Retrained Generic Antibodies Can Recognize SARS-CoV-2

Dendrimer-Peptide Conjugates for Effective Blockade of the Interactions between SARS-CoV-2 Spike Protein and Human ACE2 Receptor

The coronavirus disease 2019 (COVID-19) pandemic has threatened the stability of global healthcare, which is becoming an endemic issue. Despite the development of various treatment strategies to fight COVID-19, the currently available treatment options have shown varied efficacy. Herein, we have developed an avidity-based SARS-CoV-2 antagonist using dendrimer-peptide conjugates (DPCs) for effective COVID-19 treatment. Two different peptide fragments obtained from angiotensin-converting enzyme 2 (ACE2) were integrated into a single sequence, followed by the conjugation to poly(amidoamine) (PAMAM) dendrimers. We hypothesized that the strong multivalent binding avidity endowed by dendrimers would help peptides effectively block the interaction between SARS-CoV-2 and ACE2, and this antagonist effect would be dependent upon the generation (size) of the dendrimers. To assess this, binding kinetics of the DPCs prepared from generation 4 (G4) and G7 PAMAM dendrimers to spike protein of SARS-CoV-2 were quantitatively measured using surface plasmon resonance. The larger dendrimer-based DPCs exhibited significantly enhanced binding strength by 3 orders of magnitude compared to the free peptides, whereas the smaller one showed a 12.8-fold increase only. An in vitro assay using SARS-CoV-2-mimicking microbeads also showed the improved SARS-CoV-2 blockade efficiency of the G7-peptide conjugates compared to G4. In addition, the interaction between the DPCs and SARS-CoV-2 was analyzed using molecular dynamics (MD) simulation, providing an insight into how the dendrimer-mediated multivalent binding effect can enhance the SARS-CoV-2 blockade. Our findings demonstrate that the DPCs having strong binding to SARS-CoV-2 effectively block the interaction between ACE2 and SARS-CoV-2, providing a potential as a high-affinity drug delivery system to direct anti-COVID payloads to the virus.

Persistent Correlation in Cellular Noise Determines Longevity of Viral Infections

The slowly decaying viral dynamics, even after 2-3 weeks from diagnosis, is one of the characteristics of COVID-19 infection that is still unexplored in theoretical and experimental studies. This long-lived characteristic of viral infections in the framework of inherent variations or noise present at the cellular level is often overlooked. Therefore, in this work, we aim to understand the effect of these variations by proposing a stochastic non-Markovian model that not only captures the coupled dynamics between the immune cells and the virus but also enables the study of the effect of fluctuations. Numerical simulations of our model reveal that the long-range temporal correlations in fluctuations dictate the long-lived dynamics of a viral infection and, in turn, also affect the rates of immune response. Furthermore, predictions of our model system are in agreement with the experimental viral load data of COVID-19 patients from various countries.