Kinetic and Thermodynamic Studies of Lysozyme Adsorption on Cibacron Blue F3GA Dye-Ligand Immobilized on Aminated Nanofiber Membrane

The polyacrylonitrile (PAN) nanofiber membrane was prepared by the electrospinning technique. The nitrile group on the PAN nanofiber surface was oxidized to carboxyl group by alkaline hydrolysis. The carboxylic group on the membrane surface was then converted to dye affinity membrane through reaction with ethylenediamine (EDA) and Cibacron Blue F3GA, sequentially. The adsorption characteristics of lysozyme onto the dye ligand affinity nanofiber membrane (namely P-EDA-Dye) were investigated under various conditions (e.g., adsorption pH, EDA coupling concentration, lysozyme concentration, ionic strength, and temperature). Optimum experimental parameters were determined to be pH 7.5, a coupling concentration of EDA 40 μmol/mL, and an immobilization density of dye 267.19 mg/g membrane. To understand the mechanism of adsorption and possible rate controlling steps, a pseudo first-order, a pseudo second-order, and the Elovich models were first used to describe the experimental kinetic data. Equilibrium isotherms for the adsorption of lysozyme onto P-EDA-Dye nanofiber membrane were determined experimentally in this work. Our kinetic analysis on the adsorption of lysozyme onto P-EDA-Dye nanofiber membranes revealed that the pseudo second-order rate equation was favorable. The experimental data were satisfactorily fitted by the Langmuir isotherm model, and the thermodynamic parameters including the free energy change, enthalpy change, and entropy change of adsorption were also determined accordingly. Our results indicated that the free energy change had a negative value, suggesting that the adsorption process occurred spontaneously. Moreover, after five cycles of reuse, P-EDA-Dye nanofiber membranes still showed promising efficiency of lysozyme adsorption.

Free Energy Change during the Formation of Crystalline Contact between Lysozyme Monomers under Different Physical and Chemical Conditions

We use the MM/GBSA method to calculate the free energies of dimer formation by binding two monomers with different combinations of precipitant ions, both embedded in the structure of monomers and in the crystallization solution. It shows that the largest difference in free energy values corresponds to the most accurate dimer model, which considers all precipitant ions in their structure. In addition, it shows that in the absence of precipitant ions in the solution of lysozyme molecules, a monomer is a more energetically favorable state.

Energy of a magnetic body: −m⋅δB or +B⋅δm?

“Energy of a magnetic body: -m dB or +B dm?” addresses a fraught question: what is the free-energy change when a B-field applied to a magnetic body (magnetic moment m) is changed? We define what is meant by an “applied field”. We show that the free-energy change is -m dB. The electric analogue -p dE is also described. A balls-and-spring model helps to understand an electric dipole, and by extension a magnetic dipole. In the magnetic case, we prepare the field using a reversible current generator driving a coil. The energy change is obtained by inserting the sample and (also, alternatively) by changing the field. There is much that is new in this chapter.

dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction and de novo pathway design

Group contribution (GC) methods are conventionally used in thermodynamics analysis of metabolic pathways to estimate the standard Gibbs free energy change (ΔrG′o) of enzymatic reactions from limited experimental measurements. However, these methods are limited by their dependence on manually curated groups and inability to capture stereochemical information, leading to low reaction coverage. Herein, we introduce an automated molecular fingerprint-based thermodynamic analysis tool called dGPredictor that enables the consideration of stereochemistry within metabolite structures and thus increases reaction coverage. dGPredictor has a higher prediction accuracy as compared to existing GC methods and can capture free energy changes for isomerase and transferase reactions, which exhibit no overall group changes. We also demonstrate dGPredictor’s ability to predict the Gibbs free energy change for novel reactions and seamless integration within de novo metabolic pathway design tools such as novoStoic. This enables performing a thermodynamic analysis for synthetic pathways, thus safeguarding against the inclusion of reaction steps with infeasible directionalities. To facilitate easy access to dGPredictor, we developed a graphical user interface to predict the standard Gibbs free energy change for reactions at various pH and ionic strengths. The tool allows customized user input of known metabolites as KEGG IDs and novel metabolites as InChI strings (https://github.com/maranasgroup/dGPredictor).Author summaryThe genome-scale metabolic networks consist of a large number of biochemical reactions interconnected in a complex system. The standard Gibbs free energy change is commonly used to check for the feasibility of enzyme-catalyzed reactions as thermodynamics plays a crucial role in pathway design for biochemical synthesis. The group contribution methods using expert-defined functional groups have been extensively used for estimating standard Gibbs free energy change with limited experimental measurements. However, current methods using functional groups have major issues that limit its ability to cover all the metabolites and reactions as well as the inability to consider stereochemistry leads to erroneous estimation of free energy that undergoes only stereochemical change such as isomerases. Here, we introduce a molecular fingerprint-based thermodynamic tool dGPredictor that enables stereochemistry in metabolites and thus improves the reaction coverage with higher prediction accuracy compared to current GC methods. It also allows the ability to predict free energy change for novel reactions which can aid the de novo metabolic pathway design tool to ensure the reaction feasibility. We apply and test our method on reactions in the KEGG database and isobutanol synthesis pathway. In addition, we provide an open-source user-friendly web interface to facilitate easy access for standard Gibbs free energy change of reactions at different physiological states.

Free energy amplification by magnetic flux for driven quantum systems

Exploring the source of free energy is of practical use for thermodynamical systems. In the classical regime, the free energy change is independent of magnetism, as the Lorentz force is conservative. In contrast, here we find that the free energy change can be amplified by adding a magnetic field to driven quantum systems. Taking a recent experimental system as an example, the predicted amplification becomes 3-fold when adding a 10-tesla magnetic field under temperature 316 nanoKelvin. We further uncover the mechanism by examining the driving process. Through extending the path integral approach for quantum thermodynamics, we obtain a generalized free energy equality for both closed and open quantum systems. The equality reveals a decomposition on the source of the free energy change: one is the quantum work functional, and the other emerges from the magnetic flux passing through a closed loop of propagators. The result suggests a distinct quantum effect of magnetic flux and supports to extract additional free energy from the magnetic field.