A Mechanistic Understanding of the Modes of Ca2+ Ion Binding to the SARS-CoV-1 Fusion Peptide and Their Role in the Dynamics of Host Membrane Penetration.
ACS INFECTIOUS DISEASES(2024)
Cornell Univ
Abstract
The SARS-CoV-1 spike glycoprotein contains a fusion peptide (FP) segment that mediates the fusion of the viral and host cell membranes. Calcium ions are thought to position the FP optimally for membrane insertion by interacting with negatively charged residues in this segment (E801, D802, D812, E821, D825, and D830); however, which residues bind to calcium and in what combinations supportive of membrane insertion are unknown. Using biological assays and molecular dynamics studies, we have determined the functional configurations of FP-Ca2+ binding that likely promote membrane insertion. We first individually mutated the negatively charged residues in the SARS CoV-1 FP to assay their roles in cell entry and syncytia formation, finding that charge loss in the D802A or D830A mutants greatly reduced syncytia formation and pseudoparticle transduction of VeroE6 cells. Interestingly, one mutation (D812A) led to a modest increase in cell transduction, further indicating that FP function likely depends on calcium binding at specific residues and in specific combinations. To interpret these results mechanistically and identify specific modes of FP-Ca2+ binding that modulate membrane insertion, we performed molecular dynamics simulations of the SARS-CoV-1 FP and Ca2+ions. The preferred residue pairs for Ca2+ binding we identified (E801/D802, E801/D830, and D812/E821) include the two residues found to be essential for S function in our biological studies (D802 and D830). The three preferred Ca2+ binding pairs were also predicted to promote FP membrane insertion. We also identified a Ca2+ binding pair (E821/D825) predicted to inhibit FP membrane insertion. We then carried out simulations in the presence of membranes and found that binding of Ca2+ to SARS-CoV-1 FP residue pairs E801/D802 and D812/E821 facilitates membrane insertion by enabling the peptide to adopt conformations that shield the negative charges of the FP to reduce repulsion by the membrane phospholipid headgroups. This calcium binding mode also optimally positions the hydrophobic LLF region of the FP for membrane penetration. Conversely, Ca2+ binding to the FP E801/D802 and D821/D825 pairs eliminates the negative charge screening and instead creates a repulsive negative charge that hinders membrane penetration of the LLF motif. These computational results, taken together with our biological studies, provide an improved and nuanced mechanistic understanding of the dymanics of SARS-CoV-1 calcium binding and their potential effects on host cell entry.
MoreTranslated text
Key words
spike,calcium,fusion,entry,membrane,coronavirus
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined