Optimizing variant-specific therapeutic SARS-CoV-2 decoys using deep-learning-guided molecular dynamics simulations.

Treatment of COVID-19 with a soluble version of ACE2 that binds to SARS-CoV-2 virions before they enter host cells is a promising approach, however it needs to be optimized and adapted to emerging viral variants. The computational workflow presented here consists of molecular dynamics simulations for spike RBD-hACE2 binding affinity assessments of multiple spike RBD/hACE2 variants and a novel convolutional neural network architecture working on pairs of voxelized force-fields for efficient search-space reduction. We identified hACE2-Fc K31W and multi-mutation variants as high-affinity candidates, which we validated in vitro with virus neutralization assays. We evaluated binding affinities of these ACE2 variants with the RBDs of Omicron BA.3, Omicron BA.4/BA.5, and Omicron BA.2.75 in silico. In addition, candidates produced in Nicotiana benthamiana, an expression organism for potential large-scale production, showed a 4.6-fold reduction in half-maximal inhibitory concentration (IC50) compared with the same variant produced in CHO cells and an almost six-fold IC50 reduction compared with wild-type hACE2-Fc.

Recent changes in the mutational dynamics of the SARS-CoV-2 main protease substantiate the danger of emerging resistance to antiviral drugs.

Introduction: The current coronavirus pandemic is being combated worldwide by nontherapeutic measures and massive vaccination programs. Nevertheless, therapeutic options such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main-protease (Mpro) inhibitors are essential due to the ongoing evolution toward escape from natural or induced immunity. While antiviral strategies are vulnerable to the effects of viral mutation, the relatively conserved Mpro makes an attractive drug target: Nirmatrelvir, an antiviral targeting its active site, has been authorized for conditional or emergency use in several countries since December 2021, and a number of other inhibitors are under clinical evaluation. We analyzed recent SARS-CoV-2 genomic data, since early detection of potential resistances supports a timely counteraction in drug development and deployment, and discovered accelerated mutational dynamics of Mpro since early December 2021. Methods: We performed a comparative analysis of 10.5 million SARS-CoV-2 genome sequences available by June 2022 at GISAID to the NCBI reference genome sequence NC_045512.2. Amino-acid exchanges within high-quality regions in 69,878 unique Mpro sequences were identified and time- and in-depth sequence analyses including a structural representation of mutational dynamics were performed using in-house software. Results: The analysis showed a significant recent event of mutational dynamics in Mpro. We report a remarkable increase in mutational variability in an eight-residue long consecutive region (R188-G195) near the active site since December 2021. Discussion: The increased mutational variability in close proximity to an antiviral-drug binding site as described herein may suggest the onset of the development of antiviral resistance. This emerging diversity urgently needs to be further monitored and considered in ongoing drug development and lead optimization.

Structural bioinformatics analysis of SARS-CoV-2 variants reveals higher hACE2 receptor binding affinity for Omicron B.1.1.529 spike RBD compared to wild type reference.

To date, more than 263 million people have been infected with SARS-CoV-2 during the COVID-19 pandemic. In many countries, the global spread occurred in multiple pandemic waves characterized by the emergence of new SARS-CoV-2 variants. Here we report a sequence and structural-bioinformatics analysis to estimate the effects of amino acid substitutions on the affinity of the SARS-CoV-2 spike receptor binding domain (RBD) to the human receptor hACE2. This is done through qualitative electrostatics and hydrophobicity analysis as well as molecular dynamics simulations used to develop a high-precision empirical scoring function (ESF) closely related to the linear interaction energy method and calibrated on a large set of experimental binding energies. For the latest variant of concern (VOC), B.1.1.529 Omicron, our Halo difference point cloud studies reveal the largest impact on the RBD binding interface compared to all other VOC. Moreover, according to our ESF model, Omicron achieves a much higher ACE2 binding affinity than the wild type and, in particular, the highest among all VOCs except Alpha and thus requires special attention and monitoring.

Serine 477 plays a crucial role in the interaction of the SARS-CoV-2 spike protein with the human receptor ACE2.

Since the worldwide outbreak of the infectious disease COVID-19, several studies have been published to understand the structural mechanism of the novel coronavirus SARS-CoV-2. During the infection process, the SARS-CoV-2 spike (S) protein plays a crucial role in the receptor recognition and cell membrane fusion process by interacting with the human angiotensin-converting enzyme 2 (hACE2) receptor. However, new variants of these spike proteins emerge as the virus passes through the disease reservoir. This poses a major challenge for designing a potent antigen for an effective immune response against the spike protein. Through a normal mode analysis (NMA) we identified the highly flexible region in the receptor binding domain (RBD) of SARS-CoV-2, starting from residue 475 up to residue 485. Structurally, the position S477 shows the highest flexibility among them. At the same time, S477 is hitherto the most frequently exchanged amino acid residue in the RBDs of SARS-CoV-2 mutants. Therefore, using MD simulations, we have investigated the role of S477 and its two frequent mutations (S477G and S477N) at the RBD during the binding to hACE2. We found that the amino acid exchanges S477G and S477N strengthen the binding of the SARS-COV-2 spike with the hACE2 receptor.