Direct S669 variants; ThermoMPNN trained on a homologue-filtered Megascale set; ABYSSAL scored on 420 filtered variants.
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13 evaluations · 24 results. Different protocols are not a single leaderboard.
Applied filters: All linked evaluations
| Tested configuration | Protocol and dataset | Finding | Evidence and details |
|---|---|---|---|
| Configuration: ABYSSAL (Dieckhaus et al. 2024) | Protocol: S669 direct ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: S669 as used in Dieckhaus et al. Table 3 | 0.37 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceABYSSAL on S669 direct (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-s669 Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'ABYSSAL ( 16 )', column 'S669 PCC' |
| Configuration: ACDC-NN (Dieckhaus et al. 2024) | Protocol: S669 direct ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: S669 as used in Dieckhaus et al. Table 3 | 0.46 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceACDC-NN on S669 direct (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-s669 Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'ACDC-NN ( 30 , 36 )', column 'S669 PCC' |
| Configuration: FoldX (Dieckhaus et al. 2024) | Protocol: S669 direct ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: S669 as used in Dieckhaus et al. Table 3 | 0.22 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceFoldX on S669 direct (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-s669 Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'FoldX ( 29 , 30 )', column 'S669 PCC' |
| Configuration: FoldX (Dieckhaus et al. 2024) | Protocol: S669 direct ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: S669 as used in Dieckhaus et al. Table 3 | 2.3 root-mean-squared-error kilocalorie-per-mole · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceFoldX on S669 direct (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-s669 Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'FoldX ( 29 , 30 )', column 'S669 RMSE (kcal/mol)' |
| Configuration: MAESTRO (Dieckhaus et al. 2024) | Protocol: S669 direct ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: S669 as used in Dieckhaus et al. Table 3 | 0.5 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceMAESTRO on S669 direct (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-s669 Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'MAESTRO ( 29 , 30 )', column 'S669 PCC' |
| Configuration: MAESTRO (Dieckhaus et al. 2024) | Protocol: S669 direct ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: S669 as used in Dieckhaus et al. Table 3 | 1.44 root-mean-squared-error kilocalorie-per-mole · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceMAESTRO on S669 direct (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-s669 Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'MAESTRO ( 29 , 30 )', column 'S669 RMSE (kcal/mol)' |
| Configuration: mCSM (Dieckhaus et al. 2024) | Protocol: S669 direct ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: S669 as used in Dieckhaus et al. Table 3 | 0.36 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourcemCSM on S669 direct (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-s669 Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'mCSM ( 29 , 30 )', column 'S669 PCC' |
| Configuration: mCSM (Dieckhaus et al. 2024) | Protocol: S669 direct ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: S669 as used in Dieckhaus et al. Table 3 | 1.54 root-mean-squared-error kilocalorie-per-mole · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourcemCSM on S669 direct (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-s669 Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'mCSM ( 29 , 30 )', column 'S669 RMSE (kcal/mol)' |
| Configuration: MUPRO (Dieckhaus et al. 2024) | Protocol: S669 direct ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: S669 as used in Dieckhaus et al. Table 3 | 0.25 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceMUPRO on S669 direct (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-s669 Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'MUPRO ( 29 , 30 )', column 'S669 PCC' |
| Configuration: MUPRO (Dieckhaus et al. 2024) | Protocol: S669 direct ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: S669 as used in Dieckhaus et al. Table 3 | 1.61 root-mean-squared-error kilocalorie-per-mole · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceMUPRO on S669 direct (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-s669 Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'MUPRO ( 29 , 30 )', column 'S669 RMSE (kcal/mol)' |
| Configuration: PROSTATA (Dieckhaus et al. 2024) | Protocol: S669 direct ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: S669 as used in Dieckhaus et al. Table 3 | 0.48 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourcePROSTATA on S669 direct (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-s669 Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'PROSTATA ( 17 )', column 'S669 PCC' |
| Configuration: PROSTATA (Dieckhaus et al. 2024) | Protocol: S669 direct ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: S669 as used in Dieckhaus et al. Table 3 | 1.44 root-mean-squared-error kilocalorie-per-mole · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourcePROSTATA on S669 direct (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-s669 Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'PROSTATA ( 17 )', column 'S669 RMSE (kcal/mol)' |
| Configuration: ProteinMPNN (Dieckhaus et al. 2024) | Protocol: S669 direct ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: S669 as used in Dieckhaus et al. Table 3 | 0.26 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 S669 direct (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-s669 Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'ProteinMPNN', column 'S669 PCC' |
| Configuration: ProteinMPNN (Dieckhaus et al. 2024) | Protocol: S669 direct ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: S669 as used in Dieckhaus et al. Table 3 | 3.32 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 S669 direct (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-s669 Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'ProteinMPNN', column 'S669 RMSE (kcal/mol)' |
| Configuration: RaSP (Dieckhaus et al. 2024) | Protocol: S669 direct ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: S669 as used in Dieckhaus et al. Table 3 | 0.39 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceRaSP on S669 direct (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-s669 Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'RaSP ( 12 )', column 'S669 PCC' |
| Configuration: RaSP (Dieckhaus et al. 2024) | Protocol: S669 direct ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: S669 as used in Dieckhaus et al. Table 3 | 1.63 root-mean-squared-error kilocalorie-per-mole · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceRaSP on S669 direct (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-s669 Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'RaSP ( 12 )', column 'S669 RMSE (kcal/mol)' |
| Configuration: Rosetta (Dieckhaus et al. 2024) | Protocol: S669 direct ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: S669 as used in Dieckhaus et al. Table 3 | 0.39 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceRosetta on S669 direct (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-s669 Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'Rosetta ( 29 , 30 )', column 'S669 PCC' |
| Configuration: Rosetta (Dieckhaus et al. 2024) | Protocol: S669 direct ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: S669 as used in Dieckhaus et al. Table 3 | 2.7 root-mean-squared-error kilocalorie-per-mole · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceRosetta on S669 direct (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-s669 Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'Rosetta ( 29 , 30 )', column 'S669 RMSE (kcal/mol)' |
| Configuration: Stability Oracle (Dieckhaus et al. 2024) | Protocol: S669 direct ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: S669 as used in Dieckhaus et al. Table 3 | 0.52 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceStability Oracle on S669 direct (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-s669 Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'Stability Oracle ( 28 )', column 'S669 PCC' |
| Configuration: Stability Oracle (Dieckhaus et al. 2024) | Protocol: S669 direct ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: S669 as used in Dieckhaus et al. Table 3 | 1.43 root-mean-squared-error kilocalorie-per-mole · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceStability Oracle on S669 direct (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-s669 Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'Stability Oracle ( 28 )', column 'S669 RMSE (kcal/mol)' |
| Configuration: ThermoMPNN (Dieckhaus et al. 2024) | Protocol: S669 direct ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: S669 as used in Dieckhaus et al. Table 3 | 0.43 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceThermoMPNN on S669 direct (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-s669 Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'ThermoMPNN', column 'S669 PCC' |
| Configuration: ThermoMPNN (Dieckhaus et al. 2024) | Protocol: S669 direct ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: S669 as used in Dieckhaus et al. Table 3 | 1.52 root-mean-squared-error kilocalorie-per-mole · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceThermoMPNN on S669 direct (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-s669 Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'ThermoMPNN', column 'S669 RMSE (kcal/mol)' |
| Configuration: ThermoNet (Dieckhaus et al. 2024) | Protocol: S669 direct ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: S669 as used in Dieckhaus et al. Table 3 | 0.39 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceThermoNet on S669 direct (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-s669 Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'ThermoNet ( 11 , 30 )', column 'S669 PCC' |
| Configuration: ThermoNet (Dieckhaus et al. 2024) | Protocol: S669 direct ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: S669 as used in Dieckhaus et al. Table 3 | 1.62 root-mean-squared-error kilocalorie-per-mole · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Result quoted from another source · Source checkedMethods, coverage and sourceThermoNet on S669 direct (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-s669 Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'ThermoNet ( 11 , 30 )', column 'S669 RMSE (kcal/mol)' |
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5 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| attributes.population 669 direct variants (420 for ABYSSAL) Context-only references | Transfer learning to leverage larger datasets for improved prediction of protein stability changes Table 3 and footnotes 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 Table 3 and footnotes Context-only references | Transfer learning to leverage larger datasets for improved prediction of protein stability changes Table 3 and footnotes 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 External test set Context-only references | Transfer learning to leverage larger datasets for improved prediction of protein stability changes Table 3 and footnotes 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 Direct S669 variants; ThermoMPNN trained on a homologue-filtered Megascale set; ABYSSAL scored on 420 filtered variants. Context-only references | Transfer learning to leverage larger datasets for improved prediction of protein stability changes Table 3 and footnotes 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 S669 as used in Dieckhaus et al. Table 3 Context-only references | Transfer learning to leverage larger datasets for improved prediction of protein stability changes Table 3 and footnotes 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-s669