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1. Charting the solid‐state NMR signals of polysaccharides: A database‐driven roadmap

   https://doi.org/10.1002/mrc.5397  

   Zhao, Wancheng ; Debnath, Debkumar ; Gautam, Isha ; Fernando, Liyanage D. ; Wang, Tuo ( September 2023 , Magnetic Resonance in Chemistry)

   Solid‐state nuclear magnetic resonance (ssNMR) measurements of intact cell walls and cellular samples often generate spectra that are difficult to interpret due to the presence of many coexisting glycans and the structural polymorphism observed in native conditions. To overcome this analytical challenge, we present a statistical approach for analyzing carbohydrate signals using high‐resolution ssNMR data indexed in a carbohydrate database. We generate simulated spectra to demonstrate the chemical shift dispersion and compare this with experimental data to facilitate the identification of important fungal and plant polysaccharides, such as chitin and glucans in fungi and cellulose, hemicellulose, and pectic polymers in plants. We also demonstrate that chemically distinct carbohydrates from different organisms may produce almost identical signals, highlighting the need for high‐resolution spectra and validation of resonance assignments. Our study provides a means to differentiate the characteristic signals of major carbohydrates and allows us to summarize currently undetected polysaccharides in plants and fungi, which may inspire future investigations.

    

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2. Mass spectrometry-based technologies for probing the 3D world of plant proteins

   https://doi.org/10.1093/plphys/kiac039  

   Blackburn, Matthew R ; Minkoff, Benjamin B ; Sussman, Michael R ( February 2022 , Plant Physiology)

   Abstract Over the past two decades, mass spectrometric (MS)-based proteomics technologies have facilitated the study of signaling pathways throughout biology. Nowhere is this needed more than in plants, where an evolutionary history of genome duplications has resulted in large gene families involved in posttranslational modifications and regulatory pathways. For example, at least 5% of the Arabidopsis thaliana genome (ca. 1,200 genes) encodes protein kinases and protein phosphatases that regulate nearly all aspects of plant growth and development. MS-based technologies that quantify covalent changes in the side-chain of amino acids are critically important, but they only address one piece of the puzzle. A more crucially important mechanistic question is how noncovalent interactions—which are more difficult to study—dynamically regulate the proteome’s 3D structure. The advent of improvements in protein 3D technologies such as cryo-electron microscopy, nuclear magnetic resonance, and X-ray crystallography has allowed considerable progress to be made at this level, but these methods are typically limited to analyzing proteins, which can be expressed and purified in milligram quantities. Newly emerging MS-based technologies have recently been developed for studying the 3D structure of proteins. Importantly, these methods do not require protein samples to be purified and require smaller amounts of sample, opening the wider proteome for structural analysis in complex mixtures, crude lysates, and even in intact cells. These MS-based methods include covalent labeling, crosslinking, thermal proteome profiling, and limited proteolysis, all of which can be leveraged by established MS workflows, as well as newly emerging methods capable of analyzing intact macromolecules and the complexes they form. In this review, we discuss these recent innovations in MS-based “structural” proteomics to provide readers with an understanding of the opportunities they offer and the remaining challenges for understanding the molecular underpinnings of plant structure and function. 

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3. SVM-SulfoSite: A support vector machine based predictor for sulfenylation sites

   https://doi.org/10.1038/s41598-018-29126-x  

   AL-barakati, Hussam J. ; McConnell, Evan W. ; Hicks, Leslie M. ; Poole, Leslie B. ; Newman, Robert H. ; KC, Dukka B. ( July 2018 , Scientific Reports)

   ProteinS-sulfenylation, which results from oxidation of free thiols on cysteine residues, has recently emerged as an important post-translational modification that regulates the structure and function of proteins involved in a variety of physiological and pathological processes. By altering the size and physiochemical properties of modified cysteine residues, sulfenylation can impact the cellular function of proteins in several different ways. Thus, the ability to rapidly and accurately identify putative sulfenylation sites in proteins will provide important insights into redox-dependent regulation of protein function in a variety of cellular contexts. Though bottom-up proteomic approaches, such as tandem mass spectrometry (MS/MS), provide a wealth of information about global changes in the sulfenylation state of proteins, MS/MS-based experiments are often labor-intensive, costly and technically challenging. Therefore, to complement existing proteomic approaches, researchers have developed a series of computational tools to identify putative sulfenylation sites on proteins. However, existing methods often suffer from low accuracy, specificity, and/or sensitivity. In this study, we developed SVM-SulfoSite, a novel sulfenylation prediction tool that uses support vector machines (SVM) to identify key determinants of sulfenylation among five feature classes: binary code, physiochemical properties, k-space amino acid pairs, amino acid composition and high-quality physiochemical indices. Using 10-fold cross-validation, SVM-SulfoSite achieved 95% sensitivity and 83% specificity, with an overall accuracy of 89% and Matthew’s correlation coefficient (MCC) of 0.79. Likewise, using an independent test set of experimentally identified sulfenylation sites, our method achieved scores of 74%, 62%, 80% and 0.42 for accuracy, sensitivity, specificity and MCC, with an area under the receiver operator characteristic (ROC) curve of 0.81. Moreover, in side-by-side comparisons, SVM-SulfoSite performed as well as or better than existing sulfenylation prediction tools. Together, these results suggest that our method represents a robust and complementary technique for advanced exploration of protein S-sulfenylation.

    

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4. Embryo timelapses can be compiled and quantified to understand canonical histone dynamics across multiple cell cycles

   https://doi.org/10.1002/cm.21493  

   Smith, Lydia ; Maddox, Paul ( November 2018 , Cytoskeleton)

   In the last decade, computational processing of big datasets has facilitated the analyses of unprecedented quantities of biological data. Thus, automation and big data analysis have been revolutionary in detecting and quantifying subtle phenotypes in cell biological contexts. Analyzing similar quantities of data in larger and more complicated biological systems such as developing embryos has been more challenging due to experimental limitations on both ensemble data collection and analysis. These challenges include photosensitivity of living samples and of the fluorescently tagged proteins under study, collectively limiting the number of images that can be acquired in a single timelapse series. Here we present a streamlined workflow to quantify dynamics of fluorescently labeled proteins over the course of several cell cycles in early embryos, taking advantage of the stereotypical nature of early development that is inherent for many organisms. We benchmark this pipeline studying a fluorescently labeled histone during early embryonic development of the nematodeCaenorhabditis elegans.Our strategy allowed us to overcome biological and experimental variation among our timelapse series and quantify nuclear accumulation rate, chromatin incorporation, and turnover/stability of canonical histones. We find that histone proteins are broadly stable in earlyC. elegansdevelopment. Thus, changes in genome regulation occurring in early development do not manifest in gross changes in histone metabolism. Our method enabling characterization of cumulative protein dynamics over several cell cycles of developmental time with high temporal resolution can be applied to expand our understanding of diverse cellular and developmental processes.

    

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5. A traveling-wave solution for bacterial chemotaxis with growth

   https://doi.org/10.1073/pnas.2105138118  

   Narla, Avaneesh V. ; Cremer, Jonas ; Hwa, Terence ( November 2021 , Proceedings of the National Academy of Sciences)

   Bacterial cells navigate their environment by directing their movement along chemical gradients. This process, known as chemotaxis, can promote the rapid expansion of bacterial populations into previously unoccupied territories. However, despite numerous experimental and theoretical studies on this classical topic, chemotaxis-driven population expansion is not understood in quantitative terms. Building on recent experimental progress, we here present a detailed analytical study that provides a quantitative understanding of how chemotaxis and cell growth lead to rapid and stable expansion of bacterial populations. We provide analytical relations that accurately describe the dependence of the expansion speed and density profile of the expanding population on important molecular, cellular, and environmental parameters. In particular, expansion speeds can be boosted by orders of magnitude when the environmental availability of chemicals relative to the cellular limits of chemical sensing is high. Analytical understanding of such complex spatiotemporal dynamic processes is rare. Our analytical results and the methods employed to attain them provide a mathematical framework for investigations of the roles of taxis in diverse ecological contexts across broad parameter regimes. 

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