We report a study of self-assembled beta-pleated sheets in B. mori silk fibroin films using thermal analysis and infrared spectroscopy. B. mori silk fibroin may stand as an exemplar of fibrous proteins containing crystalline beta-sheets. Materials were prepared from concentrated solutions (2−5 wt % fibroin in water) and then dried to achieve a less ordered state without beta-sheets. Crystallization of beta-pleated sheets was effected either by heating the films above the glass transition temperature (Tg) and holding isothermally or by exposure to methanol. The fractions of secondary structural components including random coils, alpha-helices, beta-pleated sheets, turns, and side chains were evaluated using Fourier self-deconvolution (FSD) of the infrared absorbance spectra. The silk fibroin films were studied thermally using temperature-modulated differential scanning calorimetry (TMDSC) to obtain the reversing heat capacity. The increment of the reversing heat capacity ΔCp0(Tg) at the glass transition fo...

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Determining Beta Sheet Crystallinity in Fibrous Proteins by Thermal Analysis and Infrared Spectroscopy

https://pubs.rsc.org/en/content/articlepdf/2021/GC/D1GC01852C

Enzyme entrapment, biocatalyst immobilization without covalent attachment

A novel water-in-ionic liquid microemulsion and its interfacial effect on the activity of laccase B Biointerfaces

There are thousands of papers published every year investigating the properties and possible applications of ionic liquids. Industrial use of these exceptional fluids requires adequate understanding of their physical properties, in order to create the ionic liquid that will optimally suit the application. Computational property prediction arose from the urgent need to minimise the time and cost that would be required to experimentally test different combinations of ions. This review discusses the use of machine learning algorithms as property prediction tools for ionic liquids (either as standalone methods or in conjunction with molecular dynamics simulations), presents common problems of training datasets and proposes ways that could lead to more accurate and efficient models.

/pdf/a-review-on-machine-learning-algorithms-for-the-ionic-liquid-1t11a97auc.pdf

A review on machine learning algorithms for the ionic liquid chemical space

Room temperature Ionic liquids (RTILs) have evolved as a new type of solvent for biocatalysis, mainly due to their unique and tunable physical properties.[1] In addition, within the family of organic semiconductor-based sensors, organic electrochemical transistors (OECTs) have attracted particular interest.[2] Here, we present a simple and robust biosensor, based on a OECT, capable of measuring lactic acid using a gel-like polymeric materials that endow RTIL (ionogel)[3] as solid-state electrolyte both to immobilise the enzyme and to serve as a supporting electrolyte.[4] This represents the first step towards the achievement of a fast, flexible, miniaturised and cheap way of measuring lactate concentration in sweat.

Organic electrochemical transistor incorporating anionogel as solid state electrolyte for lactate sensing

Biopolymer processing and handling is greatly facilitated by the use of ionic liquids, given the increased solubility, and in some cases, structural stability imparted to these molecules. Focussing on proteins, we highlight here not just the key drivers behind protein-ionic liquid interactions that facilitate these functionalities, but address relevant current and potential applications of protein-ionic liquid interactions, including areas of future interest.

/pdf/proteins-in-ionic-liquids-reactions-applications-and-futures-10diu6kya8.pdf

Proteins in Ionic Liquids: Reactions, Applications, and Futures

Rate constants for a bimolecular nucleophilic substitution (SN2) process in a range of ionic liquids are correlated with calculated parameters associated with the charge localisation on the cation of the ionic liquid (including the molecular electrostatic potential). Simple linear regression models proved effective, though the interdependency of the descriptors needs to be taken into account when considering generality. A series of ionic liquids were then prepared and evaluated as solvents for the same process; this data set was rationally chosen to incorporate homologous series (to evaluate systematic variation) and functionalities not available in the original data set. These new data were used to evaluate and refine the original models, which were expanded to include simple artificial neural networks. Along with showing the importance of an appropriate data set and the perils of overfitting, the work demonstrates that such models can be used to reliably predict ionic liquid solvent effects on an organic process, within the limits of the data set.

Alexandra Schindl

Papers

Proteins in Ionic Liquids: Reactions, Applications, and Futures

Controlling the outcome of SN2 reactions in ionic liquids: from rational data set design to predictive linear regression models