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ProteinMPNN

ProteinMPNN designs amino-acid sequences for a supplied protein backbone, with controls for fixed residues and chains.

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Shared profile: ProteinMPNN. This page retains the exact record and its evaluation context.

At a glance

Explanatory profile: source reviewed · Automated source review, 2026-09-16. This does not change the review status of its results.

Inputs, outputs and configuration
PropertyDescription and evidence
Catalogue weight namev_48_020dauparas/ProteinMPNN official source · README.md: Full protein backbone models, CA only models and protein_mpnn_run.py arguments
OutputDesigned sequences and model scoresdauparas/ProteinMPNN official source · README.md: Full protein backbone models, CA only models and protein_mpnn_run.py arguments
Configuration in this recordv_48_020dauparas/ProteinMPNN official source · README.md: Full protein backbone models, CA only models and protein_mpnn_run.py arguments
Model typeNot extracted or verified for this record.
Training dataNot extracted or verified for this record.
Context limitsNot extracted or verified for this record.
AccessNot extracted or verified for this record.
Code licenceNot extracted or verified for this record.
Weights licenceNot extracted or verified for this record.

How it works

Conceptual procedure

Schematic of the documented input, computation and output; not an executable configuration.

Conceptual procedureBackbone structure. Then: Chain / residue constraints. Then: ProteinMPNN. Then: Conditional sequence sampling. Then: Designed sequencesBackbone structureChain / residue constraintsProteinMPNNConditional sequence samplingDesigned sequences
Read the diagram as text
  1. Backbone structure
  2. Chain / residue constraints
  3. ProteinMPNN
  4. Conditional sequence sampling
  5. Designed sequences
dauparas/ProteinMPNN official source · README.md: Full protein backbone models, CA only models and protein_mpnn_run.py arguments

A parsed structure and design constraints are supplied to the sequence-design model. It samples amino-acid sequences conditional on the backbone; sampling temperature changes diversity. Full-backbone and Cα-only weights are separate configurations.

dauparas/ProteinMPNN official source · README.md: Full protein backbone models, CA only models and protein_mpnn_run.py arguments

Benchmarks and results

Release 2026-09-16-d74d282221a9 · 0 evaluations · 0 metric rows. Different protocols are not a single leaderboard.

No evaluations linked in this release.

Strengths and limitations

Strengths supported by sources

  • Allows selected chains and positions to be redesigned while retaining specified residues.dauparas/ProteinMPNN official source · README.md: Full protein backbone models, CA only models and protein_mpnn_run.py arguments

Limitations and conditions

  • Requires a suitable input structure. Sequence generation does not itself demonstrate folding, activity or experimental success.dauparas/ProteinMPNN official source · README.md: Full protein backbone models, CA only models and protein_mpnn_run.py arguments
Profile review details

Primary project documentation or paper inspected for the explanatory claims and cited locations. Reviewed coverage concerns this narrative, not complete metadata, independent reproduction or a performance ranking.

Stable record: catalog-model-proteinmpnn

Sources and history

Release 2026-09-16-d74d282221a9 · Record review: discovered

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Technical metadata and extraction receipts

Stable ID: catalog-model-proteinmpnn

areas
proteins-complexes
method types
specialist
entity level
family
version
v_48_020
reported name
ProteinMPNN
access
Public code and checkpoints; requires a suitable protein structure.
method type
specialist
missing metadata
checkpoint revision: not_yet_extracted; training data: not_yet_extracted; licence: not_yet_extracted
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