Configuration

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

12 evaluations · 12 results

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

Inputs

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

Outputs

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

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

Evaluations and results

12 evaluations · 12 results. Different protocols are not a single leaderboard.

Dataset subset: CASP, CAMEO and de novo backbones (ProteinBench split)

Sorted by Plddt (higher is better). The best value in each column is highlighted. Decimals are rounded for display; each value links to the printed value and its source.

Tested configuration	Protocol and dataset	Plddt	Sctm	Sequence recovery
ProteinMPNNEvaluation	ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-100-PLDDT: De novo backbones based sequence design, length 100 pLDDT	94.1 (best in column)	Not reported	Not reported
ProteinMPNNEvaluation	ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-300-PLDDT: De novo backbones based sequence design, length 300 pLDDT	90.3	Not reported	Not reported
ProteinMPNNEvaluation	ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-200-PLDDT: De novo backbones based sequence design, length 200 pLDDT	89.3	Not reported	Not reported
ProteinMPNNEvaluation	ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-400-PLDDT: De novo backbones based sequence design, length 400 pLDDT	83.8	Not reported	Not reported
ProteinMPNNEvaluation	ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-500-PLDDT: De novo backbones based sequence design, length 500 pLDDT	67.1	Not reported	Not reported
ProteinMPNNEvaluation	ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-100-SCTM: De novo backbones based sequence design, length 100 scTM	Not reported	0.962 (best in column)	Not reported
ProteinMPNNEvaluation	ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-200-SCTM: De novo backbones based sequence design, length 200 scTM	Not reported	0.945	Not reported
ProteinMPNNEvaluation	ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-300-SCTM: De novo backbones based sequence design, length 300 scTM	Not reported	0.962 (best in column)	Not reported
ProteinMPNNEvaluation	ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-400-SCTM: De novo backbones based sequence design, length 400 scTM	Not reported	0.875	Not reported
ProteinMPNNEvaluation	ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-500-SCTM: De novo backbones based sequence design, length 500 scTM	Not reported	0.568	Not reported
ProteinMPNNEvaluation	ProteinBench IF-FITTING-EVOLUTION-DISTRIBUTION-CAMEO-AAR: Fitting Evolution Distribution, CAMEO AAR	Not reported	Not reported	0.468 (best in column)
ProteinMPNNEvaluation	ProteinBench IF-FITTING-EVOLUTION-DISTRIBUTION-CASP-AAR: Fitting Evolution Distribution, CASP AAR	Not reported	Not reported	0.45

All 12 result rows with coverage, uncertainty and sources

Filter evaluations

Tested model or methodProtocolDatasetEvaluation setupMetricEvidence origin

Applied filters: All linked evaluations

Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: ProteinMPNN	Task: ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-100-PLDDT: De novo backbones based sequence design, length 100 pLDDT Dataset subset: CASP, CAMEO and de novo backbones (ProteinBench split)	94.1 Plddt unitless · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source ProteinMPNN on ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-100-PLDDT: De novo backbones based sequence design, length 100 pLDDT Inverse folding: recover a sequence for a given backbone. Values are the median over repeated runs. Aggregation: Not reported proteinbench primary benchmark evidence · Table 2, row(ProteinMPNN), column(De novo backbones based sequence design, length 100 pLDDT ↑)
Configuration: ProteinMPNN	Task: ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-100-SCTM: De novo backbones based sequence design, length 100 scTM Dataset subset: CASP, CAMEO and de novo backbones (ProteinBench split)	0.962 Sctm unitless · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source ProteinMPNN on ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-100-SCTM: De novo backbones based sequence design, length 100 scTM Inverse folding: recover a sequence for a given backbone. Values are the median over repeated runs. Aggregation: Not reported proteinbench primary benchmark evidence · Table 2, row(ProteinMPNN), column(De novo backbones based sequence design, length 100 scTM ↑)
Configuration: ProteinMPNN	Task: ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-200-PLDDT: De novo backbones based sequence design, length 200 pLDDT Dataset subset: CASP, CAMEO and de novo backbones (ProteinBench split)	89.3 Plddt unitless · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source ProteinMPNN on ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-200-PLDDT: De novo backbones based sequence design, length 200 pLDDT Inverse folding: recover a sequence for a given backbone. Values are the median over repeated runs. Aggregation: Not reported proteinbench primary benchmark evidence · Table 2, row(ProteinMPNN), column(De novo backbones based sequence design, length 200 pLDDT ↑)
Configuration: ProteinMPNN	Task: ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-200-SCTM: De novo backbones based sequence design, length 200 scTM Dataset subset: CASP, CAMEO and de novo backbones (ProteinBench split)	0.945 Sctm unitless · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source ProteinMPNN on ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-200-SCTM: De novo backbones based sequence design, length 200 scTM Inverse folding: recover a sequence for a given backbone. Values are the median over repeated runs. Aggregation: Not reported proteinbench primary benchmark evidence · Table 2, row(ProteinMPNN), column(De novo backbones based sequence design, length 200 scTM ↑)
Configuration: ProteinMPNN	Task: ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-300-PLDDT: De novo backbones based sequence design, length 300 pLDDT Dataset subset: CASP, CAMEO and de novo backbones (ProteinBench split)	90.3 Plddt unitless · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source ProteinMPNN on ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-300-PLDDT: De novo backbones based sequence design, length 300 pLDDT Inverse folding: recover a sequence for a given backbone. Values are the median over repeated runs. Aggregation: Not reported proteinbench primary benchmark evidence · Table 2, row(ProteinMPNN), column(De novo backbones based sequence design, length 300 pLDDT ↑)
Configuration: ProteinMPNN	Task: ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-300-SCTM: De novo backbones based sequence design, length 300 scTM Dataset subset: CASP, CAMEO and de novo backbones (ProteinBench split)	0.962 Sctm unitless · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source ProteinMPNN on ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-300-SCTM: De novo backbones based sequence design, length 300 scTM Inverse folding: recover a sequence for a given backbone. Values are the median over repeated runs. Aggregation: Not reported proteinbench primary benchmark evidence · Table 2, row(ProteinMPNN), column(De novo backbones based sequence design, length 300 scTM ↑)
Configuration: ProteinMPNN	Task: ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-400-PLDDT: De novo backbones based sequence design, length 400 pLDDT Dataset subset: CASP, CAMEO and de novo backbones (ProteinBench split)	83.8 Plddt unitless · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source ProteinMPNN on ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-400-PLDDT: De novo backbones based sequence design, length 400 pLDDT Inverse folding: recover a sequence for a given backbone. Values are the median over repeated runs. Aggregation: Not reported proteinbench primary benchmark evidence · Table 2, row(ProteinMPNN), column(De novo backbones based sequence design, length 400 pLDDT ↑)
Configuration: ProteinMPNN	Task: ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-400-SCTM: De novo backbones based sequence design, length 400 scTM Dataset subset: CASP, CAMEO and de novo backbones (ProteinBench split)	0.875 Sctm unitless · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source ProteinMPNN on ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-400-SCTM: De novo backbones based sequence design, length 400 scTM Inverse folding: recover a sequence for a given backbone. Values are the median over repeated runs. Aggregation: Not reported proteinbench primary benchmark evidence · Table 2, row(ProteinMPNN), column(De novo backbones based sequence design, length 400 scTM ↑)
Configuration: ProteinMPNN	Task: ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-500-PLDDT: De novo backbones based sequence design, length 500 pLDDT Dataset subset: CASP, CAMEO and de novo backbones (ProteinBench split)	67.1 Plddt unitless · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source ProteinMPNN on ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-500-PLDDT: De novo backbones based sequence design, length 500 pLDDT Inverse folding: recover a sequence for a given backbone. Values are the median over repeated runs. Aggregation: Not reported proteinbench primary benchmark evidence · Table 2, row(ProteinMPNN), column(De novo backbones based sequence design, length 500 pLDDT ↑)
Configuration: ProteinMPNN	Task: ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-500-SCTM: De novo backbones based sequence design, length 500 scTM Dataset subset: CASP, CAMEO and de novo backbones (ProteinBench split)	0.568 Sctm unitless · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source ProteinMPNN on ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-500-SCTM: De novo backbones based sequence design, length 500 scTM Inverse folding: recover a sequence for a given backbone. Values are the median over repeated runs. Aggregation: Not reported proteinbench primary benchmark evidence · Table 2, row(ProteinMPNN), column(De novo backbones based sequence design, length 500 scTM ↑)
Configuration: ProteinMPNN	Task: ProteinBench IF-FITTING-EVOLUTION-DISTRIBUTION-CAMEO-AAR: Fitting Evolution Distribution, CAMEO AAR Dataset subset: CASP, CAMEO and de novo backbones (ProteinBench split)	0.468 Sequence recovery fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source ProteinMPNN on ProteinBench IF-FITTING-EVOLUTION-DISTRIBUTION-CAMEO-AAR: Fitting Evolution Distribution, CAMEO AAR Inverse folding: recover a sequence for a given backbone. Values are the median over repeated runs. Aggregation: Not reported proteinbench primary benchmark evidence · Table 2, row(ProteinMPNN), column(Fitting Evolution Distribution, CAMEO AAR ↑)
Configuration: ProteinMPNN	Task: ProteinBench IF-FITTING-EVOLUTION-DISTRIBUTION-CASP-AAR: Fitting Evolution Distribution, CASP AAR Dataset subset: CASP, CAMEO and de novo backbones (ProteinBench split)	0.45 Sequence recovery fraction · higher Uncertainty: Not yet extracted Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source ProteinMPNN on ProteinBench IF-FITTING-EVOLUTION-DISTRIBUTION-CASP-AAR: Fitting Evolution Distribution, CASP AAR Inverse folding: recover a sequence for a given backbone. Values are the median over repeated runs. Aggregation: Not reported proteinbench primary benchmark evidence · Table 2, row(ProteinMPNN), column(Fitting Evolution Distribution, CASP AAR ↑)

Source checking is not independent reproduction. Release 2026-11-02-a7f03c9e55a5.

Use this configuration

How it works, versions and access

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

This configuration

Configuration of ProteinMPNN; no settings beyond its name are recorded.

record: ProteinMPNN
configuration: Not reported
entity type: Configuration

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: discovery-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

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Claims, original sources and review scope · Release 2026-11-02-a7f03c9e55a5
Property and statement	Original source and location	Review and provenance
Relationship: family discovery-model-proteinmpnn Individual claims	proteinbench primary benchmark evidence Original source ↗ Tables 2; task methods and corresponding named row Version: 2409.06744v1 Retrieved: 2026-09-16T21:07:13.231727+00:00	source checked ["automated-source-review"] · 2026-09-23 Audit details Source review establishes this relationship only. Exact evaluated configurations and original numerical review status remain unchanged. Paper evaluates this named model under its ProteinBench harness. Association is to the family, not an assertion of checkpoint equivalence or cross-task comparability. Field: `links:family:discovery-model-proteinmpnn` Claim: model-evaluation-identity-ef61334eed18290462db Source artifact SHA-256: `4334d636223ad42bfb9ae68aae03f5a255c29ba1cebe7b8f9588fb3b9b5453b2` Hash scope: Exact retrieved primary paper artifact bytes. Inspected artifact

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Release 2026-11-02-a7f03c9e55a5 · Record review: source checked

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proteinbench primary benchmark evidence · Original source · 2409.06744v1

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Stable ID: proteinbench-method-proteinmpnn

areas: proteins-complexes
source locator: Table 2, row(ProteinMPNN)
missing metadata: checkpoint revision: reason: unreported; parameters: reason: unextracted
provenance note: Protein model evaluated by the ProteinBench authors under their harness.

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