Update README.md

README.md

@@ -360,10 +360,12 @@ configs:
 ---
 
 # 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.
+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!!** ***
 
@@ -391,7 +393,7 @@ then, from within python load the datasets library
 
 ### Load model datasets
    
-To load one of the `MIP` model datasets, use `datasets.load_dataset(...)`:
+To load one of the `MegaScale` model datasets, use `datasets.load_dataset(...)`:
 
     >>> dataset_tag = "dataset3_single"
     >>> dataset3_single = datasets.load_dataset(
@@ -447,13 +449,35 @@ which is a column oriented format that can be accessed directly, written to disk
     
 
 
-## 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)
+## 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 whil-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
+  * 
+
+**`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
+
 
 ### Target Selection
 Targets consist of natural, designed, and destabilized wild-type
@@ -518,11 +542,6 @@ Each target was analyzed and given a single quality category score G0-G11, which
   * 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)
-
 
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