--- 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.