Modulating the immune response to SARS-CoV-2 by different nanocarriers delivering an mRNA expressing trimeric RBD of the spike protein: COVARNA Consortium.

Vaccines based on mRNA technology have revolutionized the field. In fact, lipid nanoparticles (LNP) formulated with mRNA are the preferential vaccine platform used in the fight against SARS-CoV-2 infection, with wider application against other diseases. The high demand and property right protection of the most potent cationic/ionizable lipids used for LNP formulation of COVID-19 mRNA vaccines have promoted the design of alternative nanocarriers for nucleic acid delivery. In this study we have evaluated the immunogenicity and efficacy of different rationally designed lipid and polymeric-based nanoparticle prototypes against SARS-CoV-2 infection. An mRNA coding for a trimeric soluble form of the receptor binding domain (RBD) of the spike (S) protein from SARS-CoV-2 was encapsulated using different components to form nanoemulsions (NE), nanocapsules (NC) and lipid nanoparticles (LNP). The toxicity and biological activity of these prototypes were evaluated in cultured cells after transfection and in mice following homologous prime/boost immunization. Our findings reveal good levels of RBD protein expression with most of the formulations. In C57BL/6 mice immunized intramuscularly with two doses of formulated RBD-mRNA, the modified lipid nanoparticle (mLNP) and the classical lipid nanoparticle (LNP-1) were the most effective delivery nanocarriers at inducing binding and neutralizing antibodies against SARS-CoV-2. Both prototypes fully protected susceptible K18-hACE2 transgenic mice from morbidity and mortality following a SARS-CoV-2 challenge. These results highlight that modulation of mRNAs immunogenicity can be achieved by using alternative nanocarriers and support further assessment of mLNP and LNP-1 prototypes as delivery vehicles for mRNA vaccines.

High-Throughput Prediction of the Impact of Genetic Variability on Drug Sensitivity and Resistance Patterns for Clinically Relevant Epidermal Growth Factor Receptor Mutations from Atomistic Simulations.

Mutations in the kinase domain of the epidermal growth factor receptor (EGFR) can be drivers of cancer and also trigger drug resistance in patients receiving chemotherapy treatment based on kinase inhibitors. A priori knowledge of the impact of EGFR variants on drug sensitivity would help to optimize chemotherapy and design new drugs that are effective against resistant variants before they emerge in clinical trials. To this end, we explored a variety of in silico methods, from sequence-based to "state-of-the-art" atomistic simulations. We did not find any sequence signal that can provide clues on when a drug-related mutation appears or the impact of such mutations on drug activity. Low-level simulation methods provide limited qualitative information on regions where mutations are likely to cause alterations in drug activity, and they can predict around 70% of the impact of mutations on drug efficiency. High-level simulations based on nonequilibrium alchemical free energy calculations show predictive power. The integration of these "state-of-the-art" methods into a workflow implementing an interface for parallel distribution of the calculations allows its automatic and high-throughput use, even for researchers with moderate experience in molecular simulations.

BioExcel Building Blocks, a software library for interoperable biomolecular simulation workflows.

In the recent years, the improvement of software and hardware performance has made biomolecular simulations a mature tool for the study of biological processes. Simulation length and the size and complexity of the analyzed systems make simulations both complementary and compatible with other bioinformatics disciplines. However, the characteristics of the software packages used for simulation have prevented the adoption of the technologies accepted in other bioinformatics fields like automated deployment systems, workflow orchestration, or the use of software containers. We present here a comprehensive exercise to bring biomolecular simulations to the "bioinformatics way of working". The exercise has led to the development of the BioExcel Building Blocks (BioBB) library. BioBB's are built as Python wrappers to provide an interoperable architecture. BioBB's have been integrated in a chain of usual software management tools to generate data ontologies, documentation, installation packages, software containers and ways of integration with workflow managers, that make them usable in most computational environments.