PepMLM: Target Sequence-Conditioned Generation of Therapeutic Peptide Binders via Span Masked Language Modeling
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arXiv
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| Natura: | Preprint |
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2023
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| author | Chen, Tianlai Dumas, Madeleine Watson, Rio Vincoff, Sophia Peng, Christina Zhao, Lin Hong, Lauren Pertsemlidis, Sarah Shaepers-Cheu, Mayumi Wang, Tian Zi Srijay, Divya Monticello, Connor Vure, Pranay Pulugurta, Rishab Kholina, Kseniia Goel, Shrey DeLisa, Matthew P. Truant, Ray Aguilar, Hector C. Chatterjee, Pranam |
| author_facet | Chen, Tianlai Dumas, Madeleine Watson, Rio Vincoff, Sophia Peng, Christina Zhao, Lin Hong, Lauren Pertsemlidis, Sarah Shaepers-Cheu, Mayumi Wang, Tian Zi Srijay, Divya Monticello, Connor Vure, Pranay Pulugurta, Rishab Kholina, Kseniia Goel, Shrey DeLisa, Matthew P. Truant, Ray Aguilar, Hector C. Chatterjee, Pranam |
| contents | Target proteins that lack accessible binding pockets and conformational stability have posed increasing challenges for drug development. Induced proximity strategies, such as PROTACs and molecular glues, have thus gained attention as pharmacological alternatives, but still require small molecule docking at binding pockets for targeted protein degradation. The computational design of protein-based binders presents unique opportunities to access "undruggable" targets, but have often relied on stable 3D structures or structure-influenced latent spaces for effective binder generation. In this work, we introduce PepMLM, a target sequence-conditioned generator of de novo linear peptide binders. By employing a novel span masking strategy that uniquely positions cognate peptide sequences at the C-terminus of target protein sequences, PepMLM fine-tunes the state-of-the-art ESM-2 pLM to fully reconstruct the binder region, achieving low perplexities matching or improving upon validated peptide-protein sequence pairs. After successful in silico benchmarking with AlphaFold-Multimer, outperforming RFDiffusion on structured targets, we experimentally verify PepMLM's efficacy via fusion of model-derived peptides to E3 ubiquitin ligase domains, demonstrating endogenous degradation of emergent viral phosphoproteins and Huntington's disease-driving proteins. In total, PepMLM enables the generative design of candidate binders to any target protein, without the requirement of target structure, empowering downstream therapeutic applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_03842 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | PepMLM: Target Sequence-Conditioned Generation of Therapeutic Peptide Binders via Span Masked Language Modeling Chen, Tianlai Dumas, Madeleine Watson, Rio Vincoff, Sophia Peng, Christina Zhao, Lin Hong, Lauren Pertsemlidis, Sarah Shaepers-Cheu, Mayumi Wang, Tian Zi Srijay, Divya Monticello, Connor Vure, Pranay Pulugurta, Rishab Kholina, Kseniia Goel, Shrey DeLisa, Matthew P. Truant, Ray Aguilar, Hector C. Chatterjee, Pranam Biomolecules Target proteins that lack accessible binding pockets and conformational stability have posed increasing challenges for drug development. Induced proximity strategies, such as PROTACs and molecular glues, have thus gained attention as pharmacological alternatives, but still require small molecule docking at binding pockets for targeted protein degradation. The computational design of protein-based binders presents unique opportunities to access "undruggable" targets, but have often relied on stable 3D structures or structure-influenced latent spaces for effective binder generation. In this work, we introduce PepMLM, a target sequence-conditioned generator of de novo linear peptide binders. By employing a novel span masking strategy that uniquely positions cognate peptide sequences at the C-terminus of target protein sequences, PepMLM fine-tunes the state-of-the-art ESM-2 pLM to fully reconstruct the binder region, achieving low perplexities matching or improving upon validated peptide-protein sequence pairs. After successful in silico benchmarking with AlphaFold-Multimer, outperforming RFDiffusion on structured targets, we experimentally verify PepMLM's efficacy via fusion of model-derived peptides to E3 ubiquitin ligase domains, demonstrating endogenous degradation of emergent viral phosphoproteins and Huntington's disease-driving proteins. In total, PepMLM enables the generative design of candidate binders to any target protein, without the requirement of target structure, empowering downstream therapeutic applications. |
| title | PepMLM: Target Sequence-Conditioned Generation of Therapeutic Peptide Binders via Span Masked Language Modeling |
| topic | Biomolecules |
| url | https://arxiv.org/abs/2310.03842 |