Enhanced activity of hyperthermostable Pyrococcus horikoshii endoglucanase in superbase ionic liquids.

OBJECTIVES: Ionic liquids (ILs) that dissolve biomass are harmful to the enzymes that degrade lignocellulose. Enzyme hyperthermostability promotes a tolerance to ILs. Therefore, the limits of hyperthemophilic Pyrococcus horikoschii endoglucanase (PhEG) to tolerate 11 superbase ILs were explored. RESULTS: PhEG was found to be most tolerant to 1-ethyl-3-methylimidazolium acetate ([EMIM]OAc) in soluble 1% carboxymethylcellulose (CMC) and insoluble 1% Avicel substrates. At 35% concentration, this IL caused an increase in enzyme activity (up to 1.5-fold) with CMC. Several ILs were more enzyme inhibiting with insoluble Avicel than with soluble CMC. Km increased greatly in the presence ILs, indicating significant competitive inhibition. Increased hydrophobicity of the IL cation or anion was associated with the strongest enzyme inhibition and activation. Surprisingly, PhEG activity was increased 2.0-2.5-fold by several ILs in 4% substrate. Cations exerted the main role in competitive inhibition of the enzyme as revealed by their greater binding energy to the active site. CONCLUSIONS: These results reveal new ways to design a beneficial combination of ILs and enzymes for the hydrolysis of lignocellulose, and the strong potential of PhEG in industrial, high substrate concentrations in aqueous IL solutions.

Monte Carlo Simulations of Au38(SCH3)24 Nanocluster Using Distance-Based Machine Learning Methods.

We present an implementation of distance-based machine learning (ML) methods to create a realistic atomistic interaction potential to be used in Monte Carlo simulations of thermal dynamics of thiolate (SR) protected gold nanoclusters. The ML potential is trained for Au38(SR)24 by using previously published, density functional theory (DFT) based, molecular dynamics (MD) simulation data on two experimentally characterized structural isomers of the cluster and validated against independent DFT MD simulations. This method opens a door to efficient probing of the configuration space for further investigations of thermal-dependent electronic and optical properties of Au38(SR)24. Our ML implementation strategy allows for generalization and accuracy control of distance-based ML models for complex nanostructures having several chemical elements and interactions of varying strength.

A method for structure prediction of metal-ligand interfaces of hybrid nanoparticles.

Hybrid metal nanoparticles, consisting of a nano-crystalline metal core and a protecting shell of organic ligand molecules, have applications in diverse areas such as biolabeling, catalysis, nanomedicine, and solar energy. Despite a rapidly growing database of experimentally determined atom-precise nanoparticle structures and their properties, there has been no successful, systematic way to predict the atomistic structure of the metal-ligand interface. Here, we devise and validate a general method to predict the structure of the metal-ligand interface of ligand-stabilized gold and silver nanoparticles, based on information about local chemical environments of atoms in experimental data. In addition to predicting realistic interface structures, our method is useful for investigations on the steric effects at the metal-ligand interface, as well as for predicting isomers and intermediate structures induced by thermal dynamics or interactions with the environment. Our method is applicable to other hybrid nanomaterials once a suitable set of reference structures is available.