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SaLT&PepPr
To use this repository, you must abide by the UbiquiTx License: https://huggingface.co/ubiquitx/saltnpeppr/blob/main/SaLT%26PepPr%20Licence%20FINAL.pdf
An Interface-Predicting Language Model for Designing Peptide-Guided Protein Degraders
Protein-protein interactions (PPIs) are critical for biological processes and predicting the sites of these interactions is useful for both computational and experimental applications. We present a Structure-agnostic Language Transformer and Peptide Prioritization (SaLT&PepPr) pipeline to predict interaction interfaces from a protein sequence alone for the subsequent generation of peptidic binding motifs. Our model fine-tunes the ESM-2 protein language model (pLM) with a per-position prediction task to identify PPI sites using data from the PDB, and prioritizes motifs which are most likely to be involved within inter-chain binding. By only using amino acid sequence as input, our model is competitive with structural homology-based methods, but exhibits reduced performance compared with deep learning models that input both structural and sequence features. Inspired by our previous results using co-crystals to engineer target-binding “guide” peptides, we curate PPI databases to identify partners for subsequent peptide derivation. Fusing guide peptides to an E3 ubiquitin ligase domain, we demonstrate degradation of endogenous β-catenin, 4E-BP2, and TRIM8, and highlight the nanomolar binding affinity, low off-targeting propensity, and function-altering capability of our best-performing degraders in cancer cells. In total, our study suggests that prioritizing binders from natural interactions via pLMs can enable programmable protein targeting and modulation.
Please read and cite our manuscript published in Communications Biology!
Weight are in the repository and datasets are available in the datasets.zip file!
We have developed a user-friendly Colab notebook for peptide generation with SaLT&PepPr! When you download the repository, copy this Colab notebook into the repository main directory (which should be located at MyDrive/saltnpeppr) and follow the instructions to generate peptides.
Repository Authors: Garyk Brixi, Sophia Vincoff, and Pranam Chatterjee
Contact: info@ubiquitx.com