--- 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 = "single_mutant" >>> dataset = datasets.load_dataset( path = "RosettaCommons/MegaScale", name = dataset_tag, data_dir = dataset_tag) Resolving data files: 100%|█████████████████████████████████████████| 54/54 [00:00<00:00, 441.70it/s] Downloading data: 100%|███████████████████████████████████████████| 54/54 [01:34<00:00, 1.74s/files] Generating train split: 100%|███████████████████████| 211069/211069 [01:41<00:00, 2085.54 examples/s] Loading dataset shards: 100%|███████████████████████████████████████| 48/48 [00:00<00:00, 211.74it/s] and the dataset is loaded as a `datasets.arrow_dataset.Dataset` >>> dataset_models Dataset({ features: ['id', 'pdb', 'Filter_Stage2_aBefore', 'Filter_Stage2_bQuarter', 'Filter_Stage2_cHalf', 'Filter_Stage2_dEnd', 'clashes_bb', 'clashes_total', 'score', 'silent_score', 'time'], num_rows: 211069 }) which is a column oriented format that can be accessed directly, converted in to a `pandas.DataFrame`, or `parquet` format, e.g. >>> dataset_models.data.column('pdb') >>> dataset_models.to_pandas() >>> dataset_models.to_parquet("dataset.parquet") ## 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)