Critical role of backbone coordination in the mRNA recognition by RNA induced silencing complex

Despite its functional importance, the molecular mechanism underlying target mRNA recognition by Argonaute (Ago) remains largely elusive. Based on extensive all-atom molecular dynamics simulations, we constructed quasi-Markov State Model (qMSM) to reveal the dynamics during recognition at position 6-7 in the seed region of human Argonaute 2 (hAgo2). Interestingly, we found that the slowest mode of motion therein is not the gRNA-target base-pairing, but the coordination of the target phosphate groups with a set of positively charged residues of hAgo2. Moreover, the ability of Helix-7 to approach the PIWI and MID domains was found to reduce the effective volume accessible to the target mRNA and therefore facilitate both the backbone coordination and base-pair formation. Further mutant simulations revealed that alanine mutation of the D358 residue on Helix-7 enhanced a trap state to slow down the loading of target mRNA. Similar trap state was also observed when wobble pairs were introduced in g6 and g7, indicating the role of Helix-7 in suppressing non-canonical base-paring. Our study pointed to a general mechanism for mRNA recognition by eukaryotic Agos and demonstrated the promise of qMSM in investigating complex conformational changes of biomolecular systems. Lizhe Zhu et al. use a quasi Markov State Model built from extensive molecular dynamics simulations to elucidate the dynamics of RISC-mRNA interactions. Their results provide further insight on how a target nucleotide is positioned before recognition by Argonaute.

Automated summary

The study utilized extensive molecular dynamics simulations to build a quasi Markov State Model, revealing the dynamics of mRNA recognition by Argonaute. Motion modes dependent on the coordination of target phosphate groups and the role of Helix-7 in reducing target mRNA volume accessibility and facilitating base-pair formation were discovered through simulations. The study also demonstrated the potential of qMSM in investigating complex conformational changes in biomolecular systems.