Publication details
The Kink-Turn 7 Motif: An Additional Test for RNA Force Field Performance
| Authors | |
|---|---|
| Year of publication | 2025 |
| Type | Article in Periodical |
| Magazine / Source | Journal of Chemical Theory and Computation |
| MU Faculty or unit | |
| Citation | |
| web | https://doi.org/10.1021/acs.jctc.5c00776 |
| Doi | https://doi.org/10.1021/acs.jctc.5c00776 |
| Keywords | Chemical structure; Conformation; Genetics; Molecular dynamics simulations; Molecular mechanics |
| Description | The kink-turn is a recurrent RNA structural motif that induces a sharp bend (kink) in the A-form RNA helix. It is defined by key structural features, including consecutive sheared AG base pairs, an A-minor interaction, and multiple base–sugar interactions. An accurate representation of these densely packed noncanonical interactions by molecular dynamics simulations poses a significant challenge for contemporary force fields (FFs). Here, we present extended simulations of the ribosomal kink-turn 7 (Kt-7) from H.m., the so-called “consensual” kink-turn, using a broad spectrum of pair-additive and polarizable RNA FFs. None of the tested FFs manage to flawlessly describe all of the structural features of the Kt-7 although several FFs provide rather acceptable results and should not cause problems in simulations of larger RNAs containing a kink-turn. On aggregate, the widely used OL3 (ff99bsc0?OL3) and polarizable AMOEBA FFs achieve the best performance for this motif. Interestingly, some more recently parametrized FF variants struggle to describe the Kt-7’s tertiary A-minor interaction – a ubiquitous tertiary contact in RNA. This raises some concerns about the broader applicability of these FFs and suggests that they may be overfitted to small model systems, such as RNA tetranucleotides. In some cases, irreversible unkinking of the entire kink-turn motif can also be observed. The kink-turn motif is highly sensitive to variations in RNA FFs, and we strongly recommend its inclusion in training and benchmarking data sets as an important regression test to improve the robustness and accuracy of RNA FF parametrization. |
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