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

Lizhe Zhu, Hanlun Jiang, Siqin Cao, Ilona Christy Unarta, Xin Gao, Xuhui Huang

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


AbstractDespite 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.
Original languageEnglish (US)
JournalCommunications Biology
Issue number1
StatePublished - Nov 30 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-12-14
Acknowledged KAUST grant number(s): FCC/1/1976-04, URF/1/4098-01-01, URF/1/4352-01-01, URF/1/4379-01-0
Acknowledgements: We thank Dr. Fu Kit Sheong for fruitful discussions about the Argonaute proteins. This work was supported by the Hong Kong Research Grant Council [16303919, 16307718, and 16318816] to X.H., Shenzhen Science and Technology Innovation Committee [JCYJ20200109150003938] to L.Z., the National Science Foundation of China General Fund [31971179] to L.Z., research fund from Warshel Institute for Computational Biology and Longgang District Shenzhen to L.Z., and King Abdullah University of Science and Technology (KAUST) under award number FCC/1/1976-04-01, URF/1/4098-01-01, URF/1/4352-01-01, and URF/1/4379-01-01 to X.G.. This research made use of the resources of the Supercomputing Laboratory at KAUST.


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