StraD: a coarse-grained simulation of co-transcriptional RNA folding

Area of Science:

• Molecular Biology
• Computational Biology
• Biophysics

Background:

• Co-transcriptional folding is crucial for RNA function, with transient structures playing key roles in processes like molecular recognition in riboswitches.
• Understanding RNA folding mechanisms during transcription is essential for comprehending diverse RNA functions.
• Existing simulation methods may face challenges in efficiency and accuracy for complex RNA folding dynamics.

Purpose of the Study:

• To introduce a novel algorithm, StraD, for efficient and accurate simulation of RNA secondary structure kinetics during co-transcriptional folding.
• To validate StraD's performance by comparing its results with a fine-grained simulation method (Kinfold).
• To apply StraD to simulate the co-transcriptional folding of biologically relevant RNA molecules, such as Saccharomyces cerevisiae tRNAs and 5S rRNAs.

Main Methods:

• Development of StraD, a novel algorithm utilizing a coarse-grained energy landscape and a constrained local flooding approach for RNA secondary structure kinetics simulation.
• Enumeration of secondary structures based on a constrained local flooding algorithm to reduce conformational complexity.
• Application of StraD for co-transcriptional folding simulations of artificial RNA switches and biological RNAs (yeast tRNAs and 5S rRNAs).

Main Results:

• StraD successfully reproduced simulation results obtained by the fine-grained simulation method, Kinfold, for artificial RNA switches.
• Co-transcriptional folding simulations of Saccharomyces cerevisiae tRNAs and 5S rRNAs using both Kinfold and StraD yielded accurate secondary structures within the suboptimal structures.
• The constrained local search approach in StraD effectively reduced the number of conformations, enabling efficient simulations.

Conclusions:

• StraD is an efficient and accurate algorithm for simulating RNA secondary structure kinetics during co-transcriptional folding.
• The algorithm demonstrates the ability to predict biologically relevant RNA structures, including those found in yeast tRNAs and 5S rRNAs.
• StraD provides a valuable tool for elucidating RNA functions by enabling detailed studies of co-transcriptional folding mechanisms.