Low-throughput biophysical ddG measurements matched to PDB structures, homologue-free split.
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12 evaluations · 36 results. Different protocols are not a single leaderboard.
Applied filters: All linked evaluations
| Tested configuration | Protocol and dataset | Finding | Evidence and details |
|---|---|---|---|
| Configuration: ACDC-NN (Dieckhaus et al. 2024) | Protocol: Fireprot homologue-free split ddG prediction (Dieckhaus et al. 2024 Table 2) Dataset: FireProtDB homologue-free split with experimental structures | 0.57 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceACDC-NN on Fireprot homologue-free split (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-fireprot Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 2, row 'ACDC-NN', column 'Fireprot PCC' |
| Configuration: ACDC-NN (Dieckhaus et al. 2024) | Protocol: Fireprot homologue-free split ddG prediction (Dieckhaus et al. 2024 Table 2) Dataset: FireProtDB homologue-free split with experimental structures | 1.69 root-mean-squared-error kilocalorie-per-mole · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceACDC-NN on Fireprot homologue-free split (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-fireprot Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 2, row 'ACDC-NN', column 'Fireprot RMSE (kcal/mol)' |
| Configuration: ACDC-NN (Dieckhaus et al. 2024) | Protocol: Fireprot homologue-free split ddG prediction (Dieckhaus et al. 2024 Table 2) Dataset: FireProtDB homologue-free split with experimental structures | 0.51 spearman-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceACDC-NN on Fireprot homologue-free split (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-fireprot Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 2, row 'ACDC-NN', column 'Fireprot SCC' |
| Configuration: ACDC-NN-Seq (Dieckhaus et al. 2024) | Protocol: Fireprot homologue-free split ddG prediction (Dieckhaus et al. 2024 Table 2) Dataset: FireProtDB homologue-free split with experimental structures | 0.54 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceACDC-NN-Seq on Fireprot homologue-free split (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-fireprot Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 2, row 'ACDC-NN-Seq', column 'Fireprot PCC' |
| Configuration: ACDC-NN-Seq (Dieckhaus et al. 2024) | Protocol: Fireprot homologue-free split ddG prediction (Dieckhaus et al. 2024 Table 2) Dataset: FireProtDB homologue-free split with experimental structures | 1.71 root-mean-squared-error kilocalorie-per-mole · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceACDC-NN-Seq on Fireprot homologue-free split (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-fireprot Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 2, row 'ACDC-NN-Seq', column 'Fireprot RMSE (kcal/mol)' |
| Configuration: ACDC-NN-Seq (Dieckhaus et al. 2024) | Protocol: Fireprot homologue-free split ddG prediction (Dieckhaus et al. 2024 Table 2) Dataset: FireProtDB homologue-free split with experimental structures | 0.48 spearman-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceACDC-NN-Seq on Fireprot homologue-free split (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-fireprot Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 2, row 'ACDC-NN-Seq', column 'Fireprot SCC' |
| Configuration: FoldX (Dieckhaus et al. 2024) | Protocol: Fireprot homologue-free split ddG prediction (Dieckhaus et al. 2024 Table 2) Dataset: FireProtDB homologue-free split with experimental structures | 0.43 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceFoldX on Fireprot homologue-free split (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-fireprot Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 2, row 'FoldX', column 'Fireprot PCC' |
| Configuration: FoldX (Dieckhaus et al. 2024) | Protocol: Fireprot homologue-free split ddG prediction (Dieckhaus et al. 2024 Table 2) Dataset: FireProtDB homologue-free split with experimental structures | 2.77 root-mean-squared-error kilocalorie-per-mole · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceFoldX on Fireprot homologue-free split (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-fireprot Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 2, row 'FoldX', column 'Fireprot RMSE (kcal/mol)' |
| Configuration: FoldX (Dieckhaus et al. 2024) | Protocol: Fireprot homologue-free split ddG prediction (Dieckhaus et al. 2024 Table 2) Dataset: FireProtDB homologue-free split with experimental structures | 0.57 spearman-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceFoldX on Fireprot homologue-free split (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-fireprot Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 2, row 'FoldX', column 'Fireprot SCC' |
| Configuration: MAESTRO (Dieckhaus et al. 2024) | Protocol: Fireprot homologue-free split ddG prediction (Dieckhaus et al. 2024 Table 2) Dataset: FireProtDB homologue-free split with experimental structures | 0.62 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceMAESTRO on Fireprot homologue-free split (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-fireprot Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 2, row 'MAESTRO', column 'Fireprot PCC' |
| Configuration: MAESTRO (Dieckhaus et al. 2024) | Protocol: Fireprot homologue-free split ddG prediction (Dieckhaus et al. 2024 Table 2) Dataset: FireProtDB homologue-free split with experimental structures | 1.49 root-mean-squared-error kilocalorie-per-mole · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceMAESTRO on Fireprot homologue-free split (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-fireprot Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 2, row 'MAESTRO', column 'Fireprot RMSE (kcal/mol)' |
| Configuration: MAESTRO (Dieckhaus et al. 2024) | Protocol: Fireprot homologue-free split ddG prediction (Dieckhaus et al. 2024 Table 2) Dataset: FireProtDB homologue-free split with experimental structures | 0.6 spearman-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceMAESTRO on Fireprot homologue-free split (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-fireprot Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 2, row 'MAESTRO', column 'Fireprot SCC' |
| Configuration: mCSM (Dieckhaus et al. 2024) | Protocol: Fireprot homologue-free split ddG prediction (Dieckhaus et al. 2024 Table 2) Dataset: FireProtDB homologue-free split with experimental structures | 0.59 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourcemCSM on Fireprot homologue-free split (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-fireprot Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 2, row 'mCSM', column 'Fireprot PCC' |
| Configuration: mCSM (Dieckhaus et al. 2024) | Protocol: Fireprot homologue-free split ddG prediction (Dieckhaus et al. 2024 Table 2) Dataset: FireProtDB homologue-free split with experimental structures | 1.53 root-mean-squared-error kilocalorie-per-mole · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourcemCSM on Fireprot homologue-free split (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-fireprot Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 2, row 'mCSM', column 'Fireprot RMSE (kcal/mol)' |
| Configuration: mCSM (Dieckhaus et al. 2024) | Protocol: Fireprot homologue-free split ddG prediction (Dieckhaus et al. 2024 Table 2) Dataset: FireProtDB homologue-free split with experimental structures | 0.57 spearman-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourcemCSM on Fireprot homologue-free split (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-fireprot Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 2, row 'mCSM', column 'Fireprot SCC' |
| Configuration: MUPRO (Dieckhaus et al. 2024) | Protocol: Fireprot homologue-free split ddG prediction (Dieckhaus et al. 2024 Table 2) Dataset: FireProtDB homologue-free split with experimental structures | 0.58 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceMUPRO on Fireprot homologue-free split (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-fireprot Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 2, row 'MUPRO', column 'Fireprot PCC' |
| Configuration: MUPRO (Dieckhaus et al. 2024) | Protocol: Fireprot homologue-free split ddG prediction (Dieckhaus et al. 2024 Table 2) Dataset: FireProtDB homologue-free split with experimental structures | 1.54 root-mean-squared-error kilocalorie-per-mole · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceMUPRO on Fireprot homologue-free split (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-fireprot Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 2, row 'MUPRO', column 'Fireprot RMSE (kcal/mol)' |
| Configuration: MUPRO (Dieckhaus et al. 2024) | Protocol: Fireprot homologue-free split ddG prediction (Dieckhaus et al. 2024 Table 2) Dataset: FireProtDB homologue-free split with experimental structures | 0.57 spearman-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceMUPRO on Fireprot homologue-free split (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-fireprot Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 2, row 'MUPRO', column 'Fireprot SCC' |
| Configuration: PROSTATA (Dieckhaus et al. 2024) | Protocol: Fireprot homologue-free split ddG prediction (Dieckhaus et al. 2024 Table 2) Dataset: FireProtDB homologue-free split with experimental structures | 0.59 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourcePROSTATA on Fireprot homologue-free split (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-fireprot Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 2, row 'PROSTATA †', column 'Fireprot PCC' |
| Configuration: PROSTATA (Dieckhaus et al. 2024) | Protocol: Fireprot homologue-free split ddG prediction (Dieckhaus et al. 2024 Table 2) Dataset: FireProtDB homologue-free split with experimental structures | 1.68 root-mean-squared-error kilocalorie-per-mole · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourcePROSTATA on Fireprot homologue-free split (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-fireprot Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 2, row 'PROSTATA †', column 'Fireprot RMSE (kcal/mol)' |
| Configuration: PROSTATA (Dieckhaus et al. 2024) | Protocol: Fireprot homologue-free split ddG prediction (Dieckhaus et al. 2024 Table 2) Dataset: FireProtDB homologue-free split with experimental structures | 0.55 spearman-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourcePROSTATA on Fireprot homologue-free split (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-fireprot Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 2, row 'PROSTATA †', column 'Fireprot SCC' |
| Configuration: ProteinMPNN (Dieckhaus et al. 2024) | Protocol: Fireprot homologue-free split ddG prediction (Dieckhaus et al. 2024 Table 2) Dataset: FireProtDB homologue-free split with experimental structures | 0.41 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceProteinMPNN on Fireprot homologue-free split (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-fireprot Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 2, row 'ProteinMPNN', column 'Fireprot PCC' |
| Configuration: ProteinMPNN (Dieckhaus et al. 2024) | Protocol: Fireprot homologue-free split ddG prediction (Dieckhaus et al. 2024 Table 2) Dataset: FireProtDB homologue-free split with experimental structures | 2.14 root-mean-squared-error kilocalorie-per-mole · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceProteinMPNN on Fireprot homologue-free split (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-fireprot Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 2, row 'ProteinMPNN', column 'Fireprot RMSE (kcal/mol)' |
| Configuration: ProteinMPNN (Dieckhaus et al. 2024) | Protocol: Fireprot homologue-free split ddG prediction (Dieckhaus et al. 2024 Table 2) Dataset: FireProtDB homologue-free split with experimental structures | 0.49 spearman-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceProteinMPNN on Fireprot homologue-free split (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-fireprot Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 2, row 'ProteinMPNN', column 'Fireprot SCC' |
| Configuration: RaSP (Dieckhaus et al. 2024) | Protocol: Fireprot homologue-free split ddG prediction (Dieckhaus et al. 2024 Table 2) Dataset: FireProtDB homologue-free split with experimental structures | 0.47 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Independent external evaluation · Source checkedMethods, coverage and sourceRaSP on Fireprot homologue-free split (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-fireprot Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 2, row 'RaSP †', column 'Fireprot PCC' |
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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.
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6 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| attributes.denominator 2578 Context-only references | Transfer learning to leverage larger datasets for improved prediction of protein stability changes Methods 'Datasets' paragraphs 3-4; Results Version: PNAS 121(6):e2314853121, published 2024-01-29; PMC10861915 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.population 2,578 mutations in 89 proteins; across the whole Fireprot set 85 of 100 proteins have fewer than 50 measurements, and proteins with more than 250 were assigned to training Context-only references | Transfer learning to leverage larger datasets for improved prediction of protein stability changes Methods 'Datasets' paragraphs 3-4; Results Version: PNAS 121(6):e2314853121, published 2024-01-29; PMC10861915 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.source_locator Methods 'Datasets' paragraphs 3-4; Results Context-only references | Transfer learning to leverage larger datasets for improved prediction of protein stability changes Methods 'Datasets' paragraphs 3-4; Results Version: PNAS 121(6):e2314853121, published 2024-01-29; PMC10861915 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| attributes.split Homologue-free split at 25% identity to the training sets Context-only references | Transfer learning to leverage larger datasets for improved prediction of protein stability changes Methods 'Datasets' paragraphs 3-4; Results Version: PNAS 121(6):e2314853121, published 2024-01-29; PMC10861915 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| description Low-throughput biophysical ddG measurements matched to PDB structures, homologue-free split. Context-only references | Transfer learning to leverage larger datasets for improved prediction of protein stability changes Methods 'Datasets' paragraphs 3-4; Results Version: PNAS 121(6):e2314853121, published 2024-01-29; PMC10861915 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| name FireProtDB homologue-free split with experimental structures Context-only references | Transfer learning to leverage larger datasets for improved prediction of protein stability changes Methods 'Datasets' paragraphs 3-4; Results Version: PNAS 121(6):e2314853121, published 2024-01-29; PMC10861915 full-text XML | not individually reviewed No individual claim review recorded Audit detailsField: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Release 2026-10-10-6e93f504adfc · Record review: source checked
Stable ID: protein-stability-20261009-data-dieckhaus2024-fireprot-hf