Update README.md

Add a bunch of notes on quantification of the data from the main text

README.md

@@ -6,4 +6,163 @@ tags:
 pretty_name: >-
   Mega-scale experimental analysis of protein folding stability in biology and
   design
----
+---
+
+# Mega-scale experimental analysis of protein folding stability in biology and design
+Here we present cDNA display proteolysis, a method for measuring thermodynamic folding
+stability for up to 900,000 protein domains in a one-week experiment. From 1,841,285
+measurements in total, we curated a set of 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.
+
+## 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 Details
+
+
+1.8 million measurements in total
+
+
+curated a set of around 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
+   * 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)
+
+
+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.
+
+
+stability for all single substitutions, deletions and Gly and Ala insertions in 983 natural and designed domains (wild-type sequences)
+  * natural domains
+    * nearly all the small (less than 72 amino acids) monomeric domains in the PDB that were suitable for our assay (Methods)
+    * all monomeric proteins in the PDB in the 30–100 amino acid length range in June 2021
+      * excluded structures that
+        * had only a single helix
+        * contained other molecules (for example, proteins, nucleic acids or metals)
+        * were annotated to have DNAse, RNAse or protease inhibition activity
+        * included more than four cystines
+      * removed redundant sequences (amino acid sequence distance <2)
+      * predicted the structures of these PDB sequences using AlphaFold
+        * 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 domains
+    (1) previous Rosetta designs with ααα, αββα, βαββ, and ββαββ topologies (40 to 43 amino acids)
+    (2) new ββαα proteins designed using Rosetta (47 amino acids)
+    (3) new domains designed by trRosetta hallucination (46 to 69 amino acids)
+  * "destabilized wild-type backgrounds": 121
+  => 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.
+
+
+T-C := trypsin vs. chymotrypsin
+
+Datasets
+  * ProthermDB
+    * Thermodynamic data
+    * Thermal proteome profiling
+  * Rocklin2017
+  * 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)
+
+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).
+
+Classification of mutational scanning results
+  * 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 T-C correlation
+  * G6: Poor T-C 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)
+  => All mutational scans are included in only one group 
+
+
+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)
+
+