Molecular Dynamics Simulation of Permeation Through Connexin Channels.

Molecular dynamics (MD) simulations are a collection of computational tools that can be used to trace intermolecular interactions at the sub-nanometer level. They offer possibilities that are often unavailable to experimental methods, making MD an ideal complementary technique for the understanding a plethora of biological processes. Thanks to significant efforts by many groups of developers around the world, setting up and running MD simulations has become progressively simpler. However, simulating ionic permeation through membrane channels still presents significant caveats.MD simulations of connexin (Cx) hemichannels (HCs) are particularly problematic because HCs create wide pores in the plasma membrane, and the lateral sizes of the extracellular and intracellular regions are quite different. In this chapter, we provide a detailed instruction to perform MD simulations aimed at computationally modeling the permeation of inorganic ions and larger molecules through Cx HCs.

RAGE engagement by SARS-CoV-2 enables monocyte infection and underlies COVID-19 severity.

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has fueled the COVID-19 pandemic with its enduring medical and socioeconomic challenges because of subsequent waves and long-term consequences of great concern. Here, we chart the molecular basis of COVID-19 pathogenesis by analyzing patients' immune responses at single-cell resolution across disease course and severity. This approach confirms cell subpopulation-specific dysregulation in COVID-19 across disease course and severity and identifies a severity-associated activation of the receptor for advanced glycation endproducts (RAGE) pathway in monocytes. In vitro THP1-based experiments indicate that monocytes bind the SARS-CoV-2 S1-receptor binding domain (RBD) via RAGE, pointing to RAGE-Spike interaction enabling monocyte infection. Thus, our results demonstrate that RAGE is a functional receptor of SARS-CoV-2 contributing to COVID-19 severity.

Variability in excess deaths across countries with different vulnerability during 2020-2023.

Excess deaths provide total impact estimates of major crises, such as the COVID-19 pandemic. We evaluated excess death trajectories across countries with accurate death registration and population age structure data and assessed relationships with vulnerability indicators. Using the Human Mortality Database on 34 countries, excess deaths were calculated for 2020-2023 (to week 29, 2023) using 2017-2019 as reference, with adjustment for 5 age strata. Countries were divided into less and more vulnerable; the latter had per capita nominal GDP < $30,000, Gini > 0.35 for income inequality and/or at least ≥2.5% of their population living in poverty. Excess deaths (as proportion of expected deaths, p%) were inversely correlated with per capita GDP (r = -0.60), correlated with proportion living in poverty (r = 0.66), and modestly correlated with income inequality (r = 0.45). Incidence rate ratio for deaths was 1.062 (95% CI, 1.038-1.087) in more versus less vulnerable countries. Excess deaths started deviating in the two groups after the first wave. Between-country heterogeneity diminished gradually within each group. Less vulnerable countries had mean p% = -0.8% and 0.4% in 0-64 and >65-y-old strata. More vulnerable countries had mean p% = 7.0% and 7.2%, respectively. Lower death rates were seen in children of age 0-14 y during 2020-2023 versus prepandemic years. While the pandemic hit some countries earlier than others, country vulnerability dominated eventually the cumulative impact. Half the analyzed countries witnessed no substantial excess deaths versus prepandemic levels, while the others suffered major death tolls.

Development of a machine learning multiclass screening tool for periodontal health status based on non-clinical parameters and salivary biomarkers.

AIM: To develop a multiclass non-clinical screening tool for periodontal disease and assess its accuracy for differentiating periodontal health, gingivitis and different stages of periodontitis. MATERIALS AND METHODS: A cross-sectional diagnostic study on a convenience sample of 408 consecutive subjects was conducted by applying three non-clinical index tests estimating different features of the periodontal health-disease spectrum: a self-administered questionnaire, an oral rinse activated matrix metalloproteinase-8 (aMMP-8) point-of-care test (POCT) and determination of gingival bleeding on brushing (GBoB). Full-mouth periodontal examination was the reference standard. The periodontal diagnosis was made on the basis of the 2017 classification of periodontal diseases and conditions. Logistic regression and random forest (RF) analyses were performed to predict various periodontal diagnoses, and the accuracy measures were assessed. RESULTS: Four-hundred and eight subjects were enrolled in this study, including those with periodontal health (16.2%), gingivitis (15.2%) and stage I (15.9%), stage II (15.9%), stage III (29.7%) and stage IV (7.1%) periodontitis. Nine predictors, namely 'gum disease' (Q1), 'a rating of gum/teeth health' (Q2), 'tooth cleaning' (Q3a), the symptom of 'loose teeth' (Q4), 'use of floss' (Q7), aMMP-8 POCT, self-reported GBoB, haemoglobin and age, resulted in high levels of accuracy in the RF classifier. High accuracy (area under the ROC curve > 0.94) was observed for the discrimination of three (health, gingivitis and periodontitis) and six classes (health, gingivitis, stages I, II, III and IV periodontitis). Confusion matrices showed that the misclassification of a periodontitis case as health or gingivitis was less than 1%-2%. CONCLUSIONS: Machine learning-based classifiers, such as RF analyses, are promising tools for multiclass assessment of periodontal health and disease in a non-clinical setting. Results need to be externally validated in appropriately sized independent samples (ClinicalTrials.gov NCT03928080).

Variability in excess deaths across countries with different vulnerability during 2020-2023.

Excess deaths provide total impact estimates of major crises, such as the COVID-19 pandemic. We evaluated excess death's trajectories during 2020-2023 across countries with accurate death registration and population age structure data; and assessed relationships with economic indicators of vulnerability. Using the Human Mortality Database on 34 countries, excess deaths were calculated for 2020-2023 (to week 29, 2023) using 2017-2019 as reference, with weekly expected death calculations and adjustment for 5 age strata. Countries were divided into less and more vulnerable; the latter had per capita nominal GDP<$30,000, Gini>0.35 for income inequality and/or at least 2.5% of their population living in poverty. Excess deaths (as proportion of expected deaths, p%) were inversely correlated with per capita GDP (r=-0.60), correlated with proportion living in poverty (r=0.66) and modestly correlated with income inequality (r=0.45). Incidence rate ratio for deaths was 1.06 (95% confidence interval, 1.04-1.08) in the more versus less vulnerable countries. Excess deaths started deviating in the two groups after the first wave. Between-country heterogeneity diminished over time within each of the two groups. Less vulnerable countries had mean p%=-0.8% and 0.4% in 0-64 and >65 year-old strata while more vulnerable countries had mean p%=7.0% and 7.2%, respectively. Usually lower death rates were seen in children 0-14 years old during 2020-2023 versus pre-pandemic years. While the pandemic hit some countries earlier than others, country vulnerability dominated eventually the cumulative impact. Half of the analyzed countries witnessed no substantial excess deaths versus pre-pandemic levels, while the other half suffered major death tolls.

What Really Happened During the Massive SARS-CoV-2 Omicron Wave in China?

This Viewpoint discusses reports from China after its zero COVID-19 policy ended in December 2022.

Excess death estimates from multiverse analysis in 2009-2021.

Excess death estimates have great value in public health, but they can be sensitive to analytical choices. Here we propose a multiverse analysis approach that considers all possible different time periods for defining the reference baseline and a range of 1 to 4 years for the projected time period for which excess deaths are calculated. We used data from the Human Mortality Database on 33 countries with detailed age-stratified death information on an annual basis during the period 2009-2021. The use of different time periods for reference baseline led to large variability in the absolute magnitude of the exact excess death estimates. However, the relative ranking of different countries compared to others for specific years remained largely unaltered. The relative ranking of different years for the specific country was also largely independent of baseline. Averaging across all possible analyses, distinct time patterns were discerned across different countries. Countries had declines between 2009 and 2019, but the steepness of the decline varied markedly. There were also large differences across countries on whether the COVID-19 pandemic years 2020-2021 resulted in an increase of excess deaths and by how much. Consideration of longer projected time windows resulted in substantial shrinking of the excess deaths in many, but not all countries. Multiverse analysis of excess deaths over long periods of interest can offer an approach that better accounts for the uncertainty in estimating expected mortality patterns, comparative mortality trends across different countries, and the nature of observed mortality peaks.

Flaws and uncertainties in pandemic global excess death calculations.

Several teams have been publishing global estimates of excess deaths during the COVID-19 pandemic. Here, we examine potential flaws and underappreciated sources of uncertainty in global excess death calculations. Adjusting for changing population age structure is essential. Otherwise, excess deaths are markedly overestimated in countries with increasingly aging populations. Adjusting for changes in other high-risk indicators, such as residence in long-term facilities, may also make a difference. Death registration is highly incomplete in most countries; completeness corrections should allow for substantial uncertainty and consider that completeness may have changed during pandemic years. Excess death estimates have high sensitivity to modelling choice. Therefore different options should be considered and the full range of results should be shown for different choices of pre-pandemic reference periods and imposed models. Any post-modelling corrections in specific countries should be guided by pre-specified rules. Modelling of all-cause mortality (ACM) in countries that have ACM data and extrapolating these models to other countries is precarious; models may lack transportability. Existing global excess death estimates underestimate the overall uncertainty that is multiplicative across diverse sources of uncertainty. Informative excess death estimates require risk stratification, including age groups and ethnic/racial strata. Data to-date suggest a death deficit among children during the pandemic and marked socioeconomic differences in deaths, widening inequalities. Finally, causal explanations require great caution in disentangling SARS-CoV-2 deaths, indirect pandemic effects and effects from measures taken. We conclude that excess deaths have many uncertainties, but globally deaths from SARS-CoV-2 may be the minority of calculated excess deaths.

Antibody gene transfer treatment drastically improves epidermal pathology in a keratitis ichthyosis deafness syndrome model using male mice.

BACKGROUND: Keratitis ichthyosis deafness (KID) syndrome is a rare disorder caused by hemichannel (HC) activating gain-of-function mutations in the GJB2 gene encoding connexin (Cx) 26, for which there is no cure, or current treatments based upon the mechanism of disease causation. METHODS: We applied Adeno Associated Virus (AAV) mediated mAb gene transfer (AAVmAb) to treat the epidermal features of KID syndrome with a well-characterized HC blocking antibody using male mice of a murine model that replicates the skin pathology of the human disease. FINDINGS: We demonstrate that in vivo AAVmAb treatment significantly reduced the size and thickness of KID lesions, in addition to blocking activity of mutant HCs in the epidermis in vivo. We also show that AAVmAb treatment eliminated abnormal keratinocyte proliferation and enlarged cell size, decreased apoptosis, and restored the normal distribution of keratin expression. INTERPRETATION: Our findings reinforce the critical role played by increased HC activity in the skin pathology associated with KID syndrome. They also underscore the clinical potential of anti-HC mAbs coupled with genetic based delivery systems for treating the underlying mechanistic basis of this disorder. Inhibition of HC activity is an ideal therapeutic target in KID syndrome, and the genetic delivery of mAbs targeted against mutant HCs could form the basis of new therapeutic interventions to treat this incurable disease. FUNDING: Fondazione Telethon grant GGP19148 and University of Padova grant Prot. BIRD187130 to FM; Foundation for Ichthyosis and Related Skin Types (FIRST) and National Institutes of Health grant EY 026911 to TWW.

Estimates of COVID-19 deaths in Mainland China after abandoning zero COVID policy.

Background: China witnessed a surge of Omicron infections after abandoning zero COVID strategies on December 7, 2022. The authorities report very sparse deaths based on very restricted criteria, but massive deaths are speculated. Methods: We aimed to estimate the COVID-19 fatalities in Mainland China until summer 2023 using the experiences of Hong Kong and of South Korea in 2022 as prototypes. Both these locations experienced massive Omicron waves after having had very few SARS-CoV-2 infections during 2020-2021. We estimated age-stratified infection fatality rates (IFRs) in Hong Kong and South Korea during 2022 and extrapolated to the population age structure of Mainland China. We also accounted separately for deaths of residents in long-term care facilities in both Hong Kong and South Korea. Results: IFR estimates in non-elderly strata were modestly higher in Hong Kong than South Korea and projected 987,455 and 619,549 maximal COVID-19 deaths, respectively, if the entire China population was infected. Expected COVID-19 deaths in Mainland China until summer 2023 ranged from 49,962 to 691,219 assuming 25-70% of the non-elderly population being infected and variable protection of elderly (from none to three-quarter reduction in fatalities). The main analysis (45% of non-elderly population infected and fatality impact among elderly reduced by half) estimated 152,886-249,094 COVID-19 deaths until summer 2023. Large uncertainties exist regarding potential changes in dominant variant, health system strain, and impact on non-COVID-19 deaths. Conclusions: The most critical factor that can affect total COVID-19 fatalities in China is the extent to which the elderly can be protected.

Estimates of COVID-19 deaths in Mainland China after abandoning zero COVID policy.

BACKGROUND: China witnessed a surge of Omicron infections after abandoning 'zero COVID' strategies on 7 December 2022. The authorities report very sparse deaths based on very restricted criteria, but massive deaths are speculated. METHODS: We aimed to estimate the COVID-19 fatalities in Mainland China until summer 2023 using the experiences of Hong Kong and of South Korea in 2022 as prototypes. Both these locations experienced massive Omicron waves after having had very few SARS-CoV-2 infections during 2020-2021. We estimated age-stratified infection fatality rates (IFRs) in Hong Kong and South Korea during 2022 and extrapolated to the population age structure of Mainland China. We also accounted separately for deaths of residents in long-term care facilities in both Hong Kong and South Korea. RESULTS: Infection fatality rate estimates in non-elderly strata were modestly higher in Hong Kong than South Korea and projected 987,455 and 619,549 maximal COVID-19 deaths respectively, if the entire China population were infected. Expected COVID-19 deaths in Mainland China until summer 2023 ranged from 49,962 to 691,219 assuming 25-70% of the non-elderly population being infected and variable protection of elderly (from none to three-quarter reduction in fatalities). The main analysis (45% of non-elderly population infected and fatality impact among elderly reduced by half) estimated 152,886-249,094 COVID-19 deaths until summer 2023. Large uncertainties exist regarding potential changes in dominant variant, health system strain and impact on non-COVID-19 deaths. CONCLUSIONS: The most critical factor that can affect total COVID-19 fatalities in China is the extent to which the elderly can be protected.

Excess death estimates from multiverse analysis in 2009-2021.

Excess death estimates have great value in public health, but they can be sensitive to analytical choices. Here we propose a multiverse analysis approach that considers all possible different time periods for defining the reference baseline and a range of 1 to 4 years for the projected time period for which excess deaths are calculated. We used data from the Human Mortality Database on 33 countries with detailed age-stratified death information on an annual basis during the period 2009-2021. The use of different time periods for reference baseline led to large variability in the absolute magnitude of the exact excess death estimates. However, the relative ranking of different countries compared to others for specific years remained largely unaltered. The relative ranking of different years for the specific country was also largely independent of baseline. Averaging across all possible analyses, distinct time patterns were discerned across different countries. Countries had declines between 2009 and 2019, but the steepness of the decline varied markedly. There were also large differences across countries on whether the COVID-19 pandemic years 2020-2021 resulted in an increase of excess deaths and by how much. Consideration of longer projected time windows resulted in substantial shrinking of the excess deaths in many, but not all countries. Multiverse analysis of excess deaths over long periods of interest can offer a more unbiased approach to understand comparative mortality trends across different countries, the range of uncertainty around estimates, and the nature of observed mortality peaks.

Left atrial appendage occlusion in ventricular assist device patients to decrease thromboembolic events: A computer simulation study.

Atrial fibrillation (AF) is a common comorbidity in left ventricular assist device (LVAD) patients and has been identified as a risk factor for thromboembolic stroke. Blood stagnation within the left atrial appendage (LAA) is considered a possible major source of thrombosis and clinical studies have shown reduced thromboembolic risk after LAA occlusion (LAAO). Therefore, this study aims to investigate the effect of LAAO on thrombosis-related parameters using patient-specific simulations. Left ventricular and left atrial geometries of an LVAD patient were obtained from computed tomography and combined with hemodynamic data with either sinus rhythm (SR) or AF generated by a lumped parameter model. In four simulations applying contractile walls, stagnation volume and blood residence times were evaluated with or without AF and with or without LAAO. Reduced atrial contraction in AF resulted in unfavorable flow dynamics within the left atrium. The average atrial velocity was lower for the AF simulation when compared to SR, resulting in a 55% increase in the atrial stagnation volume (from 4.2 to 6.5 cm3). Moreover, blood remained in the LAA for more than 8 cardiac cycles. After LAAO the atrial stagnation decreased from 4.2 to 1.4 cm3 for SR and from 6.5 to 2.3 cm3 for the AF simulation. A significant stagnation volume was found in the LAA for both SR and AF, with larger values occurring with AF. These regions are known as potential sources for thrombus formation and can be diminished by LAAO. This significantly improved the thrombus-related flow parameters and may also lower the risk of thromboembolic events from the appendage.

Virus spread on a scale-free network reproduces the Gompertz growth observed in isolated COVID-19 outbreaks.

The counts of confirmed cases and deaths in isolated SARS-CoV-2 outbreaks follow the Gompertz growth function for locations of very different sizes. This lack of dependence on region size leads us to hypothesize that virus spread depends on the universal properties of the network of social interactions. We test this hypothesis by simulating the propagation of a virus on networks of different topologies or connectivities. Our main finding is that we can reproduce the Gompertz growth observed for many early outbreaks with a simple virus spread model on a scale-free network, in which nodes with many more neighbors than average are common. Nodes that have very many neighbors are infected early in the outbreak and then spread the infection very rapidly. When these nodes are no longer infectious, the remaining nodes that have most neighbors take over and continue to spread the infection. In this way, the rate of spread is fastest at the very start and slows down immediately. Geometrically we see that the "surface" of the epidemic, the number of susceptible nodes in contact with the infected nodes, starts to rapidly decrease very early in the epidemic and as soon as the larger nodes have been infected. In our simulation, the speed and impact of an outbreak depend on three parameters: the average number of contacts each node makes, the probability of being infected by a neighbor, and the probability of recovery. Intelligent interventions to reduce the impact of future outbreaks need to focus on these critical parameters in order to minimize economic and social collateral damage.

In Silico Maturation of a Nanomolar Antibody against the Human CXCR2.

The steady increase in computational power in the last 50 years is opening unprecedented opportunities in biology, as computer simulations of biological systems have become more accessible and can reproduce experimental results more accurately. Here, we wanted to test the ability of computer simulations to replace experiments in the limited but practically useful scope of improving the biochemical characteristics of the abN48 antibody, a nanomolar antagonist of the CXC chemokine receptor 2 (CXCR2) that was initially selected from a combinatorial antibody library. Our results showed a good correlation between the computed binding energies of the antibody to the peptide target and the experimental binding affinities. Moreover, we showed that it is possible to design new antibody sequences in silico with a higher affinity to the desired target using a Monte Carlo Metropolis algorithm. The newly designed sequences had an affinity comparable to the best ones obtained using in vitro affinity maturation and could be obtained within a similar timeframe. The methodology proposed here could represent a valid alternative for improving antibodies in cases in which experiments are too expensive or technically tricky and could open an opportunity for designing antibodies for targets that have been elusive so far.

Construction of a Deep Neural Network Energy Function for Protein Physics.

The traditional approach of computational biology consists of calculating molecule properties by using approximate classical potentials. Interactions between atoms are described by an energy function derived from physical principles or fitted to experimental data. Their functional form is usually limited to pairwise interactions between atoms and does not consider complex multibody effects. More recently, neural networks have emerged as an alternative way of describing the interactions between biomolecules. In this approach, the energy function does not have an explicit functional form and is learned bottom-up from simulations at the atomistic or quantum level. In this study, we attempt a top-down approach and use deep learning methods to obtain an energy function by exploiting the large amount of experimental data acquired with years in the field of structural biology. The energy function is represented by a probability density model learned from a large repertoire of building blocks representing local clusters of amino acids paired with their sequence signature. We demonstrated the feasibility of this approach by generating a neural network energy function and testing its validity on several applications such as discriminating decoys, assessing qualities of structural models, sampling structural conformations, and designing new protein sequences. We foresee that, in the future, our methodology could exploit the continuously increasing availability of experimental data and simulations and provide a new method for the parametrization of protein energy functions.

A Quantitative Assay for Ca2+ Uptake through Normal and Pathological Hemichannels.

Connexin (Cx) hemichannels (HCs) are large pore hexameric structures that allow the exchange of ions, metabolites and a variety of other molecules between the cell cytoplasm and extracellular milieu. HC inhibitors are attracting growing interest as drug candidates because deregulated fluxes through HCs have been implicated in a plethora of genetic conditions and other diseases. HC activity has been mainly investigated by electrophysiological methods and/or using HC-permeable dye uptake measurements. Here, we present an all-optical assay based on fluorometric measurements of ionized calcium (Ca2+) uptake with a Ca2+-selective genetically encoded indicator (GCaMP6s) that permits the optical tracking of cytosolic Ca2+ concentration ([Ca2+]cyt) changes with high sensitivity. We exemplify use of the assay in stable pools of HaCaT cells overexpressing human Cx26, Cx46, or the pathological mutant Cx26G45E, under control of a tetracycline (Tet) responsive element (TRE) promoter (Tet-on). We demonstrate the usefulness of the assay for the characterization of new monoclonal antibodies (mAbs) targeting the extracellular domain of the HCs. Although we developed the assay on a spinning disk confocal fluorescence microscope, the same methodology can be extended seamlessly to high-throughput high-content platforms to screen other kinds of inhibitors and/or to probe HCs expressed in primary cells and microtissues.

Comparison of pandemic excess mortality in 2020-2021 across different empirical calculations.

Different modeling approaches can be used to calculate excess deaths for the COVID-19 pandemic period. We compared 6 calculations of excess deaths (4 previously published [3 without age-adjustment] and two new ones that we performed with and without age-adjustment) for 2020-2021. With each approach, we calculated excess deaths metrics and the ratio R of excess deaths over recorded COVID-19 deaths. The main analysis focused on 33 high-income countries with weekly deaths in the Human Mortality Database (HMD at mortality.org) and reliable death registration. Secondary analyses compared calculations for other countries, whenever available. Across the 33 high-income countries, excess deaths were 2.0-2.8 million without age-adjustment, and 1.6-2.1 million with age-adjustment with large differences across countries. In our analyses after age-adjustment, 8 of 33 countries had no overall excess deaths; there was a death deficit in children; and 0.478 million (29.7%) of the excess deaths were in people <65 years old. In countries like France, Germany, Italy, and Spain excess death estimates differed 2 to 4-fold between highest and lowest figures. The R values' range exceeded 0.3 in all 33 countries. In 16 of 33 countries, the range of R exceeded 1. In 25 of 33 countries some calculations suggest R > 1 (excess deaths exceeding COVID-19 deaths) while others suggest R < 1 (excess deaths smaller than COVID-19 deaths). Inferred data from 4 evaluations for 42 countries and from 3 evaluations for another 98 countries are very tenuous. Estimates of excess deaths are analysis-dependent and age-adjustment is important to consider. Excess deaths may be lower than previously calculated.

Effects of the atrium on intraventricular flow patterns during mechanical circulatory support.

Simulations of the ventricular flow patterns during left ventricular assist device (LVAD) support are mainly performed with idealized cylindrical inflow, neglecting the influence of the atrial vortex. In this study, the influence of the left atrium (LA) on the intra-ventricular flow was investigated via Computational Fluid Dynamics (CFD) simulations. Ventricular flow was simulated by a combined Eulerian (carrier flow)/Lagrangian (particles) approach taking into account either the LA or a cylindrical inflow section to mimic a fully support condition. The flow deviation at the mitral valve, the blood low-velocity volume as well as the residence time and shear stress history of the particles were calculated. Inclusion of the LA deflects the flow at the mitral valve by 25°, resulting in an asymmetric flow jet entering the left ventricle. This reduced the ventricular low-velocity volume by 40% (from 6.4 to 3.9 cm3), increased (40%) the shear stress experienced by particles and correspondingly increased (27%) their residence time. Under the studied conditions, the atrial geometry plays a major role in the development of intraventricular flow patterns. A reliable prediction of blood flow dynamics and consequently thrombosis risk analysis within the ventricle requires the consideration of the LA in computational simulations.

Rapid Assessment of Binding Affinity of SARS-COV-2 Spike Protein to the Human Angiotensin-Converting Enzyme 2 Receptor and to Neutralizing Biomolecules Based on Computer Simulations.

SARS-CoV-2 infects humans and causes Coronavirus disease 2019 (COVID-19). The S1 domain of the spike glycoprotein of SARS-CoV-2 binds to human angiotensin-converting enzyme 2 (hACE2) via its receptor-binding domain, while the S2 domain facilitates fusion between the virus and the host cell membrane for entry. The spike glycoprotein of circulating SARS-CoV-2 genomes is a mutation hotspot. Some mutations may affect the binding affinity for hACE2, while others may modulate S-glycoprotein expression, or they could result in a virus that can escape from antibodies generated by infection with the original variant or by vaccination. Since a large number of variants are emerging, it is of vital importance to be able to rapidly assess their characteristics: while changes of binding affinity alone do not always cause direct advantages for the virus, they still can provide important insights on where the evolutionary pressure is directed. Here, we propose a simple and cost-effective computational protocol based on Molecular Dynamics simulations to rapidly screen the ability of mutated spike protein to bind to the hACE2 receptor and selected neutralizing biomolecules. Our results show that it is possible to achieve rapid and reliable predictions of binding affinities. A similar approach can be used to perform preliminary screenings of the potential effects of S-RBD mutations, helping to prioritize the more time-consuming and expensive experimental work.