| task_categories: | |
| - text-generation | |
| tags: | |
| - biology | |
| - protein | |
| - mutation | |
| # RipplePLM Dataset | |
| This dataset accompanies the paper [RipplePLM: Structural and Property Decoupling for Protein Mutation Effect Generation](https://huggingface.co/papers/2610.01891). It contains protein mutation data for training and evaluating models that generate natural-language descriptions of protein mutation effects. | |
| Code and usage instructions are available at [https://github.com/Lyu6PosHao/RipplePLM](https://github.com/Lyu6PosHao/RipplePLM). | |
| The dataset is organized into two splits (`structural_split/` and `temporal_split/`), each containing `train_with_label.csv`, `val_with_label.csv`, and `test_with_label.csv`. | |
| The columns include: | |
| - `entry`: Protein ID and mutation, e.g. `P12345-A123B`; residue positions are 1-based. | |
| - `protein1`: Wild-type amino acid sequence. | |
| - `protein2`: Mutant sequence; reconstructed from `entry` if absent. | |
| - `function`: Wild-type protein function used as input context. | |
| - `uniprot_description`: UniProt mutation-effect description. | |
| - `GPT_description`: Additional generated mutation-effect description. | |
| - `all_description`: Description field used to filter samples with missing annotations. | |
| - `ephod_label`: Integer class label for pH. | |
| - `pptstab_label`: Integer class label for thermostability. | |
| The reference text combines `uniprot_description` and `GPT_description`. Samples missing `function` or `all_description` are omitted. |