Genetic Code Expansion for Mechanistic Studies in Ion Channels: An (Un)natural Union of Chemistry and Biology.

Ion channels play central roles in biology and human health by catalyzing the transmembrane flow of electrical charge. These proteins are ideal targets for genetic code expansion (GCE) methods because it is feasible to measure ion channel activity from miniscule amounts of protein and to analyze the resulting data via rigorous, established biophysical methods. In an ideal scenario, the encoding of synthetic, noncanonical amino acids via GCE allows the experimenter to ask questions inaccessible to traditional methods.

Expression and purification of fluorinated proteins from mammalian suspension culture.

The site-specific encoding of noncanonical amino acids allows for the introduction of rationalized chemistry into a target protein. Of the methods that enable this technology, evolved tRNA and synthetase pairs offer the potential for expanded protein production and purification. Such an approach combines the versatility of solid-phase peptide synthesis with the scalable features of recombinant protein production.

Real-time observation of functional specialization among phosphorylation sites in CFTR.

Phosphoregulation is ubiquitous in biology. Defining the functional roles of individual phosphorylation sites within a multivalent system remains particularly challenging. We have therefore applied a chemical biology approach to light-control the state of single candidate phosphoserines in the canonical anion channel CFTR while simultaneously measuring channel activity.

Tuning phenylalanine fluorination to assess aromatic contributions to protein function and stability in cells.

The aromatic side-chains of phenylalanine, tyrosine, and tryptophan interact with their environments via both hydrophobic and electrostatic interactions. Determining the extent to which these contribute to protein function and stability is not possible with conventional mutagenesis. Serial fluorination of a given aromatic is a validated method in vitro and in silico to specifically alter electrostatic characteristics, but this approach is restricted to a select few experimental systems.

Research on the Derated Power Data Identification Method of a Wind Turbine Based on a Multi-Gaussian-Discrete Joint Probability Model.

This paper focuses on how to identify normal, derated power and abnormal data in operation data, which is key to intelligent operation and maintenance applications such as wind turbine condition diagnosis and performance evaluation. Existing identification methods can distinguish normal data from the original data, but usually remove power curtailment data as outliers. A multi-Gaussian-discrete probability distribution model was used to characterize the joint probability distribution of wind speed and power from wind turbine SCADA data, taking the derated power of the wind turbine as a hidden random variable.