Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userProton tunneling in hydrogen bonds and its implications in an induced-fit model of enzyme catalysis
124
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
The role of proton tunneling in biological catalysis is investigated here within the frameworks of quantum information theory and thermodynamics. We consider the quantum correlations generated through two hydrogen bonds between a substrate and a prototypical enzyme that first catalyzes the tautomerization of the substrate to move on to a subsequent catalysis, and discuss how the enzyme can derive its catalytic potency from these correlations. In particular, we show that classical changes induced in the binding site of the enzyme spreads the quantum correlations among all of the four hydrogen-bonded atoms thanks to the directionality of hydrogen bonds. If the enzyme rapidly returns to its initial state after the binding stage, the substrate ends in a new transition state corresponding to a quantum superposition. Open quantum system dynamics can then naturally drive the reaction in the forward direction from the major tautomeric form to the minor tautomeric form without needing any additional catalytic activity. We find that in this scenario the enzyme lowers the activation energy so much that there is no energy barrier left in the tautomerization, even if the quantum correlations quickly decay.
rate research
Read More
The crystal structures of potassium and cesium bistrifluoroacetates were determined at room temperature and at 20 K and 14 K, respectively, with the single crystal neutron diffraction technique. The crystals belong to the I2/a and A2/a monoclinic space groups, respectively, and there is no visible phase transition. For both crystals, the trifluoroacetate entities form dimers linked by very short hydrogen bonds lying across a centre of inversion. Any proton disorder or double minimum potential can be rejected. The inelastic neutron scattering spectral profiles in the OH stretching region between 500 and 1000 cm^{-1} previously published [Fillaux and Tomkinson, Chem. Phys. 158 (1991) 113] are reanalyzed. The best fitting potential has the major characteristics already reported for potassium hydrogen maleate [Fillaux et al. Chem. Phys. 244 (1999) 387]. It is composed of a narrow well containing the ground state and a shallow upper part corresponding to dissociation of the hydrogen bond.
In multi-resolution simulations, different system components are simultaneously modelled at different levels of resolution, these being smoothly coupled together. In the case of enzyme systems, computationally expensive atomistic detail is needed in the active site to capture the chemistry of substrate binding. Global properties of the rest of the protein also play an essential role, determining the structure and fluctuations of the binding site; however, these can be modelled on a coarser level. Similarly, in the most computationally efficient scheme only the solvent hydrating the active site requires atomistic detail. We present a methodology to couple atomistic and coarse-grained protein models, while solvating the atomistic part of the protein in atomistic water. This allows a free choice of which protein and solvent degrees of freedom to include atomistically, without loss of accuracy in the atomistic description. This multi-resolution methodology can successfully model stable ligand binding, and we further confirm its validity via an exploration of system properties relevant to enzymatic function. In addition to a computational speedup, such an approach can allow the identification of the essential degrees of freedom playing a role in a given process, potentially yielding new insights into biomolecular function.
The concept that catalytic enzymes can act as molecular machines transducing chemical activity into motion has conceptual and experimental support, but much of the claimed support comes from experimental conditions where the substrate concentration is higher than biologically relevant and accordingly exceeds kM, the Michaelis-Menten constant. Moreover, many of the enzymes studied experimentally to date are oligomeric. Urease, a hexamer of subunits, has been considered to be the gold standard demonstrating enhanced diffusion. Here we show that urease and certain other oligomeric enzymes of high catalytic activity above kM dissociate into their smaller subunit fragments that diffuse more rapidly, thus providing a simple physical mechanism of enhanced diffusion in this regime of concentrations. Mindful that this conclusion may be controversial, our findings are sup-ported by four independent analytical techniques, static light scattering, dynamic light scattering (DLS), size-exclusion chroma-tography (SEC), and fluorescence correlation spectroscopy (FCS). Data for urease are presented in the main text and the con-clusion is validated for hexokinase and acetylcholinesterase with data presented in supplementary information. For substrate concentration regimes below kM at which these enzymes do not dissociate, our findings from both FCS and DLS validate that enzymatic catalysis does lead to the enhanced diffusion phenomenon. INTRODUCT
Quantum tunneling events occurring through biochemical bonds are capable to generate quantum correlations between bonded systems, which in turn makes the conventional second law of thermodynamics approach insufficient to investigate these systems. This means that the utilization of these correlations in their biological functions could give an evolutionary advantage to biomolecules to an extent beyond the predictions of molecular biology that are generally based on the second law in its standard form. To explore this possibility, we first compare the tunneling assisted quantum entanglement shared in the ground states of covalent and hydrogen bonds. Only the latter appears to be useful from a quantum information point of view. Also, significant amounts of quantum entanglement can be found in the thermal state of hydrogen bond. Then, we focus on an illustrative example of ligand binding in which a receptor protein or an enzyme is restricted to recognize its ligands using the same set of proton-acceptors and donors residing on its binding site. In particular, we show that such a biomolecule can discriminate between $3^n - 1$ agonist ligands if it uses the entanglement shared in $n$ intermolecular hydrogen bonds as a resource in molecular recognition. Finally, we consider the molecular recognition events encountered in both the contemporary genetic machinery and its hypothetical primordial ancestor in pre-DNA world, and discuss whether there may have been a place for the utilization of quantum entanglement in the evolutionary history of this system.
Microbial communities are ubiquitous in nature and come in a multitude of forms, ranging from communities dominated by a handful of species to communities containing a wide variety of metabolically distinct organisms. This huge range in diversity is not a curiosity - microbial diversity has been linked to outcomes of substantial ecological and medical importance. However, the mechanisms underlying microbial diversity are still under debate, as simple mathematical models only permit as many species to coexist as there are resources. A plethora of mechanisms have been proposed to explain the origins of microbial diversity, but many of these analyses omit a key property of real microbial ecosystems: the propensity of the microbes themselves to change their growth properties within and across generations. In order to explore the impact of this key property on microbial diversity, we expand upon a recently developed model of microbial diversity in fluctuating environments. We implement changes in growth strategy in two distinct ways. First, we consider the regulation of a cells enzyme levels within short, ecological times, and second we consider evolutionary changes driven by mutations across generations. Interestingly, we find that these two types of microbial responses to the environment can have drastically different outcomes. Enzyme regulation may collapse diversity over long enough times while, conversely, strategy-randomizing mutations can produce a rich-get-poorer effect that promotes diversity. This work makes explicit, using a simple serial-dilutions framework, the conflicting ways that microbial adaptation and evolution can affect community diversity.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
