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.

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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

• PeptiDerive Colab (compute Relative Interaction Scores (RIS) for residues on the target protein): Link

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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, PhD Student, University of Pennsylvania

Pranam Chatterjee, Assistant Professor, University of Pennsylvania

Reach out to us with any questions!