Datasets:

RosettaCommons
/

MegaScale

	---
	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
	- 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: 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:
	- name: train
	num_bytes: 542077948
	num_examples: 776298
	download_size: 291488588
	dataset_size: 542077948
	- config_name: dataset3
	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: 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:
	- name: train
	num_bytes: 426187043
	num_examples: 607839
	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
	- 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: 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
	- name: split_name
	dtype: string
	splits:
	- name: train
	num_bytes: 1017283318
	num_examples: 1503063
	- name: val
	num_bytes: 110475434
	num_examples: 163968
	- name: test
	num_bytes: 116788047
	num_examples: 169032
	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
	- 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: 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: float64
	- 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
	splits:
	- name: train_0
	num_bytes: 97788595
	num_examples: 164094
	- name: train_1
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	num_examples: 160686
	- name: train_2
	num_bytes: 99485827
	num_examples: 161791
	- name: train_3
	num_bytes: 100203431
	num_examples: 162090
	- name: train_4
	num_bytes: 100206394
	num_examples: 165032
	- name: val_0
	num_bytes: 34689107
	num_examples: 55592
	- name: val_1
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	num_examples: 54953
	- name: val_2
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	- name: val_3
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	- name: val_4
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	- name: test_0
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	- name: test_1
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	- name: test_2
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	- name: test_3
	num_bytes: 32527088
	num_examples: 54487
	- name: test_4
	num_bytes: 32271722
	num_examples: 54654
	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](https://forms.gle/wuHv8MKmEu4EEMA99) 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](https://huggingface.co/docs/datasets/index) 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%\|███████████████████████████████\| 41.2M/41.2M [00:00<00:00, 57.3MB/s]
	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 `331` natural and `148` de novo designed protein domains `40–72` amino acids in length
	* comprehensive double mutations at 559 site pairs spread across `190` domains (a total of `210,118` double mutants)
	* `36` different 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
	* (`400` mutants × 3 pairs × 2 backgrounds ≈ `2,400` mutants 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 `dataset2` where 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)](https://www.pnas.org/doi/abs/10.1073/pnas.2314853121)

	`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)](https://www.pnas.org/doi/abs/10.1073/pnas.2314853121)

	`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)](https://www.science.org/doi/10.1126/science.add2187).
	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.