ChemRxiv
Not a member yet
27047 research outputs found
Sort by
A fluorogenic pseudo-infection assay to probe transfer and distribution of influenza viral contents to target vesicles
No full textFusion of enveloped viruses with endosomal membranes and subsequent release of viral genome into the cytoplasm is crucial to the viral infection cycle. It is often modelled by carrying out fusion between virus particles and target lipid vesicles. We utilized fluorescence microscopy to characterize the kinetic and spatial aspects of the transfer of influenza viral ribonucleoprotein (vRNP) complexes to target vesicles and their distribution within the fused volumes to gain deeper insight into the mechanistic aspects of endosomal escape. The fluorogenic RNA-binding dye QuantiFluor® (Promega) was found to be well-suited for direct and sensitive microscopic observation of vRNPs which facilitated background-free detection and kinetic analysis of fusion events on a single particle level. To determine the extent to which the viral contents are transferred to the target vesicles through the fusion pore, we carried out virus-vesicle fusion in a side-by-side fashion. Measurement of the Euclidean distances between the centroids of super-localized membrane and content dye signals within the fused volumes allowed determination of any symmetry (or the lack thereof) between them as expected in the event of transfer (or the lack thereof) of vRNPs, respectively. We found that in case of fusion between viruses and 100 nm target vesicles, ~39% of the events led to transfer of viral contents to the target vesicles. This methodology provides a rapid, generic and cell-free method to assess the inhibitory effects of anti-viral drugs and therapeutics on the endosomal escape behavior of enveloped viruses
Leveraging Undergraduate Learning Assistants When Implementing New Laboratory Curricula
No full textAt a large-enrollment research university, undergraduate chemistry courses for non-chemistry majors were delivered remotely during the 2020–2021 academic year, with a return to in-person instruction planned for January 2022. Because this return to in-person instruction coincided with the transition of second-year students from general chemistry labs to organic chemistry labs, the instructional staff recognized a need for remedial laboratory curricula for students with no prior in-person laboratory experience. Simultaneously, we desired to implement undergraduate Learning Assistants (LAs) in non-chemistry major organic chemistry laboratories for the first time at our university. In this paper, we describe our approach for leveraging undergraduate LAs to (1) test new laboratory curriculum and (2) address feelings of comfort and safety for students with no prior in-person laboratory experience. Benefits of our LA program perceived by students include increased laboratory efficiency and improved student learning from near-peer instructors; benefits perceived by LAs include the development of professional skills and teamwork with graduate student teaching assistants. We provide an outline of resources and strategies to enable instructors to simultaneously implement undergraduate LAs and new laboratory curriculum
“Small is beautiful” – the significance of reliable determination of low- abundant therapeutic antibody glycovariants
No full textGlycans associated with biopharmaceutical drugs play crucial roles in drug safety and efficacy, and therefore, their reliable detection and quantification is essential. Our study introduces a multi-level quantification approach for glycosylation analysis in monoclonal antibodies, focusing on minor abundant glycovariants. Mass spectrometric data is evaluated mainly employing open-source software tools. Released glycan and glycopeptide data form the basis for integrating information across different molecular and structural levels up to intact glycoproteins. A comprehensive site-specific comparison showed that indeed, variations across structural levels were observed especially for minor abundant species. Utilizing MoFi, a tool for annotating mass peaks of intact proteins, we quantify isobaric glycosylation variants at the intact protein level. Our workflow\u27s utility is demonstrated on NISTmAb, rituximab and adalimumab, profiling their minor abundant variants for the first time across diverse structural levels. This study enhances understanding and accessibility in glycosylation analysis, emphasizing the significance of minor abundant glycovariants in therapeutic antibodies
Novelty Detection in the Design of Synthesis of Energy Storage Materials: a Case Study of Garnet-Structured Solid Electrolytes
No full textRecent decades have shown arising growth-on-demand of integrating the machine learning into all areas of chemistry and materials science. In this study, we consider one of the aspects of applying these technologies to gain advantage in the search for new knowledge extracted from experimental data obtained in ever-growing number of studies. The novelty detection approaches are aimed to identify the artefacts in these data that may be of importance in many direc- tions. The analysis of "outliers" in details of the synthesis in the research studies of garnet-structured solid electrolytes was chosen as the object of demonstration of one of the practical applications of this methodology. Particular attention was paid to the choice of precursors. The thermodynamic data such as the heat of formation from the pure oxides as well as the results of drop solution calorimetry for simple oxides were involved as the descriptors of the studied systems. The overall performance of novelty/outlier detection of all types of outliers was characterized for the data described varying the complexity of description using ROC-AUC statistics and was assessed to be 0.71 – 0.72 using the Area-Under-Curve statistics. It was found that all “outlier” compounds related to those as the result of using the rare precursors in synthesis were successfully identified. The complementary regression analysis was performed to elucidate the relationship between the data diversity and the complexity of data description
Gold-catalysed Heck Reactions: Fact or Fiction?
No full textTwo recent high-profile publications reported the formation of Heck-type arylated alkenes catalysed by MeDalPhosAuCl / AgOTf (J. Am. Chem. Soc. 2023, 145, 8810) and their cyclisation to tetralines (Angew. Chem. Int. Ed. 2023, e202312786). It was claimed that these were the first demonstrations in gold catalysis of alkene insertion into Au-aryl bonds, β-H elimination and chain-walking by Au-H cations. We show here that in fact this chemistry is a two-stage process. Only the first step, the production of an alkyl triflate ester as the primary organic product by the well-known alkene heteroarylation sequence, involves gold. The subsequent formation of Heck-type olefins and their cyclisation to tetralines represent classical H+-triggered carbocationic chemistry. These steps proceed in the absence of gold with identical results. Literature claims of new gold reactivity such as chain walking by the putative [LAuH]2+ dication have no basis in fact
Predicting Two-Dimensional Semiconductors Using Conductivity Effective Mass
No full textIn this paper we investigate the relationship between the conductivity effective mass and exfoliation energy of materials to assess whether automatic sampling of the electron band structure can predict the presence of and ease of separating chemically bonded layers. We assess 22,976 materials from the Materials Project database, screen for only those that are thermodynamically stable and identify the 1,000 materials with the highest standard deviation for p-type and the 1,000 materials with the highest standard deviation for n-type internal conductivity effective mass tensors. We calculate the exfoliation energy of these 2,000 materials and report on the correlation between effective mass and exfoliation energy. A relationship is found which is used to identify a previously unconsidered two-dimensional material and could streamline the modelling of other two-dimensional materials in the future
Navigating Antibacterial Frontiers: Landscape Analysis of Antibiotics, Resistance Mechanisms and Emerging Therapeutic Strategies
No full textDeveloping effective antibacterial solutions has become paramount in maintaining global health in this era of increasing bacterial threats and rampant antibiotic resistance. Traditional antibiotics have played a significant role in combating bacterial infections throughout history. However, the emergence of novel resistant strains necessitates constant innovation in antibacterial research. We have analyzed the data on antibacterials from the CAS Content CollectionTM, the largest human-curated collection of published scientific knowledge, proven valuable for quantitative analysis of global scientific knowledge. Our analysis focuses on mining the CAS Content Collection data for recent publications (since 2012). This article aims to explore the intricate landscape of antibacterial research while reviewing the advancement from traditional antibiotics to novel and emerging antibacterial strategies. By delving into the resistance mechanisms, this paper highlights the need to find alternate strategies to address the growing concern
Exploiting Vector Pattern Diversity of Molecular Scaffolds for Cheminformatics Tasks in Drug Discovery
No full textChemical diversity is challenging to describe objectively. Despite this, various notions of chemical diversity are used throughout the medicinal chemistry optimization process in drug discovery. In this work, we show the usefulness of considering exploited vectors during different phases of the drug design process to provide a quantitative and objective description of chemical diversity. We have developed a concise and fast approach to enumerate and analyze the exploited vector patterns (EVPs) of molecular compound series, which can then be used in archetypal compound selection tasks from hit matter identification to hit expansion and lead optimization. We firstly show that EVPs can be used to assess the progressibility of compounds in a fragment library design exercise. By considering EVPs, we then show how a set of compounds can be prioritized for hit expansion using EVP-based, customizable diversity sampling approaches, reducing the time taken and mitigating human biases. We also show that EVPs are a useful tool to analyze SAR data, offering the chance to uncover correlations between different vectors without pre-determining the molecular scaffold structures. The codes used to perform these tasks are presented as easy-to-use Jupyter notebooks, which can be readily adapted for further related tasks
Bismaleimide - Aluminium sulphate blends as 3D printing ink
No full textAluminium Sulphate (AS) used a precursor for Al based compounds has the capabilities of flocculation and coagulation imparting strength to the material it gets added to. Bismaleimide (BMI) on the other hand is a high temperature resin used for aerospace applications. An AS-BMI resin has therefore been studied in this communication having potential application as a 3D printing in
Scalable and low-energy decoupled electrochemical CO2 capture
No full textElectrochemical CO2 capture with renewable electricity provides a promising avenue for efficient decarbonization but faces challenges by instability, discontinuity, high energy consumption, and difficulties in scale-up. Here, we first propose a scalable electrochemical CO2 capture strategy by separating the traditional single electrochemical redox reaction process into a stepwise electrochemical-chemical redox reaction process. Hydrogen evolution reaction and redox carrier oxidation reaction swings the pH of electrolyte at the cathode and anode to capture CO2 efficiently which avoids side effects through decoupling of electrochemical-swing for CO2 capture and redox carrier regeneration in different times and spatial domains. We demonstrate a stable electrochemical CO2 capture process over 200 hours with low energy consumption (49.15 kJ mol-1 CO2 at 10 mA cm-2). Furthermore, the system is tunable and modular. Molecular design can be used to tailor the potential and allow scalability across various process sizes, making it a promising strategy for large-scale decarbonization