SARS-CoV-2 Spike Protein Post Translational Modification Landscape and Its Impact on Protein Structure and Function via Computational Prediction

To elucidate the role of post-translational modifications (PTM) in SARS-CoV-2 spike protein's structure and virulence, we generated a high-resolution map of 87 PTMs using liquid chromatography with tandem mass spectrometry (LC-MS/MS) data on extracted spike protein from the SARS-CoV-2 virions, and then reconstituted its structure heterogeneity caused by PTMs. Nonetheless, Alphafold2, a high-accuracy artificial intelligence tool to perform protein structure prediction, relies solely on primary amino acid sequence, whereas the impact of PTM, which often modulate critical protein structure and function, are much ignored. To overcome this challenge, we proposed the mutagenesis approach: in-silico, site-directed amino-acid substitution to mimic the influence of PTMs on protein structure due to altered physicochemical properties in modified amino acids, and then reconstituted the spike protein's structure from the substituted sequences by Alphafold2. For the first time, the proposed method revealed predicted protein structures resulting from PTMs, a problem that Alphafold2 has yet to address. As an example, we performed computational analyses of the interaction of post-translationally modified spike protein with its host factors such as ACE2 to illuminate the binding affinity. Mechanistically, this study suggested that the post-translationally modified protein structural analysis via mutagenesis and deep learning exist. To summarize, the reconstructed spike protein structures showed that specific PTMs can be used to modulate host factor binding, guide antibody design, and pave the way for new therapeutic targets.