Graphical Gaussian process regression model for aqueous solvation free energy prediction of organic molecules in redox flow batteries.

The solvation free energy of organic molecules is a critical parameter in determining emergent properties such as solubility, liquid-phase equilibrium constants, pKa and redox potentials in an organic redox flow battery. In this work, we present a machine learning (ML) model that can learn and predict the aqueous solvation free energy of an organic molecule using the Gaussian process regression method based on a new molecular graph kernel. To investigate the performance of the ML model for electrostatic interaction, the nonpolar interaction contribution of the solvent and the conformational entropy of the solute in the solvation free energy, three data sets with implicit or explicit water solvent models, and contribution of the conformational entropy of the solute are tested. We demonstrate that our ML model can predict the solvation free energy of molecules at chemical accuracy with a mean absolute error of less than 1 kcal mol-1 for subsets of the QM9 dataset and the Freesolv database. To solve the general data scarcity problem for a graph-based ML model, we propose a dimension reduction algorithm based on the distance between molecular graphs, which can be used to examine the diversity of the molecular data set. It provides a promising way to build a minimum training set to improve prediction for certain test sets where the space of molecular structures is predetermined.

Magnetic field intensified bi-enzyme system with in situ cofactor regeneration supported by magnetic nanoparticles.

Efficient dynamic interactions among cofactor, enzymes and substrate molecules are of primary importance for multi-step enzymatic reactions with in situ cofactor regeneration. Here we showed for the first time that the above dynamic interactions could be significantly intensified by exerting an external alternating magnetic field on magnetic nanoparticles-supported multi-enzymatic system so that the inter-particle collisions due to Brownian motion of nanoparticles could be improved. To that end, a multienzyme system including glutamate dehydrogenase (GluDH), glucose dehydrogenase (GDH) and cofactor NAD(H) were separately immobilized on silica coated Fe3O4 magnetic nanoparticles with an average diameter of 105 nm, and the effect of magnetic field strength and frequency on the kinetics of the coupled bi-enzyme reaction was investigated. It was found that at low magnetic field frequency (25 Hz and 100 Hz), increasing magnetic field strength from 9.8 to 161.1 Gs led to only very slight increase in reaction rate of the coupled bi-enzyme reaction expressed by glucose consumption rate. At higher magnetic field of 200 Hz and 500 Hz, reaction rate increased significantly with increase of magnetic field strength. When the magnetic field frequency was kept at 500 Hz, the reaction rate increased from 3.89 µM/min to 8.11 µM/min by increasing magnetic field strength from 1.3 to 14.2 Gs. The immobilized bi-enzyme system also showed good reusability and stability in the magnetic field (500 Hz, 14.2 Gs), that about 46% of original activity could be retained after 33 repeated uses, accounting for totally 34 days continuous operation. These results demonstrated the feasibility in intensifying molecular interactions among magnetic nanoparticle-supported multienzymes by using nano-magnetic stirrer for efficient multi-step transformations.

Effect of molecular mobility on coupled enzymatic reactions involving cofactor regeneration using nanoparticle-attached enzymes.

Cofactor-dependent multi-step enzymatic reactions generally require dynamic interactions among cofactor, enzyme and substrate molecules. Maintaining such molecular interactions can be quite challenging especially when the catalysts are tethered to solid state supports for heterogeneous catalysis for either biosynthesis or biosensing. The current work examines the effects of the pattern of immobilization, which presumably impacts molecular interactions on the surface of solid supports, on the reaction kinetics of a multienzymic system including glutamate dehydrogenase, glucose dehydrogenase and cofactor NAD(H). Interestingly, particle collision due to Brownian motion of nanoparticles successfully enabled the coupled reactions involving a regeneration cycle of NAD(H) even when the enzymes and cofactor were immobilized separately onto superparamagnetic nanoparticles (124 nm). The impact of particle motion and collision was evident in that the overall reaction rate was increased by over 100% by applying a moderate alternating magnetic field (500 Hz, 17 Gs), or using additional spacers, both of which could improve the mobility of the immobilized catalysts. We further observed that integrated immobilization, which allowed the cofactor to be placed in the molecular vicinity of enzymes on the same nanoparticles, could enhance the reaction rate by 1.8 fold. These results demonstrated the feasibility in manipulating molecular interactions among immobilized catalyst components by using nanoscale fabrication for efficient multienzymic biosynthesis.