--- license: apache-2.0 --- # moPPIt: De Novo Generation of Motif-Specific Peptide Binders via Multi-Objective Discrete Flow Matching Targeting specific functional motifs, whether conserved viral epitopes, intrinsically disordered regions (IDRs), or fusion breakpoints, is essential for modulating protein function and protein-protein interactions (PPIs). Current design methods, however, depend on stable tertiary structures, limiting their utility for disordered or dynamic targets. Here, we present a motif-specific PPI targeting algorithm (moPPIt), a framework for the de novo generation of motif-specific peptide binders derived solely from target sequence data. The core of this approach is BindEvaluator, a transformer architecture that interpolates protein language model embeddings to predict peptide-protein binding site interactions with high accuracy (AUC = 0.97). We integrate this predictor into a novel Multi-Objective-Guided Discrete Flow Matching (MOG-DFM) framework, which steers generative trajectories toward peptides that simultaneously maximize binding affinity and motif specificity. After comprehensive in silico validation of binding and motif-specific targeting, we validate moPPIt in vitro by generating binders that strictly discriminate between the FN3 and IgG domains of NCAM1, confirming domain-level specificity, and further demonstrate precise targeting of IDRs by generating binders specific to the N-terminal disordered domain of β-catenin. In functional, disease-relevant assays, moPPIt-designed peptides targeting the GM-CSF receptor effectively block macrophage polarization. Finally, we demonstrate therapeutic utility in cell engineering, where binders directed against the tumor antigen AGR2 drive specific CAR T regulatory cell activation. In total, moPPIt serves as a purely sequence-based paradigm for controllably targeting the "undruggable" and disordered proteome. --- ## 1. Google Colab Notebooks We provide two Google Colab notebooks to help you run and evaluate moPPIt without any local setup: - **moPPIt Colab** (generate motif-specific binders while optimizing other therapeutic-related properties): [Link](https://colab.research.google.com/drive/16n8PIwKwAiG-oDLm171BWvv-lQH0dHMg?usp=sharing) - **PeptiDerive Colab** (compute Relative Interaction Scores (RIS) for residues on the target protein): [Link](https://colab.research.google.com/drive/1aCODZ-WRwhxr-u8nEB6ZrdrhIOTz7-UF?usp=sharing) --- ## 2. Command-line Usage You can also run **moPPIt** and **BindEvaluator** from the command line. ### 2.1 Run moPPIt Example command: ``` python -u moo.py \ --output_file './samples.csv' \ --length 10 \ --n_batches 600 \ --weights 1 1 1 4 4 2 \ --motifs '16-31,62-79' \ --motif_penalty \ --objectives Hemolysis Non-Fouling Half-Life Affinity Motif Specificity \ --target_protein MHVPSGAQLGLRPDLLARRRLKRCPSRWLCLSAAWSFVQVFSEPDGFTVIFSGLGNNAGGTMHWNDTRPAHFRILKVVLREAVAECLMDSYSLDVHGGRRTAAG ``` ### 2.2 Run BindEvaluator BindEvaluator predicts the binding sites on the target protein, given a target protein seqeunce and a binder sequence. Example command: ``` python -u bindevaluator.py \ -target MHVPSGAQLGLRPDLLARRRLKRCPSRWLCLSAAWSFVQVFSEPDGFTVIFSGLGNNAGGTMHWNDTRPAHFRILKVVLREAVAECLMDSYSLDVHGGRRTAAG \ -binder YVEICRCVVC \ -sm ./classifier_ckpt/finetuned_BindEvaluator.ckpt \ -n_layers 8 \ -d_model 128 \ -d_hidden 128 \ -n_head 8 \ -d_inner 64 ``` ## Repository Authors [Tong Chen](mailto:chentong@seas.upenn.edu), PhD Student, University of Pennsylvania [Pranam Chatterjee](mailto:pranam@seas.upenn.edu), Assistant Professor, University of Pennsylvania Reach out to us with any questions!