Tan, Z.-J. | bioRxiv Science

bioRxiv2021

Observation of reversal in twist-stretch coupling of RNA suggests a unified mechanism for the opposite couplings of DNA and RNA

The functions of DNA and RNA rely on their deformations. When stretched, both DNA and RNA duplexes change their twist angles through twist-stretch coupling. The coupling is negative for DNA but positive for RNA, which is not yet completely understood. Here, our magnetic tweezers experiments show that the coupling of RNA reverses from positive to negative by multivalent cations. Combining with the previously reported tension-induced negative-to-positive coupling-reversal of DNA, we propose a unified mechanism of the couplings of both RNA and DNA based on molecular dynamics simulations. Two deformation pathways are competing when stretched: shrinking the radius causes positive couplings but widening the major groove causes negative couplings. For RNA whose major groove is clamped by multivalent cations and canonical DNA, their radii shrink when stretched, thus exhibiting positive couplings. For elongated DNA whose radius already shrinks to the minimum and canonical RNA, their major grooves are widened when stretched, thus exhibiting negative coupling.

biophysics↗

bioRxiv2021

rsRNASP: A residue-separation-based statistical potential for RNA 3D structure evaluation

Knowledge-based statistical potentials have been shown to be rather effective in protein 3-dimensional (3D) structure evaluation and prediction. Recently, several statistical potentials have been developed for RNA 3D structure evaluation, while their performances are either still at low level for the test datasets from structure prediction models or dependent on the "black-box" process through neural networks. In this work, we have developed an all-atom distance-dependent statistical potential based on residue separation for RNA 3D structure evaluation, namely rsRNASP, which is composed of short- and long-ranged potentials distinguished by residue separation. The extensive examinations against available RNA test datasets show that, rsRNASP has apparently higher performance than the existing statistical potentials for the realistic test datasets with large RNAs from structure prediction models including the newly released RNA-Puzzles dataset, and is comparable to the existing top statistical potentials for the test datasets with small RNAs or near-native decoys. Additionally, rsRNASP is also superior to RNA3DCNN, a recently developed scoring function through 3D convolutional neural networks. rsRNASP and the relevant databases are available at website https://github.com/Tan-group/rsRNASP. SIGNIFICANCERNAs play crucial roles in catalyzing biochemical reactions and regulating gene expression, and the biological functions of RNAs are generally coupled to their structures. Complementary to experiments, developing computational models to predict RNA 3D structures can be very helpful for understanding RNA biology functions. For a computational model, a reliable energy function is essentially important either for guiding conformational folding or for structure evaluation. For this purpose, we developed a residue-separation-based distance-dependent statistical potential, named rsRNASP which distinguishes the short- and long-ranged interactions, for RNA 3D structure evaluation. Our rsRNASP were examined against extensive test sets and shows overall superior performance over existing top traditional statistical potentials and a recently developed scoring function through 3D convolutional neural networks, especially for realistic test set from various computational structure prediction models.

bioinformatics↗