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DeepFoldRNA is a deep-learning based method for de novo RNA tertiary structure prediction. Starting from an RNA sequence, it first collects an alignment of homologous sequences from multiple sequence databases. Spatial restraints (distance maps and inter-residue orientations) are then predicted by deep self-attention neural networks and converted into negative log-likelihood potentials. Finally, full-length structure models are generated using L-BFGS folding simulations based on minimization of the potential with respect to the backbone pseudo-torsion angles.

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• Sequence
• File

Sequence (in FASTA format, length in the range [5, 500] nucleotides)

The uploaded file should contain one RNA sequence in FASTA format with a length in the range [5, 500] nucleotides.

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RNA name (optional)

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Reference

• Robin Pearce, Gilbert S. Omenn, Yang Zhang. De Novo RNA Tertiary Structure Prediction at Atomic Resolution using Geometric Potentials from Deep Learning, submitted, 2022