| Configuration: ACDC-NN (Pancotti et al. 2022) | Protocol: S669 direct and reverse ddG prediction (Pancotti et al. 2022 Table 1)
Dataset: S669: 669 single-point variants from ThermoMutDB in proteins under 25% identity to S2648 and VariBench, with reverse variants | –0.98 pearson-correlation unitless · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceACDC-NN on S669 (Pancotti et al. 2022) protein-stability-20261009-protocol-pancotti2022-s669 Aggregation: Not reported Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset · Table 1, row 'ACDC-NN', column 'Antisimmetry r(d-r)' |
|---|
| Configuration: ACDC-NN (Pancotti et al. 2022) | Protocol: S669 direct and reverse ddG prediction (Pancotti et al. 2022 Table 1)
Dataset: S669: 669 single-point variants from ThermoMutDB in proteins under 25% identity to S2648 and VariBench, with reverse variants | –0.02 antisymmetry-bias kilocalorie-per-mole · unknown Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceACDC-NN on S669 (Pancotti et al. 2022) protein-stability-20261009-protocol-pancotti2022-s669 Aggregation: Not reported Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset · Table 1, row 'ACDC-NN', column 'Bias' |
|---|
| Configuration: ACDC-NN (Pancotti et al. 2022) | Protocol: S669 direct and reverse ddG prediction (Pancotti et al. 2022 Table 1)
Dataset: S669: 669 single-point variants from ThermoMutDB in proteins under 25% identity to S2648 and VariBench, with reverse variants | 1.05 mean-absolute-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 sourceACDC-NN on S669 (Pancotti et al. 2022) protein-stability-20261009-protocol-pancotti2022-s669 Aggregation: Not reported Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset · Table 1, row 'ACDC-NN', column 'Direct MAE' |
|---|
| Configuration: ACDC-NN (Pancotti et al. 2022) | Protocol: S669 direct and reverse ddG prediction (Pancotti et al. 2022 Table 1)
Dataset: S669: 669 single-point variants from ThermoMutDB in proteins under 25% identity to S2648 and VariBench, with reverse variants | 0.46 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceACDC-NN on S669 (Pancotti et al. 2022) protein-stability-20261009-protocol-pancotti2022-s669 Aggregation: Not reported Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset · Table 1, row 'ACDC-NN', column 'Direct r' |
|---|
| Configuration: ACDC-NN (Pancotti et al. 2022) | Protocol: S669 direct and reverse ddG prediction (Pancotti et al. 2022 Table 1)
Dataset: S669: 669 single-point variants from ThermoMutDB in proteins under 25% identity to S2648 and VariBench, with reverse variants | 1.49 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 sourceACDC-NN on S669 (Pancotti et al. 2022) protein-stability-20261009-protocol-pancotti2022-s669 Aggregation: Not reported Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset · Table 1, row 'ACDC-NN', column 'Direct RMSE' |
|---|
| Configuration: ACDC-NN (Pancotti et al. 2022) | Protocol: S669 direct and reverse ddG prediction (Pancotti et al. 2022 Table 1)
Dataset: S669: 669 single-point variants from ThermoMutDB in proteins under 25% identity to S2648 and VariBench, with reverse variants | 1.06 mean-absolute-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 sourceACDC-NN on S669 (Pancotti et al. 2022) protein-stability-20261009-protocol-pancotti2022-s669 Aggregation: Not reported Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset · Table 1, row 'ACDC-NN', column 'Reverse MAE' |
|---|
| Configuration: ACDC-NN (Pancotti et al. 2022) | Protocol: S669 direct and reverse ddG prediction (Pancotti et al. 2022 Table 1)
Dataset: S669: 669 single-point variants from ThermoMutDB in proteins under 25% identity to S2648 and VariBench, with reverse variants | 0.45 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceACDC-NN on S669 (Pancotti et al. 2022) protein-stability-20261009-protocol-pancotti2022-s669 Aggregation: Not reported Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset · Table 1, row 'ACDC-NN', column 'Reverse r' |
|---|
| Configuration: ACDC-NN (Pancotti et al. 2022) | Protocol: S669 direct and reverse ddG prediction (Pancotti et al. 2022 Table 1)
Dataset: S669: 669 single-point variants from ThermoMutDB in proteins under 25% identity to S2648 and VariBench, with reverse variants | 1.5 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 sourceACDC-NN on S669 (Pancotti et al. 2022) protein-stability-20261009-protocol-pancotti2022-s669 Aggregation: Not reported Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset · Table 1, row 'ACDC-NN', column 'Reverse RMSE' |
|---|
| Configuration: ACDC-NN (Pancotti et al. 2022) | Protocol: S669 direct and reverse ddG prediction (Pancotti et al. 2022 Table 1)
Dataset: S669: 669 single-point variants from ThermoMutDB in proteins under 25% identity to S2648 and VariBench, with reverse variants | 1.05 mean-absolute-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 sourceACDC-NN on S669 (Pancotti et al. 2022) protein-stability-20261009-protocol-pancotti2022-s669 Aggregation: Not reported Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset · Table 1, row 'ACDC-NN', column 'Total MAE' |
|---|
| Configuration: ACDC-NN (Pancotti et al. 2022) | Protocol: S669 direct and reverse ddG prediction (Pancotti et al. 2022 Table 1)
Dataset: S669: 669 single-point variants from ThermoMutDB in proteins under 25% identity to S2648 and VariBench, with reverse variants | 0.61 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceACDC-NN on S669 (Pancotti et al. 2022) protein-stability-20261009-protocol-pancotti2022-s669 Aggregation: Not reported Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset · Table 1, row 'ACDC-NN', column 'Total r' |
|---|
| Configuration: ACDC-NN (Pancotti et al. 2022) | Protocol: S669 direct and reverse ddG prediction (Pancotti et al. 2022 Table 1)
Dataset: S669: 669 single-point variants from ThermoMutDB in proteins under 25% identity to S2648 and VariBench, with reverse variants | 1.5 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 sourceACDC-NN on S669 (Pancotti et al. 2022) protein-stability-20261009-protocol-pancotti2022-s669 Aggregation: Not reported Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset · Table 1, row 'ACDC-NN', column 'Total RMSE' |
|---|
| Configuration: ACDC-NN-Seq (Pancotti et al. 2022) | Protocol: S669 direct and reverse ddG prediction (Pancotti et al. 2022 Table 1)
Dataset: S669: 669 single-point variants from ThermoMutDB in proteins under 25% identity to S2648 and VariBench, with reverse variants | –1 pearson-correlation unitless · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceACDC-NN-Seq on S669 (Pancotti et al. 2022) protein-stability-20261009-protocol-pancotti2022-s669 Aggregation: Not reported Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset · Table 1, row 'ACDC-NN-Seq', column 'Antisimmetry r(d-r)' |
|---|
| Configuration: ACDC-NN-Seq (Pancotti et al. 2022) | Protocol: S669 direct and reverse ddG prediction (Pancotti et al. 2022 Table 1)
Dataset: S669: 669 single-point variants from ThermoMutDB in proteins under 25% identity to S2648 and VariBench, with reverse variants | 0 antisymmetry-bias kilocalorie-per-mole · unknown Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceACDC-NN-Seq on S669 (Pancotti et al. 2022) protein-stability-20261009-protocol-pancotti2022-s669 Aggregation: Not reported Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset · Table 1, row 'ACDC-NN-Seq', column 'Bias' |
|---|
| Configuration: ACDC-NN-Seq (Pancotti et al. 2022) | Protocol: S669 direct and reverse ddG prediction (Pancotti et al. 2022 Table 1)
Dataset: S669: 669 single-point variants from ThermoMutDB in proteins under 25% identity to S2648 and VariBench, with reverse variants | 1.08 mean-absolute-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 sourceACDC-NN-Seq on S669 (Pancotti et al. 2022) protein-stability-20261009-protocol-pancotti2022-s669 Aggregation: Not reported Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset · Table 1, row 'ACDC-NN-Seq', column 'Direct MAE' |
|---|
| Configuration: ACDC-NN-Seq (Pancotti et al. 2022) | Protocol: S669 direct and reverse ddG prediction (Pancotti et al. 2022 Table 1)
Dataset: S669: 669 single-point variants from ThermoMutDB in proteins under 25% identity to S2648 and VariBench, with reverse variants | 0.42 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceACDC-NN-Seq on S669 (Pancotti et al. 2022) protein-stability-20261009-protocol-pancotti2022-s669 Aggregation: Not reported Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset · Table 1, row 'ACDC-NN-Seq', column 'Direct r' |
|---|
| Configuration: ACDC-NN-Seq (Pancotti et al. 2022) | Protocol: S669 direct and reverse ddG prediction (Pancotti et al. 2022 Table 1)
Dataset: S669: 669 single-point variants from ThermoMutDB in proteins under 25% identity to S2648 and VariBench, with reverse variants | 1.53 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 sourceACDC-NN-Seq on S669 (Pancotti et al. 2022) protein-stability-20261009-protocol-pancotti2022-s669 Aggregation: Not reported Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset · Table 1, row 'ACDC-NN-Seq', column 'Direct RMSE' |
|---|
| Configuration: ACDC-NN-Seq (Pancotti et al. 2022) | Protocol: S669 direct and reverse ddG prediction (Pancotti et al. 2022 Table 1)
Dataset: S669: 669 single-point variants from ThermoMutDB in proteins under 25% identity to S2648 and VariBench, with reverse variants | 1.08 mean-absolute-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 sourceACDC-NN-Seq on S669 (Pancotti et al. 2022) protein-stability-20261009-protocol-pancotti2022-s669 Aggregation: Not reported Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset · Table 1, row 'ACDC-NN-Seq', column 'Reverse MAE' |
|---|
| Configuration: ACDC-NN-Seq (Pancotti et al. 2022) | Protocol: S669 direct and reverse ddG prediction (Pancotti et al. 2022 Table 1)
Dataset: S669: 669 single-point variants from ThermoMutDB in proteins under 25% identity to S2648 and VariBench, with reverse variants | 0.42 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceACDC-NN-Seq on S669 (Pancotti et al. 2022) protein-stability-20261009-protocol-pancotti2022-s669 Aggregation: Not reported Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset · Table 1, row 'ACDC-NN-Seq', column 'Reverse r' |
|---|
| Configuration: ACDC-NN-Seq (Pancotti et al. 2022) | Protocol: S669 direct and reverse ddG prediction (Pancotti et al. 2022 Table 1)
Dataset: S669: 669 single-point variants from ThermoMutDB in proteins under 25% identity to S2648 and VariBench, with reverse variants | 1.53 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 sourceACDC-NN-Seq on S669 (Pancotti et al. 2022) protein-stability-20261009-protocol-pancotti2022-s669 Aggregation: Not reported Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset · Table 1, row 'ACDC-NN-Seq', column 'Reverse RMSE' |
|---|
| Configuration: ACDC-NN-Seq (Pancotti et al. 2022) | Protocol: S669 direct and reverse ddG prediction (Pancotti et al. 2022 Table 1)
Dataset: S669: 669 single-point variants from ThermoMutDB in proteins under 25% identity to S2648 and VariBench, with reverse variants | 1.08 mean-absolute-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 sourceACDC-NN-Seq on S669 (Pancotti et al. 2022) protein-stability-20261009-protocol-pancotti2022-s669 Aggregation: Not reported Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset · Table 1, row 'ACDC-NN-Seq', column 'Total MAE' |
|---|
| Configuration: ACDC-NN-Seq (Pancotti et al. 2022) | Protocol: S669 direct and reverse ddG prediction (Pancotti et al. 2022 Table 1)
Dataset: S669: 669 single-point variants from ThermoMutDB in proteins under 25% identity to S2648 and VariBench, with reverse variants | 0.59 pearson-correlation unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceACDC-NN-Seq on S669 (Pancotti et al. 2022) protein-stability-20261009-protocol-pancotti2022-s669 Aggregation: Not reported Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset · Table 1, row 'ACDC-NN-Seq', column 'Total r' |
|---|
| Configuration: ACDC-NN-Seq (Pancotti et al. 2022) | Protocol: S669 direct and reverse ddG prediction (Pancotti et al. 2022 Table 1)
Dataset: S669: 669 single-point variants from ThermoMutDB in proteins under 25% identity to S2648 and VariBench, with reverse variants | 1.53 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 sourceACDC-NN-Seq on S669 (Pancotti et al. 2022) protein-stability-20261009-protocol-pancotti2022-s669 Aggregation: Not reported Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset · Table 1, row 'ACDC-NN-Seq', column 'Total RMSE' |
|---|
| Configuration: DDGun (Pancotti et al. 2022) | Protocol: S669 direct and reverse ddG prediction (Pancotti et al. 2022 Table 1)
Dataset: S669: 669 single-point variants from ThermoMutDB in proteins under 25% identity to S2648 and VariBench, with reverse variants | –0.96 pearson-correlation unitless · lower Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceDDGun on S669 (Pancotti et al. 2022) protein-stability-20261009-protocol-pancotti2022-s669 Aggregation: Not reported Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset · Table 1, row 'DDGun', column 'Antisimmetry r(d-r)' |
|---|
| Configuration: DDGun (Pancotti et al. 2022) | Protocol: S669 direct and reverse ddG prediction (Pancotti et al. 2022 Table 1)
Dataset: S669: 669 single-point variants from ThermoMutDB in proteins under 25% identity to S2648 and VariBench, with reverse variants | –0.05 antisymmetry-bias kilocalorie-per-mole · unknown Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceDDGun on S669 (Pancotti et al. 2022) protein-stability-20261009-protocol-pancotti2022-s669 Aggregation: Not reported Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset · Table 1, row 'DDGun', column 'Bias' |
|---|
| Configuration: DDGun (Pancotti et al. 2022) | Protocol: S669 direct and reverse ddG prediction (Pancotti et al. 2022 Table 1)
Dataset: S669: 669 single-point variants from ThermoMutDB in proteins under 25% identity to S2648 and VariBench, with reverse variants | 1.25 mean-absolute-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 sourceDDGun on S669 (Pancotti et al. 2022) protein-stability-20261009-protocol-pancotti2022-s669 Aggregation: Not reported Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset · Table 1, row 'DDGun', column 'Direct MAE' |
|---|