Molecular modelling Understanding and prediction of enzyme

selectivity

Linda Fransson

Licentiate thesis

Royal Institute of Technology

School of Biotechnology

Stockholm 2009

© Linda Fransson 2009 Royal Institute of Technology School of Biotechnology AlbaNova University Center SE-106 91 Stockholm Sweden ISBN 978-91-7415-325-5 TRITA-BIO-Report 2009:11 ISSN 1654-2312 Printed in Stockholm, April 2009 Universitetsservice US-AB Drottning Kristinas väg 53 B 100 44 Stockholm

ABSTRACT

Molecular modelling strategies for evaluation of enzyme selectivity were investigated with a focus on principles of how molecular interactions could be evaluated to provide information about selectivity. Although molecular modelling provides tools for evaluation of geometrical and energy features of molecular systems, no general strategies for evaluation of enzyme selectivity exist. Geometrical analyses can be based upon inspection and reasoning about molecular interactions, which provide an easily accessible way to gain information, but suffer from the risk of bias put in by the modeller. They can also be based on geometrical features of molecular interactions such as bond lengths and hydrogen-bond for-mation. Energy analyses are appealing for their modeller independence and for the possibility to predict not only stereopreference, but also its magnitude.

In this thesis, four examples of enantio- or regioselective serine hydrolase-catalysed reaction systems are presented together with deve-loped modelling protocols for explanation, prediction or enhancement of selectivity. Geometrical as well as energy-based methodology were used, and provided an understanding of the structural basis of enzyme selectivity. In total, the protocols were successful in making qualitative ex-planations and predictions of stereoselectivity, although quantitative deter-minations were not achieved.

SAMMANFATTNING

Strategier för utvärdering av molekylmodelleringsdata för enzym-selektivitet utforskades med fokus på hur molekylära interaktioner kan ge information om selektivitet. Även om molekylmodellering erbjuder färdiga verktyg för utvärdering av geometrier och energier i molekylära system, så saknas generella strategier för analys av enzymselektivitet. Geometriska analyser kan baseras på visuellt intryck av molekylära interaktioner, vilket ger är lättillgängligt men riskerar att bli beroende av modellerarens omöme. De kan också baseras på mätbara geometriska egenskaper såsom bindningslängder och vätebindningsmönster. Energibaserade analyser är tilltalande på grund av sin kvantitativa karaktär, och för den möjlighet de erbjuder att förutsäga inte enbart stereopreferens, utan även dess magnitud.

I avhandlingen beskrivs fyra enantio-eller regioselektiva serinhydrolas-katalyserade system tillsammans med utvecklade modelleringsprotokoll som förutsade, förklarade eller förbättrade selektiviteten. Såväl geomet-riska som energibaserade analyser användes och genererade en strukturell förståelse för enzymselektiviteten. Sammantaget åstadkom protokollen kvalitativa förklaringar och förutsägelser av stereoselektivitet, medans kvantitativa bestämningar av densamma ej kunde åstadkommas.

LIST OF ARTICLES

This thesis is based on the following articles which are referred to by their roman numerals.

I Berglund P., Vallikivi I., Fransson L., Dannacher H., Holmquist M., Martinelle M., Björkling F., Parve O. and Hult K.: Switched enantiopreference of Humicola lipase for 2-phenoxyalkanoic acid ester homologs can be rationalized by different substrate binding modes. Tetrahedron: Asymmetry 1999, 10: 4191-4202.

II Heinze B., Kourist R., Fransson L., Hult K. and Bornscheuer U. T.:

Highly enantioselective kinetic resolution of two tertiary alcohols using mutants of an esterase from Bacillus subtilis. Protein Eng Des Sel 2007, 20: 125-131.

III Raza S., Fransson L. and Hult K.:

Enantioselectivity in Candida antarctica lipase B: A molecular dynamics study. Protein Sci 2001, 10: 329-338.

IV Vallikivi I., Fransson L., Hult K., Jarving I., Pehk T., Samel N.,

Tougu V., Villo L. and Parve O.: The modelling and kinetic investigation of the lipase-catalysed acetylation of stereoisomeric prostaglandins. J Mol Catal B-Enzym 2005, 35: 62-69.

TABLE OF CONTENTS

Theory 1

Examples 9

1. Explaining a switch in enantioselectivity 9

2. Suggesting mutations to improve enantioselectivity 12

3. Developing a tool for prediction of enantioselectivity 16

4. Developing a tool for prediction of reactivity 20

Concluding remarks 25

Acknowledgements 27

Theory

1

THEORY

The ability of enzymes to discriminate between different substrates or re-gions of a substrate is a prerequisite for cell metabolism and life. This in-herent selectivity can also be used in vitro for enzyme-catalyzed synthesis of chiral and other products hard to reach by other means.1,2 In this thesis, molecular modelling will be used as a tool to understand the structural basis of enzyme selectivity aiming at rationalization, prediction and improvement of selectivity for transacylation reactions catalysed by serine hydrolases. Molecular modelling Molecular modelling of enzyme selectivity is based on the assumption that selectivity is determined by enzyme-substrate interactions and their change in energy when going from the reaction ground state to its transition state.3,4 Energies of molecules or molecular systems can be calculated with a high accuracy using quantum mechanics. This approach is limited to sys-tems with considerable fewer atoms than present in enzymes. For larger systems a less detailed model must be adopted where energy calculations are based on classical mechanics, considering atoms instead of electrons as the smallest participating entities. The classical mechanics model leads to a loss of all information related to processes involving electrons, such as bond formation and breaking. Thus, reactions cannot be studied, only states. It also leads to energies being calculated on an arbitrary and varying scale, where direct comparisons of energies only are meaningful between sys-tems having identical atom connectivity. In this text, all discussed calcula-tions are performed using the classical mechanical model, and the term molecular modelling will refer to calculations in that paradigm. General

1 Schmid, A., Dordick, J. S., Hauer, B., Kiener, A., Wubbolts, M. and Witholt, B.: Industrial biocatalysis today and tomorrow. Nature 2001, 409: 258-268.

2 Pollard, D. J. and Woodley, J. M.: Biocatalysis for pharmaceutical intermediates: the future is now. Trends Biotechnol 2007, 25: 66-73.

3 Fischer, E.: Influence of configuration on the action of enzymes. Berichte der Deutschen Chemischen Gesellschaft 1894, 27: 2985-2983.

4 Pauling, L.: Nature of forces between large molecules of biological interest. Nature 1948, 161: 707-709.

Theory

2

introductions to molecular modelling in general and in biochemistry are given by Leach and McCammon, respectively.5,6

Enzyme selectivity Enzyme selectivity towards a compound A over a compound B is defined as the ratio of their specificity constants

where is the turnover number and is the Michaelis constant for substrate i. Experimentally, selectivity can be determined from the ratio of reaction rates for the two competing substrates using the relationship7

where vi is the reaction rate and the concentration of substrate i. Generally, a rate constant is related to the energy difference between the reaction ground state and its transition state according to the Eyring equation8

where is the Bolzmann constant, T the absolute temperature, the Planck constant, R the general gas constant and κ is a pre-exponential factor reflecting the likelihood for reaction taking place after the formation of a transition state. Using the Eyring equation, enzyme selectivity between compounds A and B can be related to the differences in reaction energies9

5 Leach, A. R.: Molecular modelling: principles and applications. Harlow: Prentice Hall; 2001. 6 McCammon, J. A. and Harvey, S.: Dynamics of proteins and nucleic acids. Cambridge: Cambridge

University Press; 1987. 7 Segel, I. H.: Enzyme kinetics: behavior and analysis of rapid equilibrium and steady-state enzyme

systems. New York: Wiley; 1975. 8 Eyring, H.: The activated complex and the absolute rate of chemical reactions. Chem Rev 1935, 17: 65-

77. 9 The expression is valid under the assumption that the two substrates have identical pre-exponential

factors.

(2)

(3)

(1)

(4)

Theory

3

where is the energy difference between reactant ground state and transition state for substrate i, Figure 1. A calculation of selectivity from energies thus requires energy comparisons between four different energy states in total. These energy comparisons cannot be done using molecular modelling since the atom connectivity generally is different between a ground state and the corresponding transition state.

The problem is circumvented in the special case where A and B are enantiomers. In such enantioselective systems, the selectivity is referred to as the enantiomeric ratio, E, defined as the ratio of the specificity constants for the two enantiomers. Enantiomers have the same ground-state energy, and due to symmetry also the same connectivity in transition state.10 Equation (4) then simplifies to

where is the difference in transition state energy between the R and S enantiomers.

10 Enantiomers participating in an enzymatic reaction have the same ground state energy if the reaction is carried out in an achiral solvent and with an enzyme concentration low enough to have a neg-lectable influence on the properties of the solution.

(5)

Reaction coordinate

Free energy G

Ground state Transition state ProductsB

A

Figure 1. Energy profile diagrams for two enzymatic reactions acting on substrate A and B, respectively. The selectivity of substrate A over substrate B is defined as

.

Theory

4

Molecular modelling of reactions catalysed by serine hydrolases

The tetrahedral intermediate enzyme-substrate complex as a transition state model Molecular modelling of enzyme selectivity requires a structural model of the enzyme-substrate transition state, and since delocalised electrons can-not be treated, a transition state mimic must be defined. Serine hydrolases follow ping-pong bi-bi kinetics consisting of two symmetrical half reac-tions; one acylation and one deacylation reaction, with the formation of a stable, covalently modified, acyl enzyme intermediate in between, Figure 2. In general, both these transition states contributes to the overall selectivity.11 If one of the two reactions is clearly rate limiting, the selectivity can be ap-proximated to be a result of this step only. In the work presented in this thesis, the acylation step has been assumed to be rate limiting.12 The serine hydrolase catalytic machinery consists of a catalytic triad, Ser, His and Asp or Glu, and an oxy-anion hole stabilising the oxy-anion formed in transi-tion state Figure 3a. The reaction pathway together with an energy profile diagram for an acylation reaction is shown in Figure 4.

From the reaction pathway in Figure 4 the tetrahedral intermediate can be identified as a good transition state mimic candidate, and quantum mechanical studies have established it being close to transition state both in geometry and energy.13,14 The tetrahedral intermediate, Figure 3b, is defined by its atom connectivity and by a hydrogen-bond pattern consist-ing of one or two hydrogen bonds between the catalytic aspartate and the catalytic histidine, two hydrogen bonds from the histidine HNε to the serine hydroxyl oxygen and to the substrate alcohol oxygen respectively, and at least two hydrogen bonds between the oxy-anion and the oxy-anion

11 Overbeeke, P. L. A., Ottosson, J., Hult, K., Jongejan, J. A. and Duine, J. A.: The temperature dependence of enzymatic kinetic resolutions reveals the relative contribution of enthalpy and entropy to enzymatic enantioselectivity. Biocatal Biotransform 1999, 17: 61-79.

12 Martinelle, M., Holmquist, M. and Hult, K.: On the interfacial activation of Candida antarctica lipase A and B as compared with Humicola lanuginosa lipase. Biochim Biophys Acta 1995, 1258: 272 - 276.

13 Daggett, V., Schroeder, S. and Kollman, P.: Catalytic pathway of serine proteases: classical and quantum mechanical calculations. J Am Chem Soc 1991, 113: 8926-8935.

14 Hu, C. H., Brinck, T. and Hult, K.: Ab initio and density functional theory studies of the catalytic mechanism for ester hydrolysis in serine hydrolases. Int J Quantum Chem 1998, 69: 89-103.

Theory

5

hole. A structure having a correct atom connectivity but lacking the correct hydrogen-bond pattern is considered unreactive, and thus not a valid model for a transition state structure.15,16 Generation of tetrahedral intermediate structures Experimentally determined selectivities are measured as average values over a large number of catalytic events. To mimic this averaging procedure in molecular modelling of selectivities, an ensemble of tetrahedral inter-mediate enzyme-substrate complexes is needed. In general, the substrate part of the tetrahedral intermediate enzyme-substrate complex can adopt several conformations or binding modes. All reactive binding modes, as judged from their hydrogen-bond pattern defined in Figure 3b, must be identified and included in the modelling, and a larger ensemble can be achieved by subjecting the individual tetrahedral intermediate structures to molecular

15 Haeffner, F., Norin, T. and Hult, K.: Molecular modeling of the enantioselectivity in lipase-catalyzed transesterification reactions. Biophys J 1998, 74: 1251-1262.

16 Orrenius, C., Haeffner, F., Rotticci, D., Öhrner, N., Norin, T. and Hult, K.: Chiral recognition of alcohol enantiomers in acyl transfer reactions catalysed by Candida antarctica lipase B. Biocatalysis Biotransformation 1998, 16: 1-15.

Figure 2. A transacylation reaction catalysed by a serine hydrolase. In the acylation reaction an ester is converted to an alcohol upon the formation of a covalent acyl enzyme intermediate. In the symmetrical deacylation reaction an alcohol acts as a substrate resulting in an ester and a regenerated free enzyme.

Theory

6

dynamics simulations. A recording of structures from the simulations gene-rates the ensemble of enzyme-substrate complexes. For a close imitation of the experimental situation a sampling of the complete conformational space would be needed, but in practice only a subset will be achieved. This is partly due to the practical limits for computer simulation time. The fastest enzymes known have turnover numbers in the order of 106 s-1 cor-responding to one catalytic event every μs.17 Molecular dynamics simula-tions performed in this study only simulate motion over a ps to a ns time frame.

17 Barthelmes, J., Ebeling, C., Chang, A., Schomburg, I. and Schomburg, D.: BRENDA, AMENDA and FRENDA: the enzyme information system in 2007. Nucleic Acids Res 2007, 35: D511-D514.

Figure 3. The active site in a serine hydrolase. a) An empty active site. The active site contains a catalytic triad; Ser, His and Asp, and an oxy-anion hole. b) An active site with a bound tetrahedral intermediate. The tetrahedral intermediate is defined by its atom connectivity together with its hydrogen-bonds. A tetrahedral intermediate should con-tain one or two hydrogen bonds between the catalytic aspartate and the catalytic histidine, two hydrogen bonds from the histidine HNε to the serine hydroxyl oxygen and to the substrate alcohol oxygen respectively, and at least two hydrogen bonds between the oxy-anion and the oxy-anion hole.

Theory

7

Another factor contributing to the lack of a complete coverage of the conformational space is caused by the molecular modelling starting from one specific instance of an enzyme structure. Experimental measurements on single-enzyme kinetics have shown a broad variation in reactivity between individual enzyme molecules.18 Tetrahedral intermediate enzyme-substrate complexes derived from one instance of an enzyme structure will, in the best case, only give a statistical correct behaviour for that particular individual enzyme molecule. Despite the lack of completeness, studies of a subset of a conformational space is still to be preferred over studies of one

18 Lu, H. P., Xun, L. and Xie, X. S.: Single-Molecule Enzymatic Dynamics. Science 1998, 282: 1877-1882.

Transition state 1’ Transition state 1’’

Tetrahedral intermediate

Acyl enzyme and leaving alcohol

Ground state Free enzyme and ester

Reaction coordinate

Free energy G

Transition state

N

N

His

SerO

H

OR

O R1

H

N

N

His

SerO

H

OR

O R1

H

δ+

δ−

N

N

His

Ser O

H

OR

O R1

HN

N

His

SerO

H

OR

O R1

H

δ+

δ−

N

N

His

Ser O

OR

HO R1

H

Figure 4. Reaction pathway and energy profile diagram for the acylation of a trans-acylation reaction. An enzyme and an ester form a transition state, which subsequently falls apart into an acyl enzyme and a free alcohol. The transition state in itself con-sists of two transition states separated by a covalent tetrahedral intermediate. The catalytic aspartate and the oxyanion hole are omitted for clarity.

Theory

8

single instance of a structure. The intrinsic dynamics of the enzyme itself is of importance for its catalytic activity.19,20,21

The hands-on modelling work requires a number of decisions to be made. An appropriate force field has to be chosen. The enzyme structure has to be protonated according to a certain pH. Often a method that ignores the fact that the surrounding of an amino acid significantly can in-fluence its pH profile is used.22,23,24 The enzyme structure needs to be relaxed in the force field. A model of the substrate is built into the active site, and parametrizised in a reasonable way. Enzyme selectivity is experi-mentally known to be affected by the reaction solvent.25,26 Such effects can also be included by adding an explicit solvent to the modelled system.27,28 In the work presented in this thesis, no explicit solvent was used, relying on vacuum to be a reasonable good model for an apolar solvent.29,30 Further, the structure is subjected to a heating phase, and the length of the following molecular dynamics simulations will affect the quality of statistics gained from it. In total, a careful and well thought through strategy is a necessity for achieving reliable outcomes of simulation studies.

19 Karplus, M. and Kuriyan, J.: Molecular dynamics and protein function. Proc Natl Acad Sci USA 2005, 102: 6679-6685.

20 Boehr, D. D., McElheny, D., Dyson, H. J. and Wright, P. E.: The dynamic energy landscape of dihydrofolate reductase catalysis. Science 2006, 313: 1638-1642.

21 Eisenmesser, E. Z., Millet, O., Labeikovsky, W., Korzhnev, D. M., Wolf-Watz, M., Bosco, D. A., Skalicky, J. J., Kay, L. E. and Kern, D.: Intrinsic dynamics of an enzyme underlies catalysis. Nature 2005, 438: 117-121.

22 Davoodi, J., Wakarchuk, W. W., Campbell, R. L., Carey, P. R. and Surewicz, W. K.: Abnormally high pKa of an active-site glutamic acid residue in Bacillus Circulans Xylanase. Eur J Biochem 1995, 232: 839-843.

23 Kuramitsu, S., Ikeda, K., Hamaguchi, K., Fujio, H., Amano, T., Miwa, S. and Nishina, T.: Ionization constants of Glu35 and Asp52 in hen, turkey, and human lysozymes. J Biochem 1974, 76: 671-683.

24 Koeppe, R. E. and Stroud, R. M.: Mechanism of hydrolysis by serine proteases: direct determination of the pKa's of aspartyl-102 and aspartyl-194 in bovine trypsin using difference infrared spectroscopy. Biochemistry 1976, 15: 3450-3458.

25 Klibanov, A. M.: Enzymatic catalysis in anhydrous organic solvents. Trends Biochem Sci 1989, 14: 141-144. 26 Ke, T., Tidor, B. and Klibanov, A. M.: Molecular-modeling calculations of enzymatic enantioselec-

tivity taking hydration into account. Biotechnol Bioeng 1998, 57: 741-745. 27 Brooks III, C. L. and Karplus, M.: Solvent effects on protein motion and protein effects on solvent

motion : Dynamics of the active site region of lysozyme J Mol Biol 1989, 208: 159-181. 28 Trodler, P. and Pleiss, J.: Modeling structure and flexibility of Candida antarctica lipase B in organic

solvents. BMC Struct Biol 2008, 8: 9. 29 Norin, M., Haeffner, F., Hult, K. and Edholm, O.: Molecular-dynamics simulations of an enzyme

surrounded by vacuum, water, or a hydrophobic solvent. Biophys J 1994, 67: 548-559. 30 McCabe, R. W., Rodger, A. and Taylor, A.: A study of the secondary structure of Candida antarctica lipase

B using synchrotron radiation circular dichroism measurements. Enzyme Microb Technol 2005, 36: 70 - 74.

Example 1.Rationalizing enantioselectivity

9

EXAMPLES

In molecular modelling molecular structures can be evaluated both geo-metrically and based on energy calculations, but there are no general pro-cedures available for evaluation of enzyme selectivity based on structural information. Instead system- and problem specific protocols must be de-veloped. Below four examples of such protocols will be presented. They were developed to solve different problems regarding understanding and prediction of enzyme selectivity, and will be presented with a focus on the type of analyses being used. Example 1: Explaining a switch in enantio- selectivity (Paper I)

Geometrical analysis of enzyme-substrate complexes is an easily accessible type of evaluation of enzyme-substrate interactions. The visual appearance of a complex in combination with chemical knowledge opens for intuitive explanations of experimental data, here exemplified by a study aiming at understanding a switch in enantioselectivity seen within a homologous series of substrates.

Hypothesis. Substrate reactivity is dependent on the utilization of active site binding energy, and enantioselectivity can be caused by a differ-ence in how the enantiomers exploit it for transition state stabilization. 31

Experimental. The enantioselectivities of seven homologous 2-phen-oxyalkanoic acid ethyl esters were measured for a hydrolysis reaction cata-lyzed by Thermomyces lanunginosa lipase. A shift in enantiopreference, from R to S, was seen with an increasing acyl chain length, displaying enantioselec-tivities in the span from E = 13 with R-preference, to E = 56 with S-preference, Figure 5.

31 Fersht, A.: Structure and mechanism in protein science: A guide to enzyme catalysis and protein folding. New York: W. H. Freeman; 1998.

Example 1.Rationalizing enantioselectivity

10

Modelling. Three esters, 1, 5 and 6, were selected for a modelling study. Tetrahedral intermediate enzyme-substrate complexes were con-structed, and two different binding modes were found for each enantio-mer. The lipase has a shallow active site and its binding pocket was found to harbour either of the two substituents on the acyl moiety (a phenoxy group and an alkyl chain), while the other group was directed towards the active site entrance. In the up mode the phenoxy group was directed to-wards the enzyme entrance and the acyl chain was binding in the active site binding pocket. In the down mode, the situation was reversed, Figure 6. For each substrate, both enantiomers were modelled in the two modes, resulting in a total of twelve constructed tetrahedral intermediate enzyme-substrate complexes. The complexes were subjected to molecular dyna-mics simulations, and the last recorded structure from each simulation was energy minimized and saved for further analysis.

Figure 6. Two binding modes for 2-phen-oxyalcanoic acid ethyl esters in Thermomyces lanunginosa. In the up mode, left, the phenyl group was directed towards the active site entrance, and the alkyl chain was binding in the active site crevice. In the down mode, right, the positions were reversed.

Figure 5. Experimentally determined enan-tioselectivities for seven homologous 2-phen-oxyalkanoic acid ethyl esters measured in a hydrolysis reaction catalysed by Thermomyces lanunginosa. The enantiopreference shifted from R to S, and the enantioselectivity increased with an increasing acyl chain length, until a maximal value was reached for ester 5. For esters with longer acyl chains, the enantio-selectivity decreased.

Example 1.Rationalizing enantioselectivity

11

Analysis. Evaluation of binding modes. The quality of the binding modes was evaluated by their hydrogen-bond pattern of the tetrahedral-inter-mediate enzyme-substrate complexes (Figure 3b, page 6). For all three sub-strates, the down mode of the R enantiomer and the up mode of the S enan-tiomer were found to be catalytically competent. The two remainning combinations did not fulfil the hydrogen bond requirement.

Rationalization of enantiopreference. A prominent catalytic feature of enzymes is their benefit of having a preorganized active site.32 Substrates are kept properly oriented by their binding to the active site, and so the preferred enantiomer would be the one utilizing the substrate binding energy opti-mally to lower the transition state energy.33,34 For substrate 1, having a short alkyl chain, the strongest binding would be provided by having the phen-oxy group in the acyl binding pocket, predicting an R preference. For sub-strates 5 and 6, having longer acyl chains, it would instead be more advan-tageous positioning the alkyl chain in the binding pocket, suggesting an S preference. These two suggestions were in accordance with the experimen-tal findings and provided an explanation for the switch seen in enantiopre-ference for the homologous series of substrates. Related examples from the literature The idea of using substrate binding as an indication of reactivity has also been used by others. Otto et al.35 use to the same argumentation to rationa-lize substrate specificity in the synthesis of arylaliphatic glycolipids using Candida antarctica lipase B as a catalyst, and Gayscoyne et al.36 use it to explain

32 Warshel, A.: Electrostatic origin of the catalytic power of enzymes and the role of preorganized active sites. J Biol Chem 1998, 273: 27035-27038.

33 Menger, F. M.: Analysis of ground-state and transition-state effects in enzyme catalysis. Biochemistry 1992, 31: 5368-5373.

34 Menger, F. M.: An alternative view of enzyme catalysis. 17th International Conference on Physical Organic Chemistry (ICPOC-17) 2004: 1873-1886.

35 Otto, R. T., Scheib, H., Bornscheuer, U. T., Pleiss, J., Syldatk, C. and Schmid, R. D.: Substrate specificity of lipase B from Candida antarctica in the synthesis of arylaliphatic glycolipids. Journal of Molecular Catalysis B-Enzymatic 2000, 8: 201-211.

36 Gascoyne, D. G., Finkbeiner, H. L., Chan, K. P., Gordon, J. L., Stewart, K. R. and Kazlauskas, R. J.: Molecular basis for enantioselectivity of lipase from Chromobacterium viscosum toward the diesters of 2,3-dihydro-3-(4 '-hydroxyphenyl)-1,1,3-trimethyl-1H-inden-5-ol. J Org Chem 2001, 66: 3041-3048.

Example 2. Rational design

12

the enantiopreference of chiral bisphenols seen in a hydrolytic reaction catalysed by a lipase from Chromobacterium viscosum. Otto et al.35 and Gayscoyne et. al.36 use a modelling procedure similar to the one used in Example 1, where tetrahedral intermediate enzyme-substrate complexes are constructed and evaluated for reactivity. The major methodological differ-ence lies in the procedure used for structure relaxation. Otto et al. and Gayscoyne et al. use energy minimizations to obtain structure relaxation, whereas in Example 1, the energy minimizations were preceded by mole-cular dynamics simulations. The latter approach allowed the constructed enzyme-substrate complexes to relax beyond what can be reached with energy minimizations only. Example 2. Suggesting mutations to improve enantioselectivity (Paper II)

Geometrical analyses can also be used in rational design, aiming at im-proving enzyme function. Here molecular modelling was used to suggest a point mutation to increase enantioselectivity.

Hypothesis. Reaction rate is correlated to the quality of tetrahedral intermediate enzyme-substrate complexes, as determined by the fraction of catalytically competent structures obtained during a molecular model-ling simulation. Specifically, enantioselectivity will increase if the quality of the tetrahedral intermediate complex increases for the fast-reacting enan-tiomer.

Experimental. The enantioselectivity of

3-phenylbut-1-yn-3-yl acetate, Figure 7, was measured through a hydrolysis reaction cata-lysed by Bacillus subtilis lipase 2, yielding an en-antioselectivity of 5.

Modelling. Tetrahedral intermediate enzyme-substrate complexes were constructed, and for each enantiomer three binding modes were found.

Figure 7. the racemic substrate 3-phenylbut-1-yn-3-yl acetate

O

O

Example 2. Rational design

13

Molecular dynamics simulations were undertaken for all six complexes and snapshot structures collected from the second half of the simulations were used for further analysis.

Analysis. The structures collected from the molecular simulations were analysed regarding the quality of their tetrahedral intermediate enzyme-substrate complexes as defined from their hydrogen bond content (Figure 3b, page 6). For the slow-reacting enantiomer, the hydrogen bond pattern was intact in 20% of the structures, whereas no reactive structures were found for the fast reacting enantiomer. Examination of the un-reactive structures of the fast reacting enantiomer showed that the catalytic histidine, instead of stabilising the tetrahedral intermediate complex, found an alternative hydrogen bond acceptor in Glu188, Figure 8a. From this observation a charge-conserving point mutation Glu188Asp was sug-gested, where the shorter side chain of aspartate would prohibit the catalytic histidine from forming the catalytically harmful hydrogen bond, Figure 8b. A new molecular dynamics simulation was ran to investigate the behaviour of the fast reacting enantiomer in the mutated enzyme, and 85% of the tetrahedral intermediate enzyme-structures were found to be catalytically competent. The mutant Glu188Asp was experimentally evalu-ated and the enantioselectivity was found to increase from 5 for the wild-type enzyme to 46 in the Glu188Ala mutant.

Example 2 illustrates the difficulties in describing and correctly model-ling transition state analogue structures. Here, the known favoured enan-tiomer displayed far fewer reactive structures than the unfavoured one. Still the model – although obviously contradicting the defined criteria for reactive structures – contained meaningful information regarding change in reactivity.

Related examples from the literature Molecular modelling as a tool for rational design has also been used by others. Magnusson et al. have studied selectivity of nonan-5-ol over propan-2-ol measured in a transacylation reaction catalysed by Candida antarctica

Example 2. Rational design

14

Figure 8. The fast-reacting enantiomer of 3-phenylbut-1-yn-3-yl acetate in Bacillus subtilis lipase 2 wild-type and Glu188Asp mutant. a) In the wild-type enzyme, no reactive tetrahed-ral intermediate enzyme-substrate structures were seen in the molecular dynamics simu-lations. The catalytic histidine did not participate in the stabilisation of the tetrahedral intermediate complex. Instead it formed a catalytically harmful hydrogen bond (circled) with the carboxyl group of Glu188. b) In the Glu188Asp mutant, the catalytic histidine was prohibited from forming the harmful hydrogen bond due to an increased distance to the carboxylic acid in position 188 (circled). The histidine reverted to contributing to the hydro-gen bond pattern necessary for catalysis, and as a result, reactive tetrahedral intermediate enzyme-substrate complexes were seen in 85% of the analysed structures. Images based on crystal structure 1QE3 from the protein data bank, www.rcsb.org .37,38

lipase B.39 Nonan-5-ol is a bulky substrate for the deep and narrow active site of Candida antarctica lipase B, and the wild-type enzyme strongly fa-vours the smaller propan-2-ol. Based on structural knowledge of the active site geometry and of substrate binding modes for secondary alcohols, a point mutation Trp104Ala was suggested, where a space-limiting tryp-

37 Spiller, B., Gershenson, A., Arnold, F. H. and Stevens, R. C.: A structural view of evolutionary divergence. Proc Natl Acad Sci USA 1999, 96: 12305-12310.

38 Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., Shindyalov, I. N. and Bourne, P. E.: The Protein Data Bank. Nucleic Acids Res 2000, 28: 235-242.

39 Magnusson, A. O., Rotticci-Mulder, J. C., Santagostino, A. and Hult, K.: Creating space for large secondary alcohols by rational redesign of Candida antarctica lipase B. ChemBioChem 2005, 6: 1051-1056.

Example 2. Rational design

15

tophane in the active site was replaced with an alanine. The mutant showed a 450 000 fold change in selectivity for nonan-5-ol over propan-2-ol.

The study by Magnusson et al. and Example 2 share the point mutation site, which in both cases were the amino acid prior in sequence to the catalytic serine. Both also suggested point mutations increasing the volume of the active site. In Example 2, the active site was increased in size to dimi-nish interference on the catalytic machinery from the surroundings, whe-reas in the Magnusson et al. study, the size of the active site was enlarged to increase its accessibility for larger substrates.

Another example of rational design is a study by Wilks et al. aiming at increasing the malate dehyd-rogenase activity of a lactate dehydro-genase, Figure 9.40 They assumed that the negative charge from the second carboxyl group present in malate but not in lactate was the major obstacle for malate dehydrogenase functiona-lity of the lactate dehydrogenase. By introducing an additional positive charge in the lactate dehydrogenase active site by a point mutation Gln102Arg the specificity of the enzy-me changed over 800 000 times to fa-vouring malate dehydrogenase activity over lactase dehydrogenase activity.

40 Wilks, H. M., Hart, K. W., Feeney, R., Dunn, C. R., Muirhead, H., Chia, W. N., Barstow, D. A., Atkinson, T., Clarke, A. R. and Holbrook, J. J.: A specific, highly active malate dehydrogenase by redesign of a lactate dehydrogenase framework. Science 1988, 242: 1541-1544.

Figure 9. The reactions catalysed by lactate dehydrogenase and malate dehydrogenase.

COO-

H

HH

HO

COO-

CO O -

O

H H

CO O -

CH 3

O COO -

CH3

HHO

COO-

lactate dehydrogenase

malatedehydrogenas e

lactate

malate oxaloa ce ta t e

pyru v a te

NAD+ NADH+H+

NAD+ NADH+H+

Example 3. Predicting enantioselectivity

16

Example 3. Developing a tool for prediction of enantioselectivity (Paper III)

Energy analyses for evaluation of enzyme-structure interactions opens a possibility to get quantitative, and not only qualitative, results. It provides modeller-independent evaluation criteria, where visual input from the ana-lysed structures no longer is crucial.

Hypothesis. Enantioselectivity can be predicted as a difference in ave-rage potential energy between the tetrahedral intermediate enzyme-sub-strate complexes of the two enantiomers.

Experimental. The enantio-selectivites of four secondary alco-hols were measured through a transacylation reaction catalysed by Candida antarctica lipase B. Four esters with the alcohol moiety 3-methyl-2-butanol, but with vary-ing acyl chains were studied, Figure 10. The enantioselectivity varied between 360 and 760, cor-responding to transition state ener-gies differences between enantio-mers †

SRG −ΔΔ in the range of -14.7 to -16.5 kJ/mol.

Modelling. In Candida antarctica lipase B two different binding modes

are available for tetrahedral intermediates of secondary alcohol esters, but for each enantiomer, only one of the two modes are productive.15,16 Based on this knowledge, reactive tetrahedral intermediate enzyme-substrate complexes were constructed for both enantiomers of the four studied sub-strates. Molecular dynamics simulations were ran on the enzyme-substrate complexes, and all structures resulting from the second half of the simu-lations were used for analysis.

Figure 10. Experimentally determined transition state energy differences between enantiomers, for four esters of secondary alcohols.

Experimental

-15.2 -14.7 -15.2 -16.5 -20

-10

0

kJ/molO O

OO

OO

O

Example 3. Predicting enantioselectivity

17

Analysis. Attempt 1. As a first attempt, enantioselectivity was calculated as the difference in average potential energy between the tetrahedral inter-mediate enzyme-substrate structures for the two enantiomers, Figure 11. The approach failed to predict enantioselectivity. The wrong enantioprefe-rence was predicted in one of the four cases, and the calculated transition state energy differences between enantiomers were up to 300 kJ/mol.

Attempt 2. To limit the large energy fluctuations, mostly originating from motions far from the active site, only a smaller subset of atoms from each structure was included in the calculations. A strategy used earlier is to define an energy-based subset, in which only the amino acid residues having the strongest interactions with the substrate are included.15 The strategy lead to significantly lower transition state energy differences, but the wrong enantiopreference was predicted in two cases, Figure 12.

Figure 12. Energy differences between tetra-hedral intermediate enzyme-substrate com-plexes for the two enantiomers, , calculated using an energy-based subset of the structure, where only amino acids having strong interactions with the substrate were included. The energies were in a reasonable range, but the enantiopreference was only correctly predicted in two cases.

-300

0

300

Figure 11. Experimental and predicted tran-sition state energy differences between enan-tiomers, .The correct enantioprefe-rence was only predicted in three out of four cases, and the energies differences were very large – an energy difference of 300 kJ/mol between the enantiomers corresponds to an enantioselectivity of 1052.

Experimental Predicted

kJ/mol O

OOO

OO O

O

Experimental Predicted

kJ/mol O O

OO

OO

OO

-60-30030

Example 3. Predicting enantioselectivity

18

Attempt 3. An even smaller subset was defined only considering atoms either participating in the catalytic action or hydrogen bonding to those atoms. This function-based subset is defined in Figure 13, and the results are shown in Figure 14. The enantiopreference of all four substrates were cor-rectly predicted, and the calculated energy differences between the enantiomers were in the same magnitude as the experimental values.

Model evaluation. The new tool for prediction of enantioselectivity was evaluated by studying two additional substrates, Figure 15. Overall, the function-based subset was able to predict the right enantiopreference for all six substrates, although the method was not able to make quantitative predictions of the enantioselectivity.

In this example energy analyses were used in an effort to find a proto-col for determination of enantioselectivity, a task with a poor signal-to-noise ratio: The transition state energy difference between enantiomers is in the order of 10 kJ/mol even for very enantioselective systems, whereas an enzyme structure has a force field energy in the order of 50 000 kJ/mol.

kJ/mol

Figure 13. The functional-based subset. The tetrahedral intermediate and the catalytic triad in Candida antarctica lipase B are depicted. Atoms included in the functional-based subset for calculation of enantioselecti-vity are shown in bold-face.

Experimental Predicted

Figure 14. Calculated transition state energy differences between enantiomers, , using a functional-based subset including only the atoms most central to catalysis as defined in Figure 3b, page 6. The method predicted the right enantiopreference in all four cases, and the energies were in a reasonable range.

-30-150

O O O

OO

OO

O

Example 3. Predicting enantioselectivity

19

The situation is further complicated by large energy fluctuations seen during molecular dynamics simulations, which makes calculations of aver-age energies sensitive to the number of structures included. One way to improve this would be by using longer molecular dynamics simulation times allowing more of the conformational space available for the system to be covered. In this study though, another approach was used to reduce the noise. In the developed method, only a few catalytically important atoms were included in the calculations of energy differences between the enantiomers, relying on the assumption that most of the noise came from smaller conformational changes of the outer part of the enzyme structure, and does not affect the catalytic ability. This reduced the noise problem significantly, but did not solve it. The attempt of creating a protocol for prediction of enantioselectivity described in Example 3 resulted in a energy-based method for prediction of enantiopreference.

Figure 15. The function-based subset was evaluated with two additional substrates, re-sulting in a correct prediction of the enan-tiopreference in both cases. The results for the four substrates used for method deve-lopment are included for reference.

Example4.Predicting regioselectivity

20

Example 4: Developing a tool to predict reactivity (Paper IV)

In more complex systems, it can be necessary to evaluate not only the pro-perties of a certain structure or complex, but also the likelihood of its for-mation. In those cases a combination of both geometrical and energy-based analyses can be used for establishing modelling protocols.

Hypothesis. An enzyme-catalyzed reaction will occur if a) the substrate can form a reactive tetrahedral intermediate enzyme-sub-

strate complex, b) the reactive tetrahedral intermediate complex is likely to form; that is

not being too high in energy, and c) the tetrahedral intermediate complex does not cause unfolding of the

enzyme. Specifically, for a regioselective product to be formed the corresponding group on the substrate must be accessible to the enzyme by fulfilling the three above mentioned conditions.

Experimental. Five stereo-isomers of prostaglandin F, Figure 16, were acetylated in a re-action catalysed by Candida antarc-tica lipase B for determination of regioselectivity towards the hydro-xyl groups. Seven out of the 15 possible ester products were de-tected, Table 1. Modelling. Tetrahedral-interme-diate enzyme-substrate complexes were constructed for theoretically possible acylation products. For each tetrahedral intermediate complex only one conformation fulfilling the requirements of a reactive H-bond pattern was found. Molecular dynamics

Figure 16. Structure of stereoisomers of prostaglandin F. Three stereocen-ters were studied and are shown in bold, and the three possible acetylation sites are highlighted.

HO

HO

COOH

OH

19

11 15

8

Example4.Predicting regioselectivity

21

Table 1. Product formation of five stereoisomers of prostaglandin F in a transacylation reaction catalysed by Candida antarctica lipase B. The three possible acetylation sites are located at carbon 9, 11 and 15, and the stereocenters investigated are located at carbon 8, 9 and 15, see Figure 16. For all stereoisomers, position 11 was acetylated, whereas the acetylation product on position 15 never was detected. Position 9 was acetylated for two stereoisomers. In total, seven different acetylation products were formed.

Stereocenter Hydroxyl group acetylated 8 9 15 9 11 15 R R S - Detected - S R S - Detected - R S S Detected Detected - R R R - Detected R S R Detected Detected -

simulations were undertaken for each complex, and the structures sampled during the second half of the simulation were used for further geometrical and energy-based analysis.

Analysis. Construction of an evaluation model. Structures collected from the

molecular dynamics simulations were analysed regarding the reactivity as defined from their hydrogen-bond pattern, (Figure 3b, page 6), for their functional-based energy (Figure 13, page 18), and for the distortion given as a RMS deviation of the enzyme structures compared to the initial struc-ture. Histograms of the results over the 13 studied tetrahedral intermediate enzyme-substrate complexes are shown in Figure 17-19. In all analyses outliers were seen, all of which belonging to enzyme-substrate complexes of acetylation products not experimentally detected. From the analyses, cut-off values were defined which had to be fulfilled for judging a hydroxyl group as accessible to the enzyme, and thereby able to form the cor-responding product. The cut-offs where demanding that at least 50% of the tetrahedral intermediate structures were fulfilling the hydrogen bond requirement for reactivity; that the functional-based energy would not be

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Example 4. Predicting regioselectivity

23

higher than -95 kJ/mol; and that average RMS deviation of the enzyme structure during the course of the simulation would not to exceed 3 Å. With these criteria all 13 struc-tures in the test set were correctly predicted regarding reactivity.

Evaluation of the prediction model. Two prostaglandin variants, pro-staglandin E1 and prostaglandin E2, each having two hydroxyl groups, Figure 20, were evaluated according to the prediction rules for regioselective product forma-tion. In all four cases, the predic-tions were in accordance with ex-perimental results.

Related examples from the literature In Example 4, regioselectivity of prostaglandins was predicted from tetra-hedral-intermediate enzyme-substrate complexes using quantitative criteria. A weakness with the method is that the finding of possible acetylation sites required an investigation of all theoretically available acetylated prostaglandin products. Seifert et al. have cir-cumvented this problem in a study of regioselectivity of cyto-crome P450 monooxygenase 2C9 towards warfarin, Figure 21.41

41 Seifert, A., Tatzel, S., Schmid, R. D. and Pleiss, J.: Multiple molecular dynamics simulations of human P450 monooxygenase CYP2C9: The molecular basis of substrate binding and regioselectivity toward warfarin. Proteins-Structure Function and Bioinformatics 2006, 64: 147-155.

O

OH

O

O

4

6

7

10

Figure 21. Molecular structure of warfarin. The possible hydroxylation sites are located at position 4, 6, and 7.

O

HO

C O O H

OH

1 11 15

8

HOOH

1 11 15

8

OC OO H

Figure 20. Prostaglandin E1 and E2 with their two hydroxyl groups high-lighted. For both variants, acetylated products were only formed at carbon number 11.

Example 4. Predicting regioselectivity

24

Instead of having a transition state mimic as a starting point, they investi-gated the behaviour of a free substrate positioned at the entrance of the active site. The structure was subjected to a molecular dynamics simulation in which it was seen that the substrate, when inside the active site, preferred an orientation favouring the most regioselective (position 7) product, Figure 21. They also calculated the ratio of simulation time spent by the substrate in the the most and second-most (position 6) favoured position, and got results close to experimental values of the distribution between the two corresponding products.

Quantitative criteria have also been used for prediction of enantioselec-tivity. Schulz et al.42 studied enantioselectivity of 27 secondary alcohols in hydrolysis- and esterification reactions catalysed by a lipase from Pseudo-monas cepacia. They found a strong correlation between enantioselectivity and the distance between the catalytic histidine and the tetrahedral inter-mediate alcohol oxygen for the slow-reacting enantiomer. A methodo-logical difference between Schulz et al and Example 4 is that Schulz et. al. analyze one single structure, which is an average of structures sampled from the molecular dynamics simulation. This procedure leads to a potential loss of information due to the risk of getting data from different conformations averaged out. In Example 4 all structures were studied individually, allowing statistical analyses of studied features.

42 Schulz, T., Pleiss, J. and Schmid, R. D.: Stereoselectivity of Pseudomonas cepacia lipase toward secondary alcohols: A quantitative model. Protein Sci 2000, 9: 1053-1062.

Concluding remarks

25

CONCLUDING REMARKS

The focus of this presentation has been overall analytical strategies for evaluation and prediction of enzyme selectivity from molecular modelling of enzyme-substrate interactions. Examples of studies presented here are taken from the biocatalytical research field. In parallel, much work on modelling of enzyme selectivity and molecular recognition in general is done in the field of drug design, where in silico screening is a standard tool.43

In this text, transition states have been the starting point for reasoning about reactivity. Another strategy considers the free substrate and its interactions with an enzyme as the meaningful entity. In the Near Attack Model, the frequency of formation of catalytically relevant enzyme-sub-strate complexes is studied.44 In this viewpoint, the reactivity is governed not by the transition state complex but by the rate of incidence of its creation. Instead of looking at the transition state – which obviously all (reactive) substrates passes by on their way to product formation – the likelihood of formation of such a transition state is studied. In this model, all transition states are assumed to be equal in reactivity – the whole difference lies in the frequency of which it is formed.41,45,46

Molecular modelling of selectivity is not only of importance as a tool for prediction of experimental results, but – at least, or maybe more – important for the basic knowledge it can contribute to on enzymes and enzyme action. It yields understanding of enzyme catalysis, catalyst design, as well as of the fundamentals of reaction kinetics. It can be used to gene-rate plausible explanations and predictions of selectivity as well as sugges-tions of point mutations for a changed selectivity. The methodology will

43 McInnes, C.: Virtual screening strategies in drug discovery. Curr Opin Chem Biol 2007, 11: 494-502. 44 Bruice, T. C. and Lightstone, F. C.: Ground state and transition state contributions to the rates of

intramolecular and enzymatic reactions. Acc Chem Res 1999, 32: 127-136. 45 Hur, S. and Bruice, T. C.: Just a near attack conformer for catalysis (chorismate to prephenate

rearrangements in water, antibody, enzymes, and their mutants). J Am Chem Soc 2003, 125: 10540-10542.

46 Guieysse, D., Cortes, J., Puech-Guenot, S., Barbe, S., Lafaquiere, V., Monsan, P., Simeon, T., Andre, I. and Remaud-Simeon, M.: A structure-controlled investigation of lipase enantioselectivity by a path-planning approach. ChemBioChem 2008, 9: 1308-1317.

Concluding remarks

26

continue to progress over time, hopefully reaching a state where our under-standing allows us to create predefined and general protocols for model-ling of enzyme selectivity.

27

ACKNOWLEDGEMENTS

Jag vill rikta ett varmt tack till er som gjort denna avhandling möjlig.

Först och främst vill jag tacka professor Karl Hult för handledning, stöd och entusiasm. Tack för att jag fått arbeta med dig, och för möjligheten att lägga fram denna avhandling.

Ett stort tack till alla nuvarande och tidigare medlemmar i biokatalys-gruppen för en trevlig tid.

I also want to thank all my co-authors for nice collaborations and interest-ing projects.

Och slutligen, tack till min familj och mina vänner. Ni är ovärderliga.