Current Topic Workshop: Enzyme Dynamics and Function

(May 19,2005 - May 21,2005 )

Organizers


Russ Hille
Molecular & Cellular Biochemistry, The Ohio State University
Ming-Daw Tsai
Department of Chemistry, The Ohio State University

Over the past several years, it has become increasingly appreciated that the dynamic properties of enzymes can play a significant role in modulating their catalytic properties. The motions involved can range from the vibration of individual chemical bonds or groups of bonds (taking place on the femtosecond timescale and involving distances of less than 1 A) to large domain motions (taking place on a timescale of milliseconds to seconds and involving distances as great as 10 A or more). With the accumulating experimental evidence attesting to the importance of these motions in catalysis, it has become important to develop appropriate mathematical models for enzyme behavior that provide a conceptual framework within which to understand the influence of this dynamic behavior. The MBI workshop on Enzyme Dynamics and Function will bring together leaders in this emerging field to present their recent work and to participate in discussion groups that will provide a forum for both mathematicians and enzymologists to consider the fundamentals relevant to the field.

Accepted Speakers

Thomas Bruice
Department of Chemistry & Biochemistry, University of California, Santa Barbara
Qiang Cui
Chemistry & Theoretical Chemistry Institute, University of Wisconsin
Lou Gross
Mathematics/Ecology & Evolutionary Biology, University of Tennessee
Sharon Hammes-Schiffer
Department of Chemistry, Pennsylvania State University
Kirk Jordan
Computational Science Center, IBM T.J. Watson Research Center
James Keener
Dept of Math, University of Utah
Dorothee Kern
Department of Biochemistry, Brandeis University
Judith Klinmann
Department of Chemistry, University of California, Berkeley
Alex Mogilner
Mathematics/Center for Genetics & Development, University of California, Davis
Claudia Neuhauser
Department of Ecology, Evolution, & Behavior, University of Minnesota
Charles Peskin
Courant Institute of Mathematical Sciences, New York University
Steven Schwartz
Department of Biophysics & Biochemistry, Albert Einstein College of Medicine
Frank Tobin
Scientific Computing & Mathematical Modeling, Glaxo Smith Kline
Arieh Warshel
Department of Chemistry, University of Southern California
Dongping Zhong
Physics, Chemistry, & Biochemistry, The Ohio State University
Thursday, May 19, 2005
Time Session
09:15 AM
10:15 AM
Sharon Hammes-Schiffer - Impact of Enzyme Motion on Activity

Theoretical studies of the impact of enzyme motion on proton, hydride, and proton-coupled electron transfer reactions in enzymes will be presented. The quantum mechanical effects of the active electrons and transferring proton are included in the calculations. The investigation of proton-coupled electron transfer in the enzyme lipoxygenase will be discussed [1]. The experimentally measured deuterium kinetic isotope effect of 80 at room temperature is found to arise from the small overlap of the reactant and product proton vibrational wavefunctions in this nonadiabatic reaction. The calculations illustrate that the proton donor-acceptor vibrational motion plays a vital role in the proton-coupled electron transfer reaction. The study of hydride transfer in the enzyme dihydrofolate reductase (DHFR) will also be discussed [2-4]. An analysis of the simulations leads to the identification and characterization of a network of coupled motions that extends throughout the enzyme and represents equilibrium conformational changes that facilitate the charge transfer process. Mutations distal to the active site are shown to significantly impact the catalytic rate by altering the conformational motions of the entire enzyme and thereby changing the probability of sampling conformations conducive to the catalyzed reaction [3,4].



  1. E. Hatcher, A. V. Soudackov, and S. Hammes-Schiffer, J. Am. Chem. Soc., 126, 5763-5775 (2004).

  2. P. K. Agarwal, S. R. Billeter, P. T. R. Rajagopalan, S. J. Benkovic, and S. Hammes-Schiffer, Proc. Nat. Acad. Sci. USA, 99, 2794-2799 (2002).

  3. J. B. Watney, P. K. Agarwal, and S. Hammes-Schiffer, J. Am. Chem. Soc., 125, 3745-3750 (2003).

  4. K. F. Wong, T. Selzer, S. J. Benkovic, and S. Hammes-Schiffer, Proc. Nat. Acad. Sci. USA, in press.

11:00 AM
12:00 PM
Judith Klinmann - Studies of H-Transfer in Enzymes: Insight into The Role of Protein Motions in Catalysis

Linking protein motions to the efficiency of enzymatic bond making/breaking processes presents a formidable experimental challenge. For a complete picture, it will be important to define both the time scales of particular motions (in relation to the chemical event itself) and the structural units within a protein whose motions correlate with catalysis. In this context, my laboratory has focused on the cleavage of C-H bonds, whose reactions almost uniformly show a large tunneling component. Using a modified Marcus picture to formulate the H-transfer event, it is possible to parse the process into three terms that include: (i) the Frank-Condon wave function overlap (for the donor and acceptor C-H bonds), (ii) the environmental reorganization and driving force that accompany the hydrogen transfer, and (iii) a gating term that describes the degree to which the donor and acceptor atom distance must change to achieve effective wave function overlap. With these three terms, it is possible to explain a wide range of behaviors in divergent protein systems. This talk will illustrate the insights we have learned using several illustrative enzyme systems.

02:00 PM
03:00 PM
Steven Schwartz - Quantum Dynamics and Chemical Reactions in Enzymes - A Really Complex Condensed Phase or a Quantum Machine

This seminar will describe ongoing research on the nature of chemical reactions in enzymes. Classical dogma teaches that enzymes lower the free energy barrier to reaction. We will investigate if this is necessarily so, if there are other ways to catalyze chemical reactions, and how protein dynamics can couple to chemical reaction. The work proceeds through quantum theories of chemical reaction in condensed phase to studies of how the symmetry of couple vibrational modes differentially affects reaction dynamics. Specific examples will include a variety of condensed phase chemical reactions (liquid and crystalline) and a variety of enzymatically catalyzed reactions including the reactions of alcohol dehydrogenase, lactate dehydrogenase, and purine nucleoside phosphorylase.

03:30 PM
05:00 PM
Daniel Herschlag, Vern Schramm - Panel Discussion

Panel Discussion

Friday, May 20, 2005
Time Session
09:00 AM
10:00 AM
Arieh Warshel - Dynamical Contributions to Enzyme Catalysis: Critical Tests of a Problematic Hypothesis

Biological systems were optimized by evolution to reach a maximum overall efficiency. However, the available structural, spectroscopical, and biochemical information do not allow one to determine what are the most important catalytic mechanisms. A significant part of this difficulty is associated with the ill-defined nature of some proposals and with the slow realization that computer simulation approaches provide perhaps the best way for defining and examining the issues in a unique way (1-3). This talk focuses on the proposal that dynamical effects play a major role in enzyme catalysis (e.g., see references in 4). The analysis of this proposal starts by defining it by unique terms that can be actually verified. It is also to point out that all reactions involve atomic motions but that in order to have a catalytic advantage to such motions they must behave in a different way in enzymes and solutions. A wide range of simulation techniques are used to examine the magnitude of the dynamical effects. It is found that these effects do not contribute to catalysis, regardless of the definition used (4-7). In particular, it is demonstrated that the "solvent contribution to catalysis involves similar dynamics in the enzyme and in solution and that the so-called nonequilibrium solvation effects are not dynamical effects but well defined free energy contributions. Finally, it is illustrated that enzymes work by using their preorganized polar environment to stabilize the transition state of the reacting substrates. This means that enzyme catalysis is due to enzyme-enzyme interaction and not to enzyme-substrate interaction.



  1. Computer Simulations of Chemical Reactions in Enzymes and Solutions, A. Warshel, John Wiley & Sons, (1991).

  2. Electrostatic Origin of the Catalytic Power of Enzymes and the Role of Preorganized Active Sites, A. Warshel, Mini Review, J. Biol. Chem., 273, 27035-27038 (1998).

  3. Computer Simulations of Enzyme Catalysis: Methods, Progress and Insights, A. Warshel, Ann. Rev. of Biophysics and Biomolecular Structure, 32, 425-443 (2003).

  4. Energetics and Dynamics of Enzymatic Reactions, Jordi Villa and Arieh Warshel, J. Phys. Chem. B 105, 7887-7907 (2001).

  5. Molecular Dynamics Simulations of Biological Reactions, A. Warshel, Acc. Chem. Res. 35, 385-395 (2002).

  6. Dynamics of Biochemical and Biophysical Reactions: Insight from Computer Simulations, A. Warshel and W.W. Parson, Quart. Rev. Biophys. 34, 563-679 (2001).

  7. Solute Solvent Dynamics and Energetics in Enzyme Catalysis: The SN2 Reaction of Dehalogenase as a General Benchmark, Mats H. M. Olsson and Arieh Warshel, J. Am. Chem. Soc. 126, 15170-79 (2004).

11:00 AM
12:00 PM
Dongping Zhong - Direct Mapping of DNA Repair by Photolyase and the Radical Mechanism of Catalytic Photocycle

Photolyase is a classic photoenzyme and uses blue-light energy to repair cyclobutane pyrimidine dimer (CPD), usually thymine dimer, in ultraviolet (UV)-induced DNA lesion in all three kingdoms. Extensive biochemical and biophysical studies proposed a radical mechanism with a unique catalytic photocycle. However, the key step in this hypothesis has never been observed and the intermediates have never been captured. Integrating femtosecond spectroscopy and molecular biology methods, we mapped out the entire functional evolution in real time by following the dynamics of various species. The repair process of DNA lesion was directly observed in less than one nanosecond. Active-site solvation in the enzyme was observed to continuously couple with the enzymatic reaction in the entire catalytic photocycle. This is probably the first and also simplest light-driven biological machinery which has been completely characterized to show how nature efficiently converts solar energy to perform important biological functions, for the system reported here, to repair UV-induced DNA damage.

02:00 PM
03:00 PM
Dorothee Kern - Going Beyond Static Structures: Enzymes in action

Going Beyond Static Structures: Enzymes in action

03:30 PM
04:30 PM
Daniel Herschlag, Vern Schramm - Panel Discussion

Panel Discussion

Saturday, May 21, 2005
Time Session
09:00 AM
10:15 AM
Qiang Cui - Simulation Analysis of Coupling between Chemistry and Conformational Dynamics in Biomolecules

The tight coupling between chemical events and conformational properties is believed to be crucial to the function of many biological systems. The precise mechanism behind the coupling, however, is often poorly understood and difficult to probe based on experiments alone. Our group develops and applies powerful molecular simulation techniques to explore such type of coupling in the context of catalysis, signaling and bioenergy transduction. Several recent examples will be discussed: the possible role of enzyme dynamics in the catalysis of cyclophilin A will be analyzed in connection with recent NMR studies; preliminary results regarding the mechanochemical coupling in the biomolecular motor, myosin-II, will also be presented.

11:00 AM
12:00 PM
Thomas Bruice - Kinetic Efficiency of Enzyme Reactions; Separation of the Importance of Ground State Conformations and Transition State Stabilization

The mechanisms of reaction of chorismate mutase enzymes, as well as chalcone isomerase, and the hyperthermophilic glycerol phosphate isomerase will be discussed. The mechanisms of these single substrate enzymes do not involve covalent intermediates and the rate constant for enzymatic reaction (kcat) and the uncatalyzed reaction in water (ko) are both first order. The efficiency of catalysis by an enzyme is, by convention, equal to (kcat/ko) at pH 7.0. Refering to eq. 1, these features simplify the quest to understand the roles in determining enzyme efficiency in the difference of water and enzyme stabilization of NAC and TS. [The present use of the term TS stabilization is an adduct to Pauling's suggestion of TS recognition by the addition of nucleopilic, general-base, general-acid catalysis, the release of solvent and release of strain on E?S E?TS etc].


Our tools have been molecular dynamic (MD) simulations, thermodynamic integrations (TI) and two-dimensional SCCDFTB/MM and TIP3P/MM. Both MD+TI and QM/MM results show that 85 - 90% of the advantage of the chorismate mutase reaction is due to the ability of the enzyme to stabilize NAC. Transition state stabilization is of very little importance as shown by essentially the same TS structures and charge distributions in gas phase, water and enzyme as well as near equal charge densities in enzyme bound NAC and TS.


Chalcone isomerase catalyzes a ring formation in the conversion of chalcone to S-flavanone. E. coli chorismate mutase and chalcone isomerase reactions share identical Km and kcat values. The chalcone isomerase reaction is driven by release of strain in the E?S complex, the release of three water molecules, and general acid catalysis by Lys109-NH3+ on E?S E?TS. These features are summed as TS stabilization.


How do thermophilic enzymes work? Long-term MD simulations were carried out with a indole glycerol synthase at various temperatures from 25? C to 110? C and mole fractions of E?S present as E?NAC determined. A change of 4,000 fold in the rate constant is accompanied by a change of 1,000 fold in NAC population. The effect of temperature is a ground state phenomenon. Examination of E?S structures with temperature reveal that the very favorable structure at 110? becomes distorted gradually on lowering the temperature. This is shown to be the result of electrostatic bonds becoming shorter as temperatures drops and distortion of the active site results. We propose this as, probably, a general explanation for the temperature dependence of reaction rates of thermophilic enzymes.

Name Affiliation
Araujo, Robyn araujor@mail.nih.gov Laboratory of Pathology, National Institutes of Health
Atzberger, Paul atzberg@rpi.edu Department of Mathematics, Rensselaer Polytechnic Institute
Babak, Petro petro@raunvis.hi.is Renewable Resources; Theoretical Ecology, University of Alberta
Baker, Ruth ruth.baker@maths.ox.ac.uk Centre for Mathematical Biology, University of Oxford
Bazaliy, Borys Institute of Applied Mathematics and Mechanics, National Academy of Sciences of Ukraine
Beggs, John jmbeggs@indiana.edu Physics - Biocomplexity Institute, Indiana University
Bell, Chuck bell.489@osu.edu Molecular & Cellular Biochemistry, The Ohio State University
Best, Janet jbest@mbi.osu.edu Mathematics, The Ohio State University
Best, Janet jbest@mbi.osu.edu Mathematics, The Ohio State University
Best, Janet jbest@mbi.osu.edu Mathematics, The Ohio State University
Borisyuk, Alla borisyuk@mbi.osu.edu Mathematical Biosciences Institute, The Ohio State University
Borisyuk, Alla borisyuk@mbi.osu.edu Mathematical Biosciences Institute, The Ohio State University
Borisyuk, Alla borisyuk@mbi.osu.edu Mathematical Biosciences Institute, The Ohio State University
Boushaba, Khalid boushaba@iastate.edu Department of Mathematics, Iowa State University
Bruice, Thomas tcbruice@chem.ucsb.edu Department of Chemistry & Biochemistry, University of California, Santa Barbara
Byrne, Maria abyrne@math.vanderbilt.edu Department of Mathematics, Vanderbilt University
Byrne, Mark Department of Mathematics, Vanderbilt University
Chowell, Gerardo gchowell@t7.lanl.gov Mathematical Modeling & Statistical Science, Los Alamos National Laboratory
Cintron-Arias, Ariel acintro@unity.ncsu.edu Center for Applied Mathematics, Cornell University
Cogan, Nickj cogan@math.tulane.edu Department of Mathematics, Tulane University
Coskun, Huseyin hcusckun@mbi.osu.edu Department of Mathematics, University of Iowa
Cowen, James cowan.2@osu.edu Department of Chemistry, The Ohio State University
Cracium, Gheorghe craciun@math.wisc.edu Dept. of Mathematics, University of Wisconsin-Madison
Cracium, Gheorghe craciun@math.wisc.edu Dept. of Mathematics, University of Wisconsin-Madison
Cracium, Gheorghe craciun@math.wisc.edu Dept. of Mathematics, University of Wisconsin-Madison
Crowder, Michael crowdemw@muohio.edu Department of Chemistry & Biochemistry, Miami University
Cuddington, Kim cuddingt@ohio.edu Biological Sciences, Ohio University
Cui, Qiang cui@chem.wisc.edu Chemistry & Theoretical Chemistry Institute, University of Wisconsin
Curley, Robert curley@dendrite.pharmacy.ohio-state.edu Department of Pharmacy, The Ohio State University
Dalbey, Ross dalbey.1@osu.edu Department of Chemistry, The Ohio State University
Dalton, James dalton.1@osu.edu Department of Pharmacy, The Ohio State University
Davuluri, Ramana ramana.davuluri@osumc.edu Department of Biomedical Informatics, The Ohio State University
De Leenheer, Patrick deleenhe@math.ufl.edu Department of Mathematics, Oregon State University
Dean, Donald dean.10@osu.edu Department of Biochemistry, The Ohio State University
Dougherty, Daniel dpdoughe@mbi.osu.edu Mathematical Biosciences Institute, The Ohio State University
Dougherty, Daniel dpdoughe@mbi.osu.edu Mathematical Biosciences Institute, The Ohio State University
Dougherty, Daniel dpdoughe@mbi.osu.edu Mathematical Biosciences Institute, The Ohio State University
Enciso, German German_Enciso@hms.harvard.edu Department of Mathematics, Rutgers University
Foster, Mark foster.281@osu.edu Department of Biochemistry, The Ohio State University
Fricks, John fricks@email.unc.edu Department of Mathematics, University of North Carolina, Chapel Hill
Funk, Max mfunk@utnet.utoledo.edu Department of Chemistry, University of Toledo
Gao, Jiali gao@chem.umn.edu Chemistry & Digital Technology Center, University of Minnesota
Garcia-Perez Gamarra, Javier jgg25@cornell.edu Department of Natural Resources, Cornell University
Goel, Pranay goelpra@helix.nih.gov NIDDK, Indian Institute of Science Education and Research
Goel, Pranay goelpra@helix.nih.gov NIDDK, Indian Institute of Science Education and Research
Goel, Pranay goelpra@helix.nih.gov NIDDK, Indian Institute of Science Education and Research
Gopalan, Venkat gopalan.5@osu.edu Department of Biochemistry, The Ohio State University
Grajdeanu, Paula pgrajdeanu@mbi.osu.edu Department of Mathematics, Duke University
Gross, Louis Mathematics/Ecology & Evolutionary Biology, University of Tennessee
Gunay, Cengiz cgunay@emory.edu Department of Biology, Emory University
Guo, Yixin yixin@math.drexel.edu Department of Psychology, The Ohio State University
Guo, Yixin yixin@math.drexel.edu Department of Psychology, The Ohio State University
Guo, Yixin yixin@math.drexel.edu Department of Mathematics, The Ohio State University
Guy, Robert guy@math.utah.edu Department of Mathematics, University of Utah
Hammes-Schiffer, Sharon shs@chem.psu.edu Department of Chemistry, Pennsylvania State University
Hassanali, Ali hassanali@osu.edu Department of Biophysics, The Ohio State University
Heitsch, Christine heitsch@math.gatech.edu Genome Center of Wisconsin, University of Wisconsin
Hemann, Craig hemann.1@osu.edu Molecular & Cellular Biochemistry, The Ohio State University
Herschlag, Daniel herschla@cmgm.stanford.edu Department of Biochemistry, Stanford University
Heyduk, Thomasz heydukt@slu.edu Biochemistry & Molecular Biology, St. Louis University
Hille, Russ hille.1@osu.edu Molecular & Cellular Biochemistry, The Ohio State University
Hu, Bei Department of Mathematics, University of Notre Dame
Ioschikhes, Ilya ioschikhes.1@osu.edu Department of Biomedical Informatics, The Ohio State University
Jordan, Kirk kjordan@us.ibm.com Computational Science Center, IBM T.J. Watson Research Center
Kamadurai, Hari kamadurai.1@osu.edu Department of Biochemistry , The Ohio State University
Keener, James keener@math.utah.edu Dept of Math, University of Utah
Kern, Dorothee dkern@brandeis.edu Department of Biochemistry, Brandeis University
Kim, Yongsam kimy@ices.utexas.edu Computational Engineering and Sciences, University of Texas
Kleinstein, Steven stevenk@cs.princeton.edu Department of Computer Science, Princeton University
Klinmann, Judith klinman@socrates.berkeley.edu Department of Chemistry, University of California, Berkeley
Kohen, Amnon amnon-kohen@uiowa.edu Department of Chemistry, University of Iowa
Law, Yu Kay law.72@osu.edu Biophysics Program, The Ohio State University
Lee, Andrew drewlee@unc.edu Medicinal Chemistry & Natural Products, University of North Carolina, Chapel Hill
Lim, Sookkyung limsk@math.uc.edu Department of Mathematical Sciences, University of Cincinnati
Lim, Sookkyung limsk@math.uc.edu Department of Mathematical Sciences, University of Cincinnati
Lim, Sookkyung limsk@math.uc.edu Department of Mathematical Sciences, University of Cincinnati
Lin, Shili lin.328@osu.edu Department of Statistics, The Ohio State University
Lin, Shili lin.328@osu.edu Department of Statistics, The Ohio State University
Lin, Shili lin.328@osu.edu Department of Statistics, The Ohio State University
Lladser, Manuel lladser@colorado.edu Department of Applied Mathematics, University of Colorado
MacMillan, Hugh macmilla@csit.fsu.edu School of Computational Science, Florida State University
Matveev, Victor matveev@njit.edu Department of Mathematical Sciences, New Jersey Institute of Technology
Matzavinos, Anastasios tasos@iastate.edu Department of Mathematics, University of Minnesota
Medovikov, Alexei amedovik@math.tulane.edu Department of Mathematics, Tulane University
Melfi, Vincent melfi@mbi.osu.edu Mathematics, Michigan State University
Melfi, Vincent melfi@mbi.osu.edu Mathematics, Michigan State University
Melfi, Vincent melfi@mbi.osu.edu Mathematics, Michigan State University
Mincheva, Maya mincheva@math.wisc.edu Department of Chemistry, University of Lethbridge
Mogilner, Alex mogilner@math.ucdavis.edu Mathematics/Center for Genetics & Development, University of California, Davis
Neuhauser, Claudia neuha001@umn.edu Department of Ecology, Evolution, & Behavior, University of Minnesota
Nevai, Andrew anevai@mbi.osu.edu Department of Mathematics, University of California, Los Angeles
Pace, Helen pace.82@osu.edu Molecular Virology, Immunology & Medical Genetics, The Ohio State University
Parthasarathy, Srinivasan srini@cse.ohio-state.edu Computer Science and Engineering, The Ohio State University
Peercy, Brad bpeercy@rice.edu Computational and Applied Mathematics, Rice University
Peskin, Charles peskin@cims.nyu.edu Courant Institute of Mathematical Sciences, New York University
Pol, Diego dpol@mbi.osu.edu Independent Researcher, Museo Paleontologico E. Feruglio
Pol, Diego dpol@mbi.osu.edu Independent Researcher, Museo Paleontologico E. Feruglio
Pol, Diego dpol@mbi.osu.edu Independent Researcher, Museo Paleontologico E. Feruglio
Politi, Antonio antonio.politi@rz.hu-berlin.de Theoretical Biophysics/Institute of Biology, Humboldt University Berlin
Polsinelli, Greg polsinelli.5@osu.edu Molecular & Cellular Biochemistry, The Ohio State University
Qiu, Weihong qui.20@osu.edu Department of Physics, The Ohio State University
Rassoul-Agha, Firas firas@math.ohio-state.edu Department of Mathematics, University of Utah
Rassoul-Agha, Firas firas@math.ohio-state.edu Department of Mathematics, University of Utah
Rassoul-Agha, Firas firas@math.ohio-state.edu Department of Mathematics, University of Utah
Rejniak, Katarzyna rejniak@mbi.osu.edu Mathematical Biosciences Institute, The Ohio State University
Rejniak, Katarzyna rejniak@mbi.osu.edu Mathematical Biosciences Institute, The Ohio State University
Rejniak, Katarzyna rejniak@mbi.osu.edu Mathematical Biosciences Institute, The Ohio State University
Sander, Evelyn sander@math.gmu.edu Department of Mathematical Sciences, George Mason University
Saxena, Chaitanya saxena.20@osu.edu Department of Physics, The Ohio State University
Schramm, Vern vern@aecom.yu.edu Department of Biochemistry, Albert Einstein College of Medicine
Schwartz, Steven sschwartz@aecom.yu.edu Department of Biophysics & Biochemistry, Albert Einstein College of Medicine
Shen, Lixin lixin.shen@vanderbilt.edu Department of Pharmacology, Vanderbilt University
Shim, Eunha alicia@mathpost.la.asu.edu Department of Mathematics & Statistics, Arizona State University
Singer, Sherwin singer@chemistry.ohio-state.edu Department of Chemistry, The Ohio State University
Stolarska, Magdalena mastolarska@stthomas.edu School of Mathematics, University of Minnesota
Stubna, Michael stubna@mbi.osu.edu Engineering Team Leader, Pulsar Informatics
Stubna, Michael stubna@mbi.osu.edu Engineering Team Leader, Pulsar Informatics
Stubna, Michael stubna@mbi.osu.edu Engineering Team Leader, Pulsar Informatics
Sun, Jun Feng (Jeff) sun@stat.ohio-state.edu Department of Statistics, The Ohio State University
Suo, Zucai suo.3@osu.edu Department of Biochemistry, The Ohio State University
Swenson, Richard swenson.1@osu.edu Department of Biochemistry, The Ohio State University
Tang, Joseph jtang@chemistry.ohio-state.edu Department of Chemistry, The Ohio State University
Tao, Louis tao@njit.edu Department of Mathematical Sciences, New Jersey Institute of Technology
Taylor, Sean taylor@chemistry.ohio-state.edu Department of Chemistry, The Ohio State University
Terman, David terman@math.ohio-state.edu Mathemathics Department, The Ohio State University
Terman, David terman@math.ohio-state.edu Mathemathics Department, The Ohio State University
Terman, David terman@math.ohio-state.edu Mathemathics Department, The Ohio State University
Tian, Jianjun Paul tianjj@mbi.osu.edu Mathematics, College of William and Mary
Tian, Jianjun Paul tianjj@mbi.osu.edu Mathematics, College of William and Mary
Tian, Jianjun Paul tianjj@mbi.osu.edu Mathematics, College of William and Mary
Timofeeva, Yulia yulia.timofeeva@nottingham.ac.uk Department of Mathematics, Heriot-Watt University
Tobin, Frank frank@tobins.org Scientific Computing & Mathematical Modeling, Glaxo Smith Kline
Tsai, Ming-Daw tsai@chemistry.ohio-state.edu Department of Chemistry, The Ohio State University
Verducci, Joseph verducci.1@osu.edu Department of Statistics, The Ohio State University
Verducci, Joseph verducci.1@osu.edu Department of Statistics, The Ohio State University
Verducci, Joseph verducci.1@osu.edu Department of Statistics, The Ohio State University
Viola, Ronald ron.viola@utoledo.edu Department of Chemistry, University of Toledo
Walsh, Scott walsh.220@osu.edu Molecular & Cellular Biochemistry, The Ohio State University
Wang, Zailong zlwang@mbi.osu.edu Integrated Information Sciences, Novartis
Wang, Zailong zlwang@mbi.osu.edu Integrated Information Sciences, Novartis
Wang, Lijuan lijuanwz@mps.ohio-state.edu Department of Physics, The Ohio State University
Wang, Peng (George) wang.892.osu.edu Department of Biochemistry, The Ohio State University
Wang, Zailong zlwang@mbi.osu.edu Integrated Information Sciences, Novartis
Warshel, Arieh warshel@usc.edu Department of Chemistry, University of Southern California
Wechselberger, Martin wm@mbi.osu.edu Mathematical Biosciences Insitute, The Ohio State University
Wechselberger, Martin wm@mbi.osu.edu Mathematical Biosciences Insitute, The Ohio State University
Wechselberger, Martin wm@mbi.osu.edu Mathematical Biosciences Insitute, The Ohio State University
Weitz, Joshua jsweitz@princeton.edu Department of Ecology & Evolutionary Biology, Princeton University
Wu, Zhijun zhijun@iastate.edu Math, Bioinformatics, & Computational Biology, Iowa State University
Wu, Zhengrong (Justin) wu.473@osu.edu Department of Biochemistry, The Ohio State University
Zhang, Yong-Tao zyt@math.uci.edu Department of Mathematics, University of California, Irvine
Zhang, Luyuan zhang.470@osu.edu Department of Physics, The Ohio State University
Zhong, Dongping dongping@mps.ohio-state.edu Physics, Chemistry, & Biochemistry, The Ohio State University
Zhou, Jin jzhou@mbi.osu.edu Department of Mathematics, Northern Michigan University
Zhou, Jin jzhou@mbi.osu.edu Department of Mathematics, Northern Michigan University
Zhou, Jin jzhou@mbi.osu.edu Department of Mathematics, Northern Michigan University
Kinetic Efficiency of Enzyme Reactions; Separation of the Importance of Ground State Conformations and Transition State Stabilization

The mechanisms of reaction of chorismate mutase enzymes, as well as chalcone isomerase, and the hyperthermophilic glycerol phosphate isomerase will be discussed. The mechanisms of these single substrate enzymes do not involve covalent intermediates and the rate constant for enzymatic reaction (kcat) and the uncatalyzed reaction in water (ko) are both first order. The efficiency of catalysis by an enzyme is, by convention, equal to (kcat/ko) at pH 7.0. Refering to eq. 1, these features simplify the quest to understand the roles in determining enzyme efficiency in the difference of water and enzyme stabilization of NAC and TS. [The present use of the term TS stabilization is an adduct to Pauling's suggestion of TS recognition by the addition of nucleopilic, general-base, general-acid catalysis, the release of solvent and release of strain on E?S E?TS etc].


Our tools have been molecular dynamic (MD) simulations, thermodynamic integrations (TI) and two-dimensional SCCDFTB/MM and TIP3P/MM. Both MD+TI and QM/MM results show that 85 - 90% of the advantage of the chorismate mutase reaction is due to the ability of the enzyme to stabilize NAC. Transition state stabilization is of very little importance as shown by essentially the same TS structures and charge distributions in gas phase, water and enzyme as well as near equal charge densities in enzyme bound NAC and TS.


Chalcone isomerase catalyzes a ring formation in the conversion of chalcone to S-flavanone. E. coli chorismate mutase and chalcone isomerase reactions share identical Km and kcat values. The chalcone isomerase reaction is driven by release of strain in the E?S complex, the release of three water molecules, and general acid catalysis by Lys109-NH3+ on E?S E?TS. These features are summed as TS stabilization.


How do thermophilic enzymes work? Long-term MD simulations were carried out with a indole glycerol synthase at various temperatures from 25? C to 110? C and mole fractions of E?S present as E?NAC determined. A change of 4,000 fold in the rate constant is accompanied by a change of 1,000 fold in NAC population. The effect of temperature is a ground state phenomenon. Examination of E?S structures with temperature reveal that the very favorable structure at 110? becomes distorted gradually on lowering the temperature. This is shown to be the result of electrostatic bonds becoming shorter as temperatures drops and distortion of the active site results. We propose this as, probably, a general explanation for the temperature dependence of reaction rates of thermophilic enzymes.

Simulation Analysis of Coupling between Chemistry and Conformational Dynamics in Biomolecules

The tight coupling between chemical events and conformational properties is believed to be crucial to the function of many biological systems. The precise mechanism behind the coupling, however, is often poorly understood and difficult to probe based on experiments alone. Our group develops and applies powerful molecular simulation techniques to explore such type of coupling in the context of catalysis, signaling and bioenergy transduction. Several recent examples will be discussed: the possible role of enzyme dynamics in the catalysis of cyclophilin A will be analyzed in connection with recent NMR studies; preliminary results regarding the mechanochemical coupling in the biomolecular motor, myosin-II, will also be presented.

Impact of Enzyme Motion on Activity

Theoretical studies of the impact of enzyme motion on proton, hydride, and proton-coupled electron transfer reactions in enzymes will be presented. The quantum mechanical effects of the active electrons and transferring proton are included in the calculations. The investigation of proton-coupled electron transfer in the enzyme lipoxygenase will be discussed [1]. The experimentally measured deuterium kinetic isotope effect of 80 at room temperature is found to arise from the small overlap of the reactant and product proton vibrational wavefunctions in this nonadiabatic reaction. The calculations illustrate that the proton donor-acceptor vibrational motion plays a vital role in the proton-coupled electron transfer reaction. The study of hydride transfer in the enzyme dihydrofolate reductase (DHFR) will also be discussed [2-4]. An analysis of the simulations leads to the identification and characterization of a network of coupled motions that extends throughout the enzyme and represents equilibrium conformational changes that facilitate the charge transfer process. Mutations distal to the active site are shown to significantly impact the catalytic rate by altering the conformational motions of the entire enzyme and thereby changing the probability of sampling conformations conducive to the catalyzed reaction [3,4].



  1. E. Hatcher, A. V. Soudackov, and S. Hammes-Schiffer, J. Am. Chem. Soc., 126, 5763-5775 (2004).

  2. P. K. Agarwal, S. R. Billeter, P. T. R. Rajagopalan, S. J. Benkovic, and S. Hammes-Schiffer, Proc. Nat. Acad. Sci. USA, 99, 2794-2799 (2002).

  3. J. B. Watney, P. K. Agarwal, and S. Hammes-Schiffer, J. Am. Chem. Soc., 125, 3745-3750 (2003).

  4. K. F. Wong, T. Selzer, S. J. Benkovic, and S. Hammes-Schiffer, Proc. Nat. Acad. Sci. USA, in press.

Panel Discussion

Panel Discussion

Panel Discussion

Panel Discussion

Going Beyond Static Structures: Enzymes in action

Going Beyond Static Structures: Enzymes in action

Studies of H-Transfer in Enzymes: Insight into The Role of Protein Motions in Catalysis

Linking protein motions to the efficiency of enzymatic bond making/breaking processes presents a formidable experimental challenge. For a complete picture, it will be important to define both the time scales of particular motions (in relation to the chemical event itself) and the structural units within a protein whose motions correlate with catalysis. In this context, my laboratory has focused on the cleavage of C-H bonds, whose reactions almost uniformly show a large tunneling component. Using a modified Marcus picture to formulate the H-transfer event, it is possible to parse the process into three terms that include: (i) the Frank-Condon wave function overlap (for the donor and acceptor C-H bonds), (ii) the environmental reorganization and driving force that accompany the hydrogen transfer, and (iii) a gating term that describes the degree to which the donor and acceptor atom distance must change to achieve effective wave function overlap. With these three terms, it is possible to explain a wide range of behaviors in divergent protein systems. This talk will illustrate the insights we have learned using several illustrative enzyme systems.

Panel Discussion

Panel Discussion

Panel Discussion

Panel Discussion

Quantum Dynamics and Chemical Reactions in Enzymes - A Really Complex Condensed Phase or a Quantum Machine

This seminar will describe ongoing research on the nature of chemical reactions in enzymes. Classical dogma teaches that enzymes lower the free energy barrier to reaction. We will investigate if this is necessarily so, if there are other ways to catalyze chemical reactions, and how protein dynamics can couple to chemical reaction. The work proceeds through quantum theories of chemical reaction in condensed phase to studies of how the symmetry of couple vibrational modes differentially affects reaction dynamics. Specific examples will include a variety of condensed phase chemical reactions (liquid and crystalline) and a variety of enzymatically catalyzed reactions including the reactions of alcohol dehydrogenase, lactate dehydrogenase, and purine nucleoside phosphorylase.

Dynamical Contributions to Enzyme Catalysis: Critical Tests of a Problematic Hypothesis

Biological systems were optimized by evolution to reach a maximum overall efficiency. However, the available structural, spectroscopical, and biochemical information do not allow one to determine what are the most important catalytic mechanisms. A significant part of this difficulty is associated with the ill-defined nature of some proposals and with the slow realization that computer simulation approaches provide perhaps the best way for defining and examining the issues in a unique way (1-3). This talk focuses on the proposal that dynamical effects play a major role in enzyme catalysis (e.g., see references in 4). The analysis of this proposal starts by defining it by unique terms that can be actually verified. It is also to point out that all reactions involve atomic motions but that in order to have a catalytic advantage to such motions they must behave in a different way in enzymes and solutions. A wide range of simulation techniques are used to examine the magnitude of the dynamical effects. It is found that these effects do not contribute to catalysis, regardless of the definition used (4-7). In particular, it is demonstrated that the "solvent contribution to catalysis involves similar dynamics in the enzyme and in solution and that the so-called nonequilibrium solvation effects are not dynamical effects but well defined free energy contributions. Finally, it is illustrated that enzymes work by using their preorganized polar environment to stabilize the transition state of the reacting substrates. This means that enzyme catalysis is due to enzyme-enzyme interaction and not to enzyme-substrate interaction.



  1. Computer Simulations of Chemical Reactions in Enzymes and Solutions, A. Warshel, John Wiley & Sons, (1991).

  2. Electrostatic Origin of the Catalytic Power of Enzymes and the Role of Preorganized Active Sites, A. Warshel, Mini Review, J. Biol. Chem., 273, 27035-27038 (1998).

  3. Computer Simulations of Enzyme Catalysis: Methods, Progress and Insights, A. Warshel, Ann. Rev. of Biophysics and Biomolecular Structure, 32, 425-443 (2003).

  4. Energetics and Dynamics of Enzymatic Reactions, Jordi Villa and Arieh Warshel, J. Phys. Chem. B 105, 7887-7907 (2001).

  5. Molecular Dynamics Simulations of Biological Reactions, A. Warshel, Acc. Chem. Res. 35, 385-395 (2002).

  6. Dynamics of Biochemical and Biophysical Reactions: Insight from Computer Simulations, A. Warshel and W.W. Parson, Quart. Rev. Biophys. 34, 563-679 (2001).

  7. Solute Solvent Dynamics and Energetics in Enzyme Catalysis: The SN2 Reaction of Dehalogenase as a General Benchmark, Mats H. M. Olsson and Arieh Warshel, J. Am. Chem. Soc. 126, 15170-79 (2004).

Direct Mapping of DNA Repair by Photolyase and the Radical Mechanism of Catalytic Photocycle

Photolyase is a classic photoenzyme and uses blue-light energy to repair cyclobutane pyrimidine dimer (CPD), usually thymine dimer, in ultraviolet (UV)-induced DNA lesion in all three kingdoms. Extensive biochemical and biophysical studies proposed a radical mechanism with a unique catalytic photocycle. However, the key step in this hypothesis has never been observed and the intermediates have never been captured. Integrating femtosecond spectroscopy and molecular biology methods, we mapped out the entire functional evolution in real time by following the dynamics of various species. The repair process of DNA lesion was directly observed in less than one nanosecond. Active-site solvation in the enzyme was observed to continuously couple with the enzymatic reaction in the entire catalytic photocycle. This is probably the first and also simplest light-driven biological machinery which has been completely characterized to show how nature efficiently converts solar energy to perform important biological functions, for the system reported here, to repair UV-induced DNA damage.