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This work describes an analytical procedure, single particle-inductively coupled plasma-time-of-flight-mass spectrometry (SP-ICP-TOF-MS), that was developed to determine the platinum binding efficiency of protein-coated magnetic microparticles.
Abstract Cell walls surround all plant cells, and their composition and structure are tightly regulated to maintain cellular and organismal homeostasis. In response to wall damage, the cell wall integrity (CWI) system is engaged to ameliorate effects on plant growth. Despite the central role CWI plays in plant development, our current understanding of how this system functions at the molecular level is limited. Here, we investigated the transcriptomes of etiolated seedlings of mutants of Arabidopsis thaliana with defects in three major wall polysaccharides, pectin ( quasimodo2 ), cellulose ( cellulose synthase3 je5 ), and xyloglucan ( xyloglucan xylosyltransferase1 and 2 ), to probe whether changes in the expression of cell wall‐related genes occur and are similar or different when specific wall components are reduced or missing. Many changes occurred in the transcriptomes of pectin‐ and cellulose‐deficient plants, but fewer changes occurred in the transcriptomes of xyloglucan‐deficient plants. We hypothesize that this might be because pectins interact with other wall components and/or integrity sensors, whereas cellulose forms a major load‐bearing component of the wall; defects in either appear to trigger the expression of structural proteins to maintain wall cohesion in the absence of a major polysaccharide. This core set of genes functioning in CWI in plants represents an attractive target for future genetic engineering of robust and resilient cell walls.
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Piezoelectric dispensing of proteins from borosilicate glass capillaries is a popular method of protein biochip fabrication that offers the advantages of sample recovery and noncontact with the printing substrate. However, little regard has been given to the quantitative aspects of dispensing minute volumes (1 nL or less) at the low protein concentrations (20 micrograms/mL or less) typically used in microprinting. Specifically, loss of protein sample due to nonspecific adsorption to the glass surface of the dispensing capillaries can limit the amount of protein delivered to the substrate. We demonstrate the benefits of a low ionic strength buffer containing the carrier protein BSA that effectively minimizes the ionic strength-dependent phenomenon of nonspecific protein adsorption to borosilicate glass. Over the concentration range of 20-2.5 micrograms/mL, the dispensing of a reference IgG in 10 mM PBS including 0.1% BSA resulted in the deposition of 3.6- to 44-fold more IgG compared to the deposition of IgG in standard 150 mM PBS in the absence of BSA. Furthermore, when the IgG was dispensed with carrier protein, the resulting spots exhibited a more uniform morphology. In a direct immunoassay for cholera toxin, capture antibody spots dispensed in 10 mM PBS containing 0.1% BSA produced fluorescent signals that were 2.8- to 4.3-fold more intense than antibody spots that were dispensed in 150 mM PBS without BSA. Interestingly, no differences were observed in the specific activities of the capture antibodies as a result of printing in the different buffers. The implications of these results on the future development of protein biochips are discussed.
The present invention provides for a method of culturing cells and inducing the expression of at least one gene in the cell culture. The method provides for contacting the cell with a transcription factor decoy oligonucleotide sequence directed against a nucleotide sequence encoding a shear stress response element.
Automated annotations of protein functions are error-prone because of our lack of knowledge of protein functions. For example, it is often impossible to predict the correct substrate for an enzyme or a transporter. Furthermore, much of the knowledge that we do have about the functions of proteins is missing from the underlying databases. We discuss how to use interactive tools to quickly find different kinds of information relevant to a protein’s function. Many of these tools are available via PaperBLAST (http://papers.genomics.lbl.gov). Combining these tools often allows us to infer a protein’s function. Ideally, accurate annotations would allow us to predict a bacterium’s capabilities from its genome sequence, but in practice, this remains challenging. We describe interactive tools that infer potential capabilities from a genome sequence or that search a genome to find proteins that might perform a specific function of interest.
The demand for sustainable and alternative protein sources has been on the rise, driving interest in the valorization of underutilized plants. This study evaluated Calendula officinalis (marigold), a common floral waste, as a sustainable alternative protein source for the food industry. The primary objective of this study was to investigate the physicochemical properties of protein fractions from Calendula officinalis flower to evaluate their potential as a novel protein ingredient. Extraction of the Calendula officinalis flower yielded 92.17% of the crude protein. A sequential extraction of albumin, globulin, glutelin, and prolamin from marigold flower revealed albumin as the dominant fraction (65.47%) and exhibited the highest protein functionality, including water-holding capacity (2.37 g/g), oil-holding capacity (2.49 g/g), and emulsifying capacity (65.22 mL/g). Compared with other protein fractions, glutelin showed a relatively high emulsifying and foaming capacity (EC: 59.13 mL/g; FC: 16.23%). Differential scanning calorimetry revealed high thermal stability for albumin (T p = 105.28 °C) and glutelin (T p = 97.6 °C). Sodium Dodecyl Sulfate–Polyacrylamide Gel Electrophoresis (SDS-PAGE) and Liquid Chromatography–Mass Spectrometry (LC-MS) confirmed the presence of abundant low-molecular-weight polypeptides (<37 kDa), which enhanced emulsification, while scanning electron microscopy revealed porous structures aligned with hydration properties. Antioxidant activity was higher in albumin and glutelin, linked to surface hydrophobicity. LC-MS/MS identified 33 short-chain proteins, including oxidoreductase proteins and lipid-transfer proteins. Findings highlight marigold flower proteins as a sustainable, functional ingredient for a diverse range of food applications.
Understanding the relationship between protein sequence and function is necessary to design new and useful proteins with applications in bioenergy, medicine, and agriculture. The mapping from sequence to function is tremendously complex because it involves thousands of molecular interactions that are coupled over multiple lengths and timescales. We show that neural networks can learn the sequence–function mapping from large protein datasets. Neural networks are appealing for this task because they can learn complicated relationships from data, make few assumptions about the nature of the sequence–function relationship, and can learn general rules that apply across the length of the protein sequence. We demonstrate that learned models can be applied to design new proteins with properties that exceed natural sequences.
Many gene products exhibit great structural heterogeneity because of an array of modifications. These modifications are not directly encoded in the genomic template but often affect the functionality of proteins. Protein glycosylation plays a vital role in proper protein functions. However, the analysis of glycoproteins has been challenging compared with other protein modifications, such as phosphorylation. Here, we perform an integrated proteomic and glycoproteomic analysis of 83 prospectively collected high-grade serous ovarian carcinoma (HGSC) and 23 non-tumor tissues. Integration of the expression data from global proteomics and glycoproteomics reveals tumor-specific glycosylation, uncovers different glycosylation associated with three tumor clusters, and identifies glycosylation enzymes that were correlated with the altered glycosylation. In addition to providing a valuable resource, these results provide insights into the potentialroles of glycosylation in the pathogenesis of HGSC, with the possibility of distinguishing pathological outcomes of ovarian tumors from non-tumors, as well as classifying tumor clusters.
Proteins interact to form complexes. Predicting the quaternary structure of protein complexes is useful for protein function analysis, protein engineering, and drug design. However, few user-friendly tools leveraging the latest deep learning technology for inter-chain contact prediction and the distance-based modelling to predict protein quaternary structures are available. To address this gap, we develop DeepComplex, a web server for predicting structures of dimeric protein complexes. It uses deep learning to predict inter-chain contacts in a homodimer or heterodimer. The predicted contacts are then used to construct a quaternary structure of the dimer by the distance-based modelling, which can be interactively viewed and analysed. The web server is freely accessible and requires no registration. It can be easily used by providing a job name and an email address along with the tertiary structure for one chain of a homodimer or two chains of a heterodimer. The output webpage provides the multiple sequence alignment, predicted inter-chain residue-residue contact map, and predicted quaternary structure of the dimer.
The African swine fever virus (ASFV) is an often deadly disease in swine and poses a threat to swine livestock and swine producers. With its complex genome containing more than 150 coding regions, developing effective vaccines for this virus remains a challenge due to a lack of basic knowledge about viral protein function and protein–protein interactions between viral proteins and between viral and host proteins. In this work, we identified ASFV-ASFV protein–protein interactions (PPIs) using artificial intelligence-powered protein structure prediction tools. We benchmarked our PPI identification workflow on the Vaccinia virus, a widely studied nucleocytoplasmic large DNA virus, and found that it could identify gold-standard PPIs that have been validated in vitro in a genome-wide computational screening. We applied this workflow to more than 18,000 pairwise combinations of ASFV proteins and were able to identify seventeen novel PPIs, many of which have corroborating experimental or bioinformatic evidence for their protein–protein interactions, further validating their relevance. Two protein–protein interactions, I267L and I8L, I267L__I8L, and B175L and DP79L, B175L__DP79L, are novel PPIs involving viral proteins known to modulate host immune response.
De novo protein design provides a framework to test our understanding of protein function and build proteins with cofactors and functions not found in nature. Here, we report the design of proteins designed to bind powerful photooxidants and the evaluation of the use of these proteins to generate diffusible small-molecule reactive species. Because excited-state dynamics are influenced by the dynamics and hydration of a photooxidant’s environment, it was important to not only design a binding site but also to evaluate its dynamic properties. Thus, we used computational design in conjunction with molecular dynamics (MD) simulations to design a protein, designated NBP (NDI Binding Protein), that held a naphthalenediimide (NDI), a powerful photooxidant, in a programmable molecular environment. Solution NMR confirmed the structure of the complex. We evaluated two NDI cofactors in this de novo protein using ultrafast pump–probe spectroscopy to evaluate light-triggered intra- and intermolecular electron transfer function. Moreover, we demonstrated the utility of this platform to activate multiple molecular probes for protein labeling.
Abstract A set of C43(DE3) and BL21(DE3) Escherichia coli host strains that are auxotrophic for various amino acids is briefly reviewed. These strains require the addition of a defined set of one or more amino acids in the growth medium, and have been specifically designed for overproduction of membrane or water-soluble proteins selectively labelled with stable isotopes, such as 2H, 13C and 15N. The strains described here are available for use and have been deposited into public strain banks. Although they cannot fully eliminate the possibility of isotope dilution and mixing, metabolic scrambling of the different amino acid types can be minimized through a careful consideration of the bacterial metabolic pathways. The use of a suitable auxotrophic expression host strain with an appropriately isotopically labelled growth medium ensures high levels of isotope labelling efficiency as well as selectivity for providing deeper insight into protein structure–function relationships.
Phosphorylation is a ubiquitous protein modification that is known to play important roles in many biological phenomena including cell signaling, the opening and closing of membrane protein channels, and even triggering of amyloid protein aggregation. Despite the effects phosphorylation has on protein function, the impact phosphorylation has on the structure of proteins is not well understood. Here, to determine how phosphorylation affects the structure of proteins, top-down mass spectrometry (TD-MS) and ion mobility-mass spectrometry (IM-MS) were performed on various phosphorylated proteins and their dephosphorylated proteoforms. TD-MS with collision- and electron-based fragmentation techniques was utilized to locate phosphorylation sites on the intrinsically disordered amyloid proteins β-casein and α-synuclein. TD-MS also provided evidence that alkaline phosphatase dephosphorylates β-casein from the N-terminus to the C-terminus. Furthermore, IM-MS of common phosphorylated proteins such as β-casein, α-casein, ovalbumin, and phosvitin indicates that phosphorylation promotes compaction of protein structure in denaturing as well as native conditions. Increases in abundance of more compact conformers are also observed when the disease related amyloid protein α-synuclein is phosphorylated at serine 129. We interpret the increased abundance of more compact conformers when proteins are phosphorylated as evidence that salt bridges form between negatively charged phosphates and positively charged residues, which alters protein structure. Salt bridge formation due to phosphorylation could be a mechanism for regulating protein function and be responsible for many of the phenomena observed in nature.
Kinases are a family of proteins that function as molecular switches, regulating several essential cellular activities such as cell proliferation. Dysfunctional kinases are implicated in several types of cancers and hence they are actively pursued as drug targets. Given the vast number of complex kinase structures that are available in the protein data bank (PDB), there is a necessity to develop methodologies that can identify structurally important moieties of the kinases in an automated fashion, for such techniques can be instrumental in identifying novel drug targets. In this work, we develop a graph neural network (GNN) based deep learning framework for classifying the functionally active and inactive states of a large set of eukaryotic protein kinases, making use of their 3D structure from the PDB. We show that GNN based machine learning models can classify protein states with an accuracy greater than 97%. We further use the GNN models to automatically identify regions of the kinases that are important for its function. For this purpose, Gradient-weighted Class Activation Mapping (Grad-CAM) was implemented on the protein graphs. Remarkably, Grad-CAM consistently identifies the highly conserved DFG motif as the most important part of the protein across the entire kinome, without any prior input. Other regions of the hydrophobic core such as the HRD motif were also identified by the interpretable GNN framework, consistent with the literature. We discuss the significance of each of these regions in detail.