Computational Design of Miniprotein Inhibitors Targeting SARS-CoV-2 Spike Protein
Source abstract
Abstract
The ongoing pandemic of COVID-19 caused by SARS-CoV-2 has become a global health problem. There is an urgent need to develop therapeutic drugs, effective therapies, and vaccines to prevent the spread of the virus. The virus first enters the host cell through the interaction between the receptor binding domain (RBD) of spike protein and the peptidase domain (PD) of the angiotensin-converting enzyme 2 (ACE2). Therefore, blocking the binding of RBD and ACE2 is a promising strategy to inhibit the invasion and infection of the virus in the host cell. In the study, we designed several miniprotein inhibitors against SARS-CoV-2 by single/double/triple-point mutant, based on the initial inhibitor LCB3. Molecular dynamics (MD) simulations and trajectory analysis were performed for an in-depth analysis of the structural stability, essential protein motions, and per-residue energy decomposition involved in the interaction of inhibitors with the RBD. The results showed that the inhibitors have adapted the protein RBD in the binding interface, thereby forming stable complexes. These inhibitors display low binding free energy in the MM/PBSA calculations, substantiating their strong interaction with RBD. Moreover, the binding affinity of the best miniprotein inhibitor, H6Y-M7L-L17F mutant, to RBD was ∼45 980 times (ΔG = RT ln Ki) higher than that of the initial inhibitor LCB3. Following H6Y-M7L-L17F mutant, the inhibitors with strong binding activity are successively H6Y-L17F, L17F, H6Y, and F30Y mutants. Our research proves that the miniprotein inhibitors can maintain their secondary structure and have a highly stable blocking (binding) effect on SARS-CoV-2. This study proposes novel miniprotein mutant inhibitors with enhanced binding to spike protein and provides potential guidance for the rational design of new SARS-CoV-2 spike protein inhibitors.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: SARS-CoV-2 and COVID-19 Research · Computational Drug Discovery Methods · Protein Structure and Dynamics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Theoretical Chemistry, College of Chemistry, Jilin University, Changchun 130023, Jilin, People’s Republic of China
Funding
China Postdoctoral Science Foundation · Jilin University · National Natural Science Foundation of China · GHfund B
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