Enhanced surface accessibility of SARS-CoV-2 Omicron spike protein due to an altered glycosylation profile (preprint)

SARS-CoV-2 spike (S) proteins undergo extensive glycosylation, aiding proper folding, enhancing stability, and evading host immune surveillance. In this study, we used mass spectrometric analysis to elucidate the N-glycosylation characteristics and disulfide bonding of recombinant spike proteins derived from the SARS-CoV-2 Omicron variant (B.1.1.529) in comparison with the D614G spike variant. Furthermore, we conducted microsecond-long molecular dynamics simulations on spike proteins to resolve how the different N-glycans impact spike conformational sampling in the two variants. Our findings reveal that the Omicron spike protein maintains an overall resemblance to the D614G spike variant in terms of site-specific glycan processing and disulfide bond formation. Nonetheless, alterations in glycans were observed at certain N-glycosylation sites. These changes, in synergy with mutations within the Omicron spike protein, result in increased surface accessibility of the macromolecule, including ectodomain, receptor-binding domain, and N-terminal domain. These insights contribute to our understanding of the interplay between structure and function, thereby advancing effective vaccination and therapeutic strategies. TeaserThrough mass spectrometry and molecular dynamics simulations, SARS-CoV-2 Omicron spike is found to be less covered by glycans when compared to the D614G spike variant.

Using a graph-based image segmentation algorithm for remote vital sign estimation and monitoring (preprint)

Reliable and contactless measurements of vital signs, such as respiration and heart rate, are still an unmet need in clinical and home settings. Mm-wave radar and video-based technologies are promising, but currently, the signal processing-based vital sign extraction methods are prone to body motion disruptions or illumination variations in the surrounding environment. Here we propose an image segmentation-based method to extract vital signs from the recorded video and mm-wave radar signals. The proposed method analyses time-frequency spectrograms obtained from Short-Time Fourier Transform rather than individual time-domain signals. This leads to much-improved robustness and accuracy of the heart rate and respiration rate extraction over existing methods. The experiments were conducted under no- and post-exercise conditions and repeated on multiple individuals. The results are evaluated by using four metrics against the gold standard contact-based measurements. Significant improvements are observed in terms of precision, accuracy, and stability. We believe that the proposed estimation method will help address the need for the increasingly popular remote cardiovascular sensing and diagnosing posed by Covid-19.

Critical interactions for SARS-CoV-2 spike protein binding to ACE2 identified by machine learning (preprint)

Both SARS-CoV and SARS-CoV-2 bind to the human ACE2 receptor. Based on high-resolution structures, the two viruses bind in practically identical conformations, although several residues of the receptor-binding domain (RBD) differ between them. Here we have used molecular dynamics (MD) simulations, machine learning (ML), and free energy perturbation (FEP) calculations to elucidate the differences in RBD binding by the two viruses. Although only subtle differences were observed from the initial MD simulations of the two RBD-ACE2 complexes, ML identified the individual residues with the most distinctive ACE2 interactions, many of which have been highlighted in previous experimental studies. FEP calculations quantified the corresponding differences in binding free energies to ACE2, and examination of MD trajectories provided structural explanations for these differences. Lastly, the energetics of emerging SARS-CoV-2 mutations were studied, showing that the affinity of the RBD for ACE2 is increased by N501Y and E484K mutations but is slightly decreased by K417N.