RNA sampling and crystallographic refinement using Rappertk

TL;DR

This paper introduces a new all-atom sampling method for RNA structure modeling and refinement, leveraging discrete restraints and demonstrating improved results over traditional methods in crystallography.

Contribution

It presents a novel all-atom sampling approach for RNA using Rappertk, enhancing crystallographic refinement with better accuracy than existing CNS-only methods.

Findings

Rappertk outperforms CNS in RNA crystallographic refinement

All-atom sampling effectively models RNA chains, termini, and loops

Benchmarking shows improved conformational sampling of RNA structures

Abstract

Background. Dramatic increases in RNA structural data have made it possible to recognize its conformational preferences much better than a decade ago. This has created an opportunity to use discrete restraint-based conformational sampling for modelling RNA and automating its crystallographic refinement. Results. All-atom sampling of entire RNA chains, termini and loops is achieved using the Richardson RNA backbone rotamer library and an unbiased distribution for glycosidic dihedral angle. Sampling behaviour of Rappertk on a diverse dataset of RNA chains under varying spatial restraints is benchmarked. The iterative composite crystallographic refinement protocol developed here is demonstrated to outperform CNS-only refinement on parts of tRNA(Asp) structure. Conclusion. This work opens exciting possibilities for further work in RNA modelling and crystallography.