GATE: Graph Transformers for Estimating Protein Model Accuracy

Table of Contents

1. Introduction
2. Installation
   • Clone the Repository
   • Install Mamba
   • Install Tools
   • Set Up Python Environments
   • Download Databases
3. Configuration
4. Usage
   • Required Arguments
   • Optional Arguments
   • Example Commands
5. Citing This Work

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Introduction

GATE is a tool designed for estimating protein model accuracy using advanced graph transformers. This repository contains the code, pre-trained models, and instructions for setup and usage.

The overall performance of GATE (MULTICOM_GATE) in CASP16 EMA competition in terms of Z-scores

The overall performance of GATE (MULTICOM_GATE) in CASP16 EMA competition in terms of per-target average

Predictor Name	Pearson's correlation	Spearman's correlation	Ranking loss	AUC
GuijunLab-QA	0.6479	0.4149	0.1195	0.6328
MULTICOM	0.6156	0.4380	0.1207	0.6660
MULTICOM_GATE	0.7076	0.4514	0.1221	0.6680
MULTICOM_LLM	0.6836	0.4808	0.1230	0.6685
MIEnsembles-Server	0.6072	0.4498	0.1325	0.6670
GuijunLab-PAthreader	0.5309	0.3744	0.1331	0.6237
ModFOLDdock2	0.6542	0.4640	0.1371	0.6859
ModFOLDdock2R	0.5724	0.3867	0.1375	0.6518
VifChartreuse	0.2921	0.2777	0.1440	0.6149
MQA_base	0.4331	0.2897	0.1462	0.6085
MQA_server	0.4326	0.2913	0.1468	0.6120
GuijunLab-Human	0.6327	0.4148	0.1477	0.6368
MULTICOM_human	0.5897	0.4260	0.1518	0.6576
ChaePred	0.4548	0.3971	0.1580	0.6534
AF_unmasked	0.4015	0.2731	0.1595	0.6052
VifChartreuseJaune	0.3421	0.1756	0.1630	0.5951
GuijunLab-Assembly	0.5439	0.3280	0.1636	0.6191
Guijunlab-Complex	0.4889	0.3019	0.1792	0.6054
ModFOLDdock2S	0.5285	0.3116	0.1806	0.6084
MULTICOM_AI	0.3281	0.2623	0.1913	0.6057
COAST	0.3840	0.2297	0.2091	0.6072
MQA	0.4410	0.2425	0.2183	0.5858
PIEFold_human	0.1929	0.1451	0.2306	0.5497

Installation

Clone the Repository

git clone -b public https://github.com/BioinfoMachineLearning/gate
cd gate

Install Mamba

wget "https://github.com/conda-forge/miniforge/releases/download/23.1.0-3/Mambaforge-$(uname)-$(uname -m).sh"
bash Mambaforge-$(uname)-$(uname -m).sh 
rm Mambaforge-$(uname)-$(uname -m).sh
source ~/.bashrc  

Install tools

cd tools

# Install GCPNet-EMA
git clone https://github.com/BioinfoMachineLearning/GCPNet-EMA
mkdir GCPNet-EMA/checkpoints
wget -P GCPNet-EMA/checkpoints/ https://zenodo.org/record/10719475/files/structure_ema_finetuned_gcpnet_i2d5t9xh_best_epoch_106.ckpt

# Install EnQA
git clone https://github.com/BioinfoMachineLearning/EnQA
chmod -R 755 EnQA/utils

# Install DProQA
git clone https://github.com/jianlin-cheng/DProQA

# Install Venclovas QAs
git clone https://github.com/kliment-olechnovic/ftdmp

# Install CDPred
git clone https://github.com/BioinfoMachineLearning/CDPred

# Install openstructure
docker pull registry.scicore.unibas.ch/schwede/openstructure:latest
# or
singularity pull docker://registry.scicore.unibas.ch/schwede/openstructure:latest

Set Up Python Environments

# Install python enviorment for gate
mamba install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
mamba install -c dglteam dgl-cuda11.0
mamba install pandas biopython

# Install python enviorment for GCPNet-EMA
mamba env create -f tools/GCPNet-EMA/environment.yaml
mamba activate GCPNet-EMA
pip3 install -e tools/GCPNet-EMA
pip3 install prody==2.4.1
pip3 uninstall protobuf
mamba deactivate

# Install python enviorment for EnQA
mamba env create -f envs/enqa.yaml

# Install python enviorment for DProQA
mamba env create -f envs/dproqa.yaml

# Install python enviorment for VoroMQA
mamba env create -f envs/ftdmp.yaml

# Install python enviorment for CDPred
mamba env create -f envs/cdpred.yaml

Download databases (~2.5T)

mkdir databases

# Create virtual links if the databases are stored elsewhere
sh scripts/download_bfd.sh databases/
sh scripts/download_uniref90.sh databases/

Configuration

* Replace the contents for the ROOTDIR in gate/feature/config.py with your installation path

* Set use_docker to False if using Singularity instead of Docker.

Usage

To run the GATE tool for estimating protein multimer structure accuracy, use the inference_multimer.py script with the following arguments:

Required Arguments:

• --fasta_path FASTA_PATH

  The path to the input FASTA file containing the protein sequences.

• --input_model_dir INPUT_MODEL_DIR

  The directory containing the input protein models.

• --output_dir OUTPUT_DIR

  The directory where the output results will be saved.

Optional Arguments:

• --pkldir PKLDIR

  The directory where intermediate pickle files will be stored.

• --use_af_feature USE_AF_FEATURE

  Specify whether to use AlphaFold features. Accepts True or False. Default is False.

• --sample_times SAMPLE_TIMES Number of times to sample the models. Default is 5.

Example Commands:

Here are examples of how to use the inference_multimer.py script with different settings:

1. Not using AlphaFold Features (default)

   python inference_multimer.py --fasta_path $FASTA_PATH --input_model_dir $INPUT_MODEL_DIR --output_dir $OUTPUT_DIR
   

2. Using AlphaFold Features

   python inference_multimer.py --fasta_path $FASTA_PATH --input_model_dir $INPUT_MODEL_DIR --output_dir $OUTPUT_DIR --pkldir $PKLDIR --use_af_feature True

Citing This Work

If you find this work useful, please cite:

Liu, J., Neupane, P., & Cheng, J. (2025). Estimating Protein Complex Model Accuracy Using Graph Transformers and Pairwise Similarity Graphs. bioRxiv, 2025-02 (https://www.biorxiv.org/content/10.1101/2025.02.04.636562v1)

@article {Liu2025.02.04.636562,
	author = {Liu, Jian and Neupane, Pawan and Cheng, Jianlin},
	title = {Estimating Protein Complex Model Accuracy Using Graph Transformers and Pairwise Similarity Graphs},
	elocation-id = {2025.02.04.636562},
	year = {2025},
	doi = {10.1101/2025.02.04.636562},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://doi.org/10.1101/2025.02.04.636562},
	journal = {bioRxiv}
}