It is very difficult to reconstruct computationally a large biomolecular complex in its biological entirety from experimental data. The resulting atomistic model should not contain gaps structurally and it should yield stable dynamics. We, for the first time, reconstruct from published incomplete cryo-EM density a complete MS2 virus at atomistic resolution, that is, the capsid with the genome, and validate the result by all-atom Molecular Dynamics with explicit water. The available experimental data includes a high resolution protein capsid and an inhomogeneously resolved genome map. For the genomic RNA, apart from 16 hairpins with atomistic resolution, the strands near the capsid’s inner surface were resolved up to the nucleic backbone level, and the innermost density was completely unresolved. As a result, only 242 nucleotides (out of 3569) were positioned, while only a fragmented backbone was outlined for the rest of the genome, making a detailed model reconstruction necessary. For model reconstruction, in addition to the available atomistic structure information, we extensively used the predicted secondary structure of the genome (base pairing). The technique was based on semi-automatic building of relatively large strands of RNA with subsequent manual positioning over the traced backbone. The entire virus structure (capsid+genome) was validated by a Molecular Dynamics run in physiological solution with ions at standard conditions confirming the stability of the model.
|Early online date||12 Apr 2022|
|Publication status||Published - 1 Nov 2022|
Bibliographical noteThis article is licensed under a Creative Commons Attribution 3.0 Unported Licence https://creativecommons.org/licenses/by/3.0/.
We acknowledge the use of Athena at HPC Midlands+, which was funded by the EPSRC on grant EP/P020232/1, in this research, as part of the HPC Midlands+ consortium. V. F. acknowledges the support from the Ministry of Education and Science of Ukraine (grant number 0120U101064). The collaboration was supported by the program H2020-MSCA-RISE-2018, project AMR-TB, grant ID: 823922. We acknowledge support from the EPSRC grant EP/M02735X/1 (AMR4AMR).