Sampling globally and locally correct RNA 3D structures using Ernwin, SPQR and experimental SAXS data

Author:

Thiel Bernhard C1,Bussi Giovanni2ORCID,Poblete Simón34ORCID,Hofacker Ivo L15ORCID

Affiliation:

1. Department of Theoretical Chemistry, University of Vienna , Währinger Strasse 17, Vienna 1090, Austria

2. Scuola Internazionale Superiore di Studi Avanzati , SISSA, via Bonomea 265, Trieste 34136, Italy

3. Centro BASAL Ciencia & Vida , Avenida del Valle Norte 725, Santiago 8580702, Chile

4. Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián , Bellavista 7, Santiago 8420524, Chile

5. Research group Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna , Vienna 1090, Austria

Abstract

Abstract The determination of the three-dimensional structure of large RNA macromolecules in solution is a challenging task that often requires the use of several experimental and computational techniques. Small-angle X-ray scattering can provide insight into some geometrical properties of the probed molecule, but this data must be properly interpreted in order to generate a three-dimensional model. Here, we propose a multiscale pipeline which introduces SAXS data into modelling the global shape of RNA in solution, which can be hierarchically refined until reaching atomistic precision in explicit solvent. The low-resolution helix model (Ernwin) deals with the exploration of the huge conformational space making use of the SAXS data, while a nucleotide-level model (SPQR) removes clashes and disentangles the proposed structures, leading the structure to an all-atom representation in explicit water. We apply the procedure on four different known pdb structures up to 159 nucleotides with promising results. Additionally, we predict an all-atom structure for the Plasmodium falceparum signal recognition particle ALU RNA based on SAXS data deposited in the SASBDB, which has an alternate conformation and better fit to the SAXS data than the previously published structure based on the same data but other modelling methods.

Funder

Austrian Science Fund

Fondo Nacional de Desarrollo Científico y Tecnológico

Publisher

Oxford University Press (OUP)

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