Speaker Dr Samuela Pasquali, Université Paris Cité, Laboratoire Biologie Fonctionnelle et Adaptative

Title Exploring RNA Polymorphism Combining Advanced Simulation Techniques and Experimental Data

Host Konstantin Roeder

 

Abstract RNA molecules are characterized by the existence of a multitude of stable states where the observed structures depend sensibly on experimental conditions and can depend on the initial, unfolded, structure.

Using both atomistic [1,2,3] and coarse-grained models for RNAs [4], combined with enhanced sampling methods, we investigate these systems to understand what the most relevant structures in the different conditions are [5]. In particular we focus on the energy landscape, i.e., we try to characterize the relative stability of the various structures and what are the energies needed to convert one into the others.

Because of the complexity of the landscape, it is useful to guide the modeling process with experimental data and placing the molecule in an environment similar to experiments. As proof or principle, the coarse-grained model we develop is a useful starting point to couple simulations with experimental data. We have recently developed a simulation technique allowing to bias MD coarse-grained simulations with SAXS data on the fly [6], and a theoretical framework to perform fast constant pH simulations where we can model the system considering the exchange of charges with the solvent [7]. These developments allow us to account for the environment to obtain reasonable structures to then be studied more thoroughly with high-resolution modeling, also introducing the comparison with SHAPE data from first principles [8].

 

^References:

1. ^{K Röder, G Stirnemann, AC Dock-Bregeon, DJ Wales, S Pasquali, “Structural transitions in the RNA 7SK 5′ hairpin and their effect on HEXIM binding”, Nucleic Acids Research 48 (1), 373-389 (2020)}
2. ^{K Röder, AM Barker, A Whitehouse, S Pasquali, “Investigating the structural changes due to adenosine methylation of the Kaposi’s sarcoma-associated herpes virus ORF50 transcript”, PLOS Computational Biology 2022, 18(5):e1010150, doi: 10.1371/journal.pcbi.1010150, PMID: 35617364}
3. ^{G Lazzeri, C Micheletto, S Pasquali, P Faccioli, “RNA Folding Pathways from All-Atom Simulations with a Variationally Improved History-Dependent Bias” arXiv:2205.12603 (2022)}
4. ^{T. Cragnolini, Y. Laurin, P. Derreumaux, S. Pasquali, “The coarse-grained HiRE-RNA model for de novo calculations of RNA free energy surfaces, folding, pathways and complex structure predictions”, J. Chem. Theory Comput., 11, 3510 (2015)}
5. ^{K Röder, G Stinermann, P Faccioli, S Pasquali, Computer-aided comprehensive explorations of RNA structural polymorphism through complementary simulation methods, QRB Discovery, 3:e21 (2022)}
6. ^{L Mazzanti, L Alferkh, E Frezza, S Pasquali, “Biasing RNA coarse-grained folding simulations with Small--Angle X--ray Scattering (SAXS) data”, J. Chem. Theory Comput., 17, 6509-6521 (2021)}
7. ^{S. Pasquali, E. Frezza, F.L. Barroso da Silva, Coarse-grained dynamic RNA titration simulations, Interface Focus 9: 20180066 (2019)}
8. ^{E. Frezza, A. Courban, D. Allouche, B. Sargueil, S. Pasquali, “The interplay between molecular flexibility and RNA chemical probing reactivities analyzed at the nucleotide level via an extensive molecular dynamics study”, Methods 162-163:108-127 (2019)}