Intra- and intermolecular atomic-scale interactions in the receptor binding domain of SARS-CoV-2 spike protein: implication for ACE2 receptor binding
文献情報
Puja Adhikari, Neng Li, Matthew Shin, Reidun Twarock, Wai-Yim Ching
The COVID-19 pandemic poses a severe threat to human health with unprecedented social and economic disruption. Spike (S) glycoprotein in the SARS-CoV-2 virus is pivotal in understanding the virus anatomy, since it initiates the early contact with the ACE2 receptor in the human cell. The subunit S1 in chain A of S-protein has four structural domains: the receptor binding domain (RBD), the n-terminal domain (NTD) and two subdomains (SD1, SD2). We report details of the intra- and inter-molecular binding mechanism of RBD using density functional theory, including electronic structure, interatomic bonding and partial charge distribution. We identify five strong hydrogen bonds and analyze their roles in binding. This provides a pathway to a quantum-chemical understanding of the interaction between the S-protein and the ACE2 receptor with insights into the function of conserved features in the ACE2 receptor binding domain that could inform vaccine and drug development.
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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.










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