RNA 3D Modeling with FARFAR2, Online.

Area of Science:

• Computational Biology
• Structural Biology
• Bioinformatics

Background:

• Understanding RNA's three-dimensional structure is crucial for deciphering its biological function.
• Advancements in RNA structure prediction are driven by diverse structural data and initiatives like RNA-Puzzles.
• The FARFAR2 algorithm offers near-native predictions for complex RNA structures, including model selection and accuracy estimation.

Purpose of the Study:

• To describe the utilization of a publicly available webserver for RNA modeling using the FARFAR2 algorithm.
• To demonstrate realistic RNA modeling scenarios with the FARFAR2 webserver.
• To showcase the integration of experimental data into RNA structure prediction.

Main Methods:

• Utilized the FARFAR2 webserver (https://rosie.rosettacommons.org/farfar2) for RNA structure modeling.
• Modeled a pseudoknot from the beet western yellows virus using the basic interface.
• Replicated RNA-Puzzle 20 (metagenomic twister sister ribozyme) using the advanced interface.
• Incorporated experimental data, including MOHCA-seq restraints and 1H NMR chemical shifts, for modeling specific RNA targets.

Main Results:

• Successfully predicted near-native structures for complex RNA molecules.
• Demonstrated the webserver's capability in handling diverse RNA structures and modeling challenges.
• Showcased the integration of experimental data (MOHCA-seq, 1H NMR) for enhanced RNA modeling accuracy.

Conclusions:

• The FARFAR2 webserver is a valuable resource for accurate and efficient RNA structure prediction.
• The algorithm effectively handles complex RNA structures and integrates various experimental data types.
• This tool facilitates a deeper understanding of RNA structure-function relationships in computational and structural biology research.