intracellular single molecule microscopy reveals two kinetically distinct pathways for microRNA assembly , 2012, EMBO reports.

[35]  M. Khammash,et al.  The finite state projection algorithm for the solution of the chemical master equation. , 2006, The Journal of chemical physics.

[36]  Dan ie l T. Gil lespie A rigorous derivation of the chemical master equation , 1992 .

[37]  Paul J. Choi,et al.  Quantifying E. coli Proteome and Transcriptome with Single-molecule Sensitivity in Single Cells , 2011 .

[38]  S. Schnell,et al.  The condition for pseudo-first-order kinetics in enzymatic reactions is independent of the initial enzyme concentration. , 2003, Biophysical chemistry.

[39]  A. R. Fresht Structure and Mechanism in Protein Science: A Guide to Enzyme Catalysis and Protein Folding , 1999 .

[40]  H. Qian Cooperativity and specificity in enzyme kinetics: a single-molecule time-based perspective. , 2008, Biophysical journal.

[41]  I G Darvey,et al.  The application of the theory of Markov processes to the reversible one substrate-one intermediate-one product enzymic mechanism. , 1967, Journal of theoretical biology.

[42]  Santiago Schnell,et al.  The mechanism distinguishability problem in biochemical kinetics: the single-enzyme, single-substrate reaction as a case study. , 2006, Comptes rendus biologies.

[43]  C T Zimmerle,et al.  Analysis of progress curves by simulations generated by numerical integration. , 1989, The Biochemical journal.

[44]  S. Schnell,et al.  Modelling reaction kinetics inside cells. , 2008, Essays in biochemistry.

[45]  Philipp Thomas,et al.  Computation of biochemical pathway fluctuations beyond the linear noise approximation using iNA , 2012, 2012 IEEE International Conference on Bioinformatics and Biomedicine.

[46]  Daniel T Gillespie,et al.  Stochastic simulation of chemical kinetics. , 2007, Annual review of physical chemistry.

[47]  Antoine M. van Oijen,et al.  Ever-fluctuating single enzyme molecules: Michaelis-Menten equation revisited , 2006, Nature chemical biology.

[48]  D. A. Mcquarrie,et al.  A STOCHASTIC APPROACH TO ENZYME-SUBSTRATE REACTIONS. , 1964, Biochemistry.

[49]  Paul J. Choi,et al.  Quantifying E. coli Proteome and Transcriptome with Single-Molecule Sensitivity in Single Cells , 2010, Science.

[50]  Taekjip Ha,et al.  Probing Cellular Protein Complexes via Single Molecule Pull-down , 2011, Nature.

[51]  H. Qian Cooperativity in cellular biochemical processes: noise-enhanced sensitivity, fluctuating enzyme, bistability with nonlinear feedback, and other mechanisms for sigmoidal responses. , 2012, Annual review of biophysics.

[52]  Victor Henri,et al.  [General theory of the action of some glycoside hydrolases]. , 2006, Comptes rendus biologies.

[53]  A. Bartholomay,et al.  A stochastic approach to statistical kinetics with application to enzyme kinetics. , 1962, Biochemistry.

[54]  Juliane Junker,et al.  Enzyme Kinetics A Modern Approach , 2016 .

[55]  N. Kampen,et al.  Stochastic processes in physics and chemistry , 1981 .

[56]  John E. Pearson,et al.  Microscopic simulation of chemical bistability in homogeneous systems , 1996 .

[57]  T. Elston,et al.  Stochasticity in gene expression: from theories to phenotypes , 2005, Nature Reviews Genetics.

[58]  Hong Qian,et al.  The Chemical Master Equation Approach to Nonequilibrium Steady-State of Open Biochemical Systems: Linear Single-Molecule Enzyme Kinetics and Nonlinear Biochemical Reaction Networks , 2010, International journal of molecular sciences.

[59]  Hong Qian,et al.  Single-molecule enzymology: stochastic Michaelis-Menten kinetics. , 2002, Biophysical chemistry.

[60]  Sonya M. Hanson,et al.  Reactant stationary approximation in enzyme kinetics. , 2008, The journal of physical chemistry. A.

[61]  R. Grima,et al.  An effective rate equation approach to reaction kinetics in small volumes: theory and application to biochemical reactions in nonequilibrium steady-state conditions. , 2010, The Journal of chemical physics.

[62]  E. Seibert,et al.  Fundamentals of enzyme kinetics. , 2014, Methods in molecular biology.

[63]  X. Xie,et al.  Optical studies of single molecules at room temperature. , 1998, Annual review of physical chemistry.

[64]  N. Friedman,et al.  Stochastic protein expression in individual cells at the single molecule level , 2006, Nature.

[65]  Rahul Roy,et al.  A practical guide to single-molecule FRET , 2008, Nature Methods.

[66]  John E. Pearson,et al.  Microscopic Simulation of Chemical Oscillations in Homogeneous Systems , 1990 .

[67]  Kevin R. Sanft,et al.  Legitimacy of the stochastic Michaelis-Menten approximation. , 2011, IET systems biology.

[68]  Marc R. Roussel,et al.  Accurate steady-state approximations : implications for kinetics experiments and mechanism , 1991 .

[69]  J. Tóth,et al.  A full stochastic description of the Michaelis-Menten reaction for small systems. , 1977, Acta biochimica et biophysica; Academiae Scientiarum Hungaricae.

[70]  C. Rao,et al.  Stochastic chemical kinetics and the quasi-steady-state assumption: Application to the Gillespie algorithm , 2003 .

[71]  P. Tinnefeld,et al.  Making connections — strategies for single molecule fluorescence biophysics , 2013, Current opinion in chemical biology.

[72]  R. Duggleby,et al.  Analysis of enzyme progress curves by nonlinear regression. , 1995, Methods in enzymology.

[73]  T. Jovin,et al.  FRET imaging , 2003, Nature Biotechnology.

[74]  Carmen G. Moles,et al.  Parameter estimation in biochemical pathways: a comparison of global optimization methods. , 2003, Genome research.

[75]  Ramon Grima,et al.  Investigating the robustness of the classical enzyme kinetic equations in small intracellular compartments , 2009, BMC Systems Biology.

[76]  S. Schnell,et al.  Closed Form Solution for Time-dependent Enzyme Kinetics , 1997 .

[77]  Santiago Schnell,et al.  Validity of the Michaelis–Menten equation – steady‐state or reactant stationary assumption: that is the question , 2014, The FEBS journal.

[78]  R. Grimaa Intrinsic biochemical noise in crowded intracellular conditions , 2012 .

[79]  Vahid Shahrezaei,et al.  Analytical distributions for stochastic gene expression , 2008, Proceedings of the National Academy of Sciences.

[80]  Gene-Wei Li,et al.  Central dogma at the single-molecule level in living cells , 2011, Nature.