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--- |
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task_categories: |
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- question-answering |
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- tabular-classification |
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- text-generation |
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language: |
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- en |
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tags: |
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- biology |
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- proteins |
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- amino-acids |
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size_categories: |
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- 100K<1M |
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extra_gated_prompt: "Access to this dataset requires a purchase [here](https://buy.stripe.com/6oEbJu5tPci79IQcMX)" |
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extra_gated_fields: |
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Name: text |
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Affiliation: text |
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Email: text |
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I have purchased a license: checkbox |
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--- |
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# Protein Data Stability - Single Mutation |
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This repository contains data on the change in protein stability with a single mutation. |
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There are two datasets: |
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- [Sample dataset, ~100 datapoints](https://huggingface.co/datasets/Trelis/protein_stability_single_mutation_SAMPLE). |
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- [Gated dataset, ~250k datapoints](https://huggingface.co/datasets/Trelis/protein_stability_single_mutation). |
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## Attribution of Data Sources |
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- **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) |
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- **Dataset Link**: [Zenodo Record](https://zenodo.org/record/7992926) |
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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. |
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## Sample Protein Stability Data |
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| Base Protein Sequence | Mutation | ΔΔG_ML | Classification | |
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|-------------------------------------------------------------|----------|--------------------|-----------------| |
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| FDIYVVTADYLPLGAEQDAITLREGQYVEVLDAAHPLRWLVRTKPTKSSPSRQGWVSPAYLDRRL | R63W | -0.2010871345320799 | neutral | |
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| FDIYVVTADYLPLGAEQDAITLREGQYVEVLDAAHPLRWLVRTKPTKSSPSRQGWVSPAYLDRRL | R63Y | 0.0194756159891467 | neutral | |
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| FDIYVVTADYLPLGAEQDAITLREGQYVEVLDAAHPLRWLVRTKPTKSSPSRQGWVSPAYLDRRL | R63F | 0.7231614929744659 | stabilising | |
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| FDIYVVTADYLPLGAEQDAITLREGQYVEVLDAAHPLRWLVRTKPTKSSPSRQGWVSPAYLDRRL | R63P | -0.3668887752897785 | neutral | |
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| FDIYVVTADYLPLGAEQDAITLREGQYVEVLDAAHPLRWLVRTKPTKSSPSRQGWVSPAYLDRRL | R63C | -0.5317304030261774 | destabilising | |
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## Dataset Structure |
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The dataset focuses on the differential deltaG of *unfolding* (mutation minus base) of various protein mutations. |
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- **Base Protein Sequence** (`aa_seq`): A (sometimes shortened) amino acid sequence. |
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- **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). |
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- **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. |
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- **Classification**: Classification is done purely on the basis of ddG. |
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-- Rows below -0.5 standard deviations are classified as 'destabilising' |
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-- Rows above +0.5 standard deviations are classified as 'stabilising' |
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-- Rows between -0.5 and 0.5 standard deviations are classified as 'neutral' |
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### Understanding ΔG (delta G) |
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ΔG is the Gibbs free energy change of a process, dictating whether a process is thermodynamically favorable: |
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- **Negative ΔG**: Indicates the process is energetically favorable. For protein unfolding, it implies the protein is more stable in its unfolded form. |
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- **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. |
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The **delta delta G** (ΔΔG) represents the deltaG of the mutation compared to the base protein: |
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- **Positive ΔΔG**: Suggests the mutation enhances protein stability. |
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- **Negative ΔΔG**: Suggests the mutation decreases protein stability. |
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### Data Cleanup and Validation: |
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1. Filtering: The dataset has been curated to only include examples of single mutations. |
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2. Sequence mutations were extracted from the row names. Base mutations are labelled as 'base'. |
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3. Consistency Check: Only rows with a consistent 'mutation', aligned with both the base and mutated sequences from the raw data, have been retained. |