PPLM

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Version 1.0, 03/25/2025
(Copyrighted by the Regents of the National University of Singapore, All rights reserved)

PPLM is a protein–protein language model that learns directly from paired sequences through a novel attention architecture, explicitly capturing inter-protein context. Building on PPLM, we developed PPLM-PPI, PPLM-Affinity, and PPLM-Contact for predicting protein–protein interactions, estimating binding affinity, and identifying interface residue contacts, respectively.

Authors: Jun Liu, Hungyu Chen, and Yang Zhang

Contact: junl_sg@nus.edu.sg

Citation:

Jun Liu, Hungyu Chen, Yang Zhang. A Protein-Protein Language Model for Interaction, Binding Affinity, and Interface Contact Prediction. In preparation.

Webserver: PPLM Online Submission

License: PolyForm Noncommercial License

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System Requirements

• x86_64 machine
• Linux Kernel OS

Software & Dataset Requirements (for PPLM-Contact)

1. HH-suite3 for MSA Search: Install HH-suite3
2. Uniclust Database: Download Uniclust
3. CCMpred for DCA: Install CCMpred
4. LoadHHM for PSSM Calculation: LoadHHM.py
5. ESM-MSA for Feature Generation: Install ESM

Usage

# 1. Install environment
conda env create -f environment.yml

# 2. Activate environment
conda activate PPLM

# 3. Run PPLM-PPI
python run_pplm-ppi.py example/seq_pairs.fasta example/seq_pairs.results

# 4. Run PPLM-Affinity
python run_pplm-affinity.py example/receptor.fasta example/ligand.fasta

# 5. Run PPLM-Contact (homodimer)
python run_pplm-contact.py example/protein.pdb example/protein.pdb example/homo_example

# 6. Run PPLM-Contact (heterodimer)
python run_pplm-contact.py example/protein1.pdb example/protein2.pdb example/hetero_example

# 7. Generate embeddings and attention weights
python run_pplm.py example/seq1.fasta example/seq2.fasta example/seq1-seq2.pplm.pkl

Example Outputs

PPLM-PPI

• Command:

python run_pplm-ppi.py example/seq_pairs.fasta example/seq_pairs.results

• Output format (example):

>Protein1:Protein2
Interaction Probability

PPLM-Affinity

• Command:

python run_pplm-affinity.py example/receptor.fasta example/ligand.fasta

• Output: Predicted binding affinity printed to the command line.

PPLM-Contact

• Command:

python run_pplm-contact.py example/protein.pdb example/protein.pdb example/homo_example

• Output file: homo_example/homo_example.pred_contact.txt

Format:

Rank	ResIdx1	ResType1	ResIdx2	ResType2	Contact Probability

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This README was automatically generated.