
RoseTTAFold2
Fast, accurate structure prediction with 3-track networks
RoseTTAFold2 uses a three-track network that jointly reasons over sequences, pairwise distances and coordinates to predict monomer and complex structures. It accepts single or paired MSAs and handles flexible multidomain proteins well. Predictions include per-residue confidence estimates for filtering low-quality regions.
RoseTTAFold2 is being onboarded — request access and be first in line.
At a glance
- Input
- Sequences with MSAs (FASTA, A3M)
- Output
- Predicted complex (PDB)
- Developed by
- Baker Lab, UW
- Published
- Baek et al., 2024 · 2024
#deep-learning#msa#complexes
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