Predicting RNA secondary structures using physics-based modeling and evolutionary inferences
Authors:   , Zhang Sicheng 
Published: February 2023

[EMBARGOED UNTIL 8/1/2023] Ribonucleic acid (RNA) molecules are critical to gene expression and regulation at the level of transcription, splicing, translation, and catalysis of biochemical reactions. In order to understand RNA function, we need to understand RNA structure and stability. However, because experimental approaches, which are time-consuming and laborious for structure determination, cannot keep up with the ever-growing new RNA sequences, computational prediction of RNA structures is becoming an increasingly important tool for structure determination. RNAs have four types of nucleotides: Adenine (A), Guanine (G), Cytosine (C), and Uracil (U). Through base pairing and tertiary interactions between nucleotides, RNA can form secondary (2D) and tertiary (3D) structures. During the evolution of RNA, it was found that many RNAs show structural conservation at both 2D and 3D levels. Predicting 2D and 3D structures, folding thermodynamics, and co-evolution between base pairs from one or more RNA sequences are challenging problems in computational biology. A physics-based prediction of RNA folding requires a free energy model for RNA folding. Accurate thermodynamic parameters are the basis for a free energy model. In the first project, using a virtual bond-based coarse-grained RNA conformational model, we develop a Monte Carlo simulation approach to compute loop free energy parameters through a statistical potential-guided sampling of loop conformations. The loop-free energy parameters lead to a new folding model ("Vfold2D-MC") for the prediction of RNA secondary structures and folding stabilities. Tests against structural and thermodynamic data support the validity and accuracy of Vfold2D-MC. The Vfold2D-MC model results in a new generation of our Vfold RNA folding software and the web server. RNA cellular functions and RNA nanotechnology involve RNA-RNA interactions and the multi-RNA structural assembly. In the second project, we develop a new computational model ("VfoldMCPX") to predict the folding of multi-RNA complexes. The model is novel in its ability to treat complicated structural motifs such as pseudoknots. Tests against other models indicate that the VfoldMCPX model significantly improves the structure prediction accuracy for multi-RNA complexes. Multiple sequence alignment (MSA) contains evolutionary information about the aligned biological sequences. Two important methods, Mutual Information (MI) and two-site Direct-Coupling Analysis (DCA), can predict RNA contacts from MSA by analyzing coevolution between pairs of nucleotides. However, these methods ignore several potentially important effects such as the nucleotide site proximity effect, i.e., the correlation between a nucleotide pair can involve sequentially nearby nucleotides and the high-order information such as the three-site correlation information. In the third project, we develop a three-site DCA method, which can treat three-site correlation as well as the proximity effect. This novel method, which has been tested for known structures, can enhance the prediction accuracy for both RNA base pairs and tertiary contacts in 2D structures. RNA pseudoknots frequently occur in biologically important structures. Therefore, the ability to predict RNA pseudoknotted structures from the sequence would significantly impact our understanding of RNA functions. However, pseudoknot folding models are limited by the availability of thermodynamic parameters and experimental data. In the fourth project, we develop a new model, Vfold2D-MD, for RNA pseudoknot folding. The key component of the approach is the use of coarse-grained molecular dynamics simulations to predict pseudoknot base pairs. This new model provides much-needed improvements in RNA pseudoknot structure prediction.