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# Protein Data Stability - Single Mutation

This repository contains data on the change in protein stability with a single mutation.

There are two datasets:
- [Sample dataset, ~100 datapoints](https://huggingface.co/datasets/Trelis/protein_stability_single_mutation_SAMPLE).
- [Gated dataset, ~250k datapoints](https://huggingface.co/datasets/Trelis/protein_stability_single_mutation).

## Attribution of Data Sources

- **Primary Source**: 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). [Link to the paper](https://www.nature.com/articles/s41586-023-06328-6)
- **Dataset Link**: [Zenodo Record](https://zenodo.org/record/7992926)

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 [subset of 4 columns]

| Base Protein Sequence                                       | Mutation | ΔΔG_ML             | Classification  |
|-------------------------------------------------------------|----------|--------------------|-----------------|
| FDIYVVTADYLPLGAEQDAITLREGQYVEVLDAAHPLRWLVRTKPTKSSPSRQGWVSPAYLDRRL | R63W    | -0.2010871345320799 | neutral        |
| FDIYVVTADYLPLGAEQDAITLREGQYVEVLDAAHPLRWLVRTKPTKSSPSRQGWVSPAYLDRRL | R63Y    |  0.0194756159891467 | neutral        |
| FDIYVVTADYLPLGAEQDAITLREGQYVEVLDAAHPLRWLVRTKPTKSSPSRQGWVSPAYLDRRL | R63F    |  0.7231614929744659 | stabilising    |
| FDIYVVTADYLPLGAEQDAITLREGQYVEVLDAAHPLRWLVRTKPTKSSPSRQGWVSPAYLDRRL | R63P    | -0.3668887752897785 | neutral        |
| FDIYVVTADYLPLGAEQDAITLREGQYVEVLDAAHPLRWLVRTKPTKSSPSRQGWVSPAYLDRRL | R63C    | -0.5317304030261774 | destabilising  |

## Dataset Structure

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'.

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)

ΔG is the Gibbs free energy change of a process, dictating whether a process is thermodynamically favorable:

- **Negative ΔG**: Indicates the process is energetically favorable. For protein unfolding, it implies the protein is more stable in its unfolded form.
- **Positive ΔG**: Indicates the process is not energetically favorable. In protein unfolding, it means the protein requires energy to maintain its unfolded state, i.e. it is stable in folded form.

The **delta delta G** (ΔΔG) represents the deltaG of the mutation compared to the base protein:

- **Positive ΔΔG**: Suggests the mutation enhances protein stability.
- **Negative ΔΔG**: Suggests the mutation decreases protein stability.

### Data Cleanup and Validation:

1. Filtering: The dataset has been curated to only include examples of single mutations.
2. Sequence mutations were extracted from the row names. Base mutations are labelled as 'base'.
3. Consistency Check: Only rows with a consistent 'mutation', aligned with both the base and mutated sequences from the raw data, have been retained.