metadata
license: cc-by-4.0
pretty_name: >-
Mega-scale experimental analysis of protein folding stability in biology and
design
tags:
- biology
- chemistry
repo: https://github.com/Rocklin-Lab/cdna-display-proteolysis-pipeline
citation_bibtex: >-
@article{Tsuboyama2023, title = {Mega-scale experimental analysis of protein
folding stability in biology and design}, volume = {620}, ISSN = {1476-4687},
url = {http://dx.doi.org/10.1038/s41586-023-06328-6}, DOI =
{10.1038/s41586-023-06328-6}, number = {7973}, journal = {Nature}, publisher =
{Springer Science and Business Media LLC}, author = {Tsuboyama, Kotaro and
Dauparas, Justas and Chen, Jonathan and Laine, Elodie and Mohseni
Behbahani, Yasser and Weinstein, Jonathan J. and Mangan, Niall M. and
Ovchinnikov, Sergey and Rocklin, Gabriel J.}, year = {2023}, month = jul,
pages = {434–444} }
citation_apa: >-
Tsuboyama, K., Dauparas, J., Chen, J. et al. Mega-scale experimental analysis
of protein folding stability in biology and design. Nature 620, 434–444
(2023). https://doi.org/10.1038/s41586-023-06328-6
dataset_info:
- config_name: AlphaFold_model_PDBs
features:
- name: name
dtype: string
- name: pdb
dtype: string
splits:
- name: train
num_bytes: 59951444
num_examples: 862
download_size: 22129369
dataset_size: 59951444
- config_name: dataset1
features:
- name: name
dtype: string
- name: dna_seq
dtype: string
- name: log10_K50_t
dtype: float64
- name: log10_K50_t_95CI_high
dtype: float64
- name: log10_K50_t_95CI_low
dtype: float64
- name: log10_K50_t_95CI
dtype: float64
- name: fitting_error_t
dtype: float64
- name: log10_K50unfolded_t
dtype: float64
- name: deltaG_t
dtype: float64
- name: deltaG_t_95CI_high
dtype: float64
- name: deltaG_t_95CI_low
dtype: float64
- name: deltaG_t_95CI
dtype: float64
- name: log10_K50_c
dtype: float64
- name: log10_K50_c_95CI_high
dtype: float64
- name: log10_K50_c_95CI_low
dtype: float64
- name: log10_K50_c_95CI
dtype: float64
- name: fitting_error_c
dtype: float64
- name: log10_K50unfolded_c
dtype: float64
- name: deltaG_c
dtype: float64
- name: deltaG_c_95CI_high
dtype: float64
- name: deltaG_c_95CI_low
dtype: float64
- name: deltaG_c_95CI
dtype: float64
- name: deltaG
dtype: float64
- name: deltaG_95CI_high
dtype: float64
- name: deltaG_95CI_low
dtype: float64
- name: deltaG_95CI
dtype: float64
- name: log10_K50_trypsin_ML
dtype: float64
- name: log10_K50_chymotrypsin_ML
dtype: float64
splits:
- name: train
num_bytes: 821805209
num_examples: 1841285
download_size: 562388001
dataset_size: 821805209
- config_name: dataset2
features:
- name: name
dtype: string
- name: dna_seq
dtype: string
- name: log10_K50_t
dtype: float64
- name: log10_K50_t_95CI_high
dtype: float64
- name: log10_K50_t_95CI_low
dtype: float64
- name: log10_K50_t_95CI
dtype: float64
- name: fitting_error_t
dtype: float64
- name: log10_K50unfolded_t
dtype: float64
- name: deltaG_t
dtype: float64
- name: deltaG_t_95CI_high
dtype: float64
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dtype: float64
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dtype: float64
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dtype: float64
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dtype: float64
- name: log10_K50_c_95CI_low
dtype: float64
- name: log10_K50_c_95CI
dtype: float64
- name: fitting_error_c
dtype: float64
- name: log10_K50unfolded_c
dtype: float64
- name: deltaG_c
dtype: float64
- name: deltaG_c_95CI_high
dtype: float64
- name: deltaG_c_95CI_low
dtype: float64
- name: deltaG_c_95CI
dtype: float64
- name: deltaG
dtype: float64
- name: deltaG_95CI_high
dtype: float64
- name: deltaG_95CI_low
dtype: float64
- name: deltaG_95CI
dtype: float64
- name: aa_seq_full
dtype: string
- name: aa_seq
dtype: string
- name: mut_type
dtype: string
- name: WT_name
dtype: string
- name: WT_cluster
dtype: string
- name: log10_K50_trypsin_ML
dtype: string
- name: log10_K50_chymotrypsin_ML
dtype: string
- name: dG_ML
dtype: string
- name: ddG_ML
dtype: string
- name: Stabilizing_mut
dtype: string
- name: pair_name
dtype: string
splits:
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dtype: string
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dtype: float64
- name: log10_K50_t_95CI_high
dtype: float64
- name: log10_K50_t_95CI_low
dtype: float64
- name: log10_K50_t_95CI
dtype: float64
- name: fitting_error_t
dtype: float64
- name: log10_K50unfolded_t
dtype: float64
- name: deltaG_t
dtype: float64
- name: deltaG_t_95CI_high
dtype: float64
- name: deltaG_t_95CI_low
dtype: float64
- name: deltaG_t_95CI
dtype: float64
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dtype: float64
- name: log10_K50_c_95CI_high
dtype: float64
- name: log10_K50_c_95CI_low
dtype: float64
- name: log10_K50_c_95CI
dtype: float64
- name: fitting_error_c
dtype: float64
- name: log10_K50unfolded_c
dtype: float64
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dtype: float64
- name: deltaG_c_95CI_high
dtype: float64
- name: deltaG_c_95CI_low
dtype: float64
- name: deltaG_c_95CI
dtype: float64
- name: deltaG
dtype: float64
- name: deltaG_95CI_high
dtype: float64
- name: deltaG_95CI_low
dtype: float64
- name: deltaG_95CI
dtype: float64
- name: aa_seq_full
dtype: string
- name: aa_seq
dtype: string
- name: mut_type
dtype: string
- name: WT_name
dtype: string
- name: WT_cluster
dtype: string
- name: log10_K50_trypsin_ML
dtype: string
- name: log10_K50_chymotrypsin_ML
dtype: string
- name: dG_ML
dtype: string
- name: ddG_ML
dtype: string
- name: Stabilizing_mut
dtype: string
- name: pair_name
dtype: string
splits:
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download_size: 233585731
dataset_size: 426187043
- config_name: dataset3_single
features:
- name: name
dtype: string
- name: dna_seq
dtype: string
- name: log10_K50_t
dtype: float64
- name: log10_K50_t_95CI_high
dtype: float64
- name: log10_K50_t_95CI_low
dtype: float64
- name: log10_K50_t_95CI
dtype: float64
- name: fitting_error_t
dtype: float64
- name: log10_K50unfolded_t
dtype: float64
- name: deltaG_t
dtype: float64
- name: deltaG_t_95CI_high
dtype: float64
- name: deltaG_t_95CI_low
dtype: float64
- name: deltaG_t_95CI
dtype: float64
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dtype: float64
- name: log10_K50_c_95CI_high
dtype: float64
- name: log10_K50_c_95CI_low
dtype: float64
- name: log10_K50_c_95CI
dtype: float64
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dtype: float64
- name: log10_K50unfolded_c
dtype: float64
- name: deltaG_c
dtype: float64
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dtype: float64
- name: deltaG_c_95CI_low
dtype: float64
- name: deltaG_c_95CI
dtype: float64
- name: deltaG
dtype: float64
- name: deltaG_95CI_high
dtype: float64
- name: deltaG_95CI_low
dtype: float64
- name: deltaG_95CI
dtype: float64
- name: aa_seq_full
dtype: string
- name: aa_seq
dtype: string
- name: mut_type
dtype: string
- name: WT_name
dtype: string
- name: WT_cluster
dtype: string
- name: log10_K50_trypsin_ML
dtype: string
- name: log10_K50_chymotrypsin_ML
dtype: string
- name: dG_ML
dtype: string
- name: ddG_ML
dtype: string
- name: Stabilizing_mut
dtype: string
- name: pair_name
dtype: string
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dtype: string
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- name: test
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download_size: 151448982
dataset_size: 1244546799
- config_name: dataset3_single_cv
features:
- name: name
dtype: string
- name: dna_seq
dtype: string
- name: log10_K50_t
dtype: float64
- name: log10_K50_t_95CI_high
dtype: float64
- name: log10_K50_t_95CI_low
dtype: float64
- name: log10_K50_t_95CI
dtype: float64
- name: fitting_error_t
dtype: float64
- name: log10_K50unfolded_t
dtype: float64
- name: deltaG_t
dtype: float64
- name: deltaG_t_95CI_high
dtype: float64
- name: deltaG_t_95CI_low
dtype: float64
- name: deltaG_t_95CI
dtype: float64
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dtype: float64
- name: log10_K50_c_95CI_high
dtype: float64
- name: log10_K50_c_95CI_low
dtype: float64
- name: log10_K50_c_95CI
dtype: float64
- name: fitting_error_c
dtype: float64
- name: log10_K50unfolded_c
dtype: float64
- name: deltaG_c
dtype: float64
- name: deltaG_c_95CI_high
dtype: float64
- name: deltaG_c_95CI_low
dtype: float64
- name: deltaG_c_95CI
dtype: float64
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dtype: float64
- name: deltaG_95CI_high
dtype: float64
- name: deltaG_95CI_low
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- name: deltaG_95CI
dtype: float64
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- name: aa_seq
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- name: mut_type
dtype: string
- name: WT_name
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- name: WT_cluster
dtype: string
- name: log10_K50_trypsin_ML
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- name: log10_K50_chymotrypsin_ML
dtype: float64
- name: dG_ML
dtype: float64
- name: ddG_ML
dtype: float64
- name: Stabilizing_mut
dtype: string
- name: pair_name
dtype: string
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- name: test_4
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download_size: 467205297
dataset_size: 825013458
configs:
- config_name: AlphaFold_model_PDBs
data_files:
- split: train
path: AlphaFold_model_PDBs/data/train-*
- config_name: dataset1
data_files:
- split: train
path: dataset1/data/train-*
- config_name: dataset2
data_files:
- split: train
path: dataset2/data/train-*
- config_name: dataset3
data_files:
- split: train
path: dataset3/data/train-*
- config_name: dataset3_single
data_files:
- split: train
path: dataset3_single/data/train-*
- split: val
path: dataset3_single/data/val-*
- split: test
path: dataset3_single/data/test-*
- config_name: dataset3_single_cv
data_files:
- split: train_0
path: datase3_single_cv/data/train_0-*
- split: train_1
path: datase3_single_cv/data/train_1-*
- split: train_2
path: datase3_single_cv/data/train_2-*
- split: train_3
path: datase3_single_cv/data/train_3-*
- split: train_4
path: datase3_single_cv/data/train_4-*
- split: val_0
path: datase3_single_cv/data/val_0-*
- split: val_1
path: datase3_single_cv/data/val_1-*
- split: val_2
path: datase3_single_cv/data/val_2-*
- split: val_3
path: datase3_single_cv/data/val_3-*
- split: val_4
path: datase3_single_cv/data/val_4-*
- split: test_0
path: datase3_single_cv/data/test_0-*
- split: test_1
path: datase3_single_cv/data/test_1-*
- split: test_2
path: datase3_single_cv/data/test_2-*
- split: test_3
path: datase3_single_cv/data/test_3-*
- split: test_4
path: datase3_single_cv/data/test_4-*
Mega-scale experimental analysis of protein folding stability in biology and design
The full MegaScale dataset contains 1,841,285 thermodynamic folding stability measurements
using cDNA display proteolysis of natural and designed proteins. From these 776,298 high-quality folding
stabilities (dataset2) cover all single amino acid variants and selected double mutants of 331 natural
and 148 de novo designed protein domains 40–72 amino acids in length. Of these mutations, 607,839 have
the wild-type ΔG is below 4.75 kcal mol^−1 (dataset3) allowing for the estimate of the ΔΔG of mutation.
Of these
*** IMPORTANT! Please register your use of these data so that we (the Rocklin Lab) can continue to release new useful datasets!! This will take 10 seconds!! ***
Quickstart Usage
Install HuggingFace Datasets package
Each subset can be loaded into python using the Huggingface datasets library.
First, from the command line install the datasets library
$ pip install datasets
Optionally set the cache directory, e.g.
$ HF_HOME=${HOME}/.cache/huggingface/
$ export HF_HOME
then, from within python load the datasets library
>>> import datasets
Load model datasets
To load one of the MegaScale model datasets (see available datasets below), use datasets.load_dataset(...):
>>> dataset_tag = "dataset3_single"
>>> dataset3_single = datasets.load_dataset(
path = "RosettaCommons/MegaScale",
name = dataset_tag,
data_dir = dataset_tag)
Downloading readme: 100%|██████████████████████████████| 17.0k/17.0k [00:00<00:00, 290kB/s]
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Downloading data: 100%|███████████████████████████████| 40.0M/40.0M [00:00<00:00, 43.9MB/s]
Downloading data: 100%|███████████████████████████████| 15.5M/15.5M [00:00<00:00, 26.8MB/s]
Downloading data: 100%|███████████████████████████████| 14.9M/14.9M [00:00<00:00, 29.4MB/s]
Generating train split: 100%|█████████| 1503063/1503063 [00:05<00:00, 262031.56 examples/s]
Generating test split: 100%|████████████| 169032/169032 [00:00<00:00, 264056.98 examples/s]
Generating val split: 100%|█████████████| 163968/163968 [00:00<00:00, 251806.22 examples/s]
and the dataset is loaded as a datasets.arrow_dataset.Dataset
>>> dataset3_single
DatasetDict({
train: Dataset({
features: ['name', 'dna_seq', 'log10_K50_t', 'log10_K50_t_95CI_high', 'log10_K50_t_95CI_low', 'log10_K50_t_95CI', 'fitting_error_t', 'log10_K50unfolded_t', 'deltaG_t', 'deltaG_t_95CI_high', 'deltaG_t_95CI_low', 'deltaG_t_95CI', 'log10_K50_c', 'log10_K50_c_95CI_high', 'log10_K50_c_95CI_low', 'log10_K50_c_95CI', 'fitting_error_c', 'log10_K50unfolded_c', 'deltaG_c', 'deltaG_c_95CI_high', 'deltaG_c_95CI_low', 'deltaG_c_95CI', 'deltaG', 'deltaG_95CI_high', 'deltaG_95CI_low', 'deltaG_95CI', 'aa_seq_full', 'aa_seq', 'mut_type', 'WT_name', 'WT_cluster', 'log10_K50_trypsin_ML', 'log10_K50_chymotrypsin_ML', 'dG_ML', 'ddG_ML', 'Stabilizing_mut', 'pair_name', 'split_name'],
num_rows: 1503063
})
test: Dataset({
features: ['name', 'dna_seq', 'log10_K50_t', 'log10_K50_t_95CI_high', 'log10_K50_t_95CI_low', 'log10_K50_t_95CI', 'fitting_error_t', 'log10_K50unfolded_t', 'deltaG_t', 'deltaG_t_95CI_high', 'deltaG_t_95CI_low', 'deltaG_t_95CI', 'log10_K50_c', 'log10_K50_c_95CI_high', 'log10_K50_c_95CI_low', 'log10_K50_c_95CI', 'fitting_error_c', 'log10_K50unfolded_c', 'deltaG_c', 'deltaG_c_95CI_high', 'deltaG_c_95CI_low', 'deltaG_c_95CI', 'deltaG', 'deltaG_95CI_high', 'deltaG_95CI_low', 'deltaG_95CI', 'aa_seq_full', 'aa_seq', 'mut_type', 'WT_name', 'WT_cluster', 'log10_K50_trypsin_ML', 'log10_K50_chymotrypsin_ML', 'dG_ML', 'ddG_ML', 'Stabilizing_mut', 'pair_name', 'split_name'],
num_rows: 169032
})
val: Dataset({
features: ['name', 'dna_seq', 'log10_K50_t', 'log10_K50_t_95CI_high', 'log10_K50_t_95CI_low', 'log10_K50_t_95CI', 'fitting_error_t', 'log10_K50unfolded_t', 'deltaG_t', 'deltaG_t_95CI_high', 'deltaG_t_95CI_low', 'deltaG_t_95CI', 'log10_K50_c', 'log10_K50_c_95CI_high', 'log10_K50_c_95CI_low', 'log10_K50_c_95CI', 'fitting_error_c', 'log10_K50unfolded_c', 'deltaG_c', 'deltaG_c_95CI_high', 'deltaG_c_95CI_low', 'deltaG_c_95CI', 'deltaG', 'deltaG_95CI_high', 'deltaG_95CI_low', 'deltaG_95CI', 'aa_seq_full', 'aa_seq', 'mut_type', 'WT_name', 'WT_cluster', 'log10_K50_trypsin_ML', 'log10_K50_chymotrypsin_ML', 'dG_ML', 'ddG_ML', 'Stabilizing_mut', 'pair_name', 'split_name'],
num_rows: 163968
})
})
which is a column oriented format that can be accessed directly, written to disk as a parquet file or converted in to a pandas.DataFrame, e.g.
>>> dataset3_single['train'].data.column('name')
>>> dataset3_single['train'].to_parquet("dataset3_single_train.parquet")
>>> dataset3_single.to_pandas()[[WT_name', 'mut_type', 'dG_ML', 'ddG_ML']]
WT_name mut_type dG_ML ddG_ML
0 r10_437_TrROS_Hall.pdb E1Q 2.9212264903176783 0.2949200736672686
1 r10_437_TrROS_Hall.pdb E1Q 2.9212264903176783 0.2949200736672686
2 r10_437_TrROS_Hall.pdb E1Q 2.9212264903176783 0.2949200736672686
3 r10_437_TrROS_Hall.pdb E1Q 2.9212264903176783 0.2949200736672686
4 r10_437_TrROS_Hall.pdb E1Q 2.9212264903176783 0.2949200736672686
... ... ... ... ...
1503058 HEEH_rd3_0055.pdb L43C 1.629862324762064 0.07132877903687329
1503059 HEEH_rd3_0055.pdb L43C 1.629862324762064 0.07132877903687329
1503060 HEEH_rd3_0055.pdb L43C 1.629862324762064 0.07132877903687329
1503061 HEEH_rd3_0055.pdb L43C 1.629862324762064 0.07132877903687329
1503062 HEEH_rd3_0055.pdb L43C 1.629862324762064 0.07132877903687329
Overview of Datasets
dataset1:
The whole dataset 1,841,285 stability measurements
- All mutations in G0-G11 (see below)
dataset2:
The curated a set of 776,298 high-quality folding stabilities covers
- All mutations in G0 + G1 (see below)
- all single amino acid variants and selected double mutants of
331natural and148de novo designed protein domains40–72amino acids in length - comprehensive double mutations at 559 site pairs spread across
190domains (a total of210,118double mutants) 36different 3-residue networks- all possible single and double substitutions in both the wild-type background and the background in which the third amino acid was replaced by alanine
- (
400mutants × 3 pairs × 2 backgrounds ≈2,400mutants in total for each triplet)
dataset3:
Curated set of 325,132 ΔG measurements at 17,093 sites in 365 domains
- All mutations in G0
- All mutations in
dataset2where the wild-type ΔG is below 4.75 kcal mol^−1 (dataset3) allowing for the estimate of the ΔΔG of mutation.
dataset3_single:
The single point mutations in dataset3
- Using the train/val/test splits defined in ThermoMPNN (Dieckhaus, et al., 2024)
dataset3_single_cv:
The single point mutations in dataset3
- Using the 5-fold cross validation splits (
train_[0-4]/val_[0-4]/test_[0-4]) defined in ThermoMPNN (Dieckhaus, et al., 2024)
AlphaFold_model_PDBs:
AlphaFold predicted structures of wildtype domains (even if structures exist in the Protein Databank)
Target Selection
Targets consist of natural, designed, and destabilized wild-type
983 natural targets were selected from the all monomeric proteins in the protein databank having 30–100 amino acid length range that met the following criteria:
- Conisted of more than a single helix
- Did not contain other molecules (for example, proteins, nucleic acids or metals)
- Were not annotated to have DNAse, RNAse, or protease inhibition activity
- Had at most four cysteins
- Were not sequence redundant (amino acid sequence distance <2) with another selected sequence These were then processed by
- AlphaFold was used to predict the structure (including those that had solved structures in the PDB), which was used to trim amino acids from the N- and C termini that had a low number of contacts with any other residues.
- selected domains with up to 72 amino acids after excluding N- or C-terminal flexible loops
designed targets were selected from
- previous Rosetta designs with ααα, αββα, βαββ, and ββαββ topologies (40 to 43 amino acids)
- new ββαα proteins designed using Rosetta (47 amino acids)
- new domains designed by trRosetta hallucination (46 to 69 amino acids)
121 destabilized wild-type backgrounds targets were also included.
Library construction
The cDNA proteolysis screening was conducted as four giant synthetic DNA oligonucleotide libraries and obtained K50 values for 2,520,337 sequences, 1,841,285 of these measurements are included here:
- Library 1:
- ~250 designed proteins and ~50 natural proteins that are shorter than 45 amino acids
- padding by Gly, Ala and Ser amino acids so that all sequences have 44 amino acids
- ~244,000 sequences Purchased from Agilent Technologies, length 230 nt.
- Library 2:
- ~350 natural proteins that have PDB structures that are in a monomer state and have 72 or less amino acids after removing N and C-terminal linkers
- padding by Gly, Ala and Ser amino acids so that all sequences have 72 amino acids
- ~650,000 sequences
- also includes scramble sequences to construct unfolded state model.
- Purchased from Twist Bioscience, length 250 nt.
- Library 3:
- ~150 designed proteins
- comprehensive deletion and Gly or Ala insertion of all wild-type proteins included in Library 1 and Libary 2
- amino acid sequences for comprehensive double mutant analysis on polar amino acid pairs
- ~840,000 sequences
- Purchased from Twist Bioscience, length 250 nt.
- Library 4:
- Amino acid sequences for exhaustive double mutant analysis on amino acid pairs located in close proximity
- overlapped sequences to calibrate effective protease concentration and to check consistency between libraries
- ~900,000 sequences
- Purchased from Twist Bioscience, length 300 nt.
Bayesian Stability Analysis
Each target was analyzed and given a single quality category score G0-G11, which were then sorted into one of three datasets. The quality scores are
- G0: Good (wild-type ΔG values below 4.75 kcal mol^−1)
- G1: Good but WT outside dynamic range
- G2: Too much missing data
- G3: WT dG is too low
- G4: WT dG is inconsistent
- G5: Poor trypsin vs. chymotrypsin correlation
- G6: Poor trypsin vs. chymotrypsin slope
- G7: Too many stabilizing mutants
- G8: Multiple cysteins (probably folded properly)
- G9: Multiple cysteins (probably misfolded)
- G10: Poor T-C intercept
- G11: Probably cleaved in folded state(s)
ThermoMPNN splits
ThermoMPNN is a message passing neural network that predicts protein ΔΔG of mutation
based on ProteinMPNN (Dauparas et al., 2022).
ThermoMPNN uses in part data from the MegaScale dataset. From the mutations in dataset2,
272,712 mutations across 298 proteins were curated that were single point mutants, reliable,
and where the baseline is wildtype.