PPLM/README.md

PPLM: A Protein-Protein Language Model for Interaction, Binding Affinity, and Interface Contact Prediction

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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

Download parameters

• Run the download_parameter.sh script located in the models/ folder of pplm/, pplm_ppi/, pplm_affinity/, and pplm_contact/

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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 for other applications

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: The predicted interaction probabilities are saved in example/seq_pairs.results:

>10090.ENSMUSP00000085394:10090.ENSMUSP00000116785
0.001926
>10090.ENSMUSP00000043111:10090.ENSMUSP00000102211
0.991765
>10090.ENSMUSP00000134644:10090.ENSMUSP00000131939
0.000425
>10090.ENSMUSP00000104648:10090.ENSMUSP00000095136
0.060997
>10090.ENSMUSP00000131855:10090.ENSMUSP00000118766
0.004577
>10090.ENSMUSP00000008036:10090.ENSMUSP00000046016
0.929329
...

Each entry consists of:
• Protein Pair: Represented in the format >Protein1:Protein2.
• Interaction Probability: The likelihood of interaction between the given protein pair.

PPLM-Affinity

• Command:

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

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

Predicted binding affinity: -7.6090136

PPLM-Contact

• Command:

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

• Output: The predicted contacts are saved in example/homo_example/homo_example.pred_contact.txt:

Format:
Rank      ResIdx1   ResType1  ResIdx2   ResType2  Contact_Probability
1         23:A      MET       26:B      CYS       0.976151
2         26:A      CYS       23:B      MET       0.974481
3         22:A      ILE       26:B      CYS       0.971633
4         23:A      MET       30:B      GLN       0.971191
5         30:A      GLN       22:B      ILE       0.970514
6         27:A      GLY       23:B      MET       0.970334
7         22:A      ILE       30:B      GLN       0.970124
8         30:A      GLN       23:B      MET       0.96919
9         23:A      MET       27:B      GLY       0.966725
10        23:A      MET       23:B      MET       0.966512
...