Cold Adaptation of Triosephosphate Isomerase - Biochemistry (ACS

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Cold-Adaptation of Triosephosphate Isomerase Johan Åqvist Biochemistry, Just Accepted Manuscript • DOI: 10.1021/acs.biochem.7b00523 • Publication Date (Web): 21 Jul 2017 Downloaded from http://pubs.acs.org on July 27, 2017

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Biochemistry

Cold-Adaptation of Triosephosphate Isomerase

Johan Åqvist*

Department of Cell and Molecular Biology, Uppsala University, Biomedical Center, Box 596, SE-751 24 Uppsala, Sweden

*Corresponding author: E-mail: [email protected] Phone: +46 18 471 4109

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Abstract The main problem for enzymes from psychrophilic species, that need to work near the freezing point of liquid water, is the exponential decay of reaction rates as the temperature is lowered. Cold-adapted enzymes have solved this problem by shifting the activation enthalpyentropy balance for the catalyzed reaction compared to their mesophilic orthologs. To understand the structural basis of this universal feature it is necessary to examine pairs of such orthologous enzymes, with known 3D structures, at the microscopic level. Here, we use molecular dynamics free energy calculations in combination with the empirical valence bond method to evaluate the temperature dependence of the activation free energy for differently adapted triosephosphate isomerases. The results show that the enzyme from the psychrophilic bacterium Vibrio marinus indeed displays the characteristic shift in enthalpy-entropy balance, compared to the yeast ortholog. The origin of this effect is found to be located to a few surface exposed protein loops that show differential mobilities in the two enzymes. Key mutations render these loops more mobile in the cold-adapted triosephosphate isomerase, which explains both the reduced activation enthalpy contribution from the protein surface and the lower thermostability.

Keywords: Triosephosphate isomerase, cold adaptation, temperature dependence, molecular dynamics, free energy calculations.


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Introduction A remarkable feature of cold-adapted enzymes from psychrophilic species is that their catalyzed reactions invariably show a lower enthalpy and a more negative entropy of activation than their mesophilic orthologs. This phenomenon has been extensively investigated both experimentally1-3 and by computational methods4-7 and it appears to be the major adaptive feature of these enzymes. While psychrophilic and mesophilic enzymes often have similar reaction rates around room temperature, the advantage with a redistribution of the enthalpic and entropic contributions to the activation free energy barrier is that the exponential damping of the rate at lower temperatures can be surpressed.4-7 That is, since the temperature dependence of the rate constant is ∝ ∆

‡ /

∆

‡ /

, moving part of the free

energy from enthalpy to negative entropy will make the rate decay more linear as the temperature is lowered. The rate optimum of cold-adapted enzymes is also generally shifted towards lower T, typically from around 50 °C in mesophiles to ~30 °C in psychrophiles.3 It should be noted, however, that this optimum (Topt) is usually far the actual working temperature (Twork) of psychrophilic enzymes which is often near the freezing point of liquid water as, e.g., in cold water fishes and bacteria. The optimum naturally arises as a balance between the increased rate and the decreased protein stability at higher values of T. The fact that Topt does not coincide with the working temperature is nothing strange since it is the rate at Twork that is subject to evolutionary pressure and not that at Topt. Furthermore, as KM is usually similar or even somewhat higher in cold-adapted enzymes,1-3 the main evolutionary pressure appears to be on kcat. Hence, by redistributing the activation free energy penalty from enthalpy to entropy the low temperature tail of the kcat vs. T curve can be lifted and this would thus be a logical evolutionary response to falling temperature.7

Since chemical reaction rates, spontaneous as well as enzymatic, increase at higher temperatures according to the Arrhenius equation, the main problem for enzymes working in this high-T regime is rather to avoid melting since they can take advantage of the natural rate increase at higher values of T. Hence, their temperature optimum most likely reflects a tradeoff between high rate and protein stability. With psychrophilic enzymes the situation appears to be rather different since the evolutionary pressure on stability is much less at lower


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temperatures. On the other hand, they need to cope with the intrinsic reduction in rate and the redistribution of activation enthalpy and entropy appears to be the recipe for this. The fact that Topt of cold-adapted enzymes is shifted towards lower temperatures may, at least partly, just be a consequence of genetic drift, if the evolutionary pressure is mainly on rate and not on stability. That is, reduced protein stability will emerge if excess stability neither improves nor impairs function, due the larger proportion of sequence space occupied by low stability sequences.8

Triosephosphate isomerase (TIM) is one of the most extensively studied enzymes and has been dubbed the “perfect enzyme” due to the efficient and uniform optimization of all its reaction steps.9 TIM catalyzes a sequence of proton transfer steps which converts dihydroxyacetone phosphate (DHAP) to glyceraldehyde-3-phosphate (GAP), or vice versa.9-12 These internal rate constants are very high, on the order of 103-104 s-1, and kcat for the DHAP→GAP reaction is considered to be dominated by the initial proton abstraction from DHAP by the base Glu165, yielding a transient enediolate intermediate.9,11,13-17 Both the yeast and E. coli enzymes have been extensively studied and their kinetic characteristics are very similar.9-12 In 1998, the first psychrophilic TIM from the bacterium Vibrio marinus (vTIM) was characterized and its 3D structure solved by X-ray crystallography.18 This cold-adapted TIM has 44% and 66% sequence identity with the yeast and E. coli enzymes, respectively. vTIM shows some clear characteristics of a cold-adapted enzyme, including a high catalytic rate at 10 °C with a kcat of about 7000 s-1 in the GAP→DHAP direction,18 which is similar to the yeast and E. coli enzymes at room temperature. It has also has its melting temperature shifted down to 40 °C compared to 55-60 °C for the yeast and E. coli enzymes.18,19 However, despite these characteristic signatures of a cold-adapted enzyme no actual analysis of the temperature dependence of its catalytic rates has been reported. Even more surprisingly, it appears that no Arrhenius plots for the temperature dependence of the proton transfer steps catalyzed by mesophilic TIMs have been published, despite extensive analyses of thermal unfolding and the dynamics of loop opening and closing.18-20

Psychrophilic enzymes can be considered more highly optimized than their mesophilic or thermophilic counterparts, in the sense that they have achieved a larger reduction of their 4 ACS Paragon Plus Environment

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activation enthalpy and usually a higher catalytic rate at moderate temperatures. In view of the efficient optimization of mesophilic TIMs it is therefore very interesting to ask whether evolution has been able to further improve the thermodynamic characteristics of vTIM. As no experimental data appears to be available regarding the catalytic activities at different temperatures, we decided to investigate this by computer simulations. As described in detail elsewhere,5-7,21-23 the empirical valence bond (EVB) method24,25 in combination with molecular dynamics (MD) free energy calculations provides a very efficient way of obtaining thermodynamic activation parameters of enzyme reactions, by directly computing the temperature dependence of reaction free energy profiles. Here, we apply this approach to the initial DHAP→enediolate reaction step in both the mesophilic enzyme from yeast and the psychrophilic enzyme from V. marinus. The yeast enzyme is the biochemically best characterized mesophilic ortholog that also has a high resolution structure determined. These calculations clearly show that there is a significant difference in activation enthalpy and entropy between yeast TIM and vTIM and that the latter is considerably faster at the examined temperatures ranging from 290 to 310 K. Furthermore, the structural origin of the shift in ∆ ‡ and ∆ ‡ is located in a few surface exposed loops of the protein that show distinct changes in flexibility, similar to what has been reported for differently adapted trypsins.5 These findings point to the generality of protein surface mutations as the main evolutionary route towards low temperature optimization.

Methods MD simulations. Molecular dynamics simulations were carried out with the program Q26 utilizing the OPLS-AA force field27 and initial atomic coordinates from the X-ray structures of yeast TIM28 and vTIM18 with PDB codes 1NEY and 1AW1, respectively. The conformation of the DHAP substrate was taken from 1NEY and parameters in both its keto and enolate form from the OPLS-AA force field (Maestro version 9.2, Schrödinger, LLC, New York, NY, 2011). Spherical simulation systems with a diameter of 70 Å were used and centered on the CD2 atom of Leu230 (located at the center of one monomer). This allows one entire monomer and about 22% of the second monomer, including the entire dimer interface, to move freely in the MD simulations. Atoms from the second monomer lying outside of the simulation sphere were restrained to their crystallographic positions and excluded from non5 ACS Paragon Plus Environment

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bonded interactions. The systems were solvated with TIP3P water molecules,27 while retaining crystallographically observed solvent molecules, yielding a total of about 4600 waters. At the sphere boundary, water molecules were treated with the SCAAS model and subjected to radial and polarization restraints to mimic bulk water.26,29 A direct non-bonded cutoff of 10 Å was used, except for atoms in the EVB region whose parameters change during the reaction. For these atoms a 25 Å direct cutoff was applied. Beyond these cutoffs longrange electrostatic interactions were treated with the local reaction field multipole expansion method.30 The MD simulations were carried out with a 1 fs time step and the systems were equilibrated for 1 ns, including a heating phase, prior to data collection. The uncatalyzed reference reaction corresponding to proton transfer between DHAP and a glutamate residue in water was simulated analogously, but with a 20 Å radius simulation sphere.

EVB model. The chemical reaction was described by a two-state EVB model corresponding to the reactants, DHAP and negatively charged glutamate residue, and the products, the enolate form of DHAP and protonated glutamic acid.16 The uncatalyzed reference reaction in water was parametrized as discussed in the main text (see also Ref. 16), using the pKa difference between DHAP and glutamate to determine the reaction free energy (∆ ), while the activation free energy (∆ ‡ ) was obtained from an accurate linear free energy relationship for the deprotonation of acetone by different bases.31 The pKa of DHAP was calculated by density functional theory (DFT) using the known pKa of acetone (19.2) as reference.32 The keto and enolate forms of DHAP and acetone were thus optimized and the shift in equilibrium constant for deprotonation was calculated with a continuum solvent representation. The geometry optimizations were done with M06-2X functional33 together with the SMD solvent model34 using a small basis set (6-31(d,p)). Electronic energies were calculated with the same functional and a larger basis set (6-311+g(2d,2p)), including the SMD solvent effect. Enthalpy and free energy corrections based on the rigid rotor harmonic oscillator (RRHO) approximation were obtained by performing frequency calculations. All DFT calculations were performed with Gaussian09 quantum chemistry package.35 Subsequent MD/EVB simulations of the reference reaction in the 20 Å radius water sphere yielded the two required EVB parameters, ∆α = 152.3 kcal/mol and H12 = 75.4 kcal/mol, which reproduce exactly the target values of ∆ ‡ = 23.6 and ∆ = 18.8 kcal/mol (see main text). The first of these 6 ACS Paragon Plus Environment

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parameters represent the absolute energy difference between the two EVB states, with the reactants at infinite separation, and the second parameter is the coupling matrix element of the EVB Hamiltonian.24,25

Reaction free energy profiles. The free energy perturbation (FEP) umbrella sampling approach was used to drive the simulations systems along the reaction coordinate and calculate free energy profiles for proton transfer, as described elsewhere.36 Each reaction free -windows where the MD simulations were energy profile was obtained from 21 discrete FEP λ = (0.5,0.5) window in order to avoid possible bias to either the always initiated from the λ initial or final state. Calculations of the uncatalyzed water free energy profiles encompassed ten replicate simulations with different initial conditions and a total of 10 ns simulation time. This yielded an s.e.m. for the activation barrier of 0.15 kcal/mol, which is small because the water simulations converge very fast. The enzyme simulations for TIM and vTIM each involved 60 replicas at every temperature (290, 300 and 310 K) and a total of 180 ns simulation time for each enzyme. This protocol also yielded small convergence errors with an s.e.m. of 0.12, 0.09 and 0.11 kcal/mol for TIM at 290, 300 and 310 K, respectively. The corresponding s.e.m. values for vTIM were 0.14, 0.13 and 0.17 kcal/mol. Arrhenius plots of ∆ ‡ / versus 1/ were obtained from these data, where the thermodynamic activation parameters were extracted by linear regression. It can be noted that earlier studies,4,5 employing either the Amber or OPLS force fields, in both cases reproduced the characteristic trend in activation enthalpy and entropy between psychrophilic and mesophilic enzymes, indicating that these quantities are really determined by the protein structure. Additional 10 ns plain MD simulations in the reactant state were carried out for both enzymes to obtain atomic position fluctuations. Using only the first 5 ns data for calculation of average residue-based backbone root mean square fluctuations for vTIM yielded an R2-value of 0.96 with respect to the 10 ns data, demonstrating that the backbone fluctuations are well converged.

Results and Discussion Calibration and validation of the EVB reaction surface. The uncatalyzed deprotonation reaction of DHAP in water was used to calibrate our EVB model for the enzyme reactions. In 7 ACS Paragon Plus Environment

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order to obtain the most reliable estimate for the energetics of this reference reaction, we utilized the linear free energy relationship reported by Guthrie for the deprotonation of acetone by various bases.31 This first requires that the pKa of DHAP is determined. Starting from the experimental pKa value of 19.2 for acetone,32 a correction of 0.8 pKa units (

.!"

#$%6) for the six equivalent protons of acetone yields a pKa of 20.0 for the dissociation

of a specific proton of acetone. DFT optimizations of the keto and enolate forms of acetone and DHAP yield a 2.8 kcal/mol lower reaction free energy for the latter compound, including the solvation correction, which corresponds to a 2.1 unit downshift of the pKa of DHAP compared to acetone. Hence the predicted pKa of DHAP becomes 17.9, which is very close to the values estimated by Richard and us earlier.10,16 It can be noted that this downshift primarily arises due to the intramolecular hydrogen bond between the DHAP hydroxyl group and the negative enolate oxygen. With glutamate as the proton accepting base (pKa = 4.1) the experimental free energy relationship between log() and ∆)*+ for acetone deprotonation31 predicts a rate constant of = 7.35 10 - M−1 s−1 (Figure 1a) for deprotonation of DHAP at a 1 M standard state. Correcting this for the entropic cost (2.4 kcal/mol) of bringing the acceptor oxygen in contact with the substrate carbon atom at a 55 M standard state yields our final activation free energy estimate of 23.6 kcal/mol at 300 K, while the reaction free energy is 1.36.)*+ (DHAP) − )*+ (Glu)6 = 18.8 kcal/mol. We note that this refined energetics of the uncatalyzed reference reaction is still very close to our original estimate over 20 years ago, based on semiempirical quantum calculations.16 The resulting free energy profiles from 10 independent simulations of the reference reaction are shown in Figure 1b. These yield average values of ∆ ‡ = 23.59 ± 0.15 and ∆ = 18.80 ± 0.24 kcal/mol.


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Figure 1. (a) Experimental linear free energy relationship between log() and ∆)*+ for acetone deprotonation by various bases (squares).31 The predicted rate constant for deprotonation of DHAP by a glutamate sidechain in water is also shown (diamond). (b) Calculated free energy profiles for deprotonation of DHAP by glutamate in aqueous solution. ∆ε is the generalized reaction coordinate.24,25

Predicted reaction energetics for yeast TIM and vTIM. We calculated 60 independent free energy profiles for TIM and vTIM at each of the temperatures 290, 300 and 310 K. A set of representative free energy profiles at room temperature is shown in Figure 2. The yeast enzyme is found to have an activation free energy of 12.80 ± 0.09 kcal/mol at 300 K, while that of vTIM is predicted to be 11.95 ± 0.13 kcal/mol, where the error bars denote standard errors of the mean from the 60 MD/EVB simulations. Hence, within the error bars of the simulations, the small decrease of 0.85 kcal/mol in ∆ ‡ is significant and leads to a rate constant for the enolization step that is about four times higher in vTIM than in TIM at 300 K. This is also in line with the general finding of higher rates for cold-adapted enzymes compared to their mesophilic counterparts.1-3,7 The origin of this effect is largely due to a more pronounced stabilization of the enolate intermediate, which is predicted to lie 4.8 and 1.8 kcal/mol above the ground state in yeast TIM and vTIM, respectively (Figure 2). This improved stabilization in vTIM originates as expected from electrostatic interactions with the negatively charged enolate, but these are diffuse and have many small and often compensating energetic contributions. Hence, the yeast to vTIM charge mutations L174K and 9 ACS Paragon Plus Environment

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N213K in the two phosphate binding loops are indeed found to contribute energetically to stabilization of the enolate, but they also part of surface charge clusters where other mutations are located which partly compensate for the favourable lysine interactions.

Figure 2. Calculated free energy profiles for deprotonation of DHAP in yeast TIM (red curves) and vTIM (blue curves) at 300 K. Only a few simulations are shown out of a total of 60 for each enzyme at this temperature.

Remarkably, the MD/EVB simulations at three different temperatures show not only that vTIM is a more effective catalyst, but also that it retains a considerably higher rate than yeast TIM as the temperature is lowered. Hence, at 290 K vTIM is predicted to have a rate that is 48% of that at the highest temperature (310 K), while the corresponding fraction for yeast TIM is only 28% (Table 1). At 290 K the speed of vTIM is then predicted to be about six times higher than that of the yeast enzyme. Arrhenius plots can be computed from these simulation data (Figure 3), from which the thermodynamic activation parameters in the relevant temperature range can be extracted. The resulting values for vTIM are ∆ ‡ = 6.0 and ∆ ‡ = −6.0 kcal/mol, while corresponding values for yeast TIM are ∆ ‡ = 10.7 and ∆ ‡ = −2.2 kcal/mol (at 300 K). Hence, is clear from these simulations that the two TIM 10 ACS Paragon Plus Environment

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enzymes also obey the general shift in the activation enthalpy-entropy balance observed between psychrophilic and mesophilic orthologs,1-3 which is what leads to rate adaptation to lower temperatures. The activation enthalpy is thus significantly lower for the cold-adapted enzyme, but this is partly compensated by a more negative entropy. In this context, it may be useful for comparison to note that the activation parameters indicated from the computed Arrhenius plot in Ref. 14 for the mesophilic enzyme are, in fact, very close to those obtained here. A remark on convergence may also be of interest here since we have earlier stressed the need for calculating a large number of independent free energy profiles at each temperature.5,21,37 Figure 3b shows the convergence of the ∆ ‡ term (at 300 K) as a function of the number of MD/EVB simulations accumulated at each temperature. It can be seen that with just a few simulations the energetic estimates are highly unreliable and that up to 60 simulations are really required to achieve satisfactory convergence. In this respect, it is also interesting to note that the quality of the crystal structure used for the calculations appears to affect the convergence properties. That is, the yeast structure has a much higher resolution of 1.2 Å and an Rfree value of 15% compared to vTIM, where the resolution is 2.7 Å and Rfree = 21.5%.18,28 This is reflected not only in the slower convergence of ∆ ‡ for vTIM (Figure 3b), but also in the slightly higher standard errors of the calculated activation free energies at all temperatures (see above).

Table 1. Calculated rate constants for the enolization reaction in yeast TIM and vTIM at different temperatures.a TIM

vTIM

T (K)

k (s−1)

krel

k (s−1)

krel

310

4574

1

15080

1

300

2756

0.60

11513

0.76

290

1292

0.28

7215

0.48

a

The rates are calculated using transition state theory from the activation free energies obtained from the MD/EVB simulations. Rate constants relative to that at 310 K are denoted krel.


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Figure 3. (a) Calculated Arrhenius plots of ∆ ‡ / versus 1/ for yeast TIM (red) and vTIM (blue) at the three temperatures 290, 300 and 310 K. The value of R2 for the two regression lines are 0.99 and 0.98, respectively. (b) Convergence of the entropy contribution to the activation free energy at 300 K as a function of the number of independent free energy profiles calculated at each temperature (red – yeast TIM, blue – vTIM).

Origin of the activation enthalpy-entropy shift in TIM. A prevailing hypothesis for the origin of the reduced activation enthalpies and entropies in cold-adapted enzymes has been that the reacting substrates and their surrounding active site residues are more mobile than in their mesophilic counterparts.2,3 However, this idea is somewhat problematic since the active site residues, and often also their immediate surrounding, are usually totally conserved between closely related psychrophilic and mesophilic enzyme orthologs. This would appear to make it difficult to attain significantly different mobilities of the active site and, indeed, our earlier MD/EVB simulations of differently adapted citrate synthases and trypsins did not give any support to the active site flexibility hypothesis.4-6 Instead these enzymes showed virtually identical substrate and active site mobilities between the cold- and warm-active species. The same appears to be true for TIM, where we examined the average sidechain positional root mean square fluctuations (RMSF) for active site residues, as well as the substrate RMSF. Figure 4a shows the calculated RMSF in both enzymes for the key residues Asn10, Lys12, His95, Cys126, Ile170 and Leu230 and for the DHAP substrate in the reactant complex. It is


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clear both that the overall RMSF for active site residues is small, typically on the order of 0.5 Å, and that there are no major differences between the cold- and warm-active enzymes.

Figure 4. Calculated average positional root mean square fluctuations for (a) active site sidechains and the DHAP substrate and (b) for backbone atoms of each residue of the enzyme. RMSF values for yeast TIM are shown in red and for vTIM in blue. Surface loops with different mobilities are indicated with their labels. The apparent shift of the blue vTIM curve to the right is due to a few amino acid insertions along the sequence.

Earlier computer simulations have instead identified regions of increased surface mobility as responsible for the cold-adaptation effect4-7 and it is therefore of interest to examine possible 13 ACS Paragon Plus Environment

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mobility differences in regions outside of the active site. To provide a picture of mobility differences that is unbiased by amino acid sidechain substitutions, it is particularly useful to examine backbone RMSFs averaged per residue (Figure 4b). The corresponding plot shows that just a few regions differ significantly between yeast TIM and vTIM. The first of these is the α1-β2 loop (residues 30-36) where vTIM clearly has an enhanced mobility. Here the psychrophilic loop has an insertion of one amino acid and a distinctly different loop conformation with a less tight packing against the C-terminal α8 helix. The loop structure also differs from that in the mesophilic E. coli structure,38 which is closer to the yeast conformation (Figure 5). In E. coli, the α1-β2 loop is held in contact with α8 due to interactions between the Glu252 sidechain at the end of the helix and two backbone amides in the loop. In yeast TIM, on the other hand, the same packing effect is achieved by water mediated hydrogen bonds between Asn35 and α8, together with the proline residue in position 33. These water mediated interactions are clearly seen in the 1.2 Å resolution structure of yeast TIM.28 It is notable that vTIM has an insertion of an alanine at the end of α8 which shifts the glutamate sidechain corresponding to Glu252 in E. coli so that it loses contact with the α1-β2 loop. Moreover, Asn35 in yeast TIM i substituted for a glycine in vTIM so that the possibility of water mediated interactions to α8 is also lost. Furthermore, the presence of the charged Glu31 in vTIM also affects the loop conformation as its sidechain prefers to interact with the surrounding solvent.


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Figure 5. View of the interaction between the α1-β2 loop and the α8 helix in vTIM,18 the yeast28 and the E. coli38 enzyme. Key hydrogen bonds are indicated and water molecules from the high resolution crystal structure of yeast TIM are shown as red spheres.

The second region of mobility difference that emerges from the MD simulations is the α2-β3 loop, comprising residues 55-59 (Figure 4b). This is again a surface exposed loop where the cold-adapted enzyme has a two-residue insertion, with a different loop conformation and an elongation of the α2 helix by almost one full turn (Figure 6). The α2-β3 loop is in yeast TIM connected to the α1-β2 loop discussed above by backbone hydrogen bonding to the Gln58 sidechain, which further rigidifies the structure in this region. In vTIM this residue has instead become an alanine and there are no direct interactions between the two loops anymore. There are also minor mobility differences in the surface exposed part of the long β3-α3 and the α3β4 and β4-α4 loops, where sequences and conformations are more similar. Apart from these regions the only notable remaining difference is found at the C-terminal end of the protein, where vTIM has an elongated solvent exposed tail that is partly in helical conformation (α8). It is thus striking that all regions of significant different backbone fluctuations are found in loop regions connecting the secondary structure elements and that the most pronounced effects are observed in parts where the vTIM structure has sequence insertions.


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Figure 6. Comparison of the interactions between the α2-β3 and α1-β2 loops in yeast TIM28 (yellow) and vTIM18 (cyan). Hydrogen bonds between Gln58 and α1-β2 loop backbone in the mesophilic enzyme are indicated.

Conclusions By carrying out extensive MD/EVB simulations of the enolization reaction in mesophilic and psychrophilic TIMs we have shown that the cold-adapted enzyme from Vibrio marinus obeys the seemingly universal phenomenon of a shift in the activation enthalpy-entropy balance, compared to the mesophilic ortholog. Hence, the predicted activation enthalpy for vTIM is about 5 kcal/mol lower than in the yeast enzyme, while the entropy contribution is 4 kcal/mol more negative. The calculations further show that vTIM is more efficient in catalyzing the proton transfer reaction and particularly so at the lowest temperature examined (290 K). The general trend with a large degree of activation enthalpy-entropy compensation when comparing psychrophilic and mesophilic orthologs is also seen for thermophilic enzymes,39 although more a pronounced net increase of the activation free energy is usually observed for 16 ACS Paragon Plus Environment

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such enzymes at moderate temperatures.39,40 It has also recently been suggested that a change in transition state heat capacity may play a role in enzyme temperature adaptation, the hallmark of which would be curved Arrhenius plots,41 but this appears to be a secondary effect compared to the shift in activation enthalpy-entropy balance.1-3,39,42 At any rate, it is clear that reliable computer simulations can now be used to explore the detailed origin of enzyme temperature adaptation.

A long-standing hypothesis in the field has been that the activation enthalpy-entropy shift originates from increased flexibility of active site residues and the substrates in cold-adapted enzymes. The idea is then that there is a mobility increase in the reactant complex, which is assumed to be significantly larger than in the transition state, and this would therefore lead to a greater entropy loss upon moving from the reactant to transition state. However, the few calculations reported so far, that have actually evaluated the thermodynamic activation parameters for different enzymes, have not given any support for this hypothesis.4-7 Likewise, our present simulations of TIM reveal that the mobilities of the active site residues and the DHAP substrate are virtually identical in vTIM and the yeast enzyme, which again appears to disprove the active site flexibility hypothesis.

However, calculations of the overall backbone mobilities in yeast TIM and vTIM show that there are distinct regions where the two enzymes differ in terms of flexibility and that these regions correspond to a few surface exposed loops of the protein structure. These regions are further found to display sequence differences and, in particular, amino acid insertions in the vTIM structure. The calculated average backbone RMSFs for the entire TIM monomer are 0.66 and 0.73 Å for yeast TIM and vTIM, respectively. These values are thus very similar, but the small increase in mobility for vTIM may still be significant. With regard to coldadaptation in general, it thus appears that evolution can utilize random surface mutations to select for those that confer a change in the temperature dependence of catalytic activity. Such mutations, since they are located at the surface, are less likely to be detrimental to structural stability and the catalytic reaction compared to those in the protein interior. The overall result of an altered temperature dependence may be a sum of many such mutations, where any single one only has a minor effect. An example of this may be Ser235 in mesophilic TIMs, 17 ACS Paragon Plus Environment

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which is substituted to alanine in vTIM.18 This residue is also situated in a loop (β8-α8) and the Ala→Ser mutation in vTIM was indeed shown experimentally to increase the melting temperature by 5° and reduce kcat to 70% of that of vTIM at 10°C. While this alanine in vTIM was considered as a possible unique feature of cold-adapted TIMs at the time when only the V. marinus sequence was available,18 subsequently obtained sequences for the cold-adapted salmon, Psychrobacter arcticus and piscatorii enzymes (UniProt accession numbers B5XB10, Q4FVL9 and A0A0T6DVB8, respectively) show that they have the same serine residue as mesophilic TIMs at this position.

In general, it thus appears that the main differences between the warm- and cold-active variants of TIM have to do with interactions between surface exposed loops and other parts of the structure. That is, such loops are rigidified in the mesophilic structures either by direct or water mediated interactions to other secondary structure elements, while just a few mutations in the cold-adapted enzyme can render these loops more flexible and less tightly connected to the rest of the structure. The overall effect of such mutations is essentially to make the protein structure softer, which directly translates into a lower activation enthalpy contribution from protein stiffness and a reduced thermal stability.

Author information Corresponding author *

E-mail: [email protected]

Notes The author declares no competing financial interest.

Acknowledgements I wish to thank Dr. Masoud Kazemi for assistance with DFT calculations. Support from the Swedish Research Council (VR) and the Knut and Alice Wallenberg Foundation is gratefully acknowledged.


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Cold Adaptation of Triosephosphate Isomerase - Biochemistry (ACS

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