Instructions to use ChatterjeeLab/PepMLM-650M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ChatterjeeLab/PepMLM-650M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ChatterjeeLab/PepMLM-650M")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ChatterjeeLab/PepMLM-650M") model = AutoModelForMaskedLM.from_pretrained("ChatterjeeLab/PepMLM-650M") - Inference
- Notebooks
- Google Colab
- Kaggle
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After successful *in silico* benchmarking with AlphaFold-Multimer, we experimentally verify PepMLM’s efficacy via fusion of model-derived peptides to E3 ubiquitin ligase domains, demonstrating endogenous degradation of target substrates in cellular models.
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In total, PepMLM enables the generative design of candidate binders to any target protein, without the requirement of target structure, empowering downstream programmable proteome editing applications.
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- Demo: HuggingFace Space Demo [Link](https://huggingface.co/spaces/TianlaiChen/PepMLM).
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- Colab Notebook: [Link](https://colab.research.google.com/drive/1u0i-LBog_lvQ5YRKs7QLKh_RtI-tV8qM?usp=sharing)
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- Preprint: [Link](https://arxiv.org/abs/2310.03842)
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- Nature Biotechnology: [Link](https://www.nature.com/articles/s41587-025-02761-2)
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After successful *in silico* benchmarking with AlphaFold-Multimer, we experimentally verify PepMLM’s efficacy via fusion of model-derived peptides to E3 ubiquitin ligase domains, demonstrating endogenous degradation of target substrates in cellular models.
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In total, PepMLM enables the generative design of candidate binders to any target protein, without the requirement of target structure, empowering downstream programmable proteome editing applications.
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- Demo: HuggingFace Space Demo [Link](https://huggingface.co/spaces/TianlaiChen/PepMLM).
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- Colab Notebook: [Link](https://colab.research.google.com/drive/1u0i-LBog_lvQ5YRKs7QLKh_RtI-tV8qM?usp=sharing)
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- Preprint: [Link](https://arxiv.org/abs/2310.03842)
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- Nature Biotechnology: [Link](https://www.nature.com/articles/s41587-025-02761-2)
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