bioRxivBiophysicsPreprint
Interfacial Water Molecules Make RBD of SPIKE Protein and Human ACE2 to Stick Together
Abstract
A novel coronavirus (SARS-CoV-2; COVID-19) that initially originates from Wuhan province in China has emerged as a global pandemic, an outbreak that started at the end of 2019 which claims 431,192 (Date: 15th June 2020 (https://covid19.who.in) life till now. Since then scientists all over the world are engaged in developing new vaccines, antibodies, or drug molecules to combat this new threat. Here in this work, we performed an in-silico analysis on the protein-protein interactions between the receptor-binding (RBD) domain of viral SPIKE protein and human angiotensin-converting enzyme 2 (hACE2) receptor to highlight the key alteration that happened from SARS-CoV to SARS-CoV-2. We analyzed and compared the molecular differences between these two viruses by using various computational approaches such as binding affinity calculations, computational alanine, and molecular dynamics simulations. The binding affinity calculations show SARS-CoV-2 binds little more firmly to the hACE2 receptor than that of SARS-CoV. Analysis of simulation trajectories reveals that enhanced hydrophobic contacts or the van der Waals interaction play a major role in stabilizing the protein-protein interface. The major finding obtained from molecular dynamics simulations is that the RBD-ACE2 interface is populated with water molecules and interacts strongly with both RBD and ACE2 interfacial residues during the simulation periods. We also emphasize that the interfacial water molecules play a critical role in binding and maintaining the stability of the RBD/hACE2 complex. The water-mediated hydrogen bond by the bridge water molecules is crucial for stabilizing the RBD and ACE2 domains. The structural and dynamical features presented here may serve as a guide for developing new drug molecules, vaccines, or antibodies to combat the COVID-19 pandemic.
- organism: human
- assay: protein structure
- research focus: drug response / drug discovery
- approach: mechanistic / dynamical model
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