Spherical Neutralizing Aptamer Inhibits SARS-CoV-2 Infection (preprint)

New neutralizing agents against SARS-CoV-2 and the associated mutant strains are urgently needed for the treatment and prophylaxis of COVID-19. Herein, we develop a spherical cocktail neutralizing aptamer-gold nanoparticle (SNAP) to synergistically block the interaction of SARS-CoV-2 receptor-binding domain (RBD) and angiotensin-converting enzyme-2 (ACE2). Taking advantage of the simultaneous recognition of multi-homologous and multi-heterogenous neutralizing aptamers and dimensionally matched nano-scaffolds, the SNAP exhibits increased affinity to the RBD with a dissociation constant value of 5.46 pM and potent neutralization against authentic SARS-CoV-2 with a half-maximal inhibitory concentration of 142.80 aM. Additional benefits include the multi-epitope blocking capability of the aptamer cocktail and the steric hindrance of the nano-scaffold, which further covers the ACE2 binding interfaces and affects the conformational transition of the spike protein. As a result, the SNAP strategy exhibits broad neutralizing activity, almost completely blocking the infection of N501Y and D614G mutant strains. Overall, the SNAP strategy provides a new direction for development of anti-virus infection mechanisms, both to fight the COVID-19 pandemic and serve as a powerful technical reserve for future unknown pandemics.

Network Representation Learning-Based Drug Mechanism Discovery and Anti-Inflammatory Response Against COVID-19 (preprint)

Recent studies have been demonstrated that the excessive inflammatory response is an important factor of death in COVID-19 patients. In this study, we proposed a network representation learning-based methodology, termed AIdrug2cov, to discover drug mechanism and anti-inflammatory response for patients with COVID-19. This work explores the multi-hub characteristic of a heterogeneous drug network integrating 8 unique networks. Inspired by the multi-hub characteristic, we design three billion special meta paths to train a deep representation model for learning low-dimensional vectors that integrate long-range structure dependency and complex semantic relation among network nodes. Using the representation vectors, AIdrug2cov identifies 40 potential targets and 22 high-confidence drugs that bind to tumor necrosis factor(TNF)-α or interleukin(IL)-6 to prevent excessive inflammatory responses in COVID-19 patients. Finally, we analyze mechanisms of action based on PubMed publications and ongoing clinical trials, and explore the possible binding modes between the new predicted drugs and targets via docking program. In addition, the results in 5 pharmacological application suggested that AIdrug2cov significantly outperforms 5 other state-of-the-art network representation approaches, future demonstrating the availability of AIdrug2cov in drug development field. In summary, AIdrug2cov is practically useful for accelerating COVID-19 therapeutic development. The source code and data can be downloaded from https://github.com/pengsl-lab/AIdrug2cov.git.