Model

ProteinMPNN

ProteinMPNN designs amino-acid sequences for a supplied protein backbone.

Sources (5)

dauparas/ProteinMPNN: README.md; dauparas/ProteinMPNN: training/README.md; dauparas/ProteinMPNN: protein_mpnn_utils.py; proteinmpnn: Journal full-text XML; proteinmpnn-supp: Publisher supplementary archive · ProteinMPNN paper: main-text model development; training/README.md: multi-chain training set, list.csv and cluster manifests; protein_mpnn_utils.py: ProteinMPNN; Supplementary Materials: Methods for training multi-chain models / Training data

The broader model family has published results, but their attribution to this exact checkpoint has not been verified.

View 18 metric rows for the broader family

How it worksProteinMPNN workflow

Conceptual summary of the documented data flow; optional inputs and configured downstream stages must be reported for a reproducible evaluation.

Sources (5)

Overview

Model type

Structure-conditioned message-passing sequence design model

Sources (5)

Inputs

Protein backbone coordinates, with optional fixed residues, chain choices, tied positions and amino-acid constraints.

Sources (5)

Outputs

Designed sequences, sequence scores and conditional amino-acid probabilities.

Sources (5)

Access

Official project documentation and implementation: https://github.com/dauparas/ProteinMPNN

Sources (5)

limited source coverage · Automated source review, 2026-09-16. All specifications and missing details

Results for the broader model family

Published evaluations are available for ProteinMPNN. The cited sources do not establish that this exact checkpoint was used, so those scores are kept on the family profile.

View the family’s evaluations and exact configurations

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How it works, versions and access

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

How it works

ProteinMPNN converts a supplied backbone into a graph whose edges encode interatomic distances. Message-passing layers update node and edge features, and an autoregressive decoder samples amino acids while conditioning on the backbone and previously assigned residues. Fixed residues, tied positions and chain choices change the design task and must accompany its result.

Sources (5)

Versions and reproducibility

v_48_002, v_48_010, v_48_020 and v_48_030; distinct soluble and C-alpha-only weights. Structure-size and memory dependent. README --max_length is an implementation guard, not a validated scientific context limit.

Sources (5)

Strengths, limitations and unresolved questions

Strengths and limitations

Strengths and considerations

The interface makes design constraints explicit and includes full-backbone, C-alpha-only and soluble-protein weight sets.
Sources (5)
dauparas/ProteinMPNN: README.md; dauparas/ProteinMPNN: training/README.md; dauparas/ProteinMPNN: protein_mpnn_utils.py; proteinmpnn: Journal full-text XML; proteinmpnn-supp: Publisher supplementary archive · ProteinMPNN paper: main-text model development; training/README.md: multi-chain training set, list.csv and cluster manifests; protein_mpnn_utils.py: ProteinMPNN; Supplementary Materials: Methods for training multi-chain models / Training data

Limitations and conditions

The requested backbone and constraints are part of the evaluated problem. A command-line maximum-length guard is not evidence that designs at that size have been validated.
Sources (5)
dauparas/ProteinMPNN: README.md; dauparas/ProteinMPNN: training/README.md; dauparas/ProteinMPNN: protein_mpnn_utils.py; proteinmpnn: Journal full-text XML; proteinmpnn-supp: Publisher supplementary archive · ProteinMPNN paper: main-text model development; training/README.md: multi-chain training set, list.csv and cluster manifests; protein_mpnn_utils.py: ProteinMPNN; Supplementary Materials: Methods for training multi-chain models / Training data

Profile review details

Inspected pinned official documentation, relevant implementation files and named primary-paper sections. Claims are limited to those artifacts. Remaining field extraction and identity conflicts are explicit; no new performance claims, model runs or human review are implied.

Stable record: catalog-model-proteinmpnn

Specifications

Inputs, training, access and other details

Explanatory profile: limited source coverage · Automated source review, 2026-09-16. Review applies to the cited claims; unresolved fields are listed below. Numerical results retain their own review status.

Inputs, outputs and configuration
Property	Description and evidence
Model type	Structure-conditioned message-passing sequence design model Sources (5) dauparas/ProteinMPNN: README.md; dauparas/ProteinMPNN: training/README.md; dauparas/ProteinMPNN: protein_mpnn_utils.py; proteinmpnn: Journal full-text XML; proteinmpnn-supp: Publisher supplementary archive · ProteinMPNN paper: main-text model development; training/README.md: multi-chain training set, list.csv and cluster manifests; protein_mpnn_utils.py: ProteinMPNN; Supplementary Materials: Methods for training multi-chain models / Training data
Architecture	Message-passing encoder-decoder with structural interatomic-distance features and edge updates; sequences are sampled with the configured autoregressive decoding procedure. Sources (5) dauparas/ProteinMPNN: README.md; dauparas/ProteinMPNN: training/README.md; dauparas/ProteinMPNN: protein_mpnn_utils.py; proteinmpnn: Journal full-text XML; proteinmpnn-supp: Publisher supplementary archive · ProteinMPNN paper: main-text model development; training/README.md: multi-chain training set, list.csv and cluster manifests; protein_mpnn_utils.py: ProteinMPNN; Supplementary Materials: Methods for training multi-chain models / Training data
Inputs	Protein backbone coordinates, with optional fixed residues, chain choices, tied positions and amino-acid constraints. Sources (5) dauparas/ProteinMPNN: README.md; dauparas/ProteinMPNN: training/README.md; dauparas/ProteinMPNN: protein_mpnn_utils.py; proteinmpnn: Journal full-text XML; proteinmpnn-supp: Publisher supplementary archive · ProteinMPNN paper: main-text model development; training/README.md: multi-chain training set, list.csv and cluster manifests; protein_mpnn_utils.py: ProteinMPNN; Supplementary Materials: Methods for training multi-chain models / Training data
Outputs	Designed sequences, sequence scores and conditional amino-acid probabilities. Sources (5) dauparas/ProteinMPNN: README.md; dauparas/ProteinMPNN: training/README.md; dauparas/ProteinMPNN: protein_mpnn_utils.py; proteinmpnn: Journal full-text XML; proteinmpnn-supp: Publisher supplementary archive · ProteinMPNN paper: main-text model development; training/README.md: multi-chain training set, list.csv and cluster manifests; protein_mpnn_utils.py: ProteinMPNN; Supplementary Materials: Methods for training multi-chain models / Training data
Parameters	The inspected model implementation is configured through encoder/decoder depth and feature width. The paper and training README do not state an exact total for every released checkpoint. · Not reported in inspected sources Sources (5) dauparas/ProteinMPNN: README.md; dauparas/ProteinMPNN: training/README.md; dauparas/ProteinMPNN: protein_mpnn_utils.py; proteinmpnn: Journal full-text XML; proteinmpnn-supp: Publisher supplementary archive · ProteinMPNN paper: main-text model development; training/README.md: multi-chain training set, list.csv and cluster manifests; protein_mpnn_utils.py: ProteinMPNN; Supplementary Materials: Methods for training multi-chain models / Training data
Known versions	v_48_002, v_48_010, v_48_020 and v_48_030; distinct soluble and C-alpha-only weights. Sources (5) dauparas/ProteinMPNN: README.md; dauparas/ProteinMPNN: training/README.md; dauparas/ProteinMPNN: protein_mpnn_utils.py; proteinmpnn: Journal full-text XML; proteinmpnn-supp: Publisher supplementary archive · ProteinMPNN paper: main-text model development; training/README.md: multi-chain training set, list.csv and cluster manifests; protein_mpnn_utils.py: ProteinMPNN; Supplementary Materials: Methods for training multi-chain models / Training data
Training data	Released multi-chain training set of PDB biological units, with chain metadata and validation/test cluster manifests. The documented set is dated 2 August 2021. Sources (5) dauparas/ProteinMPNN: README.md; dauparas/ProteinMPNN: training/README.md; dauparas/ProteinMPNN: protein_mpnn_utils.py; proteinmpnn: Journal full-text XML; proteinmpnn-supp: Publisher supplementary archive · ProteinMPNN paper: main-text model development; training/README.md: multi-chain training set, list.csv and cluster manifests; protein_mpnn_utils.py: ProteinMPNN; Supplementary Materials: Methods for training multi-chain models / Training data
Training cutoff	The released PDB training-set snapshot is dated 2021-08-02; preserve its chain-level deposition metadata and cluster split for a run. Sources (5) dauparas/ProteinMPNN: README.md; dauparas/ProteinMPNN: training/README.md; dauparas/ProteinMPNN: protein_mpnn_utils.py; proteinmpnn: Journal full-text XML; proteinmpnn-supp: Publisher supplementary archive · ProteinMPNN paper: main-text model development; training/README.md: multi-chain training set, list.csv and cluster manifests; protein_mpnn_utils.py: ProteinMPNN; Supplementary Materials: Methods for training multi-chain models / Training data
Context limits	Structure-size and memory dependent. README --max_length is an implementation guard, not a validated scientific context limit. Sources (5) dauparas/ProteinMPNN: README.md; dauparas/ProteinMPNN: training/README.md; dauparas/ProteinMPNN: protein_mpnn_utils.py; proteinmpnn: Journal full-text XML; proteinmpnn-supp: Publisher supplementary archive · ProteinMPNN paper: main-text model development; training/README.md: multi-chain training set, list.csv and cluster manifests; protein_mpnn_utils.py: ProteinMPNN; Supplementary Materials: Methods for training multi-chain models / Training data
Weights licence	Separate checkpoint-distribution terms are not stated in the inspected release documentation and licence material. The source-code licence alone is not recorded as an explicit weight grant. · Not reported in inspected sources Sources (6) dauparas/ProteinMPNN: README.md; dauparas/ProteinMPNN: training/README.md; dauparas/ProteinMPNN: protein_mpnn_utils.py; proteinmpnn: Journal full-text XML; proteinmpnn-supp: Publisher supplementary archive; dauparas/ProteinMPNN: LICENSE · ProteinMPNN paper: main-text model development; training/README.md: multi-chain training set, list.csv and cluster manifests; protein_mpnn_utils.py: ProteinMPNN; Supplementary Materials: Methods for training multi-chain models / Training data; LICENSE: licence text
Access	Official project documentation and implementation: https://github.com/dauparas/ProteinMPNN Sources (5) dauparas/ProteinMPNN: README.md; dauparas/ProteinMPNN: training/README.md; dauparas/ProteinMPNN: protein_mpnn_utils.py; proteinmpnn: Journal full-text XML; proteinmpnn-supp: Publisher supplementary archive · ProteinMPNN paper: main-text model development; training/README.md: multi-chain training set, list.csv and cluster manifests; protein_mpnn_utils.py: ProteinMPNN; Supplementary Materials: Methods for training multi-chain models / Training data
Code licence	MIT Sources dauparas/ProteinMPNN: LICENSE · LICENSE: licence text

Evidence

Source checking verifies the cited claim or transcription. It does not establish independent reproduction.

Evidence table

Inspect claims, sources and review details

Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.

One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.

98 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-09-23-2b89723c6dd9
Property and statement	Original source and location	Review and provenance
Diagram caption Conceptual summary of the documented data flow; optional inputs and configured downstream stages must be reported for a reproducible evaluation. Individual claims	dauparas/ProteinMPNN: protein_mpnn_utils.py Original source ↗ ProteinMPNN paper: main-text model development; training/README.md: multi-chain training set, list.csv and cluster manifests; protein_mpnn_utils.py: ProteinMPNN; Supplementary Materials: Methods for training multi-chain models / Training data Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 8907e6671bfbfc92303b5f79c4b5e6ce47cdef57 Retrieved: 2026-09-16T19:46:18.948060+00:00	source checked automated source review · 2026-09-16 Audit details Inspected pinned official documentation, relevant implementation files and named primary-paper sections. Claims are limited to those artifacts. Remaining field extraction and identity conflicts are explicit; no new performance claims, model runs or human review are implied. Field: `attributes.profile.diagram.caption` Source artifact SHA-256: `74c8f9b7553422a7a0bbd705874844ee103c8926c2c96f154a87e0b824071e1b` Hash scope: SHA-256 of retrieved original artifact bytes Format: original_artifact Inspected artifact
Diagram caption Conceptual summary of the documented data flow; optional inputs and configured downstream stages must be reported for a reproducible evaluation. Individual claims	proteinmpnn-supp: Publisher supplementary archive Original source ↗ ProteinMPNN paper: main-text model development; training/README.md: multi-chain training set, list.csv and cluster manifests; protein_mpnn_utils.py: ProteinMPNN; Supplementary Materials: Methods for training multi-chain models / Training data Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Retrieved page snapshot; no immutable publisher revision supplied Retrieved: 2026-09-16T20:43:11.978593+00:00	source checked automated source review · 2026-09-16 Audit details Inspected pinned official documentation, relevant implementation files and named primary-paper sections. Claims are limited to those artifacts. Remaining field extraction and identity conflicts are explicit; no new performance claims, model runs or human review are implied. Field: `attributes.profile.diagram.caption` Source artifact SHA-256: `002269144962619673b7b187bdda20bd250e5f3cfd9f72a529eac53e7a1ac082` Hash scope: SHA-256 of retrieved original artifact bytes Format: original_artifact Inspected artifact
Diagram caption Conceptual summary of the documented data flow; optional inputs and configured downstream stages must be reported for a reproducible evaluation. Individual claims	dauparas/ProteinMPNN: training/README.md Original source ↗ ProteinMPNN paper: main-text model development; training/README.md: multi-chain training set, list.csv and cluster manifests; protein_mpnn_utils.py: ProteinMPNN; Supplementary Materials: Methods for training multi-chain models / Training data Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 8907e6671bfbfc92303b5f79c4b5e6ce47cdef57 Retrieved: 2026-09-16T19:46:18.948060+00:00	source checked automated source review · 2026-09-16 Audit details Inspected pinned official documentation, relevant implementation files and named primary-paper sections. Claims are limited to those artifacts. Remaining field extraction and identity conflicts are explicit; no new performance claims, model runs or human review are implied. Field: `attributes.profile.diagram.caption` Source artifact SHA-256: `7cbe5f3f1cef53f1954b15710317d9755b7b7b3febace4758190850f12e23029` Hash scope: SHA-256 of retrieved original artifact bytes Format: original_artifact Inspected artifact
Diagram caption Conceptual summary of the documented data flow; optional inputs and configured downstream stages must be reported for a reproducible evaluation. Individual claims	proteinmpnn: Journal full-text XML Original source ↗ ProteinMPNN paper: main-text model development; training/README.md: multi-chain training set, list.csv and cluster manifests; protein_mpnn_utils.py: ProteinMPNN; Supplementary Materials: Methods for training multi-chain models / Training data Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Retrieved page snapshot; no immutable publisher revision supplied Retrieved: 2026-09-16T19:53:03.193318+00:00	source checked automated source review · 2026-09-16 Audit details Inspected pinned official documentation, relevant implementation files and named primary-paper sections. Claims are limited to those artifacts. Remaining field extraction and identity conflicts are explicit; no new performance claims, model runs or human review are implied. Field: `attributes.profile.diagram.caption` Source artifact SHA-256: `3e9042dc0ac2837e07654a43e74abcbcdd7dc4cf017fa81e2d9869b1fdb3c52e` Hash scope: SHA-256 of retrieved original artifact bytes Format: original_artifact Inspected artifact
Diagram caption Conceptual summary of the documented data flow; optional inputs and configured downstream stages must be reported for a reproducible evaluation. Individual claims	dauparas/ProteinMPNN: README.md Original source ↗ ProteinMPNN paper: main-text model development; training/README.md: multi-chain training set, list.csv and cluster manifests; protein_mpnn_utils.py: ProteinMPNN; Supplementary Materials: Methods for training multi-chain models / Training data Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 8907e6671bfbfc92303b5f79c4b5e6ce47cdef57 Retrieved: 2026-09-16T19:46:18.948060+00:00	source checked automated source review · 2026-09-16 Audit details Inspected pinned official documentation, relevant implementation files and named primary-paper sections. Claims are limited to those artifacts. Remaining field extraction and identity conflicts are explicit; no new performance claims, model runs or human review are implied. Field: `attributes.profile.diagram.caption` Source artifact SHA-256: `772ebe52d2ba5100a28a888910c6f0c9fd4ded1d1372e3d89f6f1c48707e0365` Hash scope: SHA-256 of retrieved original artifact bytes Format: original_artifact Inspected artifact
Diagram steps Protein backbone Structural graph features Message-passing model Constrained sequence sampling Individual claims	dauparas/ProteinMPNN: protein_mpnn_utils.py Original source ↗ ProteinMPNN paper: main-text model development; training/README.md: multi-chain training set, list.csv and cluster manifests; protein_mpnn_utils.py: ProteinMPNN; Supplementary Materials: Methods for training multi-chain models / Training data Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 8907e6671bfbfc92303b5f79c4b5e6ce47cdef57 Retrieved: 2026-09-16T19:46:18.948060+00:00	source checked automated source review · 2026-09-16 Audit details Inspected pinned official documentation, relevant implementation files and named primary-paper sections. Claims are limited to those artifacts. Remaining field extraction and identity conflicts are explicit; no new performance claims, model runs or human review are implied. Field: `attributes.profile.diagram.steps` Source artifact SHA-256: `74c8f9b7553422a7a0bbd705874844ee103c8926c2c96f154a87e0b824071e1b` Hash scope: SHA-256 of retrieved original artifact bytes Format: original_artifact Inspected artifact
Diagram steps Protein backbone Structural graph features Message-passing model Constrained sequence sampling Individual claims	proteinmpnn-supp: Publisher supplementary archive Original source ↗ ProteinMPNN paper: main-text model development; training/README.md: multi-chain training set, list.csv and cluster manifests; protein_mpnn_utils.py: ProteinMPNN; Supplementary Materials: Methods for training multi-chain models / Training data Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Retrieved page snapshot; no immutable publisher revision supplied Retrieved: 2026-09-16T20:43:11.978593+00:00	source checked automated source review · 2026-09-16 Audit details Inspected pinned official documentation, relevant implementation files and named primary-paper sections. Claims are limited to those artifacts. Remaining field extraction and identity conflicts are explicit; no new performance claims, model runs or human review are implied. Field: `attributes.profile.diagram.steps` Source artifact SHA-256: `002269144962619673b7b187bdda20bd250e5f3cfd9f72a529eac53e7a1ac082` Hash scope: SHA-256 of retrieved original artifact bytes Format: original_artifact Inspected artifact
Diagram steps Protein backbone Structural graph features Message-passing model Constrained sequence sampling Individual claims	dauparas/ProteinMPNN: training/README.md Original source ↗ ProteinMPNN paper: main-text model development; training/README.md: multi-chain training set, list.csv and cluster manifests; protein_mpnn_utils.py: ProteinMPNN; Supplementary Materials: Methods for training multi-chain models / Training data Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 8907e6671bfbfc92303b5f79c4b5e6ce47cdef57 Retrieved: 2026-09-16T19:46:18.948060+00:00	source checked automated source review · 2026-09-16 Audit details Inspected pinned official documentation, relevant implementation files and named primary-paper sections. Claims are limited to those artifacts. Remaining field extraction and identity conflicts are explicit; no new performance claims, model runs or human review are implied. Field: `attributes.profile.diagram.steps` Source artifact SHA-256: `7cbe5f3f1cef53f1954b15710317d9755b7b7b3febace4758190850f12e23029` Hash scope: SHA-256 of retrieved original artifact bytes Format: original_artifact Inspected artifact
Diagram steps Protein backbone Structural graph features Message-passing model Constrained sequence sampling Individual claims	proteinmpnn: Journal full-text XML Original source ↗ ProteinMPNN paper: main-text model development; training/README.md: multi-chain training set, list.csv and cluster manifests; protein_mpnn_utils.py: ProteinMPNN; Supplementary Materials: Methods for training multi-chain models / Training data Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Retrieved page snapshot; no immutable publisher revision supplied Retrieved: 2026-09-16T19:53:03.193318+00:00	source checked automated source review · 2026-09-16 Audit details Inspected pinned official documentation, relevant implementation files and named primary-paper sections. Claims are limited to those artifacts. Remaining field extraction and identity conflicts are explicit; no new performance claims, model runs or human review are implied. Field: `attributes.profile.diagram.steps` Source artifact SHA-256: `3e9042dc0ac2837e07654a43e74abcbcdd7dc4cf017fa81e2d9869b1fdb3c52e` Hash scope: SHA-256 of retrieved original artifact bytes Format: original_artifact Inspected artifact
Diagram steps Protein backbone Structural graph features Message-passing model Constrained sequence sampling Individual claims	dauparas/ProteinMPNN: README.md Original source ↗ ProteinMPNN paper: main-text model development; training/README.md: multi-chain training set, list.csv and cluster manifests; protein_mpnn_utils.py: ProteinMPNN; Supplementary Materials: Methods for training multi-chain models / Training data Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 8907e6671bfbfc92303b5f79c4b5e6ce47cdef57 Retrieved: 2026-09-16T19:46:18.948060+00:00	source checked automated source review · 2026-09-16 Audit details Inspected pinned official documentation, relevant implementation files and named primary-paper sections. Claims are limited to those artifacts. Remaining field extraction and identity conflicts are explicit; no new performance claims, model runs or human review are implied. Field: `attributes.profile.diagram.steps` Source artifact SHA-256: `772ebe52d2ba5100a28a888910c6f0c9fd4ded1d1372e3d89f6f1c48707e0365` Hash scope: SHA-256 of retrieved original artifact bytes Format: original_artifact Inspected artifact

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Release 2026-09-23-2b89723c6dd9 · Record review: discovered

7 source records and release history

ProteinMPNN official resource · Original source · Not reported
dauparas/ProteinMPNN: README.md · Original source · 8907e6671bfbfc92303b5f79c4b5e6ce47cdef57
dauparas/ProteinMPNN: training/README.md · Original source · 8907e6671bfbfc92303b5f79c4b5e6ce47cdef57
dauparas/ProteinMPNN: protein_mpnn_utils.py · Original source · 8907e6671bfbfc92303b5f79c4b5e6ce47cdef57
proteinmpnn: Journal full-text XML · Original source · Retrieved page snapshot; no immutable publisher revision supplied
proteinmpnn-supp: Publisher supplementary archive · Original source · Retrieved page snapshot; no immutable publisher revision supplied
dauparas/ProteinMPNN: LICENSE · Original source · 8907e6671bfbfc92303b5f79c4b5e6ce47cdef57

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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
historical missing metadata: checkpoint revision: not_yet_extracted; training data: not_yet_extracted; licence: not_yet_extracted
metadata review scope: historical_missing_metadata preserves the original discovery state. Current descriptive evidence and missingness are recorded in profile.facts; numerical-result review is separate.
entity classification: review date: 2026-09-17; rationale: The cited profile describes a named learned biological predictor or representation model/family. Preserve this identity separately from task-specific fitting, individual checkpoints, pipelines and hosted access.; source ids: evidence-official-ea568a52ea3ab476db6a; evidence-official-7b6fdf915d9c6950ad0d; evidence-official-019aff235ae2c3cf29d6; evidence-official-92b0ae57a020cbc40fe7; evidence-official-0b8003691827eb09b0a4; source locator: ProteinMPNN paper: main-text model development; training/README.md: multi-chain training set, list.csv and cluster manifests; protein_mpnn_utils.py: ProteinMPNN; Supplementary Materials: Methods for training multi-chain models / Training data; ambiguities: None recorded

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variant of: ProteinMPNN
subject: ProteinMPNN: variant of ProteinMPNN

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