SARS-CoV-2 mRNA vaccine design enabled by prototype pathogen preparedness

A vaccine for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is needed to control the coronavirus disease 2019 (COVID-19) global pandemic. Structural studies have led to the development of mutations that stabilize Betacoronavirus spike proteins in the prefusion state, improving their expression and increasing immunogenicity1. This principle has been applied to design mRNA-1273, an mRNA vaccine that encodes a SARS-CoV-2 spike protein that is stabilized in the prefusion conformation. Here we show that mRNA-1273 induces potent neutralizing antibody responses to both wild-type (D614) and D614G mutant2 SARS-CoV-2 as well as CD8+ T cell responses, and protects against SARS-CoV-2 infection in the lungs and noses of mice without evidence of immunopathology. mRNA-1273 is currently in a phase III trial to evaluate its efficacy

A method of predicting effective solvent extraction parameters for recycling of used lubricating oils

Solvent extraction technique is one of the cheapest and most efficient processes experienced in recycling of used lubricating oils. In this paper, the performance of three extracting solvents (2-propanol, 1-butanol, and methyl-ethyl-ketone (MEK) in recycling used oil was evaluated experimentally. The effect of the most critical parameters (type of solvent, solvent to oil ratio, and extraction temperature) was investigated. The results show that MEK achieved the best performance with the lowest percent oil losses, followed by 2-propanol and 1-butanol, and as the extraction temperature increases the percent oil losses decreases. The anti-solvency energy (Es), which originates from the solubility parameters difference between the solvent and oil was related to the solvent to oil ratio. It was found that the critical clarifying ratio predicted from such relations for the three solvents reasonably agrees with that measured experimentally. Relations between Es and solvent to oil ratio give a proper guideline for preliminary evaluation of the extracting solvent. It also can be used to predict the optimum solvent:oil ratio and extraction temperature based on the solvent ability to dissolve the base oil in used motor oil

String kernels of imperfect matches for offtarget detection in RNA interference

Abstract. RNA interference (RNAi) is a posttranscriptional gene silencing mechanism frequently used to study gene functions and knock down viral genes. RNAi has been regarded as a highly effective means of gene repression. However, an “off-target effect ” deteriorates its specificity and applicability. The complete off-target effects can only be characterized by examining all factors through systematic investigation of each gene in a genome. However, this complete investigation is too expensive to conduct experimentally which motivates a computational study. The sequence matching between an siRNA and its target mRNA allows for mismatches, G-U wobbles, and the secondary structure bulges, in addition to exact matches. To simulate these matching features, we propose string kernels measuring the similarity between two oligonucleotides and develop novel efficient implementations for RNAi off-target detection. We apply the algorithms for off-target errors in C. elegans and human.