| | --- |
| | 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 |
| | num_bytes: 97324359 |
| | 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 |
| | num_bytes: 32989126 |
| | num_examples: 54953 |
| | - name: val_2 |
| | num_bytes: 32527088 |
| | num_examples: 54487 |
| | - name: val_3 |
| | num_bytes: 32271722 |
| | num_examples: 54654 |
| | - name: val_4 |
| | num_bytes: 32525383 |
| | num_examples: 51545 |
| | - name: test_0 |
| | num_bytes: 32525383 |
| | num_examples: 51545 |
| | - name: test_1 |
| | num_bytes: 34689107 |
| | num_examples: 55592 |
| | - name: test_2 |
| | num_bytes: 32989126 |
| | num_examples: 54953 |
| | - 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] |
| | 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 |
| | |
| | |
| | |
| | |
| | ## 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. |
| | |
