Structure and dynamics of membrane protein in SARS-CoV-2

SARS-CoV-2 membrane (M) protein performs a variety of critical functions in virus infection cycle. However, the expression and purification of membrane protein structure is difficult despite tremendous progress. In this study, the 3 D structure is modeled followed by intensive validation and molecular dynamics simulation. The lack of suitable homologous templates (>30% sequence identities) leads us to construct the membrane protein models using template-free modeling (de novo or ab initio) approach with Robetta and trRosetta servers. Comparing with other model structures, it is evident that trRosetta (TM-score: 0.64; TM region RMSD: 2 angstrom) can provide the best model than Robetta (TM-score: 0.61; TM region RMSD: 3.3 angstrom) and I-TASSER (TM-score: 0.45; TM region RMSD: 6.5 angstrom). 100 ns molecular dynamics simulations are performed on the model structures by incorporating membrane environment. Moreover, secondary structure elements and principal component analysis (PCA) have also been performed on MD simulation data. Finally, trRosetta model is utilized for interpretation and visualization of interacting residues during protein-protein interactions. The common interacting residues including Phe103, Arg107, Met109, Trp110, Arg131, and Glu135 in the C-terminal domain of M protein are identified in membrane-spike and membrane-nucleocapsid protein complexes. The active site residues are also predicted for potential drug and peptide binding. Overall, this study might be helpful to design drugs and peptides against the modeled membrane protein of SARS-CoV-2 to accelerate further investigation. Communicated by Ramaswamy H. Sarma

Automated summary

In this study, the 3D structure of the SARS-CoV-2 membrane protein was modeled using template-free modeling approaches. The trRosetta server provided the most accurate model compared to other methods. Molecular dynamics simulations and analysis were performed to study the protein's behavior in a membrane environment. The study identified interacting residues and predicted potential drug and peptide binding sites.