Assignment of the Vibrational Spectra of Enzyme-Bound Tryptophan

Dec 1, 2009 - ... of Life Sciences, University of Manchester, 131 Princess Street, Manchester M1 7DN, United Kingdom ... Telephone: +44 161 306 2672...
0 downloads 0 Views 2MB Size
Download PDF
1212

J. Phys. Chem. A 2010, 114, 1212–1217

Assignment of the Vibrational Spectra of Enzyme-Bound Tryptophan Tryptophyl Quinones Using a Combined QM/MM Approach Jiayun Pang,†,‡ Nigel S. Scrutton,†,§ Sam P. de Visser,†,§ and Michael J. Sutcliffe*,†,‡ Manchester Interdisciplinary Biocentre, School of Chemical Engineering and Analytical Science, and Faculty of Life Sciences, UniVersity of Manchester, 131 Princess Street, Manchester M1 7DN, United Kingdom ReceiVed: October 24, 2009

Fourier transform infrared (FTIR) spectroscopy can be used to provide a detailed time-resolved probe of reaction intermediates in enzyme-catalyzed systems. Accurate assignment of the respective chemical species being studied is key to the success of this approach. The plethora of signals from the protein environment, leading to complexity in the spectra, presents a particular challenge. Here we present a combined QM/MMbased approach that can be used to assign key resonances in the FTIR spectrum of tryptophan tryptophyl quinone (TTQ) in the TTQ-dependent quinoprotein aromatic amine dehydrogenase (AADH). We show that consideration of the cofactor alone is not sufficient to identify correctly the experimentally observed resonancessinclusion of the protein is required for this. However, to enable accurate peak assignment, a stepwise approach is needed that builds up increasing levels of complexity from a simple system. This study serves as a benchmark for future QM/MM-based studies to predict the spectroscopic changes during the interconversion of intermediates in the reductive half-reaction catalyzed by AADH, and more generally for using a combined QM/MM approach to calculate spectroscopic data of protein cofactors and cofactor-based adducts. 1. Introduction Enzyme-catalyzed reactions often involve multiple kinetic steps. However, because of the complexities associated with the isolation of each intermediate species, determination of the nature of the chemical species that comprise a multistep reaction scheme is challenging. To fully understand such a multistep enzymatic reaction in detail, it is important to obtain a detailed time-resolved analysis of reaction intermediates, which informs on the nature of the chemical species formed and their rates of interconversion. Stopped-flow Fourier-transform infrared spectroscopy (FTIR)1-3 is an approach that provides such detailed insight. Such an approach can be used to provide a spectroscopic “fingerprint”sin the form of a vibrational difference spectrum, reporting on the changes taking place between the reactant and product speciessfor each reaction step studied. Computational chemistry techniques can provide the crucial link to a detailed picture of the factors at the atomic level that give rise to these fingerprints.3 Calculation of the main intermediates in a multistep enzymecatalyzed reaction is computationally demanding, and producing an accurate prediction of the spectroscopic data for the main intermediates that incorporate the impact of enzyme environment is particularly challenging. It has been shown previously that vibrational spectra of enzyme complexes comparable to the experimental measurements can be calculatedsin particular through the use of various combined quantum mechanical and molecular mechanical (QM/MM) techniques.4-15 However, due to the large memory requirements for diagonalizing the full QM/ MM Hessian matrix of a large molecule such as an enzyme, * To whom correspondence should be addressed. Telephone: +44 161 306 2672. E-mail: [email protected]. † Manchester Interdisciplinary Biocentre. ‡ School of Chemical Engineering and Analytical Science. § Faculty of Life Sciences.

the vibrational analysis is often performed by simply diagonalizing the part of the Hessian matrix with only the QM atoms.8,10,12,13 Another approach that avoids the computing of the full Hessian matrix employs algorithms such as the modetracking protocol to calculate selectively the normal modes that are of interest.15,16 In general, these previous QM/MM calculations are able to provide reliable microscopic descriptions of the experimental vibrational spectra and, in several cases, the improved agreement from spectra calculated by QM/MM approach compared to the QM approach alone highlights the need for realistic consideration of the environment.11,14 In the current study, we applied a straightforward approach where a combined QM/MM method is used to first calculate the molecular energy of an enzyme complex, then the vibrational frequencies and normal modes are derived from Hessian matrix that contains the secondary derivatives of the QM/MM energy with respect to its mass-weighted nuclear coordinates by the ONIOM implementation in G03.17,18 We have chosen as our model enzyme system the tryptophan tryptophyl quinone (TTQ)-dependent quinoprotein aromatic amine dehydrogenase (AADH),19 for which we have identified previously a multistep mechanism for tryptamine oxidation20,21 using an integrated kinetic, high resolution X-ray crystallographic and computational study. Raman spectroscopy studies on TTQ model compounds,22 AADH, and the closely related TTQ-dependent quinoprotein methylamine dehydrogenase (MADH)23 provide an excellent benchmark for the computed vibrational spectroscopic data of the apo form of AADH (i.e., containing TTQ cofactor but no substrate; intermediate I in the proposed multistep mechanism20). We validate our combined QM/MM approach against these experimental spectroscopic dataspaving the way for future QM/MM-based studies to predict the spectroscopic changes during the interconversion of intermediates in the reductive half-reaction catalyzed by AADH, and more generally validating this combined QM/MM approach

10.1021/jp910161k  2010 American Chemical Society Published on Web 12/01/2009

Vibrational Spectra of Tryptophan Tryptophyl Quinones

J. Phys. Chem. A, Vol. 114, No. 2, 2010 1213

Figure 1. (A) Structure of the TTQ-dependent AADH, with the small subunit depicted in gray cartoon, the large subunit in blue cartoon, and the TTQ in spheres. (B) The small TTQ-containing subunit. The QM atoms are depicted in sticks with C atoms in light blue, O atoms in red, and N atoms in dark blue. The residues active in the MM region of the 1st QM/MM system are depicted in green sticks, and the residues active in the larger MM region of the 2nd QM/MM system are depicted in purple cartoon. (C) Schematic of the active site of AADH, illustrating the QM/MM definition used in the calculations; the QM region is depicted in blue with link atoms depicted by (H) and the residues in the MM region that form hydrogen bonds (depicted as dashed lines), with the QM region depicted in black.

to calculate vibrational spectra for protein cofactor-based reaction intermediates. To achieve this, we address two questions: (1) the impact of the electrostatic environment of AADH on the spectroscopic characteristics of TTQ, and (2) how much of the enzyme to include in the MM region to obtain good quality spectroscopic information from combined QM/MM calculations. This is based on the premise that only atoms within a certain radius of the active site are involved in steric and electrostatic interactions with the cofactor that may have a pronounced effect on the calculated spectrumsin the experimental spectroscopic measurements a large part of the protein is “invisible”. 2. Methods A previous experimental study has demonstrated that only the tryptophyl quinone (TQ) moiety (Figure 1C) of the TTQ cofactor contributes to the Raman spectroscopy due to the lack of conjugation between the two π systems in TTQ (the two indole rings of TTQ are offset by a dihedral angle of ∼45°).22 In the current study, the vibrational modes of the TQ were calculated (i) in gas phase with a range of electrostatic environments, and (ii) in AADH using a QM/MM formulizm. 2.1. DFT Calculations on the Isolated TQ. DFT calculations of the IR spectroscopic properties of the isolated TQ were carried out using a range of scenarios: (i) in the gas phase; (ii) using the polarizable continuum model (PCM)24,25 to mimic ether as solvent (dielectric constant 4.3, which is close to the electrostatic environment in an enzyme26), and (iii) with one water molecule hydrogen bonded to C7dO of TQ (Figure 1C) in the gas phase to mimic the hydrogen bonding pattern in the active site of AADH. The system was first optimized to the minimum energy configuration, and then the frequency calculations were carried out using the density functional method B3LYP and employing the 6-31G* basis set within Gaussian 03.27 The frequencies obtained were adjusted using the widely applied scale factor of 0.9614.28 2.2. Setup of the QM/MM Simulation System. AADH adopts a R2β2 heterotetrametic structure.20 The cofactor TTQ is formed by post-translational modification of two gene-coded tryptophan residues (Tyr 109β and Trp 160β) and is tightly associated with the backbone of the rest of the enzyme structure via amide bonds (Figure 1C). The crystal structure has been solved for the apo form of AADH from Alcaligenes faecalis

(PDB accession code 2ah1).20 To set up the molecular dynamics (MD) simulations, the atom types and the corresponding force constants to describe the TTQ cofactor were assigned by analogy with similar chemical moieties and also based on a previous study of MADH.29 The equilibrium values for the bonds, angles, and dihedral angles were obtained by optimizing the TQ moiety of TTQ at the B3LYP/6-31G* level of theory. The partial atomic charge for the substrate was taken from ref 29. The full tetrameric structure of AADH (Figure 1A) was solvated in a truncated octahedral water box using the TIP3P model with 8 Å between the edge of the box and the protein. This leads to a system of 86 513 atoms in total, including 14 282 protein atoms. The protonation state of the ionizable residues was determined at pH 7 using H++ (http://biophysics.cs.vt.edu/H++),30,31 a webbased system that computes pK values of ionizable groups in enzymes. Six Na+ ions were added to neutralize the net charge of the system. MD simulations were carried out using the AMBER ff96 forcefield32 in AMBER9.33 Following minimization and a 300 ps equilibration, the production trajectory was collected for 1 ns and analyzed for overall stability using PTRAJ implemented in AMBER9 to calculate the root-mean-square deviations (RMSDs) and root-mean-square fluctuations (RMSFs). Three snapshots from the MD trajectory were selected randomly. In the subsequent QM/MM calculations, a 23 atom QM region (Figure 1C) was used, consisting of the TQ moiety of the TTQ and the carboxylate group of the active site base Asp128β with 3 link atoms connected to the MM region. A QM region containing 56 atoms was tested, but the calculated spectrum showed that there is only a very small difference between this and the 23 atom QM region we used [see Supporting Information Figure S1]. Due to the large size of AADH (∼900 residues) and the large memory required for the frequency calculations, inclusion of the whole enzyme into the MM region in the QM/ MM calculations is beyond our computational resources. Furthermore, previous computational studies have shown that there is no network of coupled long-range motion across the structure of AADH to promote catalysis.20 Thus, only the TTQcontaining a small β subunit and a 20 Å sphere of water centered on the C6 atom of TTQ were used as the MM region in the QM/MM frequency calculations, with the rest of the enzyme truncated. While the definition of the QM region remained the same, two different “active” MM regions were appliedsproducing what are referred to subsequently as the “1st QM/MM system”

1214

J. Phys. Chem. A, Vol. 114, No. 2, 2010

and the “2nd QM/MM system”, respectively (Figure 1B). The first “active” MM region contained a relatively small number of active site residues (residues Ala82β, Val83β, Asp84β, Ser108β, Trp109β, Ile110β, Asp128β, Asn159β, Trp160β, and Cys161β)sthose forming hydrogen bonds with TTQ (Ala82β and Asp84β) and those adjacent to the backbone of TTQsfree to move and with the rest of the β subunit and water molecules frozen. This gives an “active” MM region of ∼150 atoms. The second MM region is configured to allow residues within 10 Å of the TTQ to move freely while the rest of the MM region remains frozen. This gives an “active” MM region of ∼650 atoms. The QM/MM systems were then optimized to the minimum energy configuration (confirmed by the absence of imaginary frequencies) required for the computation of Hessians to derive the frequencies using B3LYP/6-31G* level of theory for the QM region and the AMBER 96 force field for the MM region using the ONIOM-type approach17,34 implemented in Gaussian 03.27 Both IR and Raman spectra (through freq)Raman in the input file) were calculated. The frequencies obtained were adjusted using the widely applied scale factor of 0.9614.28 Although this scaling factor was originally determined by using small systems with less than 10 atoms we found that, in the current QM/MM calculations, only by applying this scaling factor could a better agreement with the experimental measurements be achieved. The program MOLDEN35 was used to visualize the calculated frequencies and to fit the IR spectra using Gaussian band shapes with a bandwidth of 5 cm-1. The calculated vibrational frequencies were assigned by visual identification of the enzyme structures that most prominently contribute to the vibration, and analysis of the frequencies focused mainly within the range of 1000-1800 cm-1, the most likely frequencies to be observed by experimental FTIR studies and also the range containing peaks primarily arising from to CdC, CdO, CdN, and C-H vibrations of the quinone ring. 3. Results and Discussion The ultimate aim of these calculations is to provide FTIR spectroscopic fingerprints of the experimentally observed reactive intermediates. However, as there is currently only Raman spectroscopic data available to benchmark our computational approach, the calculated vibrational frequencies have been used to derive both IR and Raman spectra to provide complementary information in the assignment of spectral peaks. The analysis presented is mainly focused on the calculated IR spectra, and the calculated Raman spectra are provided in the Supporting Information (Figure S2 and S3). 3.1. Calculated IR Spectra of the Isolated Tryptophyl Quinone. The gas phase calculation of the isolated TQ using B3LYP/6-31G* basis set reveals (Figure 2) various peaks between the 1000 and 1800 cm-1 region that mainly originate from the CdC, C-C, CdO, and C-N vibrations. As expected, the carbonyl stretching absorptions (the C6dO and C7dO groups) exhibit strong intensity, in particular, the symmetric stretching vibration (1679 cm-1) that couples to C5-C6, C6-C7, and C7-C8 vibrations is associated with the most intense band. The antisymmetric stretching vibrations (1709 cm-1) that couples to C5-C6 and C7-C8 vibrations is also a fairly pure mode, albeit with a greatly reduced intensity compared to the symmetric stretch (Figure 2A). When TQ is placed in ether as solvent (i.e., similar dielectric constant to the electrostatic environment in an enzyme26) simulated by PCM, the symmetric stretching vibration of CdO, which occurs at 1679 cm-1 in the gas phase, downshifts to 1651 cm-1, and the antisymmetric

Pang et al. stretching vibration downshifts from 1709 to 1684 cm-1 (Figure 2B). To study the effect of hydrogen bonding on the vibration of the two CdO groups, a water molecule was placed in the vicinity of TQ with one hydrogen forming hydrogen bond to C7dO and the oxygen hydrogen bonded to N-H of the TQ. The symmetric and antisymmetric stretching vibrations downshift by 31 and 29 cm-1, respectively, to 1648 and 1680 cm-1 (Figure 2C). The electrostatic effects arising from an electrostatic environment (the implicit solvent model) and the hydrogen bonding appear to impact similarly on the vibration of the CdO groups. Among the various peaks in the 1000 and 1800 cm-1 region, the vibrations occurring between 1000 and 1600 cm-1 form several peaks, but the intensities of these peaks are significantly lower than the characteristic CdO stretching modes (Figure 2). These peaks are formed by the mixture of vibration modes of C-C, CdC, C-N, C-H, and N-H in the ring of TQ, as shown by the displacement vectors in Figure 2, where in the range 1000-1400 cm-1 the C-H and N-H bending modes are dominant; the peaks between 1400-1600 cm-1 are mostly associated with C-C, CdC, and C-N stretching. 3.2. Calculated IR Spectra of AADH using the QM/MM Approach. In the first QM/MM system, residues Asp84β and Ala82β are included in the MM regionsthe backbone amide of Asp84β forms a hydrogen bond with the carbonyl oxygen attached to C7, and the carboxylate of Ala82β is hydrogen bonded to the indole moiety of the TQ, respectively (Figure 1C). As with the calculations using the isolated TQ, the carbonyl stretching absorption still dominates, although the symmetric vibration has split into two modes, with one at ∼1580 cm-1 exhibiting a stronger intensity and the other at ∼1640 cm-1 giving a peak nearby. The antisymmetric stretching of the carbonyl also drops by 30 cm-1 to ∼1675 cm-1 compared to gas phase calculation of the isolated TQ. In the second QM/MM system, where the size of the MM region is increased to include approximately 10 Å radius of enzyme around TTQ, as the complexity of the system increases the characteristic OdC6-C7dO stretching modes gradually merge with the “background” frequencies of the enzyme, leading to more vibration modes that are coupled to the CdO stretching. Nevertheless, the symmetric stretching vibrations are observed at ∼1570 to ∼1580 cm-1 and at ∼1640 to ∼1650 cm-1, essentially identical to those obtained from the first QM/MM system (Figure 3B and Table 1). The vibrational mode of the CdO antisymmetric stretching (∼1670 cm-1) remains fairly “pure” without extensive coupling to other vibrational modes in the protein environment. The various peaks occurring between 1000 and 1500 cm-1 remain present in the computed spectra derived from both the first QM/MM system and the second QM/MM system, but the assignments are complicated by these vibrational modes being strongly coupled to the protein surroundings. Nonetheless, the vibrations that give rise to some of the peaks could be mapped based on their assignment using the less complex system comprising the isolated TQ structure. The ∼1070 cm-1 peak formed by the C-H and N-H bending is upshifted by 20 cm-1 to ∼1090 cm-1, and the ∼1240 to 1260 cm-1 peaks are downshifted to ∼1190 cm-1; the peaks between ∼1360 and 1450 cm-1 that mostly originate from the C-C, CdC, and C-N stretching exhibit a downshift of ∼60 to 70 cm-1 to between ∼1300 and ∼1380 cm-1. These changes are most likely due to the presence of the protein surroundings, in particular the tryptophan moiety of the TTQ in the combined QM/MM calculations.

Vibrational Spectra of Tryptophan Tryptophyl Quinones

J. Phys. Chem. A, Vol. 114, No. 2, 2010 1215

Figure 2. The calculated IR spectra of the isolated TQ in (A) the gas phase, (B) in solvent ether simulated by the polarizable continuum model, and (C) in the gas phase with a water molecule hydrogen bonded to C7dO, with the displacement vectors that correspond to the main vibrational modes labeled aside.

Figure 3. The calculated IR spectra of the 1st QM/MM system [A(i-iii)] and the 2nd QM/MM system [B(i-iii)]. For each system, three configurations that were randomly selected from the previous MM dynamics were used, denoted i, ii, and iii. Peaks that originate from the CdO groups are denoted by “*”.

1216

J. Phys. Chem. A, Vol. 114, No. 2, 2010

Pang et al.

TABLE 1: Characteristic OdC6-C7dO Vibrational Modes (Symmetric and Antisymmetric Vibrations) of Tryptophyl Quinone (TQ) As an Isolated Structure in Various Setup and in the Enzyme Environment Calculated by QM/MM Approach OdC6sC7dO vibrations (cm-1) system

sym

a

TQ TQb TQc 1st QM/MM 2nd QM/MM

1590 1546 1583 ∼1580(-16) ∼1580(-10) ∼1570(-18)

antisymmetric 1679 1651 1648 1650 1625 1642 ∼1650(-9) ∼1640(-12) ∼1640(-10)

1709 1684 1703 1676 1677 1674 1672(-6) 1670(-8) 1670(-6)

a TQ in gas phase. b TQ in solvent ether simulated by the polarizable continuum model. c TQ with a water molecules hydrogen bonded to C7dO in gas phase. See Figures 2 and 3 for the calculated IR spectra and Figure 1 for the definition of 1st and 2nd QM/MM systems. Also shown (bracketed) are the downshifts of these peaks upon substitution of O6 by 18O in the 2nd QM/MM.

3.3. Comparison with Experimental Spectroscopic Data. The typical CdO stretching frequency in quinone is ∼1675 cm-1,36 which is in good agreement with the symmetric stretching frequency of 1679 cm-1 for TQ in the gas phase. However, the actual frequencies of the CdO stretching vibrations are sensitive to its electrostatic environment. The symmetric stretching is downshifted substantially by ∼30 cm-1 upon the presence of a water molecule that is hydrogen bonded to C7dO or the presence of an implicit solvent model with the electrostatic constant of ∼4. The study by Moenne-Loccoz et al. measured the Raman spectroscopy of a series of indole 6,7quinone model compounds for TTQ.22 They found that presence of the protein environment of MADH leads the characteristic vibration of indole quinone to decrease by ∼30 cm-1 compared to that in TTQ model compounds. This is consistent with our calculations that predict a similar level of downshift in AADH due to the strong hydrogen bonding between the carbonyl oxygen attached to C7 and the protein. AADH and MADH are closely related in terms of their active site architecture and conserved residues in the proximity of cofactor,20,37 and therefore produce near identical vibrational frequencies and intensity in the resonance Raman spectra for the apo form of the two enzymes.19,23 The intense peak at ∼1625 cm-1 was assigned to be the in-phase symmetric stretch of the two CdO groups of the TQ moiety based on its 18O dependence.23 This is in good agreement with our calculated value of ∼1640 cm-1 for the same vibrational mode using the combined QM/MM approach. The intense peak at ∼1570 cm-1 that exhibit a downshift of ∼20 cm-1 in the 18O-equlibrated MADH should be the equivalent of the peak at ∼1580 cm-1 originating from the symmetric stretch of the CdO groups in our calculated spectra. Furthermore, a shoulder peak of 1648 cm-1 adjacent to the characteristic 1625 cm-1 peak in MADH was also sensitive in the oxygen isotope exchange (6 cm-1 downshift upon 18O exchange). This compares well with the antisymmetric stretching of the CdO groups in our calculations. In addition, we are also able to match the other intense peaks at ∼1065, ∼1162, ∼1454, and ∼1570 cm-1 in AADH and MADH that are not 18O-dependent to our calculated vibrational frequencies at ∼1040 (Raman spectra), ∼1140 (Raman spectra), ∼1460 (Raman spectra), and ∼1560 cm-1 (IR spectra), which are associated with the C-C, CdC, C-N, C-H, and N-H

Figure 4. Comparison of the three optimized structures used in the frequency calculations of the 1st QM/MM system to illustrate the dynamics of the protein environment.

vibration, respectively, of the TQ moiety (Figure 3). Overall, the QM/MM approach we applied has successfully identified the characteristic CdO vibrational modes occurring at ∼1600 cm-1 and yields an adequate description of the complex set of aromatic ring C-C, CdC, C-N, and C-H vibrational modes occurring between 1000 and 1500 cm-1. 3.4. Accuracy of the Current QM/MM Approach. The approach used in this study highlights the importance of including the protein surroundings by a combined QM/MM approach in order to obtain a calculated spectrum that is comparable to the experimental spectroscopic data. The vibrational mode at ∼1580 cm-1 formed by the CdO group stretching and its coupling to other degrees of freedom can only be observed in the calculated spectra using the QM/MM approach and it exhibits a ∼15 cm-1 downshift when the O6 in TTQ is substituted by 18O (Table 1 and Supporting Information Figure S4). This provides additional insight into the experimental spectroscopic studies of TTQ model compounds and AADH/ MADH where only the vibration at ∼1640 cm-1 (the one that corresponds to the CdO stretching) is 18O-dependent in the model compounds while two 18O-dependent modes (∼1625 and ∼1570 cm-1) are present in the Raman spectrum of MADH from Paracoccus denitrificans. During a MD simulation, the protein environment constantly fluctuates, adopting different conformations. The observed variations of the calculated characterized quinone vibrations across different snapshots, as shown in Table 1 and Figure 3, are therefore most likely the results of each conformation providing a specific electrostatic environment. This is consistent with a previous study of vibrational frequencies of a glutamate bound to the glutamate receptor protein,11 where the carboxylate vibration exhibits standard deviations of ∼10 cm-1 within 20 protein configurations taken from a MD trajectory. The optimized structures used in our frequency calculations of the first QM/MM system, where the hydrogen bonding distances between C7dO and N-H of the protein backbone vary between 2.02 Å and 2.53 Å, are shown in Figure 4. Furthermore, comparison between the first QM/MM and the second QM/MM system indicates that, although the characteristic vibrational frequencies do not exhibit significant shifts when the size of the “active” MM regionsand thus the degree of freedom of the systemsincreases, the calculated IR spectra become more “realistic” in terms of band shape and their relative intensity. It is therefore necessary to include sufficient protein environment in this QM/MM approach. Nevertheless, it is

Vibrational Spectra of Tryptophan Tryptophyl Quinones beneficial to start with a smaller MM region where the characteristic frequencies are readily identified, then map these frequencies in the calculated spectra with a larger MM region where overlapping of the frequencies make assignment difficult. Conclusions In this study, a combined QM/MM-based approach has been applied to calculate the vibrational spectra of TTQ in the TTQdependent quinoprotein AADH. Comparison between the spectra of the tryptophyl quinone (TQ) moiety of TTQ in isolation and in the active site of AADH shows that inclusion of the protein is essential to correctly identify the experimentally observed quinone resonances. In addition, the present work applies an approach that gradually builds up the size of the protein environment in the QM/MM calculations to enable accurate peak assignmentsovercoming the higher degree of overlap of the vibrational modes as the complexity of the system is increased. The results reported here have demonstrated that the combined QM/MM approach can produce calculated vibrational spectra that are comparable to experimentally measured ones for a complex enzyme such as AADH. This warrants further QM/ MM-based studies to predict the spectroscopic changes during the interconversion of intermediates in the reductive half-reaction catalyzed by AADH, and provides a useful benchmark for using a combined QM/MM approach to calculate spectroscopic data of protein cofactors and cofactor-based adducts in general. Acknowledgment. We are grateful to Peter Gardner, Cristina Porro, Kamilla Kopec, Linus Johannissen, and Sam Hay for helpful discussions. This work was funded by the UK Biotechnology and Biological Sciences Research Council (BBSRC). NSS is a BBSRC Professorial Research Fellow. Supporting Information Available: Figures showing the calculated IR spectrum with a larger QM region (Figure S1), the calculated Raman spectra of the isolated TQ (Figure S2), QM/MM systems (Figure S3), and the calculated IR spectra of the 2nd QMM system with O6 labeled as 18O (Figure S4). This material is available free of charge via the Internet at http:// pubs.acs.org. References and Notes (1) Muthusamy, M.; Burrell, M. R.; Thorneley, R. N. F.; Bornemann, S. Biochemistry 2006, 45, 10667–10673. (2) Bandaranayake, K. M. P.; Wang, R.; Johnson, T. W.; Hastings, G. Biochemistry 2006, 45, 12733–12740. (3) Hastings, G.; Bandaranayake, K. M. P.; Carrion, E. Biophys. J. 2008, 94, 4383–4392. (4) Senn, H. M.; Thiel, S. Top. Curr. Chem. 2007, 173–290. (5) Woo, T. K.; Cavallo, L.; Ziegler, T. Theor. Chem. Acc. 1998, 100, 307–313. (6) Eichinger, M.; Tavan, P.; Hutter, J.; Parrinello, M. J. Chem. Phys. 1999, 110, 10452–10467. (7) Cui, Q.; Karplus, M. J. Chem. Phys. 2000, 112, 1133–1149. (8) Nonella, M.; Mathias, G.; Eichinger, M.; Tavan, P. J. Phys. Chem. B 2003, 107, 316–322. (9) Nonella, M.; Mathias, G.; Tavan, P. J. Phys. Chem. A 2003, 107, 8638–8647. (10) Kla¨hn, M.; Mathias, G.; Kotting, C.; Nonella, M.; Schlitter, J.; Gerwert, K.; Tavan, P. J. Phys. Chem. A 2004, 108, 6186–6194.

J. Phys. Chem. A, Vol. 114, No. 2, 2010 1217 (11) Speranskiy, K.; Kurnikova, M. J. Chem. Phys. 2004, 121, 1516– 1524. (12) Kla¨hn, M.; Schlitter, J.; Gerwert, K. Biophys. J. 2005, 88, 3829– 3844. (13) Freindorf, M.; Shao, Y.; Brown, S. T.; Kong, J.; Furlani, T. R. Chem. Phys. Lett. 2006, 419, 563–566. (14) Mroginski, M. A.; Mark, F.; Thiel, W.; Hildebrandt, P. Biophys. J. 2007, 93, 1885–1894. (15) Herrmann, C.; Neugebauer, J.; Reiher, M. J. Comput. Chem. 2008, 29, 2460–2470. (16) Reiher, M.; Neugebauer, J. J. Chem. Phys. 2003, 118, 1634–1641. (17) Dapprich, S.; Koma´romi, I.; Byun, K. S.; Morokuma, K.; Frisch, M. J. J. Mol. Struct. (Theochem) 1999, 461-462, 1–21. (18) Herrmann, C.; Reiher, E. Top. Curr. Chem. 2007, 268, 85–132. (19) Govindaraj, S.; Eisenstein, E.; Jones, L. H.; Sanders-Loehr, J.; Chistoserdov, A. Y.; Davidson, V. L.; Edwards, S. L. J. Bacteriol. 1994, 176, 2922–2929. (20) Masgrau, L.; Roujeinikova, A.; Johannissen, L. O.; Hothi, P.; Basran, J.; Ranaghan, K. E.; Mulholland, A. J.; Sutcliffe, M. J.; Scrutton, N. S.; Leys, D. Science 2006, 312, 237–241. (21) Roujeinikova, A.; Hothi, P.; Masgrau, L.; Sutcliffe, M. J.; Scrutton, N. S.; Leys, D. J. Biol. Chem. 2007, 282, 23766–23777. (22) Moenne-Loccoz, P.; Nakamura, N.; Itoh, S.; Fukuzumi, S.; Gorren, A. C. F.; Duine, J. A.; Sanders-Loehr, J. Biochemistry 1996, 35, 4713– 4720. (23) Backes, G.; Davidson, V. L.; Huitema, F.; Duine, J. A.; SandersLoehr, J. Biochemistry 1991, 30, 9201–9210. (24) Cossi, M.; Rega, N.; Scalmani, G.; Barone, V. J. Comput. Chem. 2003, 24, 669–681. (25) Cammi, R.; Mennucci, B.; Tomasi, J. J. Phys. Chem. A 2000, 104, 5631–5637. (26) Gilson, M. K.; Honig, B. H. Biopolymers 1986, 25, 2097–2119. (27) Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Montgomery, J., J. A.; Vreven, T.; Kudin, K. N.; Burant, J. C.; Millam, J. M.; Iyengar, S. S.; Tomasi, J.; Barone, V.; Mennucci, B.; Cossi, M.; Scalmani, G.; Rega, N.; Petersson, G. A.; Nakatsuji, H.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Klene, M.; Li, X.; Knox, J. E.; Hratchian, H. P.; Cross, J. B.; Bakken, V.; Adamo, C.; Jaramillo, J.; Gomperts, R.; Stratmann, R. E.; Yazyev, O.; Austin, A. J.; Cammi, R.; Pomelli, C.; Ochterski, J. W.; Ayala, P. Y.; Morokuma, K.; Voth, G. A.; Salvador, P.; Dannenberg, J. J.; Zakrzewski, V. G.; Dapprich, S.; Daniels, A. D.; Strain, M. C.; Farkas, O.; Malick, D. K.; Rabuck, A. D.; Raghavachari, K.; Foresman, J. B.; Ortiz, J. V.; Cui, Q.; Baboul, A. G.; Clifford, S.; Cioslowski, J.; Stefanov, B. B.; Liu, G.; Liashenko, A.; Piskorz, P.; Komaromi, I.; Martin, R. L.; Fox, D. J.; Keith, T.; Al-Laham, M. A.; Peng, C. Y.; Nanayakkara, A.; Challacombe, M.; Gill, P. M. W.; Johnson, B.; Chen, W.; Wong, M. W.; Gonzalez, C.; Pople, J. A. Gaussian 03; Gaussian, Inc.: Wallingford CT, 2004. (28) Scott, A. P.; Radom, L. J. Phys. Chem. 1996, 100, 16502–16513. (29) Pierdominici-Sottile, G.; Echave, J.; Palma, J. J. Phys. Chem. B 2006, 110, 11592–11599. (30) Gordon, J. C.; Myers, J. B.; Folta, T.; Shoja, V.; Heath, L. S.; Onufriev, A. Nucleic Acids Res. 2005, 33, W368–371. (31) Anandakrishnan, R.; Onufriev, A. J. Comp. Biol. 2008, 15, 165– 184. (32) Cornell, W. D.; Cieplak, P.; Bayly, C. I.; Gould, I. R.; Merz, J., K. M.; Ferguson, D. M.; Spellmeyer, D. C.; Fox, T.; Caldwell, J. W.; Kollman, P. A. J. Am. Chem. Soc. 1995, 117, 5179–5197. (33) Case, D. A.; Cheatham, T. E., 3rd; Darden, T.; Gohlke, H.; Luo, R.; Merz, J., K. M.; Onufriev, A.; Simmerling, C.; Wang, B.; Woods, R. J. J. Comput. Chem. 2005, 26, 1668–1688. (34) Vreven, T.; Morokuma, K.; David, C. S., Chapter 3 Hybrid Methods: ONIOM(QM:MM) and QM/MM. In Annual Reports in Computational Chemistry, Elsevier: 2006; Vol 2, pp 35-51. (35) Schaftenaar, G.; Noordik, J. H. J. Comput.-Aided Mol. Design 2000, 14, 123–134. (36) Nonaka, Y.; Tsuboi, M.; Nakamoto, K. J. Raman Spectrosc. 1990, 21, 133–141. (37) Sukumar, N.; Chen, Z.; Ferrari, D.; Merli, A.; Rossi, G. L.; Bellamy, H. D.; Chistoserdov, A.; Davidson, V. L.; Mathews, F. S. Biochemistry 2006, 45, 13500–13510.

JP910161K