NMR Structure Determinations of Small Proteins Using only One Fractionally 20% 13C- and Uniformly 100% 15N-Labeled Sample

Carbohydrate binding modules: Compact yet potent accessories in the specific substrate binding and performance evolution of carbohydrate-active enzymes

Carbohydrate binding modules (CBMs) are independent non-catalytic domains widely found in carbohydrate-active enzymes (CAZymes), and they play an essential role in the substrate binding process of CAZymes by guiding the appended catalytic modules to the target substrates. Owing to their precise recognition and selective affinity for different substrates, CBMs have received increasing research attention over the past few decades. To date, CBMs from different origins have formed a large number of families that show a variety of substrate types, structural features, and ligand recognition mechanisms. Moreover, through the modification of specific sites of CBMs and the fusion of heterologous CBMs with catalytic domains, improved enzymatic properties and catalytic patterns of numerous CAZymes have been achieved. Based on cutting-edge technologies in computational biology, gene editing, and protein engineering, CBMs as auxiliary components have become portable and efficient tools for the evolution and application of CAZymes. With the aim to provide a theoretical reference for the functional research, rational design, and targeted utilization of novel CBMs in the future, we systematically reviewed the function-related characteristics and potentials of CAZyme-derived CBMs in this review, including substrate recognition and binding mechanisms, non-catalytic contributions to enzyme performances, module modifications, and innovative applications in various fields.

Arginine-Enhanced Antimicrobial Activity of Nanozymes against Gram-Negative Bacteria

The continuous reduction of clinically available antibiotics has made it imperative to exploit more effective antimicrobial therapies, especially for difficult-to-treat Gram-negative pathogens. Herein, it is shown that the combination of an antimicrobial nanozyme with the clinically compatible basic amino acid L-arginine affords a potent treatment for infections with Gram-negative pathogens. In particular, the antimicrobial activity of the antimicrobial nanozyme is dramatically increased by ≈1000-fold after L-arginine stimulation. Specifically, the combination therapy enhances bacterial outer and inner membrane permeability and promotes intracellular reactive oxygen species (ROS) generation. Moreover, the metabolomic and transcriptomic results reveal that combination treatment leads to the increased ROS-mediated damage by inhibiting the tricarboxylic acid cycle and oxidative phosphorylation, thereby inducing an imbalance of the antioxidant and oxidant systems. Importantly, L-arginine dramatically significantly accelerates the healing of infected wounds in mouse models of multidrug-resistant peritonitis-sepsis and skin wound infection. Overall, this work demonstrates a novel synergistic antibacterial strategy by combining the antimicrobial nanozymes with L-arginine, which substantively facilitates the nanozyme-mediated killing of pathogens by promoting ROS production.

The 100-protein NMR spectra dataset: A resource for biomolecular NMR data analysis

Multidimensional NMR spectra are the basis for studying proteins by NMR spectroscopy and crucial for the development and evaluation of methods for biomolecular NMR data analysis. Nevertheless, in contrast to derived data such as chemical shift assignments in the BMRB and protein structures in the PDB databases, this primary data is in general not publicly archived. To change this unsatisfactory situation, we present a standardized set of solution NMR data comprising 1329 2-4-dimensional NMR spectra and associated reference (chemical shift assignments, structures) and derived (peak lists, restraints for structure calculation, etc.) annotations. With the 100-protein NMR spectra dataset that was originally compiled for the development of the ARTINA deep learning-based spectra analysis method, 100 protein structures can be reproduced from their original experimental data. The 100-protein NMR spectra dataset is expected to help the development of computational methods for NMR spectroscopy, in particular machine learning approaches, and enable consistent and objective comparisons of these methods.

Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process

The conventional drug discovery approach is an expensive and time-consuming process, but its limitations have been overcome with the help of mathematical modeling and computational drug design approaches. Previously, finding a small molecular candidate as a drug against a disease was very costly and required a long time to screen a compound against a specific target. The development of novel targets and small molecular candidates against different diseases including emerging and reemerging diseases remains a major concern and necessitates the development of novel therapeutic targets as well as drug candidates as early as possible. In this regard, computational and mathematical modeling approaches for drug development are advantageous due to their fastest predictive ability and cost-effectiveness features. Computer-aided drug design (CADD) techniques utilize different computer programs as well as mathematics formulas to comprehend the interaction of a target and drugs. Traditional methods to determine small-molecule candidates as a drug have several limitations, but CADD utilizes novel methods that require little time and accurately predict a compound against a specific disease with minimal cost. Therefore, this review aims to provide a brief insight into the mathematical modeling and computational approaches for identifying a novel target and small molecular candidates for curing a specific disease. The comprehensive review mainly focuses on biological target prediction, structure-based and ligand-based drug design methods, molecular docking, virtual screening, pharmacophore modeling, quantitative structure-activity relationship (QSAR) models, molecular dynamics simulation, and MM-GBSA/MM-PBSA approaches along with valuable database resources and tools for identifying novel targets and therapeutics against a disease. This review will help researchers in a way that may open the road for the development of effective drugs and preventative measures against a disease in the future as early as possible.

The NMR structure of the engineered halophilic DnaE intein for segmental isotopic labeling using conditional protein splicing

Protein trans-splicing catalyzed by split inteins has been used for segmental isotopic labeling of proteins for alleviating the complexity of NMR signals. Whereas inteins spontaneously trigger protein splicing upon protein folding, inteins from extremely halophilic organisms require a high salinity condition to induce protein splicing. We designed and created a salt-inducible intein from the widely used DnaE intein from Nostoc punctiforme by introducing 29 mutations, which required a lower salt concentration than naturally occurring halo-obligate inteins. We determined the NMR solution structure of the engineered salt-inducible DnaE intein in 2 M NaCl, showing the essentially identical three-dimensional structure to the original one, albeit it unfolds without salts. The NMR structure of a halo-obligate intein under high salinity suggests that the stabilization of the active folded conformation is not a mere result of various intramolecular interactions but the subtle energy balance from the complex interactions, including the solvation energy, which involve waters, ions, co-solutes, and protein polypeptide chains.

Tying up the Loose Ends: A Mathematically Knotted Protein

Knots have attracted scientists in mathematics, physics, biology, and engineering. Long flexible thin strings easily knot and tangle as experienced in our daily life. Similarly, long polymer chains inevitably tend to get trapped into knots. Little is known about their formation or function in proteins despite >1,000 knotted proteins identified in nature. However, these protein knots are not mathematical knots with their backbone polypeptide chains because of their open termini, and the presence of a “knot” depends on the algorithm used to create path closure. Furthermore, it is generally not possible to control the topology of the unfolded states of proteins, therefore making it challenging to characterize functional and physicochemical properties of knotting in any polymer. Covalently linking the amino and carboxyl termini of the deeply trefoil-knotted YibK from Pseudomonas aeruginosa allowed us to create the truly backbone knotted protein by enzymatic peptide ligation. Moreover, we produced and investigated backbone cyclized YibK without any knotted structure. Thus, we could directly probe the effect of the backbone knot and the decrease in conformational entropy on protein folding. The backbone cyclization did not perturb the native structure and its cofactor binding affinity, but it substantially increased the thermal stability and reduced the aggregation propensity. The enhanced stability of a backbone knotted YibK could be mainly originated from an increased ruggedness of its free energy landscape and the destabilization of the denatured state by backbone cyclization with little contribution from a knot structure. Despite the heterogeneity in the side-chain compositions, the chemically unfolded cyclized YibK exhibited several macroscopic physico-chemical attributes that agree with theoretical predictions derived from polymer physics.