limited proteolysis
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2022 ◽  
Vol 23 (2) ◽  
pp. 923
Giulia Pesce ◽  
Frank Gondelaud ◽  
Denis Ptchelkine ◽  
Juliet F. Nilsson ◽  
Christophe Bignon ◽  

Henipaviruses are severe human pathogens within the Paramyxoviridae family. Beyond the P protein, the Henipavirus P gene also encodes the V and W proteins which share with P their N-terminal, intrinsically disordered domain (NTD) and possess a unique C-terminal domain. Henipavirus W proteins antagonize interferon (IFN) signaling through NTD-mediated binding to STAT1 and STAT4, and prevent type I IFN expression and production of chemokines. Structural and molecular information on Henipavirus W proteins is lacking. By combining various bioinformatic approaches, we herein show that the Henipaviruses W proteins are predicted to be prevalently disordered and yet to contain short order-prone segments. Using limited proteolysis, differential scanning fluorimetry, analytical size exclusion chromatography, far-UV circular dichroism and small-angle X-ray scattering, we experimentally confirmed their overall disordered nature. In addition, using Congo red and Thioflavin T binding assays and negative-staining transmission electron microscopy, we show that the W proteins phase separate to form amyloid-like fibrils. The present study provides an additional example, among the few reported so far, of a viral protein forming amyloid-like fibrils, therefore significantly contributing to enlarge our currently limited knowledge of viral amyloids. In light of the critical role of the Henipavirus W proteins in evading the host innate immune response and of the functional role of phase separation in biology, these studies provide a conceptual asset to further investigate the functional impact of the phase separation abilities of the W proteins.

2022 ◽  
Fred Lee ◽  
Xinhao Shao ◽  
Yu Gao ◽  
Alexandra Naba

The extracellular matrix (ECM) is a complex and dynamic meshwork of proteins providing structural support to cells. It also provides biochemical signals governing cellular processes including proliferation and migration. Alterations of ECM structure and/or composition has been shown to lead to, or accompany, many pathological processes including cancer and fibrosis. To understand how the ECM contributes to diseases, we first need to obtain a comprehensive characterization of the ECM of tissues and of its changes during disease progression. Over the past decade, mass-spectrometry-based proteomics has become the state-of-the-art method to profile the protein composition of ECMs. However, existing methods do not fully capture the broad dynamic range of protein abundance in the ECM, nor do they permit to achieve the high coverage needed to gain finer biochemical information, including the presence of isoforms or post-translational modifications. In addition, broadly adopted proteomic methods relying on extended trypsin digestion do not provide structural information on ECM proteins, yet, gaining insights into ECM protein structure is critical to better understanding protein functions. Here, we present the optimization of a time-lapsed proteomic method using limited proteolysis of partially denatured samples and the sequential release of peptides to achieve superior sequence coverage as compared to standard ECM proteomic workflow. Exploiting the spatio-temporal resolution of this method, we further demonstrate how 3-dimensional time-lapsed peptide mapping can identify protein regions differentially susceptible to trypsin and can thus identify sites of post-translational modifications, including protein-protein interactions. We further illustrate how this approach can be leveraged to gain insight on the role of the novel ECM protein SNED1 in ECM homeostasis. We found that the expression of SNED1 expression by mouse embryonic fibroblasts results in the alteration of overall ECM composition and the sequence coverage of certain ECM proteins, raising the possibility that SNED1 could modify accessibility to trypsin by engaging in protein-protein interactions.

2022 ◽  
Xinhao Shao ◽  
Christopher Grams ◽  
Yu Gao

Protein structure is connected with its function and interaction and plays an extremely important role in protein characterization. As one of the most important analytical methods for protein characterization, Proteomics is widely used to determine protein composition, quantitation, interaction, and even structures. However, due to the gap between identified proteins by proteomics and available 3D structures, it was very challenging, if not impossible, to visualize proteomics results in 3D and further explore the structural aspects of proteomics experiments. Recently, two groups of researchers from DeepMind and Baker lab have independently published protein structure prediction tools that can help us obtain predicted protein structures for the whole human proteome. Although there is still debate on the validity of some of the predicted structures, it is no doubt that these represent the most accurate predictions to date. More importantly, this enabled us to visualize the majority of human proteins for the first time. To help other researchers best utilize these protein structure predictions, we present the Sequence Coverage Visualizer (SCV),, a web application for protein sequence coverage 3D visualization. Here we showed a few possible usages of the SCV, including the labeling of post-translational modifications and isotope labeling experiments. These results highlight the usefulness of such 3D visualization for proteomics experiments and how SCV can turn a regular result list into structural insights. Furthermore, when used together with limited proteolysis, we demonstrated that SCV can help validate and compare different protein structures, including predicted ones and existing PDB entries. By performing limited proteolysis on native proteins at various time points, SCV can visualize the progress of the digestion. This time-series data further allowed us to compare the predicted structure and existing PDB entries. Although not deterministic, these comparisons could be used to refine current predictions further and represent an important step towards a complete and correct protein structure database. Overall, SCV is a convenient and powerful tool for visualizing proteomics results.

Yu. I. Matveev ◽  
E. V. Averyanova

The limited use of plant proteins for food is explained by their low bioavailability and poor digestibility by enzymes of the gastrointestinal tract. Partially reproduced enzymatic processes of limited proteolysis that occur during seed germination are used to modify and improve the edibility characteristics of seed proteins. The present work discusses the possibility of reducing the duration of seed germination processes by optimising the conditions and parameters of limited proteolysis. To optimise manufacturing high-quality final product, enzymes (additional to the natural enzymes in the seed) and proteolysis conditions (in this case, temperature), as well as added substances (hydrolysis activators), were selected. The influence of cysteine on the formation of domain structures of proteins (enzymes and globulins) was evaluated. The proposed expressions can be used to determine those fragments of protein molecules that form stable domains and become unstructured when exposed to enzymes. Optimal conditions for limited proteolysis were identified based on the physical mechanism of action of papain-like proteolytic enzymes on pea legumin LegA (3KSC, CAA10722). It is shown that the decomposition of protein secondary structures takes 6–8 times longer, since the formed hydrogen bonds limit the access of enzymes to the corresponding amino-acid residues. It is also demonstrated that the decomposition of hydrogen bonds, e.g. by preliminary heat treatment of proteins, will broaden the prospects for limited proteolysis.

2021 ◽  
Vol 119 (1) ◽  
pp. e2109169119
Kristen A. Gaffney ◽  
Ruiqiong Guo ◽  
Michael D. Bridges ◽  
Shaima Muhammednazaar ◽  
Daoyang Chen ◽  

Defining the denatured state ensemble (DSE) and disordered proteins is essential to understanding folding, chaperone action, degradation, and translocation. As compared with water-soluble proteins, the DSE of membrane proteins is much less characterized. Here, we measure the DSE of the helical membrane protein GlpG of Escherichia coli (E. coli) in native-like lipid bilayers. The DSE was obtained using our steric trapping method, which couples denaturation of doubly biotinylated GlpG to binding of two streptavidin molecules. The helices and loops are probed using limited proteolysis and mass spectrometry, while the dimensions are determined using our paramagnetic biotin derivative and double electron–electron resonance spectroscopy. These data, along with our Upside simulations, identify the DSE as being highly dynamic, involving the topology changes and unfolding of some of the transmembrane (TM) helices. The DSE is expanded relative to the native state but only to 15 to 75% of the fully expanded condition. The degree of expansion depends on the local protein packing and the lipid composition. E. coli’s lipid bilayer promotes the association of TM helices in the DSE and, probably in general, facilitates interhelical interactions. This tendency may be the outcome of a general lipophobic effect of proteins within the cell membranes.

Cells ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 3507
Wenhao Zhang ◽  
Ying Wei ◽  
Huaijin Zhang ◽  
Jing Liu ◽  
Zhaoyun Zong ◽  

The inflammatory response of macrophages is an orderly and complex process under strict regulation accompanied by drastic changes in morphology and functions. It is predicted that proteins will undergo structural changes during these finely regulated processes. However, changes in structural proteome in macrophages during the inflammatory response remain poorly characterized. In the present study, we applied limited proteolysis coupled mass spectrometry (LiP-MS) to identify proteome-wide structural changes in lipopolysaccharide (LPS)-activated macrophages. We identified 386 structure-specific proteolytic fingerprints from 230 proteins. Using the Gene Ontology (GO) biological process enrichment, we discovered that proteins with altered structures were enriched into protein folding-related terms, in which HSP60 was ranked as the most changed protein. We verified the structural changes in HSP60 by using cellular thermal shift assay (CETSA) and native CETSA. Our results showed that the thermal stability of HSP60 was enhanced in activated macrophages and formed an HSP10-less complex. In conclusion, we demonstrate that in situ structural systems biology is an effective method to characterize proteomic structural changes and reveal that the structures of chaperone proteins vary significantly during macrophage activation.

2021 ◽  
Antoine Reynaud ◽  
Maud Magdeleine ◽  
Amanda Patel ◽  
Anne Sophie Gay ◽  
Delphine Debayle ◽  

AbstractTumor Protein D54 (TPD54) is an abundant cytosolic protein that belongs to the TPD52 family, a family of four proteins (TPD52, 53, 54 and 55) that are overexpressed in several cancer cells. Even though the functions of these proteins remain elusive, recent investigations indicate that TPD54 binds to very small cytosolic vesicles with a diameter of ca. 30 nm, half the size of classical transport vesicles (e.g. COPI and COPII). Here, we investigated the mechanism of intracellular nanovesicle capture by TPD54. Bioinformatical analysis suggests that TPD54 contains a small coiled-coil followed by several amphipathic helices, which could fold upon binding to lipid membranes. One of these helices has the physicochemical features of an Amphipathic Lipid Packing Sensor (ALPS) motif, which, in other proteins, enables membrane binding in a curvature-dependent manner. Limited proteolysis, CD spectroscopy, tryptophan fluorescence and cysteine mutagenesis coupled to covalent binding of a membrane sensitive probe show that binding of TPD54 to small liposomes is accompanied by large structural changes in the amphipathic helix region. TPD54 binding to artificial liposomes is very sensitive to liposome size and to lipid unsaturation but is poorly dependent on lipid charge. Cellular investigations confirmed the key role of the ALPS motif in vesicle targeting. Surprisingly, the vesicles selected by TPD54 poorly overlap with those captured by the golgin GMAP-210, a long vesicle tether at the Golgi apparatus, which displays a dimeric coiled-coil architecture and an N-terminal ALPS motif. We propose that TPD54 recognizes nanovesicles through a combination of ALPS-dependent and -independent mechanisms.

2021 ◽  
Sarah Barrass ◽  
Lauri I. A. Pulkkinen ◽  
Olli Vapalahti ◽  
Suvi H. Kuivanen ◽  
Maria Anastasina ◽  

Virus-host protein-protein interactions are central to viral infection, but are challenging to identify and characterise, especially in complex systems involving intact viruses and cells. Here, we describe a proteome-wide approach to identify virus-host interactions using chemical cross-linking coupled with mass spectrometry. We adsorbed tick-borne encephalitis virus onto metabolically-stalled neuroblastoma cells, covalently cross-linked interacting virus-host proteins, and performed limited proteolysis to release primarily the surface-exposed proteins for analysis by mass spectrometry. Proteins in the sample were identified using data-dependent acquisition mass spectrometry and cross-linked peptides were identified using the software pLink2. Cross-links are validated using the intraviral cross-links as an internal control.

2021 ◽  
Philip To ◽  
Sea On Lee ◽  
Yingzi Xia ◽  
Taylor Devlin ◽  
Karen G Fleming ◽  

The journey by which proteins navigate their energy landscapes to their native structures is complex, involving (and sometimes requiring) many cellular factors and processes operating in partnership with a given polypeptide chain's intrinsic energy landscape. The cytosolic environment and its complement of chaperones play critical roles in granting proteins safe passage to their native states; however, the complexity of this medium has generally precluded biophysical techniques from interrogating protein folding under cellular-like conditions for single proteins, let alone entire proteomes. Here, we develop a limited-proteolysis mass spectrometry approach paired within an isotope-labeling strategy to globally monitor the structures of refolding E. coli proteins in the cytosolic medium and with the chaperones, GroEL/ES (Hsp60) and DnaK/DnaJ/GrpE (Hsp70/40). GroEL can refold the majority (85%) of the E. coli proteins for which we have data, and is particularly important for restoring acidic proteins and proteins with high molecular weight, trends that come to light because our assay measures the structural outcome of the refolding process itself, rather than indirect measures like binding or aggregation. For the most part, DnaK and GroEL refold a similar set of proteins, supporting the view that despite their vastly different structures, these two chaperones both unfold misfolded states, as one mechanism in common. Finally, we identify a cohort of proteins that are intransigent to being refolded with either chaperone. The data support a model in which chaperone-nonrefolders have evolved to fold efficiently once and only once, co-translationally, and remain kinetically trapped in their native conformations.

Sarah E. Biehn ◽  
Steffen Lindert

Knowledge of protein structure is crucial to our understanding of biological function and is routinely used in drug discovery. High-resolution techniques to determine the three-dimensional atomic coordinates of proteins are available. However, such methods are frequently limited by experimental challenges such as sample quantity, target size, and efficiency. Structural mass spectrometry (MS) is a technique in which structural features of proteins are elucidated quickly and relatively easily. Computational techniques that convert sparse MS data into protein models that demonstrate agreement with the data are needed. This review features cutting-edge computational methods that predict protein structure from MS data such as chemical cross-linking, hydrogen–deuterium exchange, hydroxyl radical protein footprinting, limited proteolysis, ion mobility, and surface-induced dissociation. Additionally, we address future directions for protein structure prediction with sparse MS data. Expected final online publication date for the Annual Review of Physical Chemistry, Volume 73 is April 2022. Please see for revised estimates.

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