Update README.md

README.md

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 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.
 
 ## Google Colab Notebooks
-We provide three Google Colab Notebooks so that you can use and evaluate moPPIt easily.
+We provide two Google Colab notebooks to help you run and evaluate moPPIt without any local setup:
 
-**Colab Notebook for moPPIt**: [Link](https://colab.research.google.com/drive/16n8PIwKwAiG-oDLm171BWvv-lQH0dHMg?usp=sharing)
+- **moPPIt Colab** (Generating motif-specific binders while optimizing other therapeutic-related properties): [Link](https://colab.research.google.com/drive/16n8PIwKwAiG-oDLm171BWvv-lQH0dHMg?usp=sharing)
 
-**Colab Notebook for PeptiDerive**: [Link](https://colab.research.google.com/drive/1aCODZ-WRwhxr-u8nEB6ZrdrhIOTz7-UF?usp=sharing)
+- **PeptiDerive Colab** (Computing Relative Interaction Scores (RIS) for residues on the target protein): [Link](https://colab.research.google.com/drive/1aCODZ-WRwhxr-u8nEB6ZrdrhIOTz7-UF?usp=sharing)
+
+## Command-line Usage
+You can also run **moPPIt** and **BindEvaluator** from the command line. 
+
+### 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
+```
+
+### 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
+```