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      Thiol redox proteomics: Characterization of thiol‐based post‐translational modifications

      1 , 1 , 2 , 1 , 1
      PROTEOMICS
      Wiley

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          Abstract

          Redox post‐translational modifications on cysteine thiols (redox PTMs) have profound effects on protein structure and function, thus enabling regulation of various biological processes. Redox proteomics approaches aim to characterize the landscape of redox PTMs at the systems level. These approaches facilitate studies of condition‐specific, dynamic processes implicating redox PTMs and have furthered our understanding of redox signaling and regulation. Mass spectrometry (MS) is a powerful tool for such analyses which has been demonstrated by significant advances in redox proteomics during the last decade. A group of well‐established approaches involves the initial blocking of free thiols followed by selective reduction of oxidized PTMs and subsequent enrichment for downstream detection. Alternatively, novel chemoselective probe‐based approaches have been developed for various redox PTMs. Direct detection of redox PTMs without any enrichment has also been demonstrated given the sensitivity of contemporary MS instruments. This review discusses the general principles behind different analytical strategies and covers recent advances in redox proteomics. Several applications of redox proteomics are also highlighted to illustrate how large‐scale redox proteomics data can lead to novel biological insights.

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

            The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk ) is an openly accessible, extensive database of high-accuracy protein-structure predictions. Powered by AlphaFold v2.0 of DeepMind, it has enabled an unprecedented expansion of the structural coverage of the known protein-sequence space. AlphaFold DB provides programmatic access to and interactive visualization of predicted atomic coordinates, per-residue and pairwise model-confidence estimates and predicted aligned errors. The initial release of AlphaFold DB contains over 360,000 predicted structures across 21 model-organism proteomes, which will soon be expanded to cover most of the (over 100 million) representative sequences from the UniRef90 data set.
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              Oxidative Stress: A Key Modulator in Neurodegenerative Diseases

              Oxidative stress is proposed as a regulatory element in ageing and various neurological disorders. The excess of oxidants causes a reduction of antioxidants, which in turn produce an oxidation–reduction imbalance in organisms. Paucity of the antioxidant system generates oxidative-stress, characterized by elevated levels of reactive species (oxygen, hydroxyl free radical, and so on). Mitochondria play a key role in ATP supply to cells via oxidative phosphorylation, as well as synthesis of essential biological molecules. Various redox reactions catalyzed by enzymes take place in the oxidative phosphorylation process. An inefficient oxidative phosphorylation may generate reactive oxygen species (ROS), leading to mitochondrial dysfunction. Mitochondrial redox metabolism, phospholipid metabolism, and proteolytic pathways are found to be the major and potential source of free radicals. A lower concentration of ROS is essential for normal cellular signaling, whereas the higher concentration and long-time exposure of ROS cause damage to cellular macromolecules such as DNA, lipids and proteins, ultimately resulting in necrosis and apoptotic cell death. Normal and proper functioning of the central nervous system (CNS) is entirely dependent on the chemical integrity of brain. It is well established that the brain consumes a large amount of oxygen and is highly rich in lipid content, becoming prone to oxidative stress. A high consumption of oxygen leads to excessive production of ROS. Apart from this, the neuronal membranes are found to be rich in polyunsaturated fatty acids, which are highly susceptible to ROS. Various neurodegenerative diseases such as Parkinson’s disease (PD), Alzheimer’s disease (AD), Huntington’s disease (HD), and amyotrophic lateral sclerosis (ALS), among others, can be the result of biochemical alteration (due to oxidative stress) in bimolecular components. There is a need to understand the processes and role of oxidative stress in neurodegenerative diseases. This review is an effort towards improving our understanding of the pivotal role played by OS in neurodegenerative disorders.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                PROTEOMICS
                Proteomics
                Wiley
                1615-9853
                1615-9861
                July 2023
                May 29 2023
                July 2023
                : 23
                : 13-14
                Affiliations
                [1 ] Biological Sciences Division Pacific Northwest National Laboratory Richland Washington USA
                [2 ] Department of Biological Systems Engineering Washington State University Richland Washington USA
                Article
                10.1002/pmic.202200194
                4212e8d6-f963-438d-a137-0bac9ce430b2
                © 2023

                http://creativecommons.org/licenses/by/4.0/

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