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README.md

@@ -33,7 +33,7 @@ There are two datasets:
 
 As to where the dataset comes from in this broader work, the relevant dataset (#3) is shown in `dataset_table.jpeg` of this repository's files.
 
-## Sample Protein Stability Data
+## Sample Protein Stability Data [subset of 4 columns]
 
 | Base Protein Sequence                                       | Mutation | ΔΔG_ML             | Classification  |
 |-------------------------------------------------------------|----------|--------------------|-----------------|
@@ -45,15 +45,70 @@ As to where the dataset comes from in this broader work, the relevant dataset (#
 
 ## Dataset Structure
 
-The dataset focuses on the differential deltaG of *unfolding* (mutation minus base) of various protein mutations.
+This dataset focuses on the differential deltaG of *unfolding* (mutation minus base) of various protein mutations and is derived from stability measurements (free energy of unfolding) measured by two proteases, trypsin and chymotrypsin.
+
+### Columns (Trypsin):
+
+- **name**: The name of the protein variant.
+- **dna_seq**: The DNA sequence encoding the protein variant.
+- **log10_K50_t**: The log10 of the K50 value measured with trypsin (a measure of stability).
+- **log10_K50_t_95CI_high**: The upper bound of the 95% confidence interval for log10_K50_t.
+- **log10_K50_t_95CI_low**: The lower bound of the 95% confidence interval for log10_K50_t.
+- **log10_K50_t_95CI**: The width of the 95% confidence interval for log10_K50_t.
+- **fitting_error_t**: A measure of error between the model and data for trypsin.
+- **log10_K50unfolded_t**: The predicted log10 K50 value for the unfolded state with trypsin.
+- **deltaG_t**: The ΔG stability calculated from the trypsin data.
+- **deltaG_t_95CI_high**: The upper bound of the ΔG confidence interval from trypsin.
+- **deltaG_t_95CI_low**: The lower bound of the ΔG confidence interval from trypsin.
+- **deltaG_t_95CI**: The width of the ΔG confidence interval from trypsin.
+
+### Columns (Chymotrypsin):
+
+- **log10_K50_c**: Analogous to `log10_K50_t`, but for chymotrypsin.
+- **log10_K50_c_95CI_high**: Upper bound of the 95% CI for `log10_K50_c`.
+- **log10_K50_c_95CI_low**: Lower bound of the 95% CI for `log10_K50_c`.
+- **log10_K50_c_95CI**: Width of the 95% CI for `log10_K50_c`.
+- **fitting_error_c**: A measure of error between the model and data for chymotrypsin.
+- **log10_K50unfolded_c**: Predicted log10 K50 value for the unfolded state with chymotrypsin.
+- **deltaG_c**: ΔG stability calculated from the chymotrypsin data.
+- **deltaG_c_95CI_high**: Upper bound of the ΔG CI from chymotrypsin.
+- **deltaG_c_95CI_low**: Lower bound of the ΔG CI from chymotrypsin.
+- **deltaG_c_95CI**: Width of the ΔG CI from chymotrypsin.
+
+### Combined Data:
+
+- **deltaG**: The combined ΔG estimate from both trypsin and chymotrypsin.
+- **deltaG_95CI_high**: Upper bound of the combined ΔG confidence interval.
+- **deltaG_95CI_low**: Lower bound of the combined ΔG confidence interval.
+- **deltaG_95CI**: Width of the combined ΔG confidence interval.
+
+### Protein Sequencing Data:
+
+- **aa_seq_full**: The full amino acid sequence.
+- **aa_seq**: A (sometimes shortened) amino acid sequence representing the protein.
+- **mut_type**: The type of mutation introduced to the protein.
+- **WT_name**: Name of the wild type variant.
+- **WT_cluster**: Cluster classification for the wild type variant.
+- **mutation**: Represented as a combination of amino acid and its position (e.g., F10N indicates changing the 10th amino acid (F) in a sequence for N).
+- **base_aa_seq**: The base sequence of the protein before the mutation. 
+
+### Derived Data:
+
+- **log10_K50_trypsin_ML**: Log10 value of K50 derived from a machine learning model using trypsin data.
+- **log10_K50_chymotrypsin_ML**: Log10 value of K50 derived from a machine learning model using chymotrypsin data.
+- **dG_ML**: ΔG derived from a machine learning model that makes use of stability measurements from both proteases.
+- **ddG_ML**: Differential ΔG (mutation minus base) derived from a machine learning model.
+  
+### Classification:
+
+- **Stabilizing_mut**: Indicates whether the mutation is stabilizing or not.
+- **pair_name**: Name representation combining the wild type and mutation.
+- **classification**: Classification based on `ddG_ML`:
+  - Rows below -0.5 standard deviations are classified as 'destabilising'.
+  - Rows above +0.5 standard deviations are classified as 'stabilising'.
+  - Rows between -0.5 and 0.5 standard deviations are classified as 'neutral'.
 
-- **Base Protein Sequence** (`aa_seq`): A (sometimes shortened) amino acid sequence.
-- **Mutation**: Represented as a combination of amino acid and its position (e.g., F10N indicates changing the 10th amino acid (F) in a sequence for N).
-- **delta deltaG** (`ddG_ML`): Derived from a model that makes use of stability measurements (free energy of unfolding) measured by two proteases, trypsin and chymotrypsin.
-- **Classification**: Classification is done purely on the basis of ddG.
--- Rows below -0.5 standard deviations are classified as 'destabilising'
--- Rows above +0.5 standard deviations are classified as 'stabilising'
--- Rows between -0.5 and 0.5 standard deviations are classified as 'neutral'
+This dataset offers a comprehensive view of protein mutations, their effects, and how they relate to the stability measurements made with trypsin and chymotrypsin.
   
 ### Understanding ΔG (delta G)