Optimization of a physical force-field for simulations of non-coding RNA molecules

Project Summary

The importance of the study of RNA molecules has been highlighted by the recent pandemic, with the SARS-CoV-2 virus featuring an RNA-based genome and a replication mechanism controlled by non-coding RNA. The function of these molecules strictly depends on the 3D structure adopted, but these are hard to obtain experimentally, and even harder is to study how a structure changes in response to the environment. Computational modeling using dedicated force-fields can provide a coherent view of the molecule, which can follow this dynamical behavior and include the effect of the environment.
Inspired also by the recent success of AlphaFold for protein folding, we plan to use ML to ameliorate our description of the molecule to predict structures. The main goal of this project is the optimization of our RNA model, HiRE-RNA, through ML to obtain a cutting-edge RNA force field to facilitate building functional three-dimensional structures for RNA molecules. We will employ machine learning to optimize the model, exploiting extensively the structural data available in the Nucleic Acids Database (NDB) and the sparse thermodynamic and dynamic data available from experiments. This approach will allow our model to give much more accurate and reliable structural predictions and to be deployed on systems of more complex architectures than currently possible. Our aim here is to anchor our force field model deep into the corresponding physics by adapting recent and promising Symbolic Regression algorithms to our data format and selecting the possible improvements in the functional form of the force field uncovered by this technique, based on sound physical principles.
The M2 internship will be the first step of a larger project where we propose to first use the existing functional form of the force field and train its 100+ coefficients and then to then build upon the ML pipeline developed in the first step to learn additional terms of the force field.

Samuela Pasquali