--- task_categories: - question-answering - tabular-classification - text-generation language: - en tags: - biology - proteins - amino-acids size_categories: - 100K<1M extra_gated_prompt: "Access to this dataset requires a purchase [here](https://buy.stripe.com/6oEbJu5tPci79IQcMX)" extra_gated_fields: Name: text Affiliation: text Email: text I have purchased a license: checkbox --- # 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 | 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 The dataset focuses on the differential deltaG of *unfolding* (mutation minus base) of various protein mutations. - **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' ### 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.