EvoClustRNA

EvoClustRNA predicts RNA tertiary structures by leveraging homologous sequences from the Rfam database and selecting consensus helical fragments from independent Rosetta FARFAR and SimRNA folding simulations to improve ab initio 3D structure prediction.

Key Features:

• Homologous Sequence Utilization: Selects homologous sequences from the Rfam database to incorporate evolutionary conservation into structure prediction.
• Independent Folding Simulations: Generates candidate tertiary structures using Rosetta FARFAR and SimRNA folding simulations.
• Consensus-Based Model Selection: Chooses the target model based on the most common structural arrangement observed among homologous sequences' helical fragments.

Scientific Applications:

• RNA-Puzzles benchmarking: Demonstrated top rankings, including first place for the L-glutamine riboswitch and second place for the ZMP riboswitch, indicating capability to produce near-native models.
• Riboswitch structure prediction: Applied to predict tertiary structures of riboswitches using evolutionary information and consensus selection across simulations.
• Assessing homolog foldability: Enables investigation of which homologs are amenable to accurate structure recovery and the predictability of related RNA sequences.

Methodology:

Identify homologous sequences via Rfam, perform independent folding simulations with Rosetta FARFAR and SimRNA, and select the model for the target sequence by consensus of the most common helical-fragment arrangements across homologs.