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configs:
- config_name: mutation_dg
data_files:
- split: train
path: data/subsets/mutation_dg/train.parquet
- split: validation
path: data/subsets/mutation_dg/validation.parquet
- split: test
path: data/subsets/mutation_dg/test.parquet
- config_name: mutation_ddg
data_files:
- split: train
path: data/subsets/mutation_ddg/train.parquet
- split: validation
path: data/subsets/mutation_ddg/validation.parquet
- split: test
path: data/subsets/mutation_ddg/test.parquet
- config_name: mutation_tm
data_files:
- split: train
path: data/subsets/mutation_tm/train.parquet
- split: validation
path: data/subsets/mutation_tm/validation.parquet
- split: test
path: data/subsets/mutation_tm/test.parquet
- config_name: mutation_dtm
data_files:
- split: train
path: data/subsets/mutation_dtm/train.parquet
- split: validation
path: data/subsets/mutation_dtm/validation.parquet
- split: test
path: data/subsets/mutation_dtm/test.parquet
- config_name: mutation_fitness
data_files:
- split: train
path: data/subsets/mutation_fitness/train.parquet
- split: validation
path: data/subsets/mutation_fitness/validation.parquet
- split: test
path: data/subsets/mutation_fitness/test.parquet
- config_name: mutation_binary
data_files:
- split: train
path: data/subsets/mutation_binary/train.parquet
- split: validation
path: data/subsets/mutation_binary/validation.parquet
- split: test
path: data/subsets/mutation_binary/test.parquet
license: cc-by-4.0
language:
- en
tags:
- thermal-stability
- mutations
- mutagenesis
- experimental
- structural-biology
pretty_name: FireProtDB 2.0
---
# Dataset Card for FireProtDB_2.0
Subsets of protein stability data for single-point mutants from FireProtDB, a comprehensive curated database.
## Dataset Details
Subsets of different thermal data of single-point mutations in the FireProtDB database with train/validation/test splits:
- 1. ΔG, ΔΔG
- 2. Tm, ΔTm
- 3. Fitness
- 4. Stabilizing
### Dataset Description
This dataset contains curated subsets of various thermal stability measurements derived from FireProtDB. Subsets are separated based on measurement type (ΔG, ΔΔG, Tm, ΔTm, Fitness, Binary Stabilizing). Subsets are split into 80/10/10 parititions based on sequence similarity of the various proteins in each subset. Stabilizing refers to a classification performed by FireProtDB which designates if a mutation is stabilizing or not. This datatype sets a 'true' or 'false' binary value to indicate if mutation is stabilizing or destabilizing.
- **Curated by:** Zachary Drake / zacharydrake (at) g.ucla.edu
### Dataset Sources
- **Repository:** https://loschmidt.chaemi.muni.cz/fireprotdb/
- **Paper:** Milos Musil, Simeon Borko, Joan Planas-Iglesias, David Lacko, Monika Rosinska, Petr Kabourek, Lígia O Martins, Mateusz Tataruch, Jiri Damborsky, Stanislav Mazurenko, David Bednar, FireProtDB 2.0: large-scale manually curated database of the protein stability data, Nucleic Acids Research, Volume 54, Issue D1, 6 January 2026, Pages D409–D418, https://doi.org/10.1093/nar/gkaf1211
## Uses
Useful for training models to predict various thermal stability metrics, or evaluating stability effects of mutations.
## Dataset Structure
Subsets included are:
- mutations_dg
- mutations_ddg
- mutations_tm
- mutations_dtm
- mutations_fitness
- mutations_binary
#### Data Collection and Processing
Datasets were processed using the FireProtDB CSV (https://loschmidt.chemi.muni.cz/fireprotdb/download/). The CSV was processed using a pipeline of primarily Pandas and mmseqs2 (code is available in src/).
## Citation
**BibTeX:**
```bibtex
@article{10.1093/nar/gkaf1211,
author = {Musil, Milos and Borko, Simeon and Planas-Iglesias, Joan and Lacko, David and Rosinska, Monika and Kabourek, Petr and Martins, Lígia O and Tataruch, Mateusz and Damborsky, Jiri and Mazurenko, Stanislav and Bednar, David},
title = {FireProtDB 2.0: large-scale manually curated database of the protein stability data},
journal = {Nucleic Acids Research},
volume = {54},
number = {D1},
pages = {D409-D418},
year = {2025},
month = {11},
abstract = {Thermostable proteins are crucial in numerous biomedical and biotechnological applications. However, naturally occurring proteins have evolved to function in mild conditions, and laboratory experiments aiming at improving protein stability have proven laborious and expensive. Computational methods overcome this issue by providing a cheap and scalable alternative. Despite significant progress, their reliability is still hindered by the availability of high-quality data. FireProtDB 2.0 (http://loschmidt.chemi.muni.cz/fireprotdb) is a large-scale database aggregating stability data from multiple sources. The second version builds upon its predecessor, retaining its original functionality while introducing a new approach to data storage and maintenance. The new scheme enables the introduction of both absolute and relative data types connected with measurements of wild-types, mutants, protein domains, and de novo designed proteins. Furthermore, while the original database was limited to single-point mutations, more complex data such as insertions, deletions, and multiple-point mutations are now available. As a result, the inclusion of large-scale mutagenesis has increased the size of the database from 16 000 to almost 5 500 000 experiments. Moreover, the updated abstract scheme is fully expandable with any new measurements and annotations without the need for any restructuring. Finally, the tracking of history together with fixed identifiers is in accordance with the FAIR principles.},
issn = {1362-4962},
doi = {10.1093/nar/gkaf1211},
url = {https://doi.org/10.1093/nar/gkaf1211},
eprint = {https://academic.oup.com/nar/article-pdf/54/D1/D409/65405634/gkaf1211.pdf},
}
```
**APA:**
Musil, M., Borko, S., Planas-Iglesias, J., Lacko, D., Rosinska, M., Kabourek, P., Martins, L. O., Tataruch, M., Damborsky, J., Mazurenko, S., & Bednar, D. (2026). FireProtDB 2.0: large-scale manually curated database of the protein stability data. Nucleic acids research, 54(D1), D409–D418. https://doi.org/10.1093/nar/gkaf1211
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