The Protein Journal最新文献

The multidrug-resistant (MDR) phenotype of Pseudomonas aeruginosa poses a significant clinical challenge and frequently causes severe and potentially lethal infections. The activity of efflux proteins, which are membrane transporters that use the electrochemical gradient across the bacterial membrane to extrude antimicrobials and reduce their intracellular concentrations, is a significant factor in this resistance. One of the most well-known mechanisms of multidrug resistance in Pseudomonas aeruginosa is the Resistance–Nodulation-Division (RND) efflux pump system. MexB, the inner membrane transporter, is essential for substrate recognition and drug binding in the well-characterised MexAB–OprM complex. A variety of antibiotics, such as Macrolides, Fluoroquinolones, Tetracyclines, Sulfonamides, β-Lactams, Trimethoprim, Novobiocin, and Chloramphenicol, are resistant to this pump. The overexpression of this tripartite efflux system, which consists of the outer membrane channel OprM, the membrane fusion protein MexA, and the inner membrane transporter MexB, is regulated by genes including mexR, nalC, and nalD. MexB involvement in drug resistance makes it a prime target for the development of efflux pump inhibitors (EPIs). Antibiotics and EPIs together have the potential to restore antibacterial activity by enhancing intracellular drug retention. However, restricted access to complex structural and computational tools required for logical drug design has hindered the development of EPI. This study examines novel EPIs that target P. aeruginosa RND-type efflux systems and discusses recent structural discoveries regarding the MexB transporter.

The review summarises the basic information on non-specific factors of mammals that potentially have antimicrobial action. A comparison of previously known factors with the latest literature data is carried out. The following peptide and protein factors are considered: lysozymes, transferrins, interferons, interleukin-2, antimicrobial peptides (defensins, cathelicidins, histatins) and protective glycoproteins (mucins, lectins). These major antibacterial factors perform regulatory functions in the immune system, and some are also able to resist viral and fungal infections or oncological pathologies. The study of the internal antibacterial factors of mammals and the mechanisms of their activation is of great importance for the fight against bacterial infections, including antibiotic-resistant ones. This knowledge is necessary for the development of new approaches to the treatment of humans and farm animals.

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Glycerol kinase (GK) is a key part of glycerol metabolism. It connects the metabolic pathways for lipids and carbohydrates by phosphorylating glycerol to glycerol-3-phosphate in an ATP-dependent reaction. This is essential for maintaining carbohydrate homeostasis, plasma glycerol withdrawal, and the utilization of glycerol by different tissues. Together, these processes impact glucose uptake and lipid metabolism. This review discusses the structure of GK, highlights the implications of mutations in the primary sequence, and provides insights on the roles of the various functional domains in the GK-catalyzed reaction. It also discussed the roles of GK in glycerol metabolism, energy production, and its connections with various cellular pathways and disease conditions. The proper regulation of GK activity is crucial, reflecting its critical role in various important cellular processes. Therefore, its regulation has been analyzed from the gene level to posttranslational modification and has implications for GK-linked disease. Separately, the critical role of this enzyme in some disease-causing organisms made it a promising target for inhibitor development. We here explore the current state of GK inhibitor research and discuss strategies for their development. Challenges in GK inhibitor research are identified, and approaches such as high-throughput screening, structure-based drug design, and computational modelling for discovering novel inhibitors are reviewed. Finally, the review highlights critical areas for further research, including the role of GK in synthetic biology and tumour development, among others.

Over the past three years, AlphaFold—a deep learning–based protein structure prediction system—has transformed structural biology by providing near–experimental accuracy models directly from amino acid sequences. This narrative review synthesizes applications reported in the 2022–2025 literature across human, microbial, and viral systems, drawing on peer-reviewed studies as our data source. Representative examples include modeling of SARS-CoV-2 spike and nucleocapsid proteins in virology, assisting cryo-EM interpretation of bacterial ribosomal and membrane-protein complexes in microbiology, and refining conformational hypotheses for human GPCRs in biomedicine. Across these cases, AlphaFold predictions have complemented experimental workflows by accelerating hypothesis generation, improving model fitting within ambiguous density regions (poorly resolved areas of cryo-EM maps), and guiding mutagenesis strategies to probe dynamic conformational states. We also summarize recent method extensions: AlphaFold-Multimer improves multi-chain complex assembly prediction, while molecular dynamics (MD) simulations augment AlphaFold’s static models by sampling conformational flexibility and testing stability. Despite these advances, important limitations remain—particularly for intrinsically disordered regions, protein–ligand and protein–cofactor interactions, and very large or transient assemblies—and current community benchmarks indicate that approximately one-third of residues may lack atomistic precision, underscoring uncertainty in flexible or modified segments. Framed within a clear chronological window and evidence base, our analysis highlights both the practical impact and the remaining challenges of integrating AlphaFold with experiment, outlining priorities where further methodological innovation and orthogonal validation are needed.

Although biological drugs have been considered as one of the effective and growing therapeutic approaches in the pharmaceutical industry in recent decades, the largest concern about them is the insufficient stability and rapid degradation in the bloodstream due to their structural nature. One of the effective methods for increasing the circulating half-life of peptide and protein drugs is the addition of half-life-extending tags, which prevent both the degradation of the biological drug and its glomerular filtration by various mechanisms, thereby increasing its half-life. This review focuses on peptide and protein tags used to enhance the pharmacokinetic profiles of biological drugs by increasing their half-life. It discusses various tags, including HSA (Human Serum Albumin), ABD (Albumin Binding Domain), DARPINS (Designed Ankyrin Repeat proteins), XTEN, CTP (Carboxy Terminal Peptide), ELP (Elastin Like Peptide), and others, and highlights both FDA-approved products and candidates currently in different stages of clinical development. In the meantime, special attention has been paid to albumin-binding domains and albumin-binding domain antibody (AlbudAb), which increases the half-life of biological drugs by binding to albumin, as the most abundant and stable protein in the body.

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Rotaviruses (RV) are a major cause of severe childhood diarrhoea, particularly in developing nations, necessitating stable vaccines. Therefore, the presented preliminary study aimed to assess the impact of altered physicochemical properties on the structural stability of recombinant rotavirus capsid protein VP6 (RV-VP6). The expression system used in this study was designed by genetically engineering the RV-VP6 into E. coli (NiCo21(DE3))-pET28a host-vector system and purified using liquid chromatography. The purified RV-VP6 homology detection and structure prediction were conducted using LC–MS and HHpred computational analysis, which indicated a 100% probability of 1QHD_A Viral Capsid VP6 (1.95 Å), representing the crystal structure of VP6. The secondary and tertiary structural stability of RV-VP6 was evaluated in altered pH and Ca²⁺ concentrations using far UV-CD and intrinsic tryptophan fluorescence spectroscopy, respectively. The computational analysis of the far-UV CD spectra revealed a significant increase in the composition of α-helices and β-sheets in altered pH and Ca²⁺ environments compared to the denatured protein (p < 0.0001). Intrinsic fluorescence analysis of RV-VP6 at pH 7 yielded an emission λmax of 339 nm, which shifted to 342 nm at pH 5. In 1 mM Ca²⁺, a λmax of 340 nm was observed, with an increase in intensity in 10 mM Ca²⁺, accompanied by a slight blue shift to 338 nm. Investigation of RV-VP6 under thermal stress yielded unfolding concomitant with aggregation, rendering the process irreversible and nullifying analysis using equilibrium thermodynamics. These findings form the preliminary basis for our future evaluation of manufacturing stable and enhanced RV-VP6 vaccines through the downstream process control of (1) pH, which alters the charge distribution on the surface of the protein, leading to conformational changes, and (2) Ca²⁺ ions, which interact with specific amino acid residues in the protein, thereby affecting its structure and function.

Refolding of protein from denatured structure caused by sodium dodecyl sulfate (SDS) was examined using agarose native gel electrophoresis and circular dichroism (CD). Refolding of protein from SDS complex was induced with the addition of non-ionic and zwitterionic detergents followed by agarose native gel electrophoresis. The native gel electrophoresis was done without both SDS and non-ionic detergents in the agarose gel and running buffer. The electrophoretic mobility of bovine serum albumin (BSA) drastically increased with the addition of 1% SDS to the samples indicative of SDS-BSA complex formation. The SDS-denatured BSA returned to the native mobility by the addition of non-ionic Tween 20 and Triton X-100 and zwitterionic CHAPS as a function of detergent concentration. Refolding, at least partially, was confirmed by CD, which was done in the presence of both SDS and non-ionic detergents, a condition different from the native gel electrophoresis done in their absence. When BSA was denatured by both 1% SDS and a disulfide-reducing dithiothreitol, even 10% Tween 20 was insufficient to restore the native BSA mobility on agarose native gel electrophoresis. When BSA was denatured by 1% Sarkosyl and sodium lauroyl-glutamate, Tween 20 restored the native structure at Tween 20 concentration lower than the Tween 20 concentration used for SDS denaturation. A similar refolding by non-ionic detergents was also observed for a rabbit monoclonal IgG, but not for lysozyme. The results with lysozyme suggest strong SDS binding and difficulty in dissociating the bound SDS by non-ionic detergents due to high isoelectric point of the protein and thereby more SDS binding.

Serum albumin (SA) is a key biomarker routinely used in clinical practice. Multiple analytical methods exist for its measurement, including bromocresol green (BCG) colorimetry and capillary zone electrophoresis (CZE). However, discrepancies between methods raise concerns regarding result consistency and clinical interpretation. This study aimed to compare the analytical concordance between BCG and CZE methods for SA measurement, and to assess their clinical interchangeability. A cross-sectional study conducted on 109 serum samples collected from patients undergoing serum protein electrophoresis at Bechir Hamza Children’s Hospital. SA was measured using BCG and CZE. Descriptive statistics were calculated for each method. Paired t-test and Wilcoxon signed-rank test were used to assess differences in means. Method agreement was evaluated using Passing-Bablok regression, Intraclass Correlation Coefficient (ICC), Bland–Altman plot, and a confusion matrix. BCG significantly overestimated SA compared to CZE (41.49 ± 7.30 g/L vs. 37.48 ± 6.85 g/L; p < 0.001). Passing-Bablok regression revealed a regression line of y = 1.02x + 3.21, indicating a consistent positive bias. The R² value was 0.891, suggesting strong correlation. The ICC was 0.81 (95% CI 0.081–0.932), reflecting good agreement. The Bland–Altman analysis confirmed a mean difference of 4.01 g/L. Confusion matrix analysis showed perfect concordance in low albumin values but significant misclassification at higher levels, with BCG shifting many values into higher categories. While BCG and CZE methods show strong correlation, BCG consistently overestimates albumin concentrations. These findings underscore the need for method standardization and careful interpretation of results in clinical settings.