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Rosetta (Dieckhaus et al. 2024)

Rosetta as evaluated in the cited comparison.

Overview

Rosetta as evaluated in the cited comparison.

Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.

Evaluations and results

4 evaluations · 12 results. Different protocols are not a single leaderboard.

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Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: Rosetta (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	0.45 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 Rosetta 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 'Rosetta', column 'Fireprot PCC'
Configuration: Rosetta (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	4.19 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 Rosetta 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 'Rosetta', column 'Fireprot RMSE (kcal/mol)'
Configuration: Rosetta (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	0.52 spearman-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 Rosetta 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 'Rosetta', column 'Fireprot SCC'
Configuration: Rosetta (Dieckhaus et al. 2024)	Protocol: Megascale held-out test split ddG prediction (Dieckhaus et al. 2024 Table 2) Dataset: Megascale (Tsuboyama 2023) test split, homology-clustered at 25% identity	0.53 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 Rosetta on Megascale held-out test split (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-megascale Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 2, row 'Rosetta', column 'Megascale PCC'
Configuration: Rosetta (Dieckhaus et al. 2024)	Protocol: Megascale held-out test split ddG prediction (Dieckhaus et al. 2024 Table 2) Dataset: Megascale (Tsuboyama 2023) test split, homology-clustered at 25% identity	5.18 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 Rosetta on Megascale held-out test split (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-megascale Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 2, row 'Rosetta', column 'Megascale RMSE (kcal/mol)'
Configuration: Rosetta (Dieckhaus et al. 2024)	Protocol: Megascale held-out test split ddG prediction (Dieckhaus et al. 2024 Table 2) Dataset: Megascale (Tsuboyama 2023) test split, homology-clustered at 25% identity	0.56 spearman-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 Rosetta on Megascale held-out test split (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-megascale Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 2, row 'Rosetta', column 'Megascale SCC'
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: Rosetta (Dieckhaus et al. 2024)	Protocol: Ssym direct and inverse ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: Ssym (342 direct and 342 inverse variants with experimental structures)	0.69 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 Ssym direct and inverse (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-ssym Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'Rosetta ( 29 , 30 )', column 'Ssym (direct) PCC'
Configuration: Rosetta (Dieckhaus et al. 2024)	Protocol: Ssym direct and inverse ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: Ssym (342 direct and 342 inverse variants with experimental structures)	2.31 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 Ssym direct and inverse (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-ssym Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'Rosetta ( 29 , 30 )', column 'Ssym (direct) RMSE (kcal/mol)'
Configuration: Rosetta (Dieckhaus et al. 2024)	Protocol: Ssym direct and inverse ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: Ssym (342 direct and 342 inverse variants with experimental structures)	0.43 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 Ssym direct and inverse (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-ssym Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'Rosetta ( 29 , 30 )', column 'Ssym (inverse) PCC'
Configuration: Rosetta (Dieckhaus et al. 2024)	Protocol: Ssym direct and inverse ddG prediction (Dieckhaus et al. 2024 Table 3) Dataset: Ssym (342 direct and 342 inverse variants with experimental structures)	2.61 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 Rosetta on Ssym direct and inverse (Dieckhaus et al. 2024) protein-stability-20261009-protocol-dieckhaus2024-ssym Aggregation: Not reported Transfer learning to leverage larger datasets for improved prediction of protein stability changes · Table 3, row 'Rosetta ( 29 , 30 )', column 'Ssym (inverse) RMSE (kcal/mol)'

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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-config-dieckhaus2024-rosetta

areas: proteins-complexes
contexts: research
method types: conventional_pipeline
reported name: Rosetta
foundation model eligible: false
source locator: Tables 2-3; Results 'Comparison with literature methods'
protocol: Published model or software with accessible code, run by the authors (Table 2) or values taken from the cited literature (Table 3)
missing metadata: version: reason: unreported

Related records

configuration of: Rosetta
system: Rosetta on Fireprot homologue-free split (Dieckhaus et al. 2024)
system: Rosetta on Megascale held-out test split (Dieckhaus et al. 2024)
system: Rosetta on S669 direct (Dieckhaus et al. 2024)
system: Rosetta on Ssym direct and inverse (Dieckhaus et al. 2024)

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