| --- |
| library_name: multimolecule |
| license: agpl-3.0 |
| pipeline: mean-ribosome-load |
| pipeline_tag: other |
| tags: |
| - Biology |
| - RNA |
| - 5' UTR |
| - Translation |
| - rna |
| widget: |
| - example_title: microRNA 21 |
| pipeline_tag: mean-ribosome-load |
| sequence_type: ncRNA |
| task: mean-ribosome-load |
| text: UAGCUUAUCAGACUGAUGUUGA |
| - example_title: microRNA 146a |
| pipeline_tag: mean-ribosome-load |
| sequence_type: ncRNA |
| task: mean-ribosome-load |
| text: UGAGAACUGAAUUCCAUGGGUU |
| - example_title: microRNA 155 |
| pipeline_tag: mean-ribosome-load |
| sequence_type: ncRNA |
| task: mean-ribosome-load |
| text: UUAAUGCUAAUCGUGAUAGGGGUU |
| - example_title: RNA component of mitochondrial RNA processing endoribonuclease |
| pipeline_tag: mean-ribosome-load |
| sequence_type: ncRNA |
| task: mean-ribosome-load |
| text: GGUUCGUGCUGAAGGCCUGUAUCCUAGGCUACACACUGAGGACUCUGUUCCUCCCCUUUCCGCCUAGGGGAAAGUCCCCGGACCUCGGGCAGAGAGUGCCACGUGCAUACGCACGUAGACAUUCCCCGCUUCCCACUCCAAAGUCCGCCAAGAAGCGUAUCCCGCUGAGCGGCGUGGCGCGGGGGCGUCAUCCGUCAGCUCCCUCUAGUUACGCAGGCAGUGCGUGUCCGCGCACCAACCACACGGGGCUCAUUCUCAGCGCGGCUGUAAAAAAAAA |
| - example_title: 7SK small nuclear RNA |
| pipeline_tag: mean-ribosome-load |
| sequence_type: ncRNA |
| task: mean-ribosome-load |
| text: GGAUGUGAGGGCGAUCUGGCUGCGACAUCUGUCACCCCAUUGAUCGCCAGGGUUGAUUCGGCUGAUCUGGCUGGCUAGGCGGGUGUCCCCUUCCUCCCUCACCGCUCCAUGUGCGUCCCUCCCGAAGCUGCGCGCUCGGUCGAAGAGGACGACCAUCCCCGAUAGAGGAGGACCGGUCUUCGGUCAAGGGUAUACGAGUAGCUGCGCUCCCCUGCUAGAACCUCCAAACAAGCUCUCAAGGUCCAUUUGUAGGAGAACGUAGGGUAGUCAAGCUUCCAAGACUCCAGACACAUCCAAAUGAGGCGCUGCAUGUGGCAGUCUGCCUUUCUUUU |
| - example_title: telomerase RNA component |
| pipeline_tag: mean-ribosome-load |
| sequence_type: ncRNA |
| task: mean-ribosome-load |
| text: GGGUUGCGGAGGGUGGGCCUGGGAGGGGUGGUGGCCAUUUUUUGUCUAACCCUAACUGAGAAGGGCGUAGGCGCCGUGCUUUUGCUCCCCGCGCGCUGUUUUUCUCGCUGACUUUCAGCGGGCGGAAAAGCCUCGGCCUGCCGCCUUCCACCGUUCAUUCUAGAGCAAACAAAAAAUGUCAGCUGCUGGCCCGUUCGCCCCUCCCGGGGACCUGCGGCGGGUCGCCUGCCCAGCCCCCGAACCCCGCCUGGAGGCCGCGGUCGGCCCGGGGCUUCUCCGGAGGCACCCACUGCCACCGCGAAGAGUUGGGCUCUGUCAGCCGCGGGUCUCUCGGGGGCGAGGGCGAGGUUCAGGCCUUUCAGGCCGCAGGAAGAGGAACGGAGCGAGUCCCCGCGCGCGGCGCGAUUCCCUGAGCUGUGGGACGUGCACCCAGGACUCGGCUCACACAUGC |
| - example_title: vault RNA 2-1 |
| pipeline_tag: mean-ribosome-load |
| sequence_type: ncRNA |
| task: mean-ribosome-load |
| text: CGGGUCGGAGUUAGCUCAAGCGGUUACCUCCUCAUGCCGGACUUUCUAUCUGUCCAUCUCUGUGCUGGGGUUCGAGACCCGCGGGUGCUUACUGACCCUUUUAUGCAA |
| - example_title: brain cytoplasmic RNA 1 |
| pipeline_tag: mean-ribosome-load |
| sequence_type: ncRNA |
| task: mean-ribosome-load |
| text: GGCCGGGCGCGGUGGCUCACGCCUGUAAUCCCAGCUCUCAGGGAGGCUAAGAGGCGGGAGGAUAGCUUGAGCCCAGGAGUUCGAGACCUGCCUGGGCAAUAUAGCGAGACCCCGUUCUCCAGAAAAAGGAAAAAAAAAAACAAAAGACAAAAAAAAAAUAAGCGUAACUUCCCUCAAAGCAACAACCCCCCCCCCCCUUU |
| - example_title: HIV-1 TAR-WT |
| pipeline_tag: mean-ribosome-load |
| sequence_type: ncRNA |
| task: mean-ribosome-load |
| text: GGUCUCUCUGGUUAGACCAGAUCUGAGCCUGGGAGCUCUCUGGCUAACUAGGGAACC |
| - example_title: prion protein (Kanno blood group) |
| pipeline_tag: mean-ribosome-load |
| sequence_type: mRNA |
| task: mean-ribosome-load |
| text: AUGGCGAACCUUGGCUGCUGGAUGCUGGUUCUCUUUGUGGCCACAUGGAGUGACCUGGGCCUCUGC |
| - example_title: interleukin 10 |
| pipeline_tag: mean-ribosome-load |
| sequence_type: mRNA |
| task: mean-ribosome-load |
| text: AUGCACAGCUCAGCACUGCUCUGUUGCCUGGUCCUCCUGACUGGGGUGAGGGCC |
| - example_title: Zaire ebolavirus |
| pipeline_tag: mean-ribosome-load |
| sequence_type: mRNA |
| task: mean-ribosome-load |
| text: AAUGUUCAAACACUUUGUGAAGCUCUGUUAGCUGAUGGUCUUGCUAAAGCAUUUCCUAGCAAUAUGAUGGUAGUCACAGAGCGUGAGCAAAAAGAAAGCUUAUUGCAUCAAGCAUCAUGGCACCACACAAGUGAUGAUUUUGGUGAGCAUGCCACAGUUAGAGGGAGUAGCUUUGUAACUGAUUUAGAGAAAUACAAUCUUGCAUUUAGAUAUGAGUUUACAGCACCUUUUAUAGAAUAUUGUAACCGUUGCUAUGGUGUUAAGAAUGUUUUUAAUUGGAUGCAUUAUACAAUCCCACAGUGUUAU |
| - example_title: SARS coronavirus |
| pipeline_tag: mean-ribosome-load |
| sequence_type: mRNA |
| task: mean-ribosome-load |
| text: AUGUUUAUUUUCUUAUUAUUUCUUACUCUCACUAGUGGUAGUGACCUUGACCGGUGCACCACUUUUGAUGAUGUUCAAGCUCCUAAUUACACUCAACAUACUUCAUCUAUGAGGGGGGUUUACUAUCCUGAUGAAAUUUUUAGAUCAGACACUCUUUAUUUAACUCAGGAUUUAUUUCUUCCAUUUUAUUCUAAUGUUACAGGGUUUCAUACUAUUAAUCAUACGUUUGACAACCCUGUCAUACCUUUUAAGGAUGGUAUUUAUUUUGCUGCCACAGAGAAAUCAAAUGUUGUCCGUGGUUGGGUUUUUGGUUCUACCAUGAACAACAAGUCACAGUCGGUGAUUAUUAUUAACAAUUCUACUAAUGUUGUUAUACGAGCAUGUAACUUUGAAUUGUGUGACAACCCUUUCUUUGCUGUUUCUAAACCCAUGGGUACACAGACACAUACUAUGAUAUUCGAUAAUGCAUUUAAAUGCACUUUCGAGUACAUAUCU |
| - example_title: insulin |
| pipeline_tag: mean-ribosome-load |
| sequence_type: mRNA |
| task: mean-ribosome-load |
| text: AUGGCCCUGUGGAUGCGCCUCCUGCCCCUGCUGGCGCUGCUGGCCCUCUGGGGACCUGACCCAGCCGCAGCCUUUGUGAACCAACACCUGUGCGGCUCACACCUGGUGGAAGCUCUCUACCUAGUGUGCGGGGAACGAGGCUUCUUCUACACACCCAAGACCCGCCGGGAGGCAGAGGACCUGCAGGUGGGGCAGGUGGAGCUGGGCGGGGGCCCUGGUGCAGGCAGCCUGCAGCCCUUGGCCCUGGAGGGGUCCCUGCAGAAGCGUGGCAUUGUGGAACAAUGCUGUACCAGCAUCUGCUCCCUCUACCAGCUGGAGAACUACUGCAACUAG |
| - example_title: cyclin dependent kinase inhibitor 2A |
| pipeline_tag: mean-ribosome-load |
| sequence_type: mRNA |
| task: mean-ribosome-load |
| text: AUGGAGCCGGCGGCGGGGAGCAGCAUGGAGCCUUCGGCUGACUGGCUGGCCACGGCCGCGGCCCGGGGUCGGGUAGAGGAGGUGCGGGCGCUGCUGGAGGCGGGGGCGCUGCCCAACGCACCGAAUAGUUACGGUCGGAGGCCGAUCCAGGUCAUGAUGAUGGGCAGCGCCCGAGUGGCGGAGCUGCUGCUGCUCCACGGCGCGGAGCCCAACUGCGCCGACCCCGCCACUCUCACCCGACCCGUGCACGACGCUGCCCGGGAGGGCUUCCUGGACACGCUGGUGGUGCUGCACCGGGCCGGGGCGCGGCUGGACGUGCGCGAUGCCUGGGGCCGUCUGCCCGUGGACCUGGCUGAGGAGCUGGGCCAUCGCGAUGUCGCACGGUACCUGCGCGCGGCUGCGGGGGGCACCAGAGGCAGUAACCAUGCCCGCAUAGAUGCCGCGGAAGGUCCCUCAGACAUCCCCGAUUGA |
| - example_title: human papillomavirus type 16 E6 |
| pipeline_tag: mean-ribosome-load |
| sequence_type: mRNA |
| task: mean-ribosome-load |
| text: AUGCACCAAAAGAGAACUGCAAUGUUUCAGGACCCACAGGAGCGACCCAGAAAGUUACCACAGUUAUGCACAGAGCUGCAAACAACUAUACAUGAUAUAAUAUUAGAAUGUGUGUACUGCAAGCAACAGUUACUGCGACGUGAGGUAUAUGACUUUGCUUUUCGGGAUUUAUGCAUAGUAUAUAGAGAUGGGAAUCCAUAUGCUGUAUGUGAUAAAUGUUUAAAGUUUUAUUCUAAAAUUAGUGAGUAUAGACAUUAUUGUUAUAGUUUGUAUGGAACAACAUUAGAACAGCAAUACAACAAACCGUUGUGUGAUUUGUUAAUUAGGUGUAUUAACUGUCAAAAGCCACUGUGUCCUGAAGAAAAGCAAAGACAUCUGGACAAAAAGCAAAGAUUCCAUAAUAUAAGGGGUCGGUGGACCGGUCGAUGUAUGUCUUGUUGCAGAUCAUCAAGAACACGUAGAGAAACCCAGCUGUAA |
| - example_title: NRAS proto-oncogene |
| pipeline_tag: mean-ribosome-load |
| sequence_type: 5' UTR |
| task: mean-ribosome-load |
| text: GGGGCCGGAAGUGCCGCUCCUUGGUGGGGGCUGUUCAUGGCGGUUCCGGGGUCUCCAACAUUUUUCCCGGCUGUGGUCCUAAAUCUGUCCAAAGCAGAGGCAGUGGAGCUUGAGGUUCUUGCUGGUGUGAA |
| - example_title: amyloid beta precursor protein |
| pipeline_tag: mean-ribosome-load |
| sequence_type: 5' UTR |
| task: mean-ribosome-load |
| text: GUCAGUUUCCUCGGCAGCGGUAGGCGAGAGCACGCGGAGGAGCGUGCGCGGGGGCCCCGGGAGACGGCGGCGGUGGCGGCGCGGGCAGAGCAAGGACGCGGCGGAUCCCACUCGCACAGCAGCGCACUCGGUGCCCCGCGCAGGGUCGCG |
| - example_title: RUNX family transcription factor 1 |
| pipeline_tag: mean-ribosome-load |
| sequence_type: 5' UTR |
| task: mean-ribosome-load |
| text: ACUUCUUUGGGCCUCAUAAACAACCACAGAACCACAAGUUGGGUAGCCUGGCAGUGUCAGAAGUCUGAACCCAGCAUAGUGGUCAGCAGGCAGGACGAAUCACACUGAAUGCAAACCACAGGGUUUCGCAGCGUGGUAAAAGAAAUCAUUGAGUCCCCCGCCUUCAGAAGAGGGUGCAUUUUCAGGAGGAAGCG |
| - example_title: fragile X messenger ribonucleoprotein 1 |
| pipeline_tag: mean-ribosome-load |
| sequence_type: 5' UTR |
| task: mean-ribosome-load |
| text: CUCAGUCAGGCGCUCAGCUCCGUUUCGGUUUCACUUCCGGUGGAGGGCCGCCUCUGAGCGGGCGGCGGGCCGACGGCGAGCGCGGGCGGCGGCGGUGACGGAGGCGCCGCUGCCAGGGGGCGUGCGGCAGCGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGGAGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCUGGGCCUCGAGCGCCCGCAGCCCACCUCUCGGGGGCGGGCUCCCGGCGCUAGCAGGGCUGAAGAGAAG |
| - example_title: MYC proto-oncogene |
| pipeline_tag: mean-ribosome-load |
| sequence_type: 5' UTR |
| task: mean-ribosome-load |
| text: AACUCGCUGUAGUAAUUCCAGCGAGAGGCAGAGGGAGCGAGCGGGCGGCCGGCUAGGGUGGAAGAGCCGGGCGAGCAGAGCUGCGCUGCGGGCGUCCUGGGAAGGGAGAUCCGGAGCGAAUAGGGGGCUUCGCCUCUGGCCCAGCCCUCCCGCUGAUCCCCCAGCCAGCGGUCCGCAACCCUUGCCGCAUCCACGAAACUUUGCCCAUAGCAGCGGGCGGGCACUUUGCACUGGAACUUACAACACCCGAGCAAGGACGCGACUCUCCCGACGCGGGGAGGCUAUUCUGCCCAUUUGGGGACACUUCCCCGCCGCUGCCAGGACCCGCUUCUCUGAAAGGCUCUCCUUGCAGCUGCUUAGACG |
| - example_title: activating transcription factor 4 |
| pipeline_tag: mean-ribosome-load |
| sequence_type: 5' UTR |
| task: mean-ribosome-load |
| text: CAUUUCUACUUUGCCCGCCCACAGAUGUAGUUUUCUCUGCGCGUGUGCGUUUUCCCUCCUCCCCGCCCUCAGGGUCCACGGCCACCAUGGCGUAUUAGGGGCAGCAGUGCCUGCGGCAGCAUUGGCCUUUGCAGCGGCGGCAGCAGCACCAGGCUCUGCAGCGGCAACCCCCAGCGGCUUAAGCCAUGGCGCUUCUCACGGCAUUCAGCAGCAGCGUUGCUGUAACCGACAAAGACACCUUCGAAUUAAGCACAUUCCUCGAUUCCAGCAAAGCACCGCAAC |
| - example_title: Human GPI protein p137 |
| pipeline_tag: mean-ribosome-load |
| sequence_type: 3' UTR |
| task: mean-ribosome-load |
| text: UUUUUAAAAGGAAAAGAUACCAAAUGCCUGCUGCUACCACCCUUUUCAAUUGCUAUGUUUUGAAAGGCACCAGUAUGUGUUUUAGAUUGAUUUAAAUGUUUCAUUUAAAUCACGGACAGUAGUUUCAGUUCUGAUGGUAUAAGCAAAACAAAUAAAACGUUUAUAAAAGUUGUAUCUUGAAACACUGGUGUUCAACAGCUAGCAGCUUAUGUGAUUCACCCCAUGCCACGUUAGUGUCACAAAUUUUAUGGUUUAUCUCCAGCAACAUUUCUCUAGUACUUGCACUUAUUAUCUGAAUUC |
| - example_title: nucleophosmin 1 |
| pipeline_tag: mean-ribosome-load |
| sequence_type: 3' UTR |
| task: mean-ribosome-load |
| text: GAAAAUAGUUUAAACAAUUUGUUAAAAAAUUUUCCGUCUUAUUUCAUUUCUGUAACAGUUGAUAUCUGGCUGUCCUUUUUAUAAUGCAGAGUGAGAACUUUCCCUACCGUGUUUGAUAAAUGUUGUCCAGGUUCUAUUGCCAAGAAUGUGUUGUCCAAAAUGCCUGUUUAGUUUUUAAAGAUGGAACUCCACCCUUUGCUUGGUUUUAAGUAUGUAUGGAAUGUUAUGAUAGGACAUAGUAGUAGCGGUGGUCAGACAUGGAAAUGGUGGGGAGACAAAAAUAUACAUGUGAAAUAAAACUCAGUAUUUUAAUAAAGUAGCACGGUUUCUAUUGA |
| - example_title: superoxide dismutase 1 |
| pipeline_tag: mean-ribosome-load |
| sequence_type: 3' UTR |
| task: mean-ribosome-load |
| text: ACAUUCCCUUGGAUGUAGUCUGAGGCCCCUUAACUCAUCUGUUAUCCUGCUAGCUGUAGAAAUGUAUCCUGAUAAACAUUAAACACUGUAAUCUUAAAAGUGUAAUUGUGUGACUUUUUCAGAGUUGCUUUAAAGUACCUGUAGUGAGAAACUGAUUUAUGAUCACUUGGAAGAUUUGUAUAGUUUUAUAAAACUCAGUUAAAAUGUCUGUUUCAAUGACCUGUAUUUUGCCAGACUUAAAUCACAGAUGGGUAUUAAACUUGUCAGAAUUUCUUUGUCAUUCAAGCCUGUGAAUAAAAACCCUGUAUGGCACUUAUUAUGAGGCUAUUAAAAGAAUCCAAAUUCAAACUAAA |
| - example_title: hemoglobin subunit alpha 2 |
| pipeline_tag: mean-ribosome-load |
| sequence_type: 3' UTR |
| task: mean-ribosome-load |
| text: CUGGAGCCUCGGUAGCCGUUCCUCCUGCCCGCUGGGCCUCCCAACGGGCCCUCCUCCCCUCCUUGCACCGGCCCUUCCUGGUCUUUGAAUAAAGUCUGAGUGGGCAGCA |
| - example_title: BRAF proto-oncogene |
| pipeline_tag: mean-ribosome-load |
| sequence_type: 3' UTR |
| task: mean-ribosome-load |
| text: AACAAAUGAGUGAGAGAGUUCAGGAGAGUAGCAACAAAAGGAAAAUAAAUGAACAUAUGUUUGCUUAUAUGUUAAAUUGAAUAAAAUACUCUCUUUUUUUUUAAGGUGAACCAAAGAACACUUGUGUGGUUAAAGACUAGAUAUAAUUUUUCCCCAAACUAAAAUUUAUACUUAACAUUGGAUUUUUAACAUCCAAGGGUUAAAAUACAUAGACAUUGCUAAAAAUUGGCAGAGCCUCUUCUAGAGGCUUUACUUUCUGUUCCGGGUUUGUAUCAUUCACUUGGUUAUUUUAAGUAGUAAACUUCAGUUUCUCAUGCAACUUUUGUUGCCAGCUAUCACAUGUCCACUAGGGACUCCAGAAGAAGACCCUACCUAUGCCUGUGUUUGCAGGUGAGAAGUUGGCAGUCGGUUAGCCUGGG |
| - example_title: H3 clustered histone 1 |
| pipeline_tag: mean-ribosome-load |
| sequence_type: 3' UTR |
| task: mean-ribosome-load |
| text: UUACUGUGGUCUCUCUGACGGUCCAAGCAAAGGCUCUUUUCAGAGCCACCACCUUUUC |
| --- |
| |
| # Optimus 5-Prime |
|
|
| Convolutional neural network that predicts the mean ribosome load (MRL) of a fixed 50 nt human 5' untranslated region (5'UTR) from sequence alone. |
|
|
| ## Disclaimer |
|
|
| This is an UNOFFICIAL implementation of [Human 5' UTR design and variant effect prediction from a massively parallel translation assay](https://doi.org/10.1038/s41587-019-0164-5) by Paul J. Sample, et al. |
|
|
| The OFFICIAL repository of Optimus 5-Prime is at [pjsample/human_5utr_modeling](https://github.com/pjsample/human_5utr_modeling). |
|
|
| > [!TIP] |
| > The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation. |
|
|
| **The team releasing Optimus 5-Prime did not write this model card for this model so this model card has been written by the MultiMolecule team.** |
|
|
| ## Model Details |
|
|
| Optimus 5-Prime is a simple, fully feed-forward 1D convolutional network trained on a massively parallel polysome-profiling assay of ~280,000 random 50 nt 5'UTRs upstream of an eGFP reporter expressed in HEK293T. The network ingests a fixed 50 nt 5'UTR one-hot tensor, applies three stacked `padding="same"` 1D convolutions (120 filters, kernel 8, ReLU) with dropout between the second/third convolutions, flattens the per-position activations channels-last, and emits a single standardized mean ribosome load (MRL) regression score through a 40-unit fully connected layer and a linear regression head. Please refer to the [Training Details](#training-details) section for more information on the training process. |
|
|
| The MRL scalar is the per-sequence mean of polysome-profile-derived ribosome loading and is used by the original authors both to score natural human 5'UTRs and to engineer new sequences with predictable translation efficiency. Variant-effect scoring is performed externally by computing the MRL difference between the reference and alternative sequences; the model itself takes a single sequence as input. |
|
|
| ### Model Specification |
|
|
| | Num Layers | Hidden Size | Num Parameters (M) | FLOPs (M) | MACs (M) | Max Num Tokens | |
| | ---------- | ----------- | ------------------ | --------- | -------- | -------------- | |
| | 4 | 40 | 0.48 | 24.04 | 12.00 | 50 | |
|
|
| ### Links |
|
|
| - **Code**: [multimolecule.optimus5prime](https://github.com/DLS5-Omics/multimolecule/tree/master/multimolecule/models/optimus5prime) |
| - **Data**: Massively parallel polysome-profiling MRL library on randomized 50 nt 5'UTRs in HEK293T, GEO [GSE114002](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE114002) |
| - **Paper**: [Human 5' UTR design and variant effect prediction from a massively parallel translation assay](https://doi.org/10.1038/s41587-019-0164-5) |
| - **Developed by**: Paul J. Sample, Ban Wang, David W. Reid, Vlad Presnyak, Iain J. McFadyen, David R. Morris, Georg Seelig |
| - **Model type**: 1D CNN for mean ribosome load (MRL) regression from a fixed 50 nt 5'UTR sequence |
| - **Original Repository**: [pjsample/human_5utr_modeling](https://github.com/pjsample/human_5utr_modeling) |
|
|
| ## Usage |
|
|
| The model file depends on the [`multimolecule`](https://multimolecule.danling.org) library. You can install it using pip: |
|
|
| ```bash |
| pip install multimolecule |
| ``` |
|
|
| ### Direct Use |
|
|
| #### Mean Ribosome Load Prediction |
|
|
| You can use this model directly to predict the mean ribosome load (MRL) of a fixed 50 nt 5'UTR sequence: |
|
|
| ```python |
| >>> from multimolecule import RnaTokenizer, Optimus5PrimeForSequencePrediction |
| |
| >>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/optimus5prime") |
| >>> model = Optimus5PrimeForSequencePrediction.from_pretrained("multimolecule/optimus5prime") |
| >>> output = model(**tokenizer("GGGACAUAGCUAGCUAGCUAGCUAGCUAGCUAGCUAGCUAGCUAGCUAGC", return_tensors="pt")) |
| |
| >>> output.keys() |
| odict_keys(['logits']) |
| ``` |
|
|
| The pre-regression dense representation is exposed on the backbone: |
|
|
| ```python |
| >>> from multimolecule import RnaTokenizer, Optimus5PrimeModel |
| |
| >>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/optimus5prime") |
| >>> model = Optimus5PrimeModel.from_pretrained("multimolecule/optimus5prime") |
| >>> output = model(**tokenizer("GGGACAUAGCUAGCUAGCUAGCUAGCUAGCUAGCUAGCUAGCUAGCUAGC", return_tensors="pt")) |
| |
| >>> output.keys() |
| odict_keys(['pooler_output']) |
| ``` |
|
|
| ### Interface |
|
|
| - **Input length**: fixed 50 nt 5'UTR sequence |
| - **Padding**: shorter sequences are right-padded with zeros to 50 nt; longer sequences are truncated to the first 50 nt |
| - **Alphabet**: RNA (`A`, `C`, `G`, `U`); `N` is encoded as an all-zero channel |
| - **Special tokens**: none added; `input_ids` are consumed positionally as one-hot channels |
| - **Output**: standardized mean ribosome load score (`logits`) of shape `(batch_size, 1)`; raw-MRL calibration requires the external scaler used by the upstream training workflow |
|
|
| ### Variant Effect |
|
|
| Optimus 5-Prime is a single-sequence regression model. To score the effect of a variant on translation, run the reference and alternative 5'UTRs through the model independently and compute the difference between their predicted MRL values: |
|
|
| ```python |
| >>> from multimolecule import RnaTokenizer, Optimus5PrimeForSequencePrediction |
| >>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/optimus5prime") |
| >>> model = Optimus5PrimeForSequencePrediction.from_pretrained("multimolecule/optimus5prime") |
| >>> ref = "GGGACAUAGCUAGCUAGCUAGCUAGCUAGCUAGCUAGCUAGCUAGCUAGC" |
| >>> alt = "GGGACAUAGCUAGCUAGCUAGCUAGCUAGCUAGCUAGCUAGCUAGAUAGC" |
| >>> ref_mrl = model(**tokenizer(ref, return_tensors="pt"))["logits"] |
| >>> alt_mrl = model(**tokenizer(alt, return_tensors="pt"))["logits"] |
| >>> delta = (alt_mrl - ref_mrl).item() |
| ``` |
|
|
| ## Training Details |
|
|
| Optimus 5-Prime was trained to regress the per-sequence mean ribosome load (MRL) derived from polysome profiling on a massively parallel reporter assay. |
|
|
| ### Training Data |
|
|
| Optimus 5-Prime was trained on approximately 280,000 randomized 50 nt 5'UTRs placed upstream of an eGFP reporter and expressed in HEK293T cells. Mean ribosome load was computed per sequence from polysome-fractionation read counts. The raw sequencing data are available at GEO accession [GSE114002](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE114002). |
|
|
| ### Training Procedure |
|
|
| #### Pre-training |
|
|
| The published `main_MRL_model` was trained with mean-squared-error loss against standardized per-sequence MRL values. The optimizer was Adam with learning rate 1e-3, batch size 128, betas (0.9, 0.999), and epsilon 1e-8. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{sample2019human, |
| author = {Sample, Paul J. and Wang, Ban and Reid, David W. and Presnyak, Vlad and McFadyen, Iain J. and Morris, David R. and Seelig, Georg}, |
| title = {Human 5' UTR design and variant effect prediction from a massively parallel translation assay}, |
| journal = {Nature Biotechnology}, |
| volume = {37}, |
| number = {7}, |
| pages = {803--809}, |
| year = {2019}, |
| publisher = {Springer Science and Business Media LLC}, |
| doi = {10.1038/s41587-019-0164-5} |
| } |
| ``` |
|
|
| > [!NOTE] |
| > The artifacts distributed in this repository are part of the MultiMolecule project. |
| > If MultiMolecule supports your research, please cite the MultiMolecule project as follows: |
|
|
| ```bibtex |
| @software{chen_2024_12638419, |
| author = {Chen, Zhiyuan and Zhu, Sophia Y.}, |
| title = {MultiMolecule}, |
| doi = {10.5281/zenodo.12638419}, |
| publisher = {Zenodo}, |
| url = {https://doi.org/10.5281/zenodo.12638419}, |
| year = 2024, |
| month = may, |
| day = 4 |
| } |
| ``` |
|
|
| ## Contact |
|
|
| Please use GitHub issues of [MultiMolecule](https://github.com/DLS5-Omics/multimolecule/issues) for any questions or comments on the model card. |
|
|
| Please contact the authors of the [Optimus 5-Prime paper](https://doi.org/10.1038/s41587-019-0164-5) for questions or comments on the paper/model. |
|
|
| ## License |
|
|
| This model implementation is licensed under the [GNU Affero General Public License](license.md). |
|
|
| For additional terms and clarifications, please refer to our [License FAQ](license-faq.md). |
|
|
| ```spdx |
| SPDX-License-Identifier: AGPL-3.0-or-later |
| ``` |
