Kinetics of H2O2-driven catalysis by a lytic polysaccharide monooxygenase from the fungus Trichoderma reesei

Owing to their ability to break glycosidic bonds in recalcitrant crystalline polysaccharides such as cellulose, the catalysis effected by lytic polysaccharide monooxygenases (LPMOs) is of major interest. Kinetics of these reductant-dependent, monocopper enzymes is complicated by the insoluble nature of the cellulose substrate and parallel, enzyme-dependent, and enzyme-independent side reactions between the reductant and oxygen-containing cosubstrates. Here, we provide kinetic characterization of cellulose peroxygenase (oxidative cleavage of glycosidic bonds in cellulose) and reductant peroxidase (oxidation of the reductant) activities of the LPMO TrAA9A of the cellulose-degrading model fungus Trichoderma reesei. The catalytic efficiency (kcat/Km(H2O2)) of the cellulose peroxygenase reaction (k_cat = 8.5 s⁻¹, and Km(H2O2)=30μM) was an order of magnitude higher than that of the reductant (ascorbic acid) peroxidase reaction. The turnover of H₂O₂ in the ascorbic acid peroxidase reaction followed the ping-pong mechanism and led to irreversible inactivation of the enzyme with a probability of 0.0072. Using theoretical analysis, we suggest a relationship between the half-life of LPMO, the values of kinetic parameters, and the concentrations of the reactants.     

Correlation of a Decline in Aerobic Capacity with Development of Emphysema in Patients with Chronic Obstructive Pulmonary Disease: A Prospective Observational Study

In patients with COPD, CT assessment of emphysema and airway disease is known to be associated with lung function and 6-minute walk distance. However, it remains to be determined whether low attenuation area (LAA) on CT is associated with aerobic capacity assessed using cardiopulmonary exercise testing (CPET). In this prospective observational study, we repeatedly conducted high-resolution CT and CPET using a treadmill in 81 COPD patients over a median interval of 3.5 years. Two investigators independently scored LAA on images obtained at the aortic arch level, tracheal bifurcation level, and supradiaphragmatic level. Grades for the images of each lung were added to yield the total LAA score. Total LAA score was negatively correlated with peak aerobic capacity (V˙O2) (p<0.001, r = -0.485). LAA scores of the upper (aortic arch level) and the lower (supradiaphragmatic level) lungs were both significantly associated with peak V˙O2. There was a significant correlation between total LAA score and peak CO₂ output (V˙CO2) (p<0.001, r = -0.433). Total LAA score was correlated with oxygen saturation at peak exercise (p<0.001, r = -0.634) and the estimated dead space fraction (p<0.001, r = 0.416). The mean annual change in total LAA score was significantly correlated with those in peak V˙O2 (p<0.001, r = -0.546) and peak V˙CO2 (p<0.001, r = -0.488). The extent of emphysema measured by CT was associated with the results of CPET. The time-dependent changes in CPET data were also correlated with that in total LAA score. CT assessment could be a non-invasive tool to predict aerobic capacity in patients with COPD.     

Influence of Adjuvant Therapy in Cancer Survivors on Endothelial Function and Skeletal Muscle Deoxygenation

The cardiotoxic effects of adjuvant cancer treatments (i.e., chemotherapy and radiation treatment) have been well documented, but the effects on peripheral cardiovascular function are still unclear. We hypothesized that cancer survivors i) would have decreased resting endothelial function; and ii) altered muscle deoxygenation response during moderate intensity cycling exercise compared to cancer-free controls. A total of 8 cancer survivors (~70 months post-treatment) and 9 healthy controls completed a brachial artery FMD test, an index of endothelial-dependent dilation, followed by an incremental exercise test up to the ventilatory threshold (VT) on a cycle ergometer during which pulmonary V˙O2 and changes in near-infrared spectroscopy (NIRS)-derived microvascular tissue oxygenation (TOI), total hemoglobin concentration ([Hb]_total), and muscle deoxygenation ([HHb] ≈ fractional O₂ extraction) were measured. There were no significant differences in age, height, weight, and resting blood pressure between cancer survivors and control participants. Brachial artery FMD was similar between groups (P = 0.98). During exercise at the VT, TOI was similar between groups, but [Hb]_total and [HHb] were significantly decreased in cancer survivors compared to controls (P < 0.01) The rate of change for TOI (ΔTOIΔ/V˙O2) and [HHb] (Δ[HHb]/ΔV˙O2) relative to ΔV˙O2 were decreased in cancer survivors compared to controls (P = 0.02 and P = 0.03 respectively). In cancer survivors, a decreased skeletal muscle microvascular function was observed during moderate intensity cycling exercise. These data suggest that adjuvant cancer therapies have an effect on the integrated relationship between O₂ extraction, V˙O2 and O₂ delivery during exercise.     

Physicochemical Properties and Amino Acid Composition of Alkaline Proteinase from Aspergillus sojae

Some physicochemical properties and amino acid composition of alkaline proteinase from Aspergillus sojae were found to be as follows: The isoelectric point was at pH 5.1. The molecular weight was 25,500 using the Sheraga-Mandelkern’s formula, based upon the values of the sedimentation coefficient $(s_{20,w}^{°}=\text{?}2.82\text{?}\text{S})$, the intrinsic viscosity ([η] = 0.027 dl/g), and the partial specific volume $({\displaystyle \overline{V}}\text{?}=\text{?}0.726\text{?}\text{ml}/\text{g})$. The enzyme contains 16.8% of nitrogen and is composed of 250 residues of amino acid; Asp₃₁ Glu₁₉, Gly₂₇, Ala₃₂, Val₁₈, Leu₁₄, Ile₁₄, Ser₂₈, Thr₁₈, ${\text{(}{\displaystyle \stackrel{}{{\displaystyle \stackrel{?}{\text{Cys C}}}}}\text{ys})}_1$, Met₂, Pro₆, Phe₇, Tyr₈, Trp₂, His₅, Lys₁₄, Arg₃, (amide-NH₃)₂₀.     

Controlling Electron Transfer between the Two Cofactor Chains of Photosystem I by the Redox State of One of Their Components

Two functional electron transfer (ET) chains, related by a pseudo-C₂ symmetry, are present in the reaction center of photosystem I (PSI). Due to slight differences in the environment around the cofactors of the two branches, there are differences in both the kinetics of ET and the proportion of ET that occurs on the two branches. The strongest evidence that this is indeed the case relied on the observation that the oxidation rates of the reduced phylloquinone (PhQ) cofactor differ by an order of magnitude. Site-directed mutagenesis of residues involved in the respective PhQ-binding sites resulted in a specific alteration of the rates of semiquinone oxidation. Here, we show that the PsaA-F689N mutation results in an ∼100-fold decrease in the observed rate of PhQA− oxidation. This is the largest change of PhQA− oxidation kinetics observed so far for a single-point mutation, resulting in a lifetime that exceeds that of the terminal electron donor, P700+. This situation allows a second photochemical charge separation event to be initiated before PhQA− has decayed, thereby mimicking in PSI a situation that occurs in type II reaction centers. The results indicate that the presence of PhQA− does not impact the overall quantum yield and leads to an almost complete redistribution of the fractional utilization of the two functional ET chains, in favor of the one that does not bear the charged species. The evolutionary implications of these results are also briefly discussed.     

Analysis of Amylose-borate Interaction by Frontal Gel Filtration

Amylose-borate interaction has been analyzed by frontal gel chromatography, using the constituent velocity data alone. The constituent Velocity equation was reformulated in terms of elution volume for a type of interacting system described by$\text{A}+i\text{B}={\text{AB}}_i(i=1,2,3\dots n)$

Detailed examination of the binding data indicates that, in the complex formation between amylose and borate, this type of equilibria operates predominantly, if not solely. Use of the constituent elution volume equation enabled us, for the first time, to evaluate the association constant (K) and number of binding site pertaining to this system, i.e., K = 4.9 10² and n = 1. There was no evidence indicating the occurrence of the formation of inclusion complex.     

Protein crystal’s shape and polymorphism prediction within the limits resulting from the exploration of the Miyazawa–Jernigan matrix

A computer study of the prediction of the protein crystal’s shape and polymorphism of crystal’s structures within the limits resulting from the exploration of the Miyazawa–Jernigan matrix is presented. In this study, a coarse-graining procedure was applied to prepare a two-dimensional growth unit, where instead of full atom representation of the protein a two-type (hydrophobic–hydrophilic, HP) aminoacidal representation was used. The interaction energies between hydrophobic ($E_{\text{HH}}$) aminoacids were chosen from the well-known HP-type models ($E_{\text{HH}}\in [-4\text{,}-3\text{,}-2.3\text{,}-1]$), whereas interaction energies between hydrophobic and hydrophilic aminoacids ($E_{\text{HP}}$) as well as interaction energies between hydrophilic aminoacids ($E_{\text{PP}}$) were chosen from the range: $<-1\text{,}1>$, but not all values from this range fulfiled limitations resulting from the exploration of the Miyazawa–Jernigan matrix. Exploring every positively vetted combinations of energy interactions a polymorphism of the unit cell was observed what led to the fact that different final crystal’s shapes were obtained.     

Isolation and Structure of Sclerothionine,a Metabolite of Sclerotinia Fungus

A new sulfur-containing imidazole compound, m.p. 218~223°C (decomp.), ${[\alpha ]}_{\text{D}}^{24}+{7.4}^{°}$ in water), C₁₁H₁₉N₃O₃S was isolated from sclerotia of Sclerotinia libertiana and named sclerothionine. The chemical structure of sclerothionine was identified with 2-hydroxyethyl-ergothioneine which was synthesized from ethylene chlorhydrine and ergothioneine.     

Conversion of Orotic Acid to Uridine-5′-monophosphate by Enzymes of Micrococcus glutamicus

An enzyme system which could convert orotic acid to uridine-5′-monophosphate (5′-UMP) was found in cell-free extract of a threonine-requiring auxotroph of Micrococcus glutamicus (Syn. Corynebacterium glutamicum) 534 Co-147. This reaction required 5-phosphoribosylpyrophosphate (PRPP) and magnesium ion as essential components. The product of the enzyme reaction was separated by ion exchange resin chromatography and identified to be uridine-5′-monopbosphate. From the stoichiometric studies and other characteristics, it became evident that this enzyme reaction proceeded according to the following equation and was assumed to be catalyzed by orotidine-5′-monophosphate pyrophosphorylase and orotidine-5′-monophosphate decarboxylase. $\text{Orotic}\text{ acid + PRPP}\underset{{\text{Mg}}^{++}}{\to }{5}^{\prime }?\text{UMP}+\text{PPi}+{\text{CO}}_2$     

Kinetics and Energetics of Biomolecular Folding and Binding

The ability of biomolecules to fold and to bind to other molecules is fundamental to virtually every living process. Advanced experimental techniques can now reveal how single biomolecules fold or bind against mechanical force, with the force serving as both the regulator and the probe of folding and binding transitions. Here, we present analytical expressions suitable for fitting the major experimental outputs from such experiments to enable their analysis and interpretation. The fit yields the key determinants of the folding and binding processes: the intrinsic on-rate and the location and height of the activation barrier.Dynamic processes in living cells are regulated through conformational changes in biomolecules—their folding into a particular shape or binding to selected partners. The ability of biomolecules to fold and to bind enables them to act as switches, assembly factors, pumps, or force- and displacement-generating motors (1). Folding and binding transitions are often hindered by a free energy barrier. Overcoming the barrier requires energy-demanding rearrangements such as displacing water from the sites of native contacts and breaking nonnative electrostatic contacts, as well as loss of configurational entropy. Once the barrier is crossed, the folded and bound states are stabilized by short-range interactions: hydrogen bonds, favorable hydrophobic effects, and electrostatic and van der Waals attractions (2).Mechanistic information about folding and binding processes is detailed in the folding and binding trajectories of individual molecules: observing an ensemble of molecules may obscure the inherent heterogeneity of these processes. Single-molecule trajectories can be induced, and monitored, by applying force to unfold/unbind a molecule and then relaxing the force until folding or binding is observed (3–5) (Fig. 1). Varying the force relaxation rate shifts the range of forces at which folding or binding occurs, thus broadening the explorable spectrum of molecular responses to force and revealing conformational changes that are otherwise too fast to detect. The measured force-dependent kinetics elucidates the role of force in physiological processes (6) and provides ways to control the timescales, and even the fate, of these processes. The force-dependent data also provides a route to understanding folding and binding in the absence of force—by extrapolating the data to zero force via a fit to a theory.Open in a separate windowFigure 1Schematic of the output from a force-relaxation experiment. The applied force is continuously relaxed from the initial value F₀ until the biomolecule folds or binds, as signified by a sharp increase in the measured force. From multiple repeats of this experiment, distributions of the folding or binding forces are collected (inset). Fitting the force distributions with the derived analytical expression yields the key parameters that determine the kinetics and energetics of folding or binding.In this letter, we derive an analytical expression for the distribution of transition forces, the major output of force-relaxation experiments that probe folding and binding processes. The expression extracts the key determinants of these processes: the on-rate and activation barrier in the absence of force. The theory is first developed in the context of biomolecular folding, and is then extended to cover the binding of a ligand tethered to a receptor. In contrast to unfolding and unbinding, the reverse processes of folding and binding require a theory that accounts for the compliance of the unfolded state, as well as the effect of the tether, to recover the true kinetic parameters of the biomolecule of interest.In a force-relaxation experiment, an unfolded biomolecule or unbound ligand-receptor complex is subject to a stretching force, which is decreased from the initial value F₀ as the pulling device approaches the sample at speed V until a folding or binding transition is observed (Fig. 1) (3–5). Define S(t) as the probability that the molecule has not yet escaped from the unfolded (implied: or unbound) state at time t. When escape is limited by one dominant barrier, S(t) follows the first-order rate equationS˙(t)≡dS(t)dt=−k←(F(t))S(t),where k_←(F(t)) is the on-rate at force F at time t. Because, prior to the transition, the applied force decreases monotonically with time, the distribution of transition forces, p(F), is related to S(t) through p(F)dF=S˙(t)dt, yieldingp(F)=−k←(F)F˙(F)e−∫F0Fk←(F′)F˙(F′)dF′.(1)Here F˙(F)≡dF(t)/dt<0 is the force relaxation rate. The proper normalization of p(F) is readily confirmed by integrating Eq. 1 from the initial force F₀ to negative infinity, the latter accounting for transitions that do not occur by the end of the experiment. Note that the expression for the distribution of folding/binding forces in Eq. 1 differs from its analog for the unfolding process (7) by the limits of integration and a negative sign, reflecting the property of a relaxation experiment to decrease the survival probability S(t) by decreasing the force. Converting the formal expression in Eq. 1 into a form suitable for fitting experimental data requires establishing functional forms for k_←(F) and F˙(F) and analytically solving the integral. These steps are accomplished below.The on-rate k_←(F) is computed by treating the conformational dynamics of the molecule as a random walk on the combined free energy profile G(x,t) = G₀(x) + G_pull(x,t) along the molecular extension x. Here G₀(x) is the intrinsic molecular potential and G_pull(x,t) is the potential of the pulling device. When G(x,t) features a high barrier on the scale of k_BT (k_B is the Boltzmann constant and T the temperature), the dynamics can be treated as diffusive. The unfolded region of the intrinsic potential for a folding process, unlike that for a barrierless process (8), can be captured by the functionG0(x)=ΔG‡ν1−ν(xx‡)11−ν−ΔG‡ν(xx‡),which has a sharp (if ν = 1/2, Fig. 2, inset) or smooth (if ν = 2/3) barrier of height ΔG^‡ and location x^‡. The potential of a pulling device of stiffness κ_S is G_pull(x,t) = κ_S/2(X₀ – Vt – x)² with an initial minimum at X₀ (corresponding to F₀). Applying Kramers formalism (9) to the combined potential G(x,t), we establish the analytical form of the on-rate at force F(t),k←(F)=k0(1+κSκU(F))1ν−12(1+νFx‡ΔG‡)1ν−1×eβΔG‡[1−(1+κSκU(F))2ν1−ν−1(1+νFx‡ΔG‡)1ν],where k₀ is the intrinsic on-rate, β ≡ (k_BT)⁻¹, andκU(F)=ν(1−ν)2ΔG‡x‡2(1+νFx‡ΔG‡)2−1νis the stiffness of the unfolded biomolecule under force F (see the Supporting Material for details on all derivations). The full nonlinear form of G_pull(x,t) was necessary in the derivation because, in contrast to the typically stiff folded state, the unfolded state may be soft (to be exact, 1/2κ_S x^‡2(F) << k_BT may not be satisfied) and thus easily deformed by the pulling device. Because of this deformation, the folding transition faces an extra contribution (regulated by the ratio κ_S/κ_U(F)) to the barrier height, typically negligible for unfolding, that decreases the on-rate in addition to the applied force F.Open in a separate windowFigure 2Contributions to the free energy profile for folding (inset) and binding (main figure). The derived expression (Eq. 2) extracts the on-rate and the location and height of the activation barrier to folding. When applied to binding data, the expression extracts the parameters of the ligand-tether-receptor (LTR) potential G˜0 (x); the proposed algorithm (Eqs. 3 and 4) removes the contribution of the tether potential G_teth(x) to recover the parameters of the intrinsic ligand-receptor (LR) potential G₀(x).The last piece required for Eq. 1, the loading rate F˙(F), is computed as the time derivative of the force F(t) on the unfolded molecule at its most probable extension at time t:F˙(F)=−κSV1+κS/κU(F).Finally, we realize that the integral in Eq. 1 can be solved analytically exactly, both for ν = 1/2 and ν = 2/3, resulting in the analytical expression for the distribution of folding forces:p(F)=k←(F)|F˙(F)|e−k←(F)β|F˙(F)|x‡(1+κSκU(F))νν−1(1+νFx‡ΔG‡)1−1ν.(2)Equation 2 can be readily applied to (normalized) histograms from force-relaxation experiments to extract the parameters of the intrinsic kinetics and energetics of folding. Being exact for ν = 1/2 and ν = 2/3, Eq. 2 is also an accurate approximation for any ν in the interval 1/2 < ν < 2/3 as long as κ_S ≲ κ_U (F) (see Fig. S1 in the Supporting Material). For simplicity, in Eq. 2 we have omitted the term containing F₀ as negligible if F₀ is large enough to prevent folding events.The solution in Eq. 2 reveals properties of the distribution of folding forces that distinguish it from its unfolding counterpart (7):

• 1.The distribution has a positive skew (Fig. 3), as intuitively expected: the rare folding events occur at high forces when the barrier is still high.Open in a separate windowFigure 3Force histograms from folding (left) and binding (right) simulations at several values of the force-relaxation speed (in nanometers per second, indicated at each histogram). Fitting the histograms with the analytical expression in Eq. 2 (lines) recovers the on-rate and activation barrier for folding or binding (2.Increasing the relaxation speed shifts the distribution to lower forces (Fig. 3): faster force relaxation leaves less time for thermal fluctuations to push the system over a high barrier, causing transitions to occur later (i.e., at lower forces), when the barrier is lower.
• 3.The stiffness κ_S and speed V enter Eq. 2 separately, providing independent routes to control the range of folding forces and thus enhance the robustness of a fit.

The application of the above framework to binding experiments on a ligand and receptor connected by a tether (3) involves an additional step—decoupling the effect of the tether—to reconstruct the parameters of ligand-receptor binding. Indeed, the parameters extracted from a fit of experimental histograms to Eq. 2 characterize the ligand-tether-receptor (LTR) potential (k˜0, x˜‡, ΔG˜‡, ν) (Fig. 2). The parameters of the natural ligand-receptor (LR) potential (k₀, x^‡, ΔG^‡) can be recovered using three characteristics of the tether: contour length L; persistence length p; and extension Δℓ of the tether along the direction of the force in the LTR transition state. The values of L and p can be determined from the force-extension curve of the tether (10); these define the tether potential G_teth(x) (Fig. 2). The value of Δℓ can be found from an unbinding experiment (7) on LTR and the geometry of the tether attachment points (see Fig. S3). Approximating the region of the LR potential between the transition and unbound states as harmonic, with no assumptions about the shape of the potential beyond x^‡, the ligand-receptor barrier parameters are thenx‡=α−1α−2x˜‡,ΔG‡=(α−1)22(α−2)x˜‡Fteth(Δℓ+x˜‡),(3)and the intrinsic unimolecular association rate isk0≈k˜0(βΔG‡)32(βΔG˜‡)1ν−12(x˜‡x‡)2eβ(ΔG˜‡−ΔG‡).(4)Here, the force value Fteth(Δℓ+x˜‡) is extracted from the force-extension curve of the tether at extension Δℓ+x˜‡ andα=2(ΔG˜‡−Gteth(Δℓ)+Gteth(Δℓ+x˜‡))x˜‡Fteth(Δℓ+x˜‡),where G_teth(x) is the wormlike-chain potential (see Eq. S13 in the Supporting Material). Equations 3–4 confirm that a tether decreases the height and width of the barrier (see Fig. 2), thus increasing the on-rate.In Fig. 3, the developed analytical framework is applied to folding and binding force histograms from Brownian dynamics simulations at parameters similar to those in the analogous experimental and computational studies (3,5,11) (for details on simulations and fitting procedure, see the Supporting Material). For the stringency of the test, the simulations account for the wormlike-chain nature of the molecular unfolded and LTR unbound states that is not explicitly accounted for in the theory. With optimized binning (12) of the histograms and a least-squares fit, Eqs. 2–4 recover the on-rate, the location and the height of the activation barrier, and the value of ν that best captures how the kinetics scale with force (

1.Multiple relaxation speeds,

2.Folding/binding events at low forces, and

3.A large number of events at each speed.

Table 1

On-rate and the location and height of the activation barrier from the fit of simulated data to the theory in Eq. 2

Biogas production from boreal herbaceous grasses – Specific methane yield and methane yield per hectare

The objective of this study was to determine the specific methane yields of four grass species (cocksfoot, tall fescue, reed canary grass and timothy) cultivated under boreal conditions as well as how harvesting time and year of cultivation affects the specific methane yields per ha. The specific methane yields of all grasses and all harvests varied from 253 to 394 Nl CH₄/kg volatile solids (VS) added. The average specific methane yield of the 1st harvest of all grasses was higher than the 2nd harvests. In this study the methane and energy yields from different harvest years were ranged from 1200 to 3600 Nm³ CH₄/ha/a, corresponding from 12 to 36 MWh_CH4${\text{MWh}}_{{\text{CH}}_4}$/ha/a. The methane yield per hectare of the 1st harvest was always higher than that of the 2nd harvest per hectare because of the higher dry matter yield and specific methane yield. High biomass yield per hectare, good digestibility and regrowth ability after harvesting are important factors when choosing grass species for biogas production. If 30% of fallow and the second harvest of grassland were cultivated grasses and harvested for biogas production in Finland, the energy produced could be 4.9 TWh_CH4${\text{TWh}}_{{\text{CH}}_4}$.