Protocol

S669 direct ddG prediction (Dieckhaus et al. 2024 Table 3)

RMSE and correlation of predicted against experimental ddG on the S669 direct.

Overview

RMSE and correlation of predicted against experimental ddG on the S669 direct.

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13 recorded evaluations, 24 metric rows. A comparison chart has not yet been validated for these results. The table retains the individual findings and their sources.

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Results

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All evaluations

13 evaluations · 24 results. Different protocols are not a single leaderboard.

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Tested model or methodProtocolDatasetEvaluation setupMetricEvidence origin

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Exact evaluated configurations and original reported results
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 checked Methods, coverage and source ABYSSAL 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 checked Methods, coverage and source ACDC-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 checked Methods, coverage and source FoldX 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 checked Methods, coverage and source FoldX 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 checked Methods, coverage and source MAESTRO 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 checked Methods, coverage and source MAESTRO 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 checked Methods, coverage and source mCSM 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 checked Methods, coverage and source mCSM 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 checked Methods, coverage and source MUPRO 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 checked Methods, coverage and source MUPRO 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 checked Methods, coverage and source PROSTATA 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 checked Methods, coverage and source PROSTATA 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 checked Methods, coverage and source ProteinMPNN 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 checked Methods, coverage and source ProteinMPNN 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 checked Methods, coverage and source RaSP 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 checked Methods, coverage and source RaSP 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 checked Methods, coverage and source Rosetta 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 checked Methods, coverage and source Rosetta 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 checked Methods, coverage and source Stability 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 checked Methods, coverage and source Stability 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 checked Methods, coverage and source ThermoMPNN 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 checked Methods, coverage and source ThermoMPNN 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 checked Methods, coverage and source ThermoNet 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 checked Methods, coverage and source ThermoNet 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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Author-reported evaluations: 1
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paper compilation: 11

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Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Original source · PNAS 121(6):e2314853121, published 2024-01-29; PMC10861915 full-text XML

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Technical metadata and extraction receipts

Stable ID: protein-stability-20261009-protocol-dieckhaus2024-s669

areas: proteins-complexes
contexts: research
protocol: Predicted ddG compared with experimental ddG in kcal/mol; PCC Pearson and SCC Spearman correlation.
version: Table 3
metric: pearson-correlation
limitations: Most rows are compiled from Pancotti et al. 2022 (reference 30); the S669 values here are not an independent rerun.; ThermoMPNN was trained on a homologue-filtered Megascale set for this column (footnote *); ABYSSAL was scored on 420 filtered variants (footnote dagger).; The ACDC-NN RMSE printed here (1.60) disagrees with the source it cites: Pancotti et al. 2022 Table 1 prints 1.49 for the direct variants with the same Pearson 0.46, and 1.60 is DDGun3D's value there. That result is disputed; the citation '(30, 36)' also points at an unrelated reference 36.; No uncertainty is printed.
source locator: Table 3; Methods 'Datasets'

Related records

uses data: S669 as used in Dieckhaus et al. Table 3
assessment: ABYSSAL on S669 direct (Dieckhaus et al. 2024)
assessment: ACDC-NN on S669 direct (Dieckhaus et al. 2024)
assessment: FoldX on S669 direct (Dieckhaus et al. 2024)
assessment: MAESTRO on S669 direct (Dieckhaus et al. 2024)
assessment: mCSM on S669 direct (Dieckhaus et al. 2024)
assessment: MUPRO on S669 direct (Dieckhaus et al. 2024)
assessment: PROSTATA on S669 direct (Dieckhaus et al. 2024)
assessment: ProteinMPNN on S669 direct (Dieckhaus et al. 2024)
assessment: RaSP on S669 direct (Dieckhaus et al. 2024)
assessment: Rosetta on S669 direct (Dieckhaus et al. 2024)
assessment: Stability Oracle on S669 direct (Dieckhaus et al. 2024)
assessment: ThermoMPNN on S669 direct (Dieckhaus et al. 2024)
assessment: ThermoNet on S669 direct (Dieckhaus et al. 2024)
assessed by: Assess methods for protein stability experiments

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