Severe acute respiratory
syndrome coronavirus 2 (SARS-CoV-2) uses
a spike protein (S-protein) to recognize the receptor protein ACE2
of human cells and initiate infection, during which the conformational
transition of the S-protein from inactive (down) state to active (up)
state is one of the key molecular events determining the infectivity
but the underlying mechanism remains poorly understood. In this work,
we investigated the activation pathways and free energy landscape
of the S-protein of SARS-CoV-2 and compared with those of the closely
related counterpart SARS-CoV using molecular dynamics simulations.
Our results revealed a large difference between the activation pathways
of the two S-proteins. The transition from inactive to an active state
for the S-protein of SARS-CoV-2 is more cooperative, involving simultaneous
disruptions of several key interfacial hydrogen bonds, and the transition
encounters a much higher free energy barrier. In addition, the conformational
equilibrium of the SARS-CoV-2 S-protein is more biased to the inactive
state compared to that of the SARS-CoV S-protein, suggesting the transient
feature of the active state before binding to the receptor protein
of the host cell. The key interactions contributing to the difference
of the activation pathways and free energy landscapes were discussed.
The results provide insights into the molecular mechanism involved
in the initial stage of the SARS-CoV-2 infection.
As an energy substitution analysis & prediction tool with flexible parameter structure, LEAP model can provide strong support and guidance for guiding the electricity substitution work. On the basis of accurate parameters in LEAP model, this paper proposes a specific parameter classification prediction method. First, a targeted data structure is established, and the parameters that need to be input into the LEAP model are classified into general parameters and scenario parameters according to their degree of certainty. Secondly, predict the general parameters using improved GM(1,1) model by modifying background values and initial conditions. Thirdly, a Grey-Monte Carlo model was proposed to predict scenario parameters and their occurrence probability. Finally, the correctness of the parameter classification and the parameter prediction model are verified through example analysis, and it is proved that the co-application of them improves the accuracy of the parameters and further improves the accuracy of the electric energy substitution prediction.
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