Isothermal Chemical Denaturation: Data Analysis, Error Detection, and Correction by PARAFAC2

Fluorescence-Based Protein Stability Monitoring-A Review

Proteins are large biomolecules with a specific structure that is composed of one or more long amino acid chains. Correct protein structures are directly linked to their correct function, and many environmental factors can have either positive or negative effects on this structure. Thus, there is a clear need for methods enabling the study of proteins, their correct folding, and components affecting protein stability. There is a significant number of label-free methods to study protein stability. In this review, we provide a general overview of these methods, but the main focus is on fluorescence-based low-instrument and -expertise-demand techniques. Different aspects related to thermal shift assays (TSAs), also called differential scanning fluorimetry (DSF) or ThermoFluor, are introduced and compared to isothermal chemical denaturation (ICD). Finally, we discuss the challenges and comparative aspects related to these methods, as well as future opportunities and assay development directions.

Tensor methods in data analysis of chromatography/mass spectroscopy-based plant metabolomics

Plant metabolomics is an important research area in plant science. Chemometrics is a useful tool for plant metabolomic data analysis and processing. Among them, high-order chemometrics represented by tensor modeling provides a new and promising technical method for the analysis of complex multi-way plant metabolomics data. This paper systematically reviews different tensor methods widely applied to the analysis of complex plant metabolomic data. The advantages and disadvantages as well as the latest methodological advances of tensor models are reviewed and summarized. At the same time, application of different tensor methods in solving plant science problems are also reviewed and discussed. The reviewed applications of tensor methods in plant metabolomics cover a wide range of important plant science topics including plant gene mutation and phenotype, plant disease and resistance, plant pharmacology and nutrition analysis, and plant products ingredient characterization and quality evaluation. It is evident from the review that tensor methods significantly promote the automated and intelligent process of plant metabolomics analysis and profoundly affect the paradigm of plant science research. To the best of our knowledge, this is the first review to systematically summarize the tensor analysis methods in plant metabolomic data analysis.

Creatinase: Using Increased Entropy to Improve the Activity and Thermostability

Improving protein thermostability in mutagenesis-based enzyme engineering was often achieved by enhancing interresidue interactions via mutation to increase the enthalpy penalty of unfolding. However, this approach may trade off the functional activity due to the loss of structural flexibility of the biomolecule. Here, by performing X-ray crystallography, enzymatic kinetic experiments, neutron scattering, and thermodynamical measurements, we compared the structures, catalytic behaviors, dynamics, and thermostability between a wild-type creatinase and its four-point mutant. We found that the mutant is an entropy-driven thermostable protein with higher structural flexibility, i.e., higher conformational entropy, in the folded state compared to the wild type. The increased conformational entropy of the mutant in the folded state can reduce the entropy gain during unfolding and thus renders it greater thermostability. Moreover, the increased structural flexibility, particularly around the catalytic site, can broaden the mutant's working temperature range and considerably improve its activity at ambient conditions, which is crucial for its application in diagnosing kidney diseases. Complementary all-atom molecular dynamics simulations indicated that the four mutations replaced several of the strong interresidue interactions (electrostatic interactions and hydrogen bonds) with weak hydrophobic interactions. These substitutions not only release the structural flexibility to promote the thermostability and enzymatic activity of the protein but they also preserve the protein structure from collapsing. Our findings may pave a route for the entropy-driven strategy to design proteins with high thermostability and activity.

Self-Interactions of Two Monoclonal Antibodies: Small-Angle X-ray Scattering, Light Scattering, and Coarse-Grained Modeling

Using light scattering (LS), small-angle X-ray scattering (SAXS), and coarse-grained Monte Carlo (MC) simulations, we studied the self-interactions of two monoclonal antibodies (mAbs), PPI03 and PPI13. With LS measurements, we obtained the osmotic second virial coefficient, B₂₂, and the molecular weight, M_w, of the two mAbs, while with SAXS measurements, we studied the mAbs' self-interaction behavior in the high protein concentration regime up to 125 g/L. Through SAXS-derived coarse-grained representations of the mAbs, we performed MC simulations with either a one-protein or a two-protein model to predict B₂₂. By comparing simulation and experimental results, we validated our models and obtained insights into the mAbs' self-interaction properties, highlighting the role of both ion binding and charged patches on the mAb surfaces. Our models provide useful information about mAbs' self-interaction properties and can assist the screening of conditions driving to colloidal stability.