The Protein Journal最新文献

It is well known that preslaughter (antemortem) stress such as rough handling, transportation, a negative environment, physical discomfort, lack of consistent routine, and bad feed quality has a big impact on meat quality. The antemortem-induced poor meat quality is characterized by low pH, a pale and exudative appearance, and a soft texture. Previous studies indicate that antemortem stress plays a key role in regulating protein acetylation and glycolysis in postmortem (PM) muscle. However, the underlying molecular and biochemical mechanism is not clearly understood yet. In this study, we investigated the relationship between antemortem and protein acetylation and glycolysis using murine longissimus dorsi muscle isolated from ICR mice and murine muscle cell line C₂C₁₂ treated with epinephrine hydrochloride. Because adrenaline secretion increases in stressed animals, epinephrine hydrochloride was intraperitoneally injected epinephrine into mice to simulate pre-slaughter stress in this study to facilitate experimental operations and save experimental costs. Our findings demonstrated that protein acetylation in pyruvate kinase M1 (PKM1) form is significantly reduced by antemortem, and the reduced acetylation subsequently leads to an increase in PKM1 enzymatic activity which causes increased glycolysis in PM muscle. By using molecular approaches, we identified lysine 141 in PKM1 as a critical residue for acetylation. Our results in this study provide useful insight for controlling or improving meat quality in the future.

Abstract

To solve the large size faultiness of Oryza sativa recombinant human serum albumin nanoparticle (OsrHSA NP), the structural discrepancies between OsrHSA and plasma-derived human serum albumin (pdHSA) were analyzed deeply in this research. It demonstrated that there were some subtle structural discrepancies located in subdomain IA and IIA between OsrHSA and pdHSA, which included peptide backbone, disulphide bridge and some amino acids. Firstly, the structural discrepancies were investigated through literature comparison, it inferred that the structural discrepancies resulted from the fatty acid (FA) binding to OsrHSA at site 2 of subdomain IA and IIA. To form a cavity for accommodation of FA molecule in OsrHSA, the peptide backbone structure of subdomain IA and IIA would change, accompanied by the conformational transition of disulphide bridges and side chain structure change of some amino acids in subdomain IA and IIA. These alterations induced the exposure of tryptophan (Trp) and tyrosine (Tyr) residues in subdomain IA and IIA and the decrease of net negative charges of molecular surface. The former would promote more OsrHSA molecules aggregate, and the latter would weaken the electrostatic repulsion. As a result, the size of OsrHSA NP was more extensive than that of pdHSA NP (175.84 ± 15.63 nm vs. 31.67 ± 1.31 nm) when the concentration of Dimethyl Sulphoxide (DMSO) was 30% (v/v). In this study, the experimental scheme of OsrHSA NP preparation was improved. There were two changes in the enhanced preparation scheme: pH 8.2 PBS buffer and 63% DMSO. It indicated that the improved OsrHSA NP carrier was comparable to the pdHSA NP carrier. The size and drug loading of paclitaxel-loaded improved OsrHSA NP were 53.57 ± 3.63 nm and 7.25 ± 0.46% (w/w), and those of docetaxel-loaded improved OsrHSA NP were 44.75 ± 2.26 nm and 8.43 ± 0.74% (w/w). Moreover, both NPs exhibited good stability for 168 h at 7.4 pH values. It is established that the improved OsrHSA NP is comparable to the pdHSA NP as a taxane delivery system.

Molecular dynamics (MD) simulations are routinely performed of biomolecules in solution, because this is their native environment. However, the structures used in such simulations are often obtained with X-ray crystallography, which provides the atomic coordinates of the biomolecule in a crystal environment. With the advent of free electron lasers and time-resolved techniques, X-ray crystallography can now also access metastable states that are intermediates in a biochemical process. Such experiments provide additional data, which can be used, for example, to optimize MD force fields. Doing so requires that the simulation of the biomolecule is also performed in the crystal environment. However, in contrast to simulations of biomolecules in solution, setting up a crystal is challenging. In particular, because not all solvent molecules are resolved in X-ray crystallography, adding a suitable number of solvent molecules, such that the properties of the crystallographic unit cell are preserved in the simulation, can be difficult and typically is a trial-and-error based procedure requiring manual interventions. Such interventions preclude high throughput applications. To overcome this bottleneck, we introduce gmXtal, a tool for setting up crystal simulations for MD simulations with GROMACS. With the information from the protein data bank (rcsb.org) gmXtal automatically (i) builds the crystallographic unit cell; (ii) sets the protonation of titratable residues; (iii) builds missing residues that were not resolved experimentally; and (iv) adds an appropriate number of solvent molecules to the system. gmXtal is available as a standalone tool https://gitlab.com/pbuslaev/gmxtal .

Biological macromolecules are found in different shapes and sizes. Among these, enzymes catalyze biochemical reactions and are essential in all organisms, but is there a limit size for them to function properly? Large enzymes such as catalases have hundreds of kDa and are formed by multiple subunits, whereas most enzymes are smaller, with molecular weights of 20–60 kDa. Enzymes smaller than 10 kDa could be called microenzymes and the present literature review brings together evidence of their occurrence in nature. Additionally, bioactive peptides could be a natural source for novel microenzymes hidden in larger peptides and molecular downsizing could be useful to engineer artificial enzymes with low molecular weight improving their stability and heterologous expression. An integrative approach is crucial to discover and determine the amino acid sequences of novel microenzymes, together with their genomic identification and their biochemical biological and evolutionary functions.

Protein aggregation is related to numerous pathological conditions like Alzheimer’s and Parkinson’s disease. In our study, we have shown that an already existing FDA-approved drug; methotrexate (MTX) can be reprofiled on preformed α-chymotrypsinogen A (α-Cgn A) aggregates. The zymogen showed formation of aggregates upon interaction with mercuric ions, with increasing concentration of Hg₂Cl₂ (0-150 µM). The hike in ThT and ANS fluorescence concomitant with blue shift, bathochromic shift and the hyperchromic effect in the CR absorbance, RLS and turbidity measurements, substantiate the zymogen β-rich aggregate formation. The secondary structural alterations of α- Cgn A as analyzed by CD measurements, FTIR and Raman spectra showed the transformation of native β-barrel conformation to β-inter-molecular rich aggregates. The native α- Cgn A have about 30% α-helical content which was found to be about 3% in presence of mercuric ions suggesting the formation of aggregates. The amorphous aggregates were visualized by SEM. On incubation of Hg₂Cl₂ treated α- Cgn A with increasing concentration of the MTX resulted in reversing aggregates to the native-like structure. These results were supported by remarkable decrease in ThT and ANS fluorescence intensities and CR absorbance and also consistent with CD, FTIR, and Raman spectroscopy data. MTX was found to increase the α-helical content of the zymogen from 3 to 15% proposing that drug is efficient in disrupting the β-inter-molecular rich aggregates and reverting it to native like structure. The SEM images are in accordance with CD data showing the disintegration of aggregates. The most effective concentration of the drug was found to be 120 µM. Molecular docking analysis showed that MTX molecule was surrounded by the hydrophobic residues including Phe39, His40, Arg145, Tyr146, Thr151, Gly193, Ser195, and Gly216 and conventional hydrogen bonds, including Gln73 (bond length: 2.67Å), Gly142 (2.59Å), Thr144 (2.81Å), Asn150 (2.73Å), Asp153 (2.71Å), and Cys191 (2.53Å). This investigation will help to find the use of already existing drugs to cure protein misfolding-related abnormalities.

S100A8 and S100A9 belong to the calcium-binding, damage associated molecular pattern (DAMP) proteins shown to aggravate the pathogenesis of rheumatoid arthritis (RA) through their interaction with the TLR4, RAGE and CD36 receptors. S100A8 and S100A9 proteins tend to exist in monomeric, homo and heterodimeric forms, which have been implicated in the pathogenesis of RA, via interacting with Pattern Recognition receptors (PRRs). The study aims to assess the influence of changes in the structure and biological assembly of S100A8 and S100A9 proteins as well as their interaction with significant receptors in RA through computational methods and surface plasmon resonance (SPR) analysis. Molecular docking analysis revealed that the S100A9 homodimer and S100A8/A9 heterodimer showed higher binding affinity towards the target receptors. Most S100 proteins showed good binding affinity towards TLR4 compared to other receptors. Based on the 50 ns MD simulations, TLR4, RAGE, and CD36 formed stable complexes with the monomeric and dimeric forms of S100A8 and S100A9 proteins. However, SPR analysis showed that the S100A8/A9 heterodimers formed stable complexes and exhibited high binding affinity towards the receptors. SPR data also indicated that TLR4 and its interactions with S100A8/A9 proteins may play a primary role in the pathogenesis of RA, with additional contributions from CD36 and RAGE interactions. Subsequent in vitro and in vivo investigations are warranted to corroborate the involvement of S100A8/A9 and the expression of TLR4, RAGE, and CD36 in the pathophysiology of RA.

Abstract

Protein–protein interactions are crucial for the entry of viruses into the cell. Understanding the mechanism of interactions is essential in studying human-virus association, developing new biologics and drug candidates, as well as viral infections and antiviral responses. Experimental methods to analyze human-virus protein–protein interactions based on protein sequence data are time-consuming and labor-intensive, so machine learning models are being developed to predict interactions and determine large-scale interactomes between species. The present work highlights the importance of sequence features in classifying interacting and non-interacting proteins from the protein sequence data. Higher dimensional amino acid sequence features such as Amino Acid Composition (AAC), Dipeptide Composition (DPC), Grouped Amino Acid Composition (GAAC), Pseudo-Amino Acid Composition (PAAC) etc., are extracted. Following feature extraction, three datasets were created: Dataset 1 contains all of the extracted features. While Datasets 2 and 3 contain the most relevant features obtained through dimensionality reduction. To analyze the importance of high-dimensional features and their participation in protein–protein interactions, a random forest classifier is trained on three datasets. With dimensionality reduction, the model exhibited exceptional accuracy, indicating that dimensionality reduction fails to capture the complexity of interactions and the underlying relationships between human and viral proteins. As a result of retaining high-dimensional features, it is possible to capture all the characteristics of protein–protein interactions that resemble host–pathogen associations, leading to the development of biologically meaningful models. Our proposed approach is a more realistic and comprehensive classification model, leading to deeper insights and better applications in virology and drug development.

Recent clinical data have identified infant patients with lethal ITPA deficiencies. ITPA is known to modulate ITP concentrations in cells and has a critical function in neural development which is not understood. Polymorphism of the ITPA gene affects outcomes for both ribavirin and thiopurine based therapies and nearly one third of the human population is thought to harbor ITPA polymorphism. In a previous site-directed mutagenesis alanine screen of the ITPA substrate selectivity pocket, we identified the ITPA mutant, E22A, as a gain-of function mutant with enhanced ITP hydrolysis activity. Here we report a rational enzyme engineering experiment to investigate the biochemical properties of position 22 ITPA mutants and find that the E22D ITPA has two- and four-fold improved substrate selectivity for ITP over the canonical purine triphosphates ATP and GTP, respectively, while maintaining biological activity. The novel E22D ITPA should be considered as a platform for further development of ITPA therapies.