Model type
Structure-conditioned message-passing sequence design model
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 dataHow 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)
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 dataOverview
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)
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 datalimited 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.
Filter evaluations
Applied filters: All linked evaluations
| 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 score · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceInverse 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 checkedMethods, coverage and sourceInverse 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 checkedMethods, coverage and sourceInverse 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 checkedMethods, coverage and sourceInverse 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 checkedMethods, coverage and sourceInverse 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 checkedMethods, coverage and sourceInverse 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 checkedMethods, coverage and sourceInverse 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 checkedMethods, coverage and sourceInverse 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 checkedMethods, coverage and sourceInverse 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 checkedMethods, coverage and sourceInverse 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 checkedMethods, coverage and sourceInverse 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 checkedMethods, coverage and sourceInverse 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 2026-09-29-06401fd5b220.
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How it works, versions and access
Related profile: ProteinMPNN. This page retains the exact record and its evaluation context.
This configuration
Protein model evaluated by the ProteinBench authors under their harness.
- record
- ProteinMPNN
- configuration
- Not reported
- entity type
- Configuration
How it works
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)
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 dataVersions 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)
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 dataStrengths, 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-proteinmpnnSpecifications
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.
| Property | Description and evidence |
|---|---|
| Model type | Structure-conditioned message-passing sequence design modelSources (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 sourcesSources (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 sourcesSources (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/ProteinMPNNSources (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 | MITSourcesdauparas/ProteinMPNN: LICENSE · LICENSE: licence text |
Evidence
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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.
1 evidence row matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Relationship: family discovery-model-proteinmpnn Individual claims | proteinbench primary benchmark evidence Tables 2; task methods and corresponding named row Version: 2409.06744v1 | source checked automated source review · 2026-09-23 Audit detailsSource 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: Claim: model-evaluation-identity-ef61334eed18290462db Source artifact SHA-256: Hash scope: Exact retrieved primary paper artifact bytes. |
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Release 2026-09-29-06401fd5b220 · Record review: source checked
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- proteinbench primary benchmark evidence · Original source · 2409.06744v1
Technical metadata and extraction receipts
Stable ID: proteinbench-method-proteinmpnn
- areas
- proteins-complexes
- source locator
- Table 2, row(ProteinMPNN)
- missing metadata
- checkpoint revision: unreported; parameters: unextracted
Related records
- family: ProteinMPNN
- subject: ProteinMPNN: family ProteinMPNN
- model: ProteinMPNN on ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-100-PLDDT: De novo backbones based sequence design, length 100 pLDDT
- model: ProteinMPNN on ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-100-SCTM: De novo backbones based sequence design, length 100 scTM
- model: ProteinMPNN on ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-200-PLDDT: De novo backbones based sequence design, length 200 pLDDT
- model: ProteinMPNN on ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-200-SCTM: De novo backbones based sequence design, length 200 scTM
- model: ProteinMPNN on ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-300-PLDDT: De novo backbones based sequence design, length 300 pLDDT
- model: ProteinMPNN on ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-300-SCTM: De novo backbones based sequence design, length 300 scTM
- model: ProteinMPNN on ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-400-PLDDT: De novo backbones based sequence design, length 400 pLDDT
- model: ProteinMPNN on ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-400-SCTM: De novo backbones based sequence design, length 400 scTM
- model: ProteinMPNN on ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-500-PLDDT: De novo backbones based sequence design, length 500 pLDDT
- model: ProteinMPNN on ProteinBench IF-DE-NOVO-BACKBONES-BASED-SEQUENCE-DESIGN-LENGTH-500-SCTM: De novo backbones based sequence design, length 500 scTM
- model: ProteinMPNN on ProteinBench IF-FITTING-EVOLUTION-DISTRIBUTION-CAMEO-AAR: Fitting Evolution Distribution, CAMEO AAR
- model: ProteinMPNN on ProteinBench IF-FITTING-EVOLUTION-DISTRIBUTION-CASP-AAR: Fitting Evolution Distribution, CASP AAR