| --- |
| license: apache-2.0 |
| library_name: pytorch |
| base_model: Bitbol-Lab/ProteomeLM-L |
| tags: |
| - biology |
| - proteomics |
| - gene-essentiality |
| - protein-language-model |
| - proteomelm |
| --- |
| |
| # ProteomeLM-ess |
|
|
| Gene-essentiality classifier of [ProteomeLM](https://github.com/Bitbol-Lab/ProteomeLM) |
| ([PNAS 2026](https://www.pnas.org/doi/10.1073/pnas.2524201123), Fig. 5). Given a whole |
| proteome, it gives each protein a probability of being essential. |
|
|
| ## Model |
|
|
| - **Input**: a whole proteome (one genome), as a protein FASTA file. |
| - **Embeddings**: every protein is embedded with ESM-C 600M (mean over residues, first |
| 4096 residues). The frozen backbone [Bitbol-Lab/ProteomeLM-L](https://huggingface.co/Bitbol-Lab/ProteomeLM-L) then reads the |
| whole proteome at once, in bfloat16, with each protein's own ESM-C embedding as input and |
| group embedding. |
| - **Head**: a two-layer classifier (linear 1152 → 2048, ReLU, dropout 0.5, linear |
| 2048 → 2) on the backbone's `hidden_states[8]` (index 0 is the input projection). |
| Class 0 is *essential*, class 1 *non-essential*; the output is `p_essential` = softmax class 0. |
| - **Files**: `config.json` (backbone, layer, dimensions, labels) and `model.safetensors` |
| (2,365,442 parameters, stored in bfloat16; inference runs the head in float32). |
|
|
| ## Training data |
|
|
| Essentiality labels (E / NE) from [OGEE](https://v3.ogee.info) for 82 genomes, mostly |
| bacteria plus several eukaryotes (e.g. mouse, zebrafish, *Drosophila*, *C. elegans*, |
| *Arabidopsis*, fission yeast). The head was trained on the training folds of the paper's |
| gene-level train / validation / test split, with Adam (learning rate 1e-3) and early stopping |
| on the validation-fold AUPR (ProteomeLM-L layer 8, seed 42). |
|
|
| Seven OGEE genomes were held out of training entirely: *S. cerevisiae* S288C (taxid 580240), |
| *E. coli* K-12 MG1655 (83333), and taxids 199310, 679895, 941322, 1380365 and 1124478. |
|
|
| ## Performance |
|
|
| Whole-genome predictions on genomes held out of training (as in Fig. 5B of the paper): the N |
| highest-ranked proteins are called essential, with N the number of labelled essential genes. |
| AUROC is computed on the genes labelled essential or non-essential. |
|
|
| | genome | proteins | labelled essential (N) | AUROC | labelled essential genes in the top N | |
| |---|---|---|---|---| |
| | *E. coli* K-12 MG1655 (taxid 83333) | 4,361 | 286 | 0.952 | 201 (70.3%) | |
| | *S. cerevisiae* S288C (taxid 580240) | 5,975 | 937 | 0.810 | 432 (46.1%) | |
| | JCVI-Syn1.0 (taxid 766747) | 820 | 202 | 0.879 | 121 (59.9%) | |
| | JCVI-Syn3A (taxid 2144189) | 455 | 199 | 0.830 | 130 (65.3%) | |
|
|
| Labels: OGEE (*E. coli*, *S. cerevisiae*), Hutchison et al. 2016 (JCVI-Syn1.0) and SynWiki |
| (JCVI-Syn3A). The embeddings are computed in bfloat16, so scores vary slightly across GPUs |
| and library versions (top-N counts by a gene or two). |
|
|
| ## Usage |
|
|
| ```bash |
| pip install "proteomelm @ git+https://github.com/Bitbol-Lab/ProteomeLM" |
| python -m proteomelm.essentiality --fasta proteome.fasta --out scores.tsv --top-fraction 0.1 |
| ``` |
|
|
| The TSV lists every protein, most essential first: `protein_id`, `p_essential`, `rank` and, |
| with `--top-n N`, `--top-fraction F` or `--threshold T`, `predicted_essential`. Ranks are |
| more reliable than the raw probabilities, which are not calibrated across organisms (the |
| fraction of essential genes ranges from a few percent in free-living bacteria to about half |
| in minimal cells), so a top-N or top-fraction rule is recommended. |
|
|
| or, from Python: |
|
|
| ```python |
| from proteomelm.essentiality import EssentialityPredictor, read_fasta |
| |
| ids, sequences = read_fasta("proteome.fasta") |
| predictor = EssentialityPredictor.from_pretrained("Bitbol-Lab/ProteomeLM-ess", device="cuda") |
| table = predictor.predict(ids, sequences, top_fraction=0.1) # pandas DataFrame, sorted by rank |
| ``` |
|
|
| The input should be one complete proteome: ProteomeLM scores every protein in the context |
| of all the others. A GPU is recommended (a bacterial proteome takes about a minute, most of |
| it ESM-C); the first run downloads ESM-C 600M and Bitbol-Lab/ProteomeLM-L. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{malbranke2026proteomelm, |
| title={ProteomeLM: A proteome-scale language model enables accurate and rapid prediction of protein-protein interactions and gene essentiality across taxa}, |
| author={Malbranke, Cyril and Zalaffi, Gionata Paolo and Bitbol, Anne-Florence}, |
| journal={Proceedings of the National Academy of Sciences}, |
| volume={123}, |
| number={21}, |
| pages={e2524201123}, |
| year={2026}, |
| publisher={National Academy of Sciences}, |
| doi={10.1073/pnas.2524201123}, |
| url={https://www.pnas.org/doi/10.1073/pnas.2524201123} |
| } |
| ``` |
|
|
