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RFdiffusion3 + MPNN Binder Design
License: This generator can use multiple tools, each under its own license. See the Tools Used tab and each tool’s page for license details.

This generator is open source. Any third-party models, product names, or trademarks referenced are the property of their respective owners, and Proto is not affiliated with them.

evo-design/proto-language/proto_language/generator/rfdiffusion_mpnn_binder_generator.py
View source De-novo protein binder generator chaining RFdiffusion3 and an inverse-folding model. For each sample() call the generator diffuses binder backbones docked to a fixed target (RFdiffusion3), then designs each backbone’s binder-chain sequence with the selected inverse-folding model (proteinmpnn or ligandmpnn) while keeping the target chains fixed as structural context. With ligandmpnn the binder conditions on the target’s ligand/nucleotide/metal atoms, so the target may be a protein, DNA, or RNA chain (with ligand/metal cofactors as context). The designed binder sequence is written to proposal.sequence and the RFdiffusion3 target+binder complex to proposal.structure (its binder chain carries RFdiffusion3’s co-designed sequence, so downstream structure-prediction constraints should re-fold proposal.sequence). The binder length is the assigned segment’s length. The generator fills a length-only segment, so its category is "mutation" despite being a de-novo designer (same convention as RandomProteinGenerator).

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API Reference

ConfigRFdiffusionMPNNBinderGeneratorConfig Source
Configuration object for RFdiffusionMPNNBinderGenerator.Top-level fields describe what binder to design (the target, its chains, the epitope hotspots, and which inverse-folding model designs the sequence); the per-tool knobs live in nested rfdiffusion3_config and an inverse-folding config (proteinmpnn_config or ligandmpnn_config), matching how constraints nest tool configs (e.g. esmfold_config). Only the config matching inverse_folding is used; it defaults to its tool default when omitted. The binder length is taken from the assigned segment’s length, not from this config.The generator injects the fields it owns at sample time and respects everything else: on rfdiffusion3_config it sets n_batches (to produce enough backbones for the proposal count) and seed; on the inverse-folding config it sets seed and reads num_sequences_per_structure as the number of sequences designed per backbone.
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target_structure
Structure | string
required
Target to bind (file path, PDB/CIF content, or Structure); may include DNA/RNA/ligand chains.
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target_chains
List[string]
Target chain IDs kept fixed; the binder is emitted after these chains.
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hotspots
array
Target hotspot residues as ‘<chain><resnum>’ (e.g. [‘A37’, ‘A39’]).
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inverse_folding
enum
default:"proteinmpnn"
Sequence-design model: ‘proteinmpnn’ (protein only) or ‘ligandmpnn’ (ligand/DNA/RNA/metal-aware).Options: proteinmpnn, ligandmpnn
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rfdiffusion3_config
RFdiffusion3Config
Advanced RFdiffusion3 backbone-generation configuration.
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proteinmpnn_config
ProteinMPNNSampleConfig
Advanced ProteinMPNN settings; used when inverse_folding=‘proteinmpnn’.
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ligandmpnn_config
LigandMPNNSampleConfig
Advanced LigandMPNN settings; used when inverse_folding=‘ligandmpnn’.

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Usage

Build a two-segment binder program: a length-only binder (designed) plus a fixed target segment derived from the same structure, so a scoring constraint can fold the complex via inputs=[binder, target]. The generator is assigned only to the binder; the target reaches it through config. See examples/scripts/binder_design_rfdiffusion_mpnn.py for the full program.
python
>>> from proto_tools import Structure
>>> from proto_language.core import Construct, Segment
>>> from proto_language.generator import (
...     RFdiffusionMPNNBinderGenerator,
...     RFdiffusionMPNNBinderGeneratorConfig,
... )
>>> target_structure = Structure.from_file("target.pdb")
>>> target_seq = target_structure.get_chain_sequence("A", remove_non_standard=True)
>>> binder = Segment(length=80, sequence_type="protein", label="binder")
>>> target = Segment(sequence=target_seq, sequence_type="protein", label="target")
>>> construct = Construct([binder, target])  # target is fixed: no generator
>>> gen = RFdiffusionMPNNBinderGenerator(
...     RFdiffusionMPNNBinderGeneratorConfig(
...         target_structure=target_structure, target_chains=["A"], hotspots=["A37"]
...     )
... )
>>> gen.assign(binder)  # generator touches only the binder
>>> gen.sample()  # fills num_proposals binders; a constraint scores [binder, target]

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Metadata

PropertyValue
Keyrfdiffusion-mpnn-binder
ClassRFdiffusionMPNNBinderGenerator
Categorymutation
Input Typestarting_sequence
Uses GPUTrue
Supported Sequence Typesprotein
Allows Empty StartTrue