Although clinical trials and real-world studies have affirmed the effectiveness and safety of the FDA-authorized COVID-19 vaccines, reports of breakthrough infections and persistent emergence of new variants highlight the need to vigilantly monitor the effectiveness of these vaccines. Here we compare the effectiveness of two full-length Spike protein-encoding mRNA vaccines from Moderna (mRNA-1273) and Pfizer/BioNTech (BNT162b2) in the Mayo Clinic Health System over time from January to July 2021, during which either the Alpha or Delta variant was highly prevalent. We defined cohorts of vaccinated and unvaccinated individuals from Minnesota (n = 25,589 each) matched on age, sex, race, history of prior SARS-CoV-2 PCR testing, and date of full vaccination. Both vaccines were highly effective during this study period against SARS-CoV-2 infection (mRNA-1273: 86%, 95%CI: 81-90.6%; BNT162b2: 76%, 95%CI: 69-81%) and COVID-19 associated hospitalization (mRNA-1273: 91.6%, 95% CI: 81-97%; BNT162b2: 85%, 95% CI: 73-93%). In July, vaccine effectiveness against hospitalization has remained high (mRNA-1273: 81%, 95% CI: 33-96.3%; BNT162b2: 75%, 95% CI: 24-93.9%), but effectiveness against infection was lower for both vaccines (mRNA-1273: 76%, 95% CI: 58-87%; BNT162b2: 42%, 95% CI: 13-62%), with a more pronounced reduction for BNT162b2. Notably, the Delta variant prevalence in Minnesota increased from 0.7% in May to over 70% in July whereas the Alpha variant prevalence decreased from 85% to 13% over the same time period. Comparing rates of infection between matched individuals fully vaccinated with mRNA-1273 versus BNT162b2 across Mayo Clinic Health System sites in multiple states (Minnesota, Wisconsin, Arizona, Florida, and Iowa), mRNA-1273 conferred a two-fold risk reduction against breakthrough infection compared to BNT162b2 (IRR = 0.50, 95% CI: 0.39-0.64). In Florida, which is currently experiencing its largest COVID-19 surge to date, the risk of infection in July after full vaccination with mRNA-1273 was about 60% lower than after full vaccination with BNT162b2 (IRR: 0.39, 95% CI: 0.24-0.62). Our observational study highlights that while both mRNA COVID-19 vaccines strongly protect against infection and severe disease, further evaluation of mechanisms underlying differences in their effectiveness such as dosing regimens and vaccine composition are warranted.

Background Two FDA-authorized mRNA COVID-19 vaccines, BNT162b2 (Pfizer/BioNTech) and mRNA-1273 (Moderna), have demonstrated high efficacies in large Phase 3 randomized clinical trials. It is important to assess their effectiveness in a real-world setting. Methods This is a retrospective analysis of 136,532 individuals in the Mayo Clinic health system (Arizona, Florida, Iowa, Minnesota, Wisconsin) with PCR testing data between December 1, 2020 and April 20, 2021. We compared clinical outcomes for a vaccinated cohort of 68,266 individuals who received at least one dose of either vaccine (n BNT162b2 = 51,795; n mRNA-1273 = 16,471) and an unvaccinated control cohort of 68,266 individuals propensity-matched based on relevant demographic, clinical, and geographic features. We estimated real-world vaccine effectiveness by comparing incidence rates of positive SARS-CoV-2 PCR testing and COVID-19 associated hospitalization and ICU admission starting 7 days after the second vaccine dose. Findings The real-world vaccine effectiveness in preventing SARS-CoV-2 infection was 86.1% (95% CI: 82.4-89.1%) for BNT162b2 and 93.3% (95% CI: 85.7-97.4%) for mRNA-1273. BNT162b2 and mRNA-1273 were 88.8% (95% CI: 75.5-95.7%) and 86.0% (95% CI: 71.6-93.9%) effective in preventing COVID-19 associated hospitalization. Both vaccines were 100% effective (95% CI BNT162b2 : 51.4-100%; 95% CI mRNA-1273 : 43.3-100%) in preventing COVID-19 associated ICU admission. Conclusions BNT162b2 and mRNA-1273 are both effective in a real-world setting and are associated with reduced rates of SARS-CoV-2 infection and decreased burden of COVID-19 on the healthcare system.

G-protein coupled receptors (GPCRs) are allosteric membrane proteins mediating cellular signaling. GPCRs exhibit multiple inactive and active conformations, and the population balance between these conformations is altered upon binding of signaling molecules (or ligands). However, the nature of the conformational ensemble or the mechanism of the conformational transitions is not well understood. We present a multiscale computational approach combining a coarse-grained discrete conformational sampling method with fine-grained molecular dynamics investigating the effect of various ligands binding on the ensemble of conformations sampled by human β2-adrenergic receptor (β2AR). We show that the receptor, in the absence of any ligand, samples an extensive conformational space that includes breathing of the orthosteric ligand binding site and shear motion of the transmembrane helices 5 and 6 against the other helices. The shear motion is similar to the reorganization of the intracellular regions of TM3, TM5, and TM6 observed in the crystal structure of the active state of GPCRs. The binding of agonist norepinephrine or partial agonist salbutamol leads to the selection of a subset of conformations including active and inactive state conformations, while inverse agonist carazolol selects only inactive state conformations. The dynamics of water observed during the simulations provides an explanation for the conformational changes observed in the solution-based fluorescence spectroscopic measurements on agonist activated β2AR, which could not be explained by the agonist bound β2AR crystal structure. This study shows that the receptor activation depends on both the low energy states and the range of the conformations sampled by the receptor.

We present a coarse-grained simulation model that is capable of simulating the minute-timescale dynamics of protein translocation and membrane integration via the Sec translocon, while retaining sufficient chemical and structural detail to capture many of the sequence-specific interactions that drive these processes. The model includes accurate geometric representations of the ribosome and Sec translocon, obtained directly from experimental structures, and interactions parameterized from nearly 200 μs of residue-based coarse-grained molecular dynamics simulations. A protocol for mapping amino-acid sequences to coarse-grained beads enables the direct simulation of trajectories for the co-translational insertion of arbitrary polypeptide sequences into the Sec translocon. The model reproduces experimentally observed features of membrane protein integration, including the efficiency with which polypeptide domains integrate into the membrane, the variation in integration efficiency upon single amino-acid mutations, and the orientation of transmembrane domains. The central advantage of the model is that it connects sequence-level protein features to biological observables and timescales, enabling direct simulation for the mechanistic analysis of co-translational integration and for the engineering of membrane proteins with enhanced membrane integration efficiency.

Internal coordinate molecular dynamics (ICMD) methods provide a more natural description of a protein by using bond, angle and torsional coordinates instead of a Cartesian coordinate representation. Freezing high frequency bonds and angles in the ICMD model gives rise to constrained ICMD (CICMD) models. There are several theoretical aspects that need to be developed in order to make the CICMD method robust and widely usable. In this paper we have designed a new framework for 1) initializing velocities for non-independent CICMD coordinates, 2) efficient computation of center of mass velocity during CICMD simulations, 3) using advanced integrators such as Runge-Kutta, Lobatto and adaptive CVODE for CICMD simulations, and 4) cancelling out the “flying ice cube effect” that sometimes arises in Nosé-Hoover dynamics. The Generalized Newton-Euler Inverse Mass Operator (GNEIMO) method is an implementation of a CICMD method that we have developed to study protein dynamics. GNEIMO allows for a hierarchy of coarse-grained simulation models based on the ability to rigidly constrain any group of atoms. In this paper, we perform tests on the Lobatto and Runge-Kutta integrators to determine optimal simulation parameters. We also implement an adaptive coarse graining tool using the GNEIMO Python interface. This tool enables the secondary structure-guided “freezing and thawing” of degrees of freedom in the molecule on the fly during MD simulations, and is shown to fold four proteins to their native topologies. With these advancements we envision the use of the GNEIMO method in protein structure prediction, structure refinement, and in studying domain motion.