Hugging Face's logo

Datasets:
RosettaCommons
/
MegaScale

Modalities:
Tabular
Text
Formats:
parquet
Size:
1M - 10M
Tags:
biology
chemistry
Libraries:
Datasets
Dask
License:
Dataset card Data Studio Files
xet
Community
2
MegaScale / README.md
maom's picture
maom
Update README.md
c1fb9e5 verified
preview code
|
raw
history blame
20.7 kB
 ---
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: 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
configs:
- 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-*
---
# Mega-scale experimental analysis of protein folding stability in biology and design
The Mega-scale dataset contains 1,841,285 thermodynamic folding stability measurements using
cDNA display proteolysis of natural and designed proteins from which 776,298 high-quality folding
stabilities covering 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.
*** **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 `MIP` model datasets, 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]
Downloading data: 100%|███████████████████████████████| 39.8M/39.8M [00:01<00:00, 36.9MB/s]
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
## Dataset Overview
The curated a set of 776,298 high-quality folding stabilities covers
* 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)
### 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
XXX **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)
The datasets 1-3 with three being the highest quality are defined by:
* Dataset 3 (for ddG ML) (G0: 325,132 ΔG measurements at 17,093 sites in 365 domains)
* Dataset 2 (for dG ML) (G0+G1: 478 domains)
* Dataset 1 (all data)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
1.8 million measurements in total
We determine ΔG using each sequence’s
* measured K50, a predicted sequence-specific K50 for the unfolded state (K50,U)
* a universal K50 for the folded state (K50,F)
published studies using purified protein samples for 1,188 variants of 10 proteins (Fig. 1g and Supplementary Fig. 1 for more details on GB129)
Our measurements for these sequences were all performed in libraries of 244,000–900,000 total sequences.
Other Datasets for comparison
* ProthermDB
* Thermodynamic data
* Thermal proteome profiling
* Rocklin2017
Tsuboyama2023_Dataset2_Dataset3_20230416.csv
* All sequences in dataset 2 and dataset 3 are included
* All sequences in this file have an inferred ΔG estimate
* only sequences in dataset 3 have a tabulated ΔΔG estimate
* datasets 2 and 3 include a very small number of sequences with low-quality data (wide confidence intervals)
because these sequences come from mutational scans that are high quality overall
* low-quality data (including mutant data filtered in Stage 3) have been filtered out and
replaced by a "–"" symbol in the columns labelled ‘_ML’ (for machine learning).
predicting wild-type amino acids from the folding stabilities (ΔG) of each protein variant
* 99,156 ΔG measurements (5,214 sites in 90 non-redundant natural domains)