OpenFold3 and Co-Folding with Templates
a new and different co-folding model; co-folding conditioned with user-specified templates; protein structure overlays; support for the mmCIF file format
OpenFold3
Today, we’re adding support for OpenFold3 (technically “OpenFold3-preview2”), an open-source reproduction of DeepMind’s AlphaFold-3 co-folding model developed by the OpenFold consortium.
OpenFold3 supports protein, nucleic acid, and small-molecule systems, and can run either with Rowan-generated MSAs or in single-sequence mode. It also supports generating multiple diffusion samples from a single prediction run.
We’re excited about OpenFold3 for a few reasons:
It’s a genuinely different model. If Boltz-2 or Chai-1 struggle with your system, OpenFold3 gives you another state-of-the-art option to try.
It can outperform Chai-1 and Boltz-2, particularly on targets that are less similar to structures seen during training.
Generating multiple samples is much cheaper than in many other diffusion-based models. OpenFold3 produces five samples in roughly the same amount of time required to generate a single sample, making it practical to explore multiple candidate structures.
Its accuracy approaches AlphaFold 3 on protein–protein, protein–DNA, and protein–RNA complexes.
OpenFold3 runs can be requested by selecting “OpenFold3-preview” as your model on the protein–ligand co-folding submit page:
Co-Folding with Templates
You can now provide structural templates for Boltz-2 and OpenFold3 predictions. In Boltz-2, templates can optionally be reinforced using physics-based potentials, with a configurable maximum-distance parameter that controls how closely the prediction follows the supplied template.
Templates can be provided either by uploading a structure file or by entering a PDB ID at the bottom of the co-folding submission page:
The effect of a template can vary considerably, ranging from a soft structural prior to a nearly fixed scaffold.
For Boltz-2, template adherence generally increases when physics-based potentials are enabled and the maximum-distance parameter is set to a smaller value. Disabling MSA generation can also encourage the model to follow the supplied template more closely, as discussed in this thread.
OpenFold3’s user-supplied template support was added recently and has not yet been included in an official release (although you can run it through Rowan). There are still some known limitations around template adherence; we hope this feature will improve over the coming months.
Overlay Protein Structures
To make it easier to judge template adherence and compare co-folding predictions, you can now overlay protein structures in Rowan’s protein viewer. Our GUI matches sequences using the Needleman-Wunsch algorithm and then runs iterative Kabsch alignment on alpha carbon positions to ensure good visual overlap on similar regions.
If you have thoughts about other places in Rowan’s GUI where this overlay capability should be accessible, please share your thoughts with contact@rowansci.com!
Macromolecular CIF support
Finally, you can now input and export protein structures using the macromolecular crystallographic information file (mmCIF) format.
Because PDB files can only store up to 99,999 atom records, mmCIF files are a better choice for handling and storing solvated protein systems and very large protein complexes. Additionally, mmCIFs are a newer format and some modern tools no longer support PDB files.
If you’re really in a pinch, you can use https://labs.rowansci.com/protein-editor to convert from mmCIF to PDB or vice versa (though there’s a also very robust converter available here: https://mmcif.pdbj.org/converter/index.php). If you notice any fields that are missing from mmCIF files exported from Rowan, please let us know!