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license: mit |
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**PepMLM: Target Sequence-Conditioned Generation of Peptide Binders via Masked Language Modeling** |
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In this work, we introduce **PepMLM**, a purely target sequence-conditioned *de novo* generator of linear peptide binders. |
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By employing a novel masking strategy that uniquely positions cognate peptide sequences at the terminus of target protein sequences, |
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PepMLM tasks the state-of-the-art ESM-2 pLM to fully reconstruct the binder region, |
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achieving low perplexities matching or improving upon previously-validated peptide-protein sequence pairs. |
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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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**Graphical Summary**: |
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``` |
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# Load model directly |
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from transformers import AutoTokenizer, AutoModelForMaskedLM |
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tokenizer = AutoTokenizer.from_pretrained("TianlaiChen/PepMLM-650M") |
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model = AutoModelForMaskedLM.from_pretrained("TianlaiChen/PepMLM-650M") |
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``` |
