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

14 evaluations · 18 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

14 evaluations · 18 results. Different protocols are not a single leaderboard.

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: Zero-shot substitution mutation effects: stability Dataset: ProteinGym substitution DMS: stability assays	0.566 Mean Spearman rho unitless · unknown Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source ProteinMPNN: Zero-shot substitution mutation effects: stability Zero-shot mutation scores; average Spearman across stability-category assays. Aggregation: Not reported ProteinGym: Large-Scale Benchmarks for Protein Design and Fitness Prediction · Table A7, ProteinMPNN row, Stability column
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 score · higher Uncertainty: Not reported 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 score · higher Uncertainty: Not reported 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 score · higher Uncertainty: Not reported 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 score · higher Uncertainty: Not reported 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 score · higher Uncertainty: Not reported 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 score · higher Uncertainty: Not reported 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 score · higher Uncertainty: Not reported 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 score · higher Uncertainty: Not reported 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 score · higher Uncertainty: Not reported 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 score · higher Uncertainty: Not reported 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 aar score · higher Uncertainty: Not reported 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 aar score · higher Uncertainty: Not reported 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 ↑)
Configuration: ProteinMPNN	Task: ProteinGym ZS-SUB-AUC: Zero-shot substitutions, AUC Dataset subset: ProteinGym substitution DMS assays (ProteinGym split)	0.639 auc fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source ProteinMPNN on ProteinGym ZS-SUB-AUC: Zero-shot substitutions, AUC Zero-shot scoring of substitution assays, averaged over assays with the correction the paper describes. Aggregation: Not reported ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design · Table 2, row(ProteinMPNN), column(Zero-shot substitutions, AUC)
Configuration: ProteinMPNN	Task: ProteinGym ZS-SUB-MCC: Zero-shot substitutions, MCC Dataset subset: ProteinGym substitution DMS assays (ProteinGym split)	0.196 mcc correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source ProteinMPNN on ProteinGym ZS-SUB-MCC: Zero-shot substitutions, MCC Zero-shot scoring of substitution assays, averaged over assays with the correction the paper describes. Aggregation: Not reported ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design · Table 2, row(ProteinMPNN), column(Zero-shot substitutions, MCC)
Configuration: ProteinMPNN	Task: ProteinGym ZS-SUB-NDCG: Zero-shot substitutions, NDCG@10% Dataset subset: ProteinGym substitution DMS assays (ProteinGym split)	0.713 ndcg fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source ProteinMPNN on ProteinGym ZS-SUB-NDCG: Zero-shot substitutions, NDCG@10% Zero-shot scoring of substitution assays, averaged over assays with the correction the paper describes. Aggregation: Not reported ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design · Table 2, row(ProteinMPNN), column(Zero-shot substitutions, NDCG@10%)
Configuration: ProteinMPNN	Task: ProteinGym ZS-SUB-RECALL: Zero-shot substitutions, top 10% recall Dataset subset: ProteinGym substitution DMS assays (ProteinGym split)	0.186 recall fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source ProteinMPNN on ProteinGym ZS-SUB-RECALL: Zero-shot substitutions, top 10% recall Zero-shot scoring of substitution assays, averaged over assays with the correction the paper describes. Aggregation: Not reported ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design · Table 2, row(ProteinMPNN), column(Zero-shot substitutions, top 10% recall)
Configuration: ProteinMPNN	Task: ProteinGym ZS-SUB-SPEARMAN: Zero-shot substitutions, Spearman Dataset subset: ProteinGym substitution DMS assays (ProteinGym split)	0.258 spearman correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source ProteinMPNN on ProteinGym ZS-SUB-SPEARMAN: Zero-shot substitutions, Spearman Zero-shot scoring of substitution assays, averaged over assays with the correction the paper describes. Aggregation: Not reported ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design · Table 2, row(ProteinMPNN), column(Zero-shot substitutions, Spearman)

Source checking is not independent reproduction. Release 2026-09-29-06401fd5b220.

Use this model

How it works, versions and access

Versions and evaluated configurations

ProteinMPNN · variant of
ProteinMPNN · family
ProteinMPNN · family
ProteinMPNN · family

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

Applicable tests and references

Applicability is distinct from a completed evaluation.

ProteinBench · Proposed association

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.

97 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-09-29-06401fd5b220
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 2026-09-29-06401fd5b220 · Record review: discovered

7 source records and release history

dauparas/ProteinMPNN official source · Original source · 8907e6671bfbfc92303b5f79c4b5e6ce47cdef57
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: discovery-model-proteinmpnn

areas: protein-structure
access: official_source_linked
benchmark applicability: candidate; not evidence of a reported evaluation
candidate benchmark ids: discovery-benchmark-proteinbench
entity level: family
reported name: ProteinMPNN
version: Not reported
historical missing metadata: checkpoint: unextracted; code licence: unextracted; parameters: unextracted; training cutoff: unextracted; training data: unextracted; version: unextracted; weights licence: unextracted
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

Related records

applicable to: ProteinBench
variant of: ProteinMPNN
model: Protein sequence recovery specialist
family: ProteinMPNN
family: ProteinMPNN
family: ProteinMPNN

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