Antigenic Structural Similarity as a Predictor for Antibody Cross-Reactivity

Antibodies are an essential component of the adaptive immune system that function to neutralize foreign invaders, such as bacterial and parasitic pathogens. However, B-cell epitopes remain difficult to predict due to their general indistinguishability from other protein regions. Epitope prediction tools in the past have largely relied on amino acid sequence similarity; however, implementing three-dimensional protein structure analyses into the epitope prediction algorithms has been shown to increase detection accuracy. Furthermore, structural comparisons between antigenic proteins for their potential to bind cross-reactive antibodies have not been explored extensively in the literature. Recent studies have pointed to the utility of looking at shared epitope structures in predicting antibody cross- reactivity, which may shed light on cross-immunity between infectious pathogens and autoimmune diseases induced after infection. Thus, herein, the potential impact of including structural similarity comparisons in detecting shared epitopes is discussed. With the large amount of structural information being determined by three-dimensional computational protein modelling methods, the ability to perform these analyses is becoming more feasible.Antibiotic Research UK (PHZJ/687

Identification of a cDNA encoding a novel C18-Δ9 polyunsaturated fatty acid-specific elongating activity from the docosahexaenoic acid (DHA)-producing microalga, Isochrysis galbana11The nucleotide sequence reported in this paper has been submitted to the GenBank™/EBI Data Bank with accession number AF390174.

AbstractIsochrysis galbana, a marine prymnesiophyte microalga, is rich in long chain polyunsaturated fatty acids such as docosahexaenoic acid (C22:6n-3, Δ4,7,10,13,16,19). We used a polymerase chain reaction-based strategy to isolate a cDNA, designated IgASE1, encoding a polyunsaturated fatty acid-elongating activity from I. galbana. The coding region of 263 amino acids predicts a protein of 30 kDa that shares only limited homology to animal and fungal proteins with elongating activity. Functional analysis of IgASE1, by expression in Saccharomyces cerevisiae, was used to determine its activity and substrate specificity. Transformed yeast cells specifically elongated the C18-Δ9 polyunsaturated fatty acids, linoleic acid (C18:2n-6, Δ9,12) and α-linolenic acid (C18:3n-3, Δ9,12,15), to eicosadienoic acid (C20:2n-6, Δ11,14) and eicosatrienoic acid (C20:3n-3, Δ11,14,17), respectively. To our knowledge this is the first time such an elongating activity has been functionally characterised. The results also suggest that a major route for eicosapentaenoic acid (C20:5n-3, Δ5,8,11,14,17) and docosahexaenoic acid syntheses in I. galbana may involve a Δ8 desaturation pathway

Searching for New Z-DNA/Z-RNA Binding Proteins Based on Structural Similarity to Experimentally Validated Zα Domain.

Z-DNA and Z-RNA are functionally important left-handed structures of nucleic acids, which play a significant role in several molecular and biological processes including DNA replication, gene expression regulation and viral nucleic acid sensing. Most proteins that have been proven to interact with Z-DNA/Z-RNA contain the so-called Zα domain, which is structurally well conserved. To date, only eight proteins with Zα domain have been described within a few organisms (including human, mouse, Danio rerio, Trypanosoma brucei and some viruses). Therefore, this paper aimed to search for new Z-DNA/Z-RNA binding proteins in the complete PDB structures database and from the AlphaFold2 protein models. A structure-based similarity search found 14 proteins with highly similar Zα domain structure in experimentally-defined proteins and 185 proteins with a putative Zα domain using the AlphaFold2 models. Structure-based alignment and molecular docking confirmed high functional conservation of amino acids involved in Z-DNA/Z-RNA, suggesting that Z-DNA/Z-RNA recognition may play an important role in a variety of cellular processes

Stabilization of Basic Oxygen Furnace Slag by Hot-stage Carbonation Treatment.

Treatment and disposal of Basic Oxygen Furnace (BOF) slag, a residue of the steel production process characterized by high basicity and propensity for heavy metal leaching, is a costly burden on metallurgical plants; a sustainable valorization route is desired. The stabilization of BOF slag utilizing hot-stage carbonation treatment was investigated; this approach envisions carbonation during the hot-to-cold pathway followed by the material after the molten slag is poured and solidified. Three experimental methodologies were employed: (i) in-situ thermogravimetric analyzer (TGA) carbonation was used to assess carbonation reaction kinetics and thermodynamic equilibrium at high temperatures; (ii) pressurized basket reaction carbonation was used to assess the effects of pressurization, steam addition and slag particle size; and (iii) atmospheric furnace carbonation was used to assess the effect of carbonation on the mineralogy, basicity and heavy metal leaching properties of the slag. Free lime was found to be the primary mineral participating in direct carbonation of BOF slag. Initial carbonation kinetics were comparable at temperatures ranging from 500 to 800 oC, but higher temperatures aided in solid state diffusion of CO2 into the unreacted particle core, thus increasing overall CO2 uptake. The optimum carbonation temperature of both BOF slag and pure lime lies just below the transition temperature between carbonation stability and carbonate decomposition: 830-850 oC and 750-770 oC at 1 atm and 0.2 atm CO2 partial pressures, respectively. Pressurization and steam addition contribute marginally to CO2 uptake. CO2 uptake progressively decreases with increasing particle size, but basicity reduction is similar independent of particle size. The solubility of some heavy metals reduced after carbonation (barium, cobalt and nickel), but vanadium and chromium leaching increased

SARS-CoV-2 3D database: Understanding the Coronavirus Proteome and Evaluating Possible Drug Targets.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a rapidly growing infectious disease, widely spread with high mortality rates. Since the release of the SARS-CoV-2 genome sequence in March 2020, there has been an international focus on developing target-based drug discovery, which also requires knowledge of the 3D structure of the proteome. Where there are no experimentally solved structures, our group has created 3D models with coverage of 97.5% and characterised them using state-of-the-art computational approaches. Models of protomers and oligomers, together with predictions of substrate and allosteric binding sites, protein- ligand docking, SARS-CoV-2 protein interactions with human proteins, impacts of mutations, and mapped solved experimental structures are freely available for download. These are imple- mented in SARS CoV-2 3D, a comprehensive and user-friendly database, available at https://sars3d.com/. This provides essential information for drug discovery, both to evaluate targets and design new potential therapeutics.This work is supported and funded by King Abdullah scholarship (Saudi Arabia research coun- cil), and American Leprosy Missions grants (G88726), SET is funded by the Cystic Fibrosis Trust (RG 70975) and Fondation Botnar (RG91317). A.R.J is funded by the Biotechnology and Biological Sciences Research Council (BBSRC) DTP studentship (BB/M011194/1). B.B. is funded by the Cystic Fibrosis Trust and L.C. on a studentship from Ipsen. T.L.B. is funded by a the Wellcome Trust Investigator Award, PHZJ/489 RG83114 (2016-2021

Unheeded SARS-CoV-2 proteins? A deep look into negative-sense RNA.

SARS-CoV-2 is a novel positive-sense single-stranded RNA virus from the Coronaviridae family (genus Betacoronavirus), which has been established as causing the COVID-19 pandemic. The genome of SARS-CoV-2 is one of the largest among known RNA viruses, comprising of at least 26 known protein-coding loci. Studies thus far have outlined the coding capacity of the positive-sense strand of the SARS-CoV-2 genome, which can be used directly for protein translation. However, it has been recently shown that transcribed negative-sense viral RNA intermediates that arise during viral genome replication from positive-sense viruses can also code for proteins. No studies have yet explored the potential for negative-sense SARS-CoV-2 RNA intermediates to contain protein-coding loci. Thus, using sequence and structure-based bioinformatics methodologies, we have investigated the presence and validity of putative negative-sense ORFs (nsORFs) in the SARS-CoV-2 genome. Nine nsORFs were discovered to contain strong eukaryotic translation initiation signals and high codon adaptability scores, and several of the nsORFs were predicted to interact with RNA-binding proteins. Evolutionary conservation analyses indicated that some of the nsORFs are deeply conserved among related coronaviruses. Three-dimensional protein modeling revealed the presence of higher order folding among all putative SARS-CoV-2 nsORFs, and subsequent structural mimicry analyses suggest similarity of the nsORFs to DNA/RNA-binding proteins and proteins involved in immune signaling pathways. Altogether, these results suggest the potential existence of still undescribed SARS-CoV-2 proteins, which may play an important role in the viral lifecycle and COVID-19 pathogenesis

Are There Hidden Genes in DNA/RNA Vaccines?

Due to the fast global spreading of the Severe Acute Respiratory Syndrome Coronavirus - 2 (SARS-CoV-2), prevention and treatment options are direly needed in order to control infection-related morbidity, mortality, and economic losses. Although drug and inactivated and attenuated virus vaccine development can require significant amounts of time and resources, DNA and RNA vaccines offer a quick, simple, and cheap treatment alternative, even when produced on a large scale. The spike protein, which has been shown as the most antigenic SARS-CoV-2 protein, has been widely selected as the target of choice for DNA/RNA vaccines. Vaccination campaigns have reported high vaccination rates and protection, but numerous unintended effects, ranging from muscle pain to death, have led to concerns about the safety of RNA/DNA vaccines. In parallel to these studies, several open reading frames (ORFs) have been found to be overlapping SARS-CoV-2 accessory genes, two of which, ORF2b and ORF-Sh, overlap the spike protein sequence. Thus, the presence of these, and potentially other ORFs on SARS-CoV-2 DNA/RNA vaccines, could lead to the translation of undesired proteins during vaccination. Herein, we discuss the translation of overlapping genes in connection with DNA/RNA vaccines. Two mRNA vaccine spike protein sequences, which have been made publicly-available, were compared to the wild-type sequence in order to uncover possible differences in putative overlapping ORFs. Notably, the Moderna mRNA-1273 vaccine sequence is predicted to contain no frameshifted ORFs on the positive sense strand, which highlights the utility of codon optimization in DNA/RNA vaccine design to remove undesired overlapping ORFs. Since little information is available on ORF2b or ORF-Sh, we use structural bioinformatics techniques to investigate the structure-function relationship of these proteins. The presence of putative ORFs on DNA/RNA vaccine candidates implies that overlapping genes may contribute to the translation of smaller peptides, potentially leading to unintended clinical outcomes, and that the protein-coding potential of DNA/RNA vaccines should be rigorously examined prior to administration

Computational linguistics for metadata building: Aggregating text processing technologies for enhanced image access

We present a system which applies text mining using computational linguistic techniques to automatically extract, categorize, disambiguate and filter metadata for image access. Candidate subject terms are identified through standard approaches; novel semantic categorization using machine learning and disambiguation using both WordNet and a domain specific thesaurus are applied. The resulting metadata can be manually edited by image catalogers or filtered by semi-automatic rules. We describe the implementation of this workbench created for, and evaluated by, image catalogers. We discuss the system\u27s current functionality, developed under the Computational Linguistics for Metadata Building (CLiMB) research project. The CLiMB Toolkit has been tested with several collections, including: Art Images for College Teaching (AICT), ARTStor, the National Gallery of Art (NGA), the Senate Museum, and from collaborative projects such as the Landscape Architecture Image Resource (LAIR) and the field guides of the Vernacular Architecture Group (VAG)