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1.
Signal Transduct Target Ther ; 7(1): 166, 2022 05 21.
Article in English | MEDLINE | ID: covidwho-1947279

ABSTRACT

The therapeutic use of messenger RNA (mRNA) has fueled great hope to combat a wide range of incurable diseases. Recent rapid advances in biotechnology and molecular medicine have enabled the production of almost any functional protein/peptide in the human body by introducing mRNA as a vaccine or therapeutic agent. This represents a rising precision medicine field with great promise for preventing and treating many intractable or genetic diseases. In addition, in vitro transcribed mRNA has achieved programmed production, which is more effective, faster in design and production, as well as more flexible and cost-effective than conventional approaches that may offer. Based on these extraordinary advantages, mRNA vaccines have the characteristics of the swiftest response to large-scale outbreaks of infectious diseases, such as the currently devastating pandemic COVID-19. It has always been the scientists' desire to improve the stability, immunogenicity, translation efficiency, and delivery system to achieve efficient and safe delivery of mRNA. Excitingly, these scientific dreams have gradually been realized with the rapid, amazing achievements of molecular biology, RNA technology, vaccinology, and nanotechnology. In this review, we comprehensively describe mRNA-based therapeutics, including their principles, manufacture, application, effects, and shortcomings. We also highlight the importance of mRNA optimization and delivery systems in successful mRNA therapeutics and discuss the key challenges and opportunities in developing these tools into powerful and versatile tools to combat many genetic, infectious, cancer, and other refractory diseases.


Subject(s)
COVID-19 , COVID-19/genetics , COVID-19/therapy , Humans , Pandemics , Proteins , RNA, Messenger/genetics
2.
Sci Rep ; 12(1): 9434, 2022 06 08.
Article in English | MEDLINE | ID: covidwho-1947486

ABSTRACT

The present study aims to assess the effects of thermal and chemical inactivating procedures, that can be used for SARS-CoV-2 inactivation, on different salivary analytes. SDS-Polyacrylamide Gel Electrophoresis (SDS-PAGE) protein profile and a panel of 25 specific biomarkers of oxidative status, stress, metabolism and tissue damage were evaluated in samples subjected to different treatments: thermal (65 °C or 92 °C) and chemical with detergents [sodium dodecyl sulphate (SDS), Triton X-100 or NP-40]. Salivary SDS-PAGE profile was most affected by heating at 92 °C, with three and two protein bands decreasing and increasing their expression levels, respectively. This treatment also affected the results of several enzymes, with some of them being also affected by heating at 65 °C and incubation with SDS. The use of Triton X-100 or NP-40 resulted in increased values of cortisol, triglycerides and glucose, not affecting the other tested biomarkers. The present results will help researchers and clinicians to select the best protocols to work in safe conditions with saliva, taking into account the target analyte planned to be measured.


Subject(s)
COVID-19 , Saliva , Electrophoresis, Polyacrylamide Gel , Humans , Octoxynol/pharmacology , Proteins , SARS-CoV-2
3.
Methods Mol Biol ; 2511: 375-394, 2022.
Article in English | MEDLINE | ID: covidwho-1941391

ABSTRACT

Machine learning is being employed for the development of diagnostic methods for several diseases, but prognostic techniques are still poorly explored. The development of such approaches is essential to assist healthcare workers to ensure the most appropriate treatment for patients. In this chapter, we demonstrate a detailed protocol for the application of machine learning to MALDI-TOF MS spectra of COVID-19-infected plasma samples for risk classification and biomarker identification.


Subject(s)
COVID-19 , Biomarkers/analysis , COVID-19/diagnosis , Humans , Machine Learning , Proteins , Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization/methods
4.
Front Immunol ; 13: 932525, 2022.
Article in English | MEDLINE | ID: covidwho-1933700

ABSTRACT

Posttranslational modifications (PTMs) allow to control molecular and cellular functions in response to specific signals and changes in the microenvironment of cells. They regulate structure, localization, stability, and function of proteins in a spatial and temporal manner. Among them, specific thiol modifications of cysteine (Cys) residues facilitate rapid signal transduction. In fact, Cys is unique because it contains the highly reactive thiol group that can undergo different reversible and irreversible modifications. Upon inflammation and changes in the cellular microenvironment, many extracellular soluble and membrane proteins undergo thiol modifications, particularly dithiol-disulfide exchange, S-glutathionylation, and S-nitrosylation. Among others, these thiol switches are essential for inflammatory signaling, regulation of gene expression, cytokine release, immunoglobulin function and isoform variation, and antigen presentation. Interestingly, also the redox state of bacterial and viral proteins depends on host cell-mediated redox reactions that are critical for invasion and infection. Here, we highlight mechanistic thiol switches in inflammatory pathways and infections including cholera, diphtheria, hepatitis, human immunodeficiency virus (HIV), influenza, and coronavirus disease 2019 (COVID-19).


Subject(s)
COVID-19 , Sulfhydryl Compounds , Cysteine , Extracellular Space/metabolism , Humans , Inflammation , Proteins/metabolism , Sulfhydryl Compounds/chemistry , Sulfhydryl Compounds/metabolism
5.
Int J Mol Sci ; 23(13)2022 Jun 21.
Article in English | MEDLINE | ID: covidwho-1934117

ABSTRACT

RNA-protein complexes regulate a variety of biological functions. Thus, it is essential to explore and visualize RNA-protein structural interaction features, especially pocket interactions. In this work, we develop an easy-to-use bioinformatics resource: RPpocket. This database provides RNA-protein complex interactions based on sequence, secondary structure, and pocket topology analysis. We extracted 793 pockets from 74 non-redundant RNA-protein structures. Then, we calculated the binding- and non-binding pocket topological properties and analyzed the binding mechanism of the RNA-protein complex. The results showed that the binding pockets were more extended than the non-binding pockets. We also found that long-range forces were the main interaction for RNA-protein recognition, while short-range forces strengthened and optimized the binding. RPpocket could facilitate RNA-protein engineering for biological or medical applications.


Subject(s)
Proteins , RNA , Binding Sites , Databases, Protein , Ligands , Models, Molecular , Proteins/chemistry
6.
J Am Chem Soc ; 144(29): 13026-13031, 2022 Jul 27.
Article in English | MEDLINE | ID: covidwho-1931307

ABSTRACT

Post-translational protein-protein conjugation produces bioconjugates that are unavailable via genetic fusion approaches. A method for preparing protein-protein conjugates using π-clamp-mediated cysteine arylation with pentafluorophenyl sulfonamide functional groups is described. Two computationally designed antibodies targeting the SARS-CoV-2 receptor binding domain were produced (KD = 146, 581 nM) with a π-clamp sequence near the C-terminus and dimerized using this method to provide a 10-60-fold increase in binding (KD = 8-15 nM). When two solvent-exposed cysteine residues were present on the second protein domain, the π-clamp cysteine residue was selectively modified over an Asp-Cys-Glu cysteine residue, allowing for subsequent small-molecule conjugation. With this strategy, we build molecule-protein-protein conjugates with complete chemical control over the sites of modification.


Subject(s)
COVID-19 , Single-Domain Antibodies , Cysteine/chemistry , Humans , Proteins/chemistry , SARS-CoV-2
7.
Acta Crystallogr D Struct Biol ; 78(Pt 7): 806-816, 2022 Jul 01.
Article in English | MEDLINE | ID: covidwho-1922451

ABSTRACT

The availability of new artificial intelligence-based protein-structure-prediction tools has radically changed the way that cryo-EM maps are interpreted, but it has not eliminated the challenges of map interpretation faced by a microscopist. Models will continue to be locally rebuilt and refined using interactive tools. This inevitably results in occasional errors, among which register shifts remain one of the most difficult to identify and correct. Here, checkMySequence, a fast, fully automated and parameter-free method for detecting register shifts in protein models built into cryo-EM maps, is introduced. It is shown that the method can assist model building in cases where poorer map resolution hinders visual interpretation. It is also shown that checkMySequence could have helped to avoid a widely discussed sequence-register error in a model of SARS-CoV-2 RNA-dependent RNA polymerase that was originally detected thanks to a visual residue-by-residue inspection by members of the structural biology community. The software is freely available at https://gitlab.com/gchojnowski/checkmysequence.


Subject(s)
Artificial Intelligence , COVID-19 , Cryoelectron Microscopy/methods , Humans , Models, Molecular , Proteins/chemistry , RNA, Viral , SARS-CoV-2
8.
Database (Oxford) ; 20222022 06 30.
Article in English | MEDLINE | ID: covidwho-1922225

ABSTRACT

During infection, the pathogen's entry into the host organism, breaching the host immune defense, spread and multiplication are frequently mediated by multiple interactions between the host and pathogen proteins. Systematic studying of host-pathogen interactions (HPIs) is a challenging task for both experimental and computational approaches and is critically dependent on the previously obtained knowledge about these interactions found in the biomedical literature. While several HPI databases exist that manually filter HPI protein-protein interactions from the generic databases and curated experimental interactomic studies, no comprehensive database on HPIs obtained from the biomedical literature is currently available. Here, we introduce a high-throughput literature-mining platform for extracting HPI data that includes the most comprehensive to date collection of HPIs obtained from the PubMed abstracts. Our HPI data portal, PHILM2Web (Pathogen-Host Interactions by Literature Mining on the Web), integrates an automatically generated database of interactions extracted by PHILM, our high-precision HPI literature-mining algorithm. Currently, the database contains 23 581 generic HPIs between 157 host and 403 pathogen organisms from 11 609 abstracts. The interactions were obtained from processing 608 972 PubMed abstracts, each containing mentions of at least one host and one pathogen organisms. In response to the coronavirus disease 2019 (COVID-19) pandemic, we also utilized PHILM to process 25 796 PubMed abstracts obtained by the same query as the COVID-19 Open Research Dataset. This COVID-19 processing batch resulted in 257 HPIs between 19 host and 31 pathogen organisms from 167 abstracts. The access to the entire HPI dataset is available via a searchable PHILM2Web interface; scientists can also download the entire database in bulk for offline processing. Database URL: http://philm2web.live.


Subject(s)
COVID-19 , Databases, Factual , Host-Pathogen Interactions/physiology , Humans , Proteins/metabolism , PubMed
9.
Sci Rep ; 12(1): 5867, 2022 04 07.
Article in English | MEDLINE | ID: covidwho-1921658

ABSTRACT

SARS-CoV-2 pandemic first emerged in late 2019 in China. It has since infected more than 298 million individuals and caused over 5 million deaths globally. The identification of essential proteins in a protein-protein interaction network (PPIN) is not only crucial in understanding the process of cellular life but also useful in drug discovery. There are many centrality measures to detect influential nodes in complex networks. Since SARS-CoV-2 and (H1N1) influenza PPINs pose 553 common human proteins. Analyzing influential proteins and comparing these networks together can be an effective step in helping biologists for drug-target prediction. We used 21 centrality measures on SARS-CoV-2 and (H1N1) influenza PPINs to identify essential proteins. We applied principal component analysis and unsupervised machine learning methods to reveal the most informative measures. Appealingly, some measures had a high level of contribution in comparison to others in both PPINs, namely Decay, Residual closeness, Markov, Degree, closeness (Latora), Barycenter, Closeness (Freeman), and Lin centralities. We also investigated some graph theory-based properties like the power law, exponential distribution, and robustness. Both PPINs tended to properties of scale-free networks that expose their nature of heterogeneity. Dimensionality reduction and unsupervised learning methods were so effective to uncover appropriate centrality measures.


Subject(s)
COVID-19 , Influenza A Virus, H1N1 Subtype , Influenza, Human , Humans , Influenza A Virus, H1N1 Subtype/metabolism , Protein Interaction Maps , Proteins/metabolism , SARS-CoV-2
10.
J Nanobiotechnology ; 20(1): 314, 2022 Jul 06.
Article in English | MEDLINE | ID: covidwho-1916963

ABSTRACT

Acute respiratory distress syndrome (ARDS), caused by noncardiogenic pulmonary edema (PE), contributes significantly to Coronavirus 2019 (COVID-19)-associated morbidity and mortality. We explored the effect of transmembrane osmotic pressure (OP) gradients in PE using a fluorescence resonance energy transfer-based Intermediate filament (IF) tension optical probe. Angiotensin-II- and bradykinin-induced increases in intracellular protein nanoparticle (PN)-OP were associated with inflammasome production and cytoskeletal depolymerization. Intracellular protein nanoparticle production also resulted in cytomembrane hyperpolarization and L-VGCC-induced calcium signals, which differed from diacylglycerol-induced calcium increment via TRPC6 activation. Both pathways involve voltage-dependent cation influx and OP upregulation via SUR1-TRPM4 channels. Meanwhile, intra/extracellular PN-induced OP gradients across membranes upregulated pulmonary endothelial and alveolar barrier permeability. Attenuation of intracellular PN, calcium signals, and cation influx by drug combinations effectively relieved intracellular OP and pulmonary endothelial nonselective permeability, and improved epithelial fluid absorption and PE. Thus, PN-OP is pivotal in pulmonary edema in ARDS and COVID-19, and transmembrane OP recovery could be used to treat pulmonary edema and develop new drug targets in pulmonary injury.


Subject(s)
COVID-19 , Nanoparticles , Pulmonary Edema , Respiratory Distress Syndrome , COVID-19/drug therapy , Calcium , Humans , Osmotic Pressure , Proteins , Pulmonary Edema/complications , Pulmonary Edema/drug therapy , Respiratory Distress Syndrome/drug therapy
11.
PLoS Comput Biol ; 18(6): e1010271, 2022 06.
Article in English | MEDLINE | ID: covidwho-1910466

ABSTRACT

While deep learning models have seen increasing applications in protein science, few have been implemented for protein backbone generation-an important task in structure-based problems such as active site and interface design. We present a new approach to building class-specific backbones, using a variational auto-encoder to directly generate the 3D coordinates of immunoglobulins. Our model is torsion- and distance-aware, learns a high-resolution embedding of the dataset, and generates novel, high-quality structures compatible with existing design tools. We show that the Ig-VAE can be used with Rosetta to create a computational model of a SARS-CoV2-RBD binder via latent space sampling. We further demonstrate that the model's generative prior is a powerful tool for guiding computational protein design, motivating a new paradigm under which backbone design is solved as constrained optimization problem in the latent space of a generative model.


Subject(s)
COVID-19 , RNA, Viral , Humans , Immunoglobulins , Proteins/chemistry , SARS-CoV-2
12.
J Phys Chem Lett ; 13(27): 6250-6258, 2022 Jul 14.
Article in English | MEDLINE | ID: covidwho-1908078

ABSTRACT

Calculating the standard binding free energies of protein-protein and protein-ligand complexes from atomistic molecular dynamics simulations in explicit solvent is a problem of central importance in computational biophysics. A rigorous strategy for carrying out such calculations is the so-called "geometrical route". In this method, two molecular objects are progressively separated from one another in the presence of orientational and conformational restraints serving to control the change in configurational entropy that accompanies the dissociation process, thereby allowing the computations to converge within simulations of affordable length. Although the geometrical route provides a rigorous theoretical framework, a tantalizing computational shortcut consists of simply leaving out such orientational and conformational degrees of freedom during the separation process. Here the accuracy and convergence of the two approaches are critically compared in the case of two protein-ligand complexes (Abl kinase-SH3:p41 and MDM2-p53:NVP-CGM097) and three protein-protein complexes (pig insulin dimer, SARS-CoV-2 spike RBD:ACE2, and CheA kinase-P2:CheY). The results of the simulations that strictly follow the geometrical route match the experimental standard binding free energies within chemical accuracy. In contrast, simulations bereft of geometrical restraints converge more poorly, yielding inconsistent results that are at variance with the experimental measurements. Furthermore, the orientational and positional time correlation functions of the protein in the unrestrained simulations decay over several microseconds, a time scale that is far longer than the typical simulation times of the geometrical route, which explains why those simulations fail to sample the relevant degrees of freedom during the separation process of the complexes.


Subject(s)
COVID-19 , SARS-CoV-2 , Animals , Entropy , Ligands , Molecular Dynamics Simulation , Protein Binding , Proteins/chemistry , Swine , Thermodynamics
13.
J Cell Biol ; 221(7)2022 Jul 04.
Article in English | MEDLINE | ID: covidwho-1890797

ABSTRACT

Wang et al. report in this issue (2022. J. Cell Biol.https://doi.org/10.1083/jcb.202108015) that the SARS-CoV-2 protein ORF10 increases the activity of the E3 ligase CUL2ZYG11B, leading to the degradation of multiple ciliary proteins. The resulting loss of cilia may facilitate the spread of SARS-CoV-2 in the respiratory tree.


Subject(s)
COVID-19 , Cilia , Ubiquitin-Protein Ligases , COVID-19/pathology , Cell Cycle Proteins , Cilia/pathology , Cullin Proteins , Genes, Viral , Humans , Proteins/metabolism , SARS-CoV-2 , Ubiquitin-Protein Ligases/metabolism
14.
Nat Methods ; 19(6): 730-739, 2022 06.
Article in English | MEDLINE | ID: covidwho-1873535

ABSTRACT

Predicting the functional sites of a protein from its structure, such as the binding sites of small molecules, other proteins or antibodies, sheds light on its function in vivo. Currently, two classes of methods prevail: machine learning models built on top of handcrafted features and comparative modeling. They are, respectively, limited by the expressivity of the handcrafted features and the availability of similar proteins. Here, we introduce ScanNet, an end-to-end, interpretable geometric deep learning model that learns features directly from 3D structures. ScanNet builds representations of atoms and amino acids based on the spatio-chemical arrangement of their neighbors. We train ScanNet for detecting protein-protein and protein-antibody binding sites, demonstrate its accuracy-including for unseen protein folds-and interpret the filters learned. Finally, we predict epitopes of the SARS-CoV-2 spike protein, validating known antigenic regions and predicting previously uncharacterized ones. Overall, ScanNet is a versatile, powerful and interpretable model suitable for functional site prediction tasks. A webserver for ScanNet is available from http://bioinfo3d.cs.tau.ac.il/ScanNet/ .


Subject(s)
COVID-19 , Deep Learning , Binding Sites , Humans , Protein Binding , Proteins/chemistry , SARS-CoV-2 , Spike Glycoprotein, Coronavirus
15.
J Chem Inf Model ; 62(11): 2869-2879, 2022 06 13.
Article in English | MEDLINE | ID: covidwho-1860271

ABSTRACT

The three-dimensional conformations of a protein influence its function and select for the ligands it can interact with. The total free energy change during protein-ligand complex formation includes enthalphic and entropic components, which together report on the binding affinity and conformational states of the complex. However, determining the entropic contribution is computationally burdensome. Here, we apply kinematic flexibility analysis (KFA) to efficiently estimate vibrational frequencies from static protein and protein-ligand structures. The vibrational frequencies, in turn, determine the vibrational entropies of the structures and their complexes. Our estimates of the vibrational entropy change caused by ligand binding compare favorably to values obtained from a dynamic Normal Mode Analysis (NMA). Higher correlation factors can be achieved by increasing the distance cutoff in the potential energy model. Furthermore, we apply our new method to analyze the entropy changes of the SARS CoV-2 main protease when binding with different ligand inhibitors, which is relevant for the design of potential drugs.


Subject(s)
COVID-19 , Severe Acute Respiratory Syndrome , Biomechanical Phenomena , Entropy , Humans , Ligands , Protein Binding , Proteins/chemistry
16.
Methods Mol Biol ; 2452: 197-212, 2022.
Article in English | MEDLINE | ID: covidwho-1844268

ABSTRACT

As the knowledge of biomolecules is increasing from the last decades, it is helping the researchers to understand the unsolved issues regarding virology. Recent technologies in high-throughput sequencing are providing the swift generation of SARS-CoV-2 genomic data with the basic inside of viral infection. Owing to various virus-host protein interactions, high-throughput technologies are unable to provide complete details of viral pathogenesis. Identifying the virus-host protein interactions using bioinformatics approaches can assist in understanding the mechanism of SARS-CoV-2 infection and pathogenesis. In this chapter, recent integrative bioinformatics approaches are discussed to help the virologists and computational biologists in the identification of structurally similar proteins of human and SARS-CoV-2 virus, and to predict the potential of virus-host interactions. Considering experimental and time limitations for effective viral drug development, computational aided drug design (CADD) can reduce the gap between drug prediction and development. More research with respect to evolutionary solutions could be helpful to make a new pipeline for virus-host protein-protein interactions and provide more understanding to disclose the cases of host switch, and also expand the virulence of the pathogen and host range in developing viral infections.


Subject(s)
COVID-19 , Computational Biology , Host Microbial Interactions , Host-Pathogen Interactions/genetics , Humans , Proteins , SARS-CoV-2/genetics
17.
Biomed Res Int ; 2022: 2273648, 2022.
Article in English | MEDLINE | ID: covidwho-1832664

ABSTRACT

Protein is the material foundation of living things, and it directly takes part in and runs the process of living things itself. Predicting protein complexes helps us understand the structure and function of complexes, and it is an important foundation for studying how cells work. Genome-wide protein interaction (PPI) data is growing as high-throughput experiments become more common. The aim of this research is that it provides a dual-tree complex wavelet transform which is used to find out about the structure of proteins. It also identifies the secondary structure of protein network. Many computer-based methods for predicting protein complexes have also been developed in the field. Identifying the secondary structure of a protein is very important when you are studying protein characteristics and properties. This is how the protein sequence is added to the distance matrix. The scope of this research is that it can confidently predict certain protein complexes rapidly, which compensates for shortcomings in biological research. The three-dimensional coordinates of C atom are used to do this. According to the texture information in the distance matrix, the matrix is broken down into four levels by the double-tree complex wavelet transform because it has four levels. The subband energy and standard deviation in different directions are taken, and then, the two-dimensional feature vector is used to show the secondary structure features of the protein in a way that is easy to understand. Then, the KNN and SVM classifiers are used to classify the features that were found. Experiments show that a new feature called a dual-tree complex wavelet can improve the texture granularity and directionality of the traditional feature extraction method, which is called secondary structure.


Subject(s)
Computational Biology , Support Vector Machine , Protein Structure, Secondary , Proteins/chemistry , Wavelet Analysis
18.
Adv Biol (Weinh) ; 6(7): e2200006, 2022 Jul.
Article in English | MEDLINE | ID: covidwho-1825811

ABSTRACT

Phase separation is a hot research field at present. It involves almost all aspects of cells and plays a significant role in cells, promising to be "a master key" in unlocking the mysteries of nature. In this review, the factors that affect phase separation are introduced, such as own component, electrostatic interaction, and chemical modification. Furthermore, the physiological roles of phase separation in cells, including molecules transport channel, gene expression and regulation, cellular division and differentiation, stress response, proteins refolding and degradation, cell connections, construction of skin barrier, and cell signals transmission, are highlighted. However, the disorder of phase separation leads to pathological condensates, which are associated with neurodegenerative diseases, tumors, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This relationship is considered the potential target for developing corresponded drugs and therapy. Some drugs targeting phase separation have improved meaningful, such as tankyrase, lipoamide, oligonucleotides, elvitagravir, nilotinib, CVL218, PJ34. All in all, mystery phase separation provides a new viewpoint for researchers to explore cells, and is expected to solve many unknown phenomena in nature.


Subject(s)
COVID-19 , Neurodegenerative Diseases , Cell Division , Humans , Neurodegenerative Diseases/metabolism , Proteins , SARS-CoV-2
19.
Ann N Y Acad Sci ; 1510(1): 79-99, 2022 04.
Article in English | MEDLINE | ID: covidwho-1822055

ABSTRACT

Targeted protein degradation is critical for proper cellular function and development. Protein degradation pathways, such as the ubiquitin proteasomes system, autophagy, and endosome-lysosome pathway, must be tightly regulated to ensure proper elimination of misfolded and aggregated proteins and regulate changing protein levels during cellular differentiation, while ensuring that normal proteins remain unscathed. Protein degradation pathways have also garnered interest as a means to selectively eliminate target proteins that may be difficult to inhibit via other mechanisms. On June 7 and 8, 2021, several experts in protein degradation pathways met virtually for the Keystone eSymposium "Targeting protein degradation: from small molecules to complex organelles." The event brought together researchers working in different protein degradation pathways in an effort to begin to develop a holistic, integrated vision of protein degradation that incorporates all the major pathways to understand how changes in them can lead to disease pathology and, alternatively, how they can be leveraged for novel therapeutics.


Subject(s)
Proteasome Endopeptidase Complex , Ubiquitin , Autophagy/physiology , Humans , Organelles , Proteasome Endopeptidase Complex/metabolism , Proteins/metabolism , Proteolysis , Ubiquitin/metabolism
20.
Trends Biochem Sci ; 47(5): 372-374, 2022 05.
Article in English | MEDLINE | ID: covidwho-1821500

ABSTRACT

Modifications of cysteine residues in redox-sensitive proteins are key to redox signaling and stress response in all organisms. A novel type of redox switch was recently discovered that comprises lysine and cysteine residues covalently linked by an nitrogen-oxygen-sulfur (NOS) bridge. Here, we discuss chemical and biological implications of this discovery.


Subject(s)
Cysteine , Lysine , Cysteine/chemistry , Lysine/metabolism , Oxidation-Reduction , Oxidative Stress , Protein Processing, Post-Translational , Proteins/chemistry
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