| --- |
| 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_K50unfol\ |
| ded_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_typ\ |
| e', '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_K50unfol\ |
| ded_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_typ\ |
| e', '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_K50unfol\ |
| ded_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_typ\ |
| e', '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. |
|
|
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| Other Datasets for comparison |
| * ProthermDB |
| * Thermodynamic data |
| * Thermal proteome profiling |
| * Rocklin2017 |
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| 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). |
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| 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) |
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