Ernwin

Ernwin predicts coarse-grained RNA tertiary structures from a provided secondary structure to sample global helix and loop arrangements and generate structural ensembles.

Key Features:

• Coarse-Grained Modeling: Employs a helix-based coarse-grained model that represents helices and loops and reduces degrees of freedom relative to all-atom models.
• Energy Function Integration: Incorporates an energy function that relies solely on the positions of stems and loops to evaluate tertiary configurations.
• Global Arrangement Exploration: Explores global arrangements of helices and loops that are not apparent from sequence data or secondary structure alone.
• Efficiency in Sampling: Samples conformational space more efficiently than all-atom models coupled with fine-grain energy functions, enabling wider exploration with fewer iterations.
• Ensemble Prediction: Generates an ensemble of predicted structures rather than focusing only on a few low-energy conformations.

Scientific Applications:

• RNA tertiary structure prediction: Produces coarse-grained tertiary models from secondary structure input for use in structural analysis.
• Studying RNA function and regulation: Provides tertiary-structure insights that inform understanding of molecular interactions and regulatory mechanisms.
• Large-scale or resource-limited studies: Supports exploration of many conformations when computational resources are limited.

Methodology:

Uses a helix-based coarse-grained representation, an energy function dependent on stem and loop positions, and sampling of global helix-loop arrangements to generate structural ensembles with fewer degrees of freedom than all-atom models.