Prediction of novel high-risk variants through co-occurrence analysis of mutation hotspots
Various forms of S proteins of severe acute respiratory syndrome coronavirus 2 have given rise to high-risk variants capable of avoiding antibody immunity or increasing binding affinity with hACE2. We propose a statistical analysis method for predicting high-risk variants by analyzing co-occurrence...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-07-01
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| Series: | Heliyon |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844025019498 |
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| Summary: | Various forms of S proteins of severe acute respiratory syndrome coronavirus 2 have given rise to high-risk variants capable of avoiding antibody immunity or increasing binding affinity with hACE2. We propose a statistical analysis method for predicting high-risk variants by analyzing co-occurrence of mutation hotspots using spike protein sequences. We identified S494P and V503I as high-risk variants. Interestingly, S494P was predicted to possess significantly increased binding affinity based on molecular docking and quantum mechanical energy calculations. In addition, we examined viral entry of high-risk variants using pseudotyped viruses (PV). Compared to PVs of spike Delta, PVs of spike Delta-S494P or spike Delta-V503I exhibited improved viral entrance in A549 cells. Our proposed analysis method can be used to predict novel high-risk variants and corresponding binding affinities. |
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| ISSN: | 2405-8440 |