Author: Xiaoqiang Huang; Robin Pearce; Yang Zhang
Title: Computational Design of Peptides to Block Binding of the SARS-CoV-2 Spike Protein to Human ACE2 Document date: 2020_3_31
ID: imkeghfd_31
Snippet: In summary, detailed computational analyses showed that the designed peptide binders were quite reasonable, as indicated by the recapitulation of critical binding interactions at the proteinpeptide interface and the introduction of new favorable binding interactions, as well as the preservation of secondary structures to maintain these interactions. We have selected dozens of designed peptides (with/without evolutionary information) that had good.....
Document: In summary, detailed computational analyses showed that the designed peptide binders were quite reasonable, as indicated by the recapitulation of critical binding interactions at the proteinpeptide interface and the introduction of new favorable binding interactions, as well as the preservation of secondary structures to maintain these interactions. We have selected dozens of designed peptides (with/without evolutionary information) that had good EvoEF2 binding energy to SARS-CoV-2 RBD to perform wet-lab experimental validation on. Specifically, we are carrying out in vitro experiments to determine their binding affinity and inhibitory potency, as well as in vivo experiments to examine their potential to disrupt the binding of SARS-CoV-2 to hACE2. Due to the urgent situation caused by COVID-19 worldwide, we would like to share our computational data to the scientific community so that researchers can work together to test them. Although we do not have experimental data yet, we believe that our computational results are well examined and reasonable, and these data may help favorably combat the COVID-19 pandemic.
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