Overview of Symmetry in RFD3¶

Specifying symmetry in your input specifications¶

Symmetry configurations are specified within the input JSON or YAML file, nested under its own specific configuration. The symmetry specific config has the following:

symmetry: {
    "id": "C3",
    "is_unsym_motif": "Y1-11,Z16-25",
    "is_symmetric_motif": true

}

symmetry:
    id: "C3"
    is_unsym_motif: "Y1-11,Z16-25"
    is_symmetric_motif: true

id : Symmetry group ID. Supported symmetry types:
- Cyclic (C): e.g. “C3” for a cyclic protein with 3 subunits
- Dihedral (D): e.g. “D2” for a dihedral protein with 4 subunits (2×2)
- Tetrahedral (T): “T” for tetrahedral symmetry with 12 subunits
- Octahedral (O): “O” for octahedral symmetry with 24 subunits
- Icosahedral (I): “I” for icosahedral symmetry with 60 subunits
is_unsym_motif : Comma separated string list of contig/ligand names that should NOT be symmetrized (e.g. DNA strands). If not provided, all motifs are assumed to be symmetrized. See Designs with motifs section for details.
is_symmetric_motif: Boolean value whether the input motif is symmetric. Currently only symmetric input motifs are supported, therefore, true by default.

⚠️ Memory Warning: Memory requirements scale quadratically with the number of subunits. For larger complexes (especially T, O, and I symmetries), memory usage can become very high. Always use diffusion_batch_size=1 for symmetry, and consider enabling low_memory_mode=True for higher-order symmetries. The memory footprint increases dramatically as both the number of subunits and the length of each subunit increase. Note: many higher order symmetires run out of memory even on large cards such as H200s, we are working on optimizations to make these networks more memory efficient.

Example command¶

You can run the following example command:

./src/modelhub/inference.py inference_sampler.kind=symmetry out_dir=logs/inference_outs/sym_demo/0 ckpt_path=$cur_ckpt inputs=./projects/aa_design/tests/test_data/sym_tests.json diffusion_batch_size=1 

inference_sampler.kind: Set symmetry to turn on symmetry mode.
diffusion_batch_size : Must be set to 1 for all symmetry types due to memory limitations. Memory scales quadratically with the number of subunits.
low_memory_mode : Strongly recommended for T, O, and I symmetries. Set to True if you encounter memory errors (e.g. “CUDA error: out of memory”). Note that this will significantly slow the inference but is often necessary for larger complexes.

Unconditional multimer design¶

The following provides a general overview of the supported symmetry types and examples of how to run:

Cyclic (C)¶

Cyclic symmetry with n-fold rotational symmetry around a single axis. Generates n identical subunits.

{
    "uncond_C15": {
        "length": 100,
        "is_non_loopy": true,
        "symmetry": {
            "id": "C15"
        }
    }
}

Dihedral (D)¶

Dihedral symmetry combines n-fold rotational symmetry with a 2-fold rotation perpendicular to the main axis. Generates 2n identical subunits.

{
    "uncond_D4": { 
        "length": 100,
        "is_non_loopy": true,
        "symmetry": {
            "id": "D4"
        }
    }
}

Tetrahedral (T)¶

Tetrahedral symmetry based on the symmetry of a tetrahedron. Generates 12 identical subunits.

{
    "uncond_T": { 
        "length": 100,
        "symmetry": {
            "id": "T"
        }
    }
}

Octahedral (O)¶

Octahedral symmetry based on the symmetry of a cube/octahedron. Generates 24 identical subunits.

{
    "uncond_O": { 
        "length": 100,
        "symmetry": {
            "id": "O"
        }
    }
}

Icosahedral (I)¶

Icosahedral symmetry based on the symmetry of an icosahedron/dodecahedron. Generates 60 identical subunits. This is the largest point group symmetry and is commonly found in viral capsids.

{
    "uncond_I": { 
        "length": 100,
        "symmetry": {
            "id": "I"
        }
    }
}

⚠️ Memory Warning: Memory requirements increase dramatically with the number of subunits and subunit length. The memory footprint scales approximately quadratically with the number of subunits due to pairwise interactions. For higher-order symmetries (T: 12 subunits, O: 24 subunits, I: 60 subunits), it is essential to:

Set diffusion_batch_size=1 (required for all symmetry types)

Enable low_memory_mode=True for T, O, and I symmetries

Consider reducing subunit length if you encounter out-of-memory errors

For icosahedral symmetry (60 subunits), expect memory usage to be 25-100× higher than a single chain design

Designs with motifs¶

Symmetry sampling currently only supports pre-symmetrized motifs around the origin. Therefore, is_symmetric_motif is set to true by default. The following are example JSON specifications for different symmetric motif scaffolding. You can also find the corresponding input PDBs in docs/input_pdbs/symmetry_examples. Although we only give JSON examples, you can also use YAML for everything shown below.

The tasks that these examples describe are as follows:

unindexed_C2_1j79, unindexed_C2_1e3v: Unindexed motif scaffolding for symmetric enzyme active sites. The motifs are located within a subunit; no inter-subunit motifs.
indexed_unsym_C2_1bfr: Indexed motif scaffolding for a single active site held by a symmetric enzyme. is_unsym_motif specifies the ligand that shouldn’t be symmetrized.
uncond_unsym_C3_6t8h: Unconditional generation of C3 proteins around a DNA helix. The DNA chains are the motifs. is_unsym_motif specifies the DNA strands that shouldn’t be symmetrized.

{
    "unindexed_C2_1j79": {
        "symmetry": {
            "id": "C2",
            "is_symmetric_motif": true
        },
        "input": "symmetry_examples/M0630_1j79_symmedORO.pdb",
        "ligand": "ORO,ZN",
        "unindex": "A250",
        "length": 130,
        "select_fixed_atoms": {
            "A250": "OD1,CG"
        }
    },
    "unindexed_C2_1e3v": {
        "symmetry": {
            "id": "C2",
            "is_symmetric_motif": true
        },
        "input": "symmetry_examples/M0349_1e3v.pdb",
        "ligand": "DXC",
        "unindex": "A16,A40,A100,A103",
        "length": 80,
        "select_fixed_atoms": {
            "A16": "OH,CZ,CE1,CE2",
            "A40": "OD2,CG",
            "A100": "N,CA,C,CB",
            "A103": "OD2,CG"
        }
    },
    "indexed_unsym_C2_1bfr": {
        "symmetry": {
            "id": "C2",
            "is_symmetric_motif": true,
            "is_unsym_motif": "HEM"
        },
        "input": "symmetry_examples/1bfr_C2.pdb",
        "ligand": "HEM",
        "contig": "51,M52,80",
        "length": null,
        "select_fixed_atoms": {
            "M52": "CG,SD,CE"
        }
    },
    "unsym_C3_6t8h": {
        "symmetry": {
            "id": "C3",
            "is_symmetric_motif": true,
            "is_unsym_motif": "Y1-11,Z16-25"
        },
        "input": "symmetry_examples/6t8h_C3.pdb",
        "contig": "150-150,/0,Y1-11,/0,Z16-25",
        "length": null,
        "is_non_loopy": true
    }
}