Features:

• Implementation based on microservices architecture
• Launch all with one docker-compose up
• Services run in isolated docker container
• Submit tasks with RESTful API (FastAPI)
• Separated task queues and concurrence control
• Flower for monitoring the Celery tasks

AIRFold is

Please follow these steps:

1. Install Docker.

   • Install NVIDIA Container Toolkit for GPU support.
   • Setup running Docker as a non-root user.

2. Clone this repository and cd into it.

   git clone https://github.com/health-air/AIRFold
   cd ./AIRFold
   docker-compose up

3. Check the page:

   • Submit page: http://127.0.0.1
   • FastAPI page: http://127.0.0.1:8081/docs
   • Tasks monitor page (powered by flower): http://127.0.0.1:5555

Note: please change IP address and ports accordingly, they are specified in docker-compose.yml

Genomics and metagenomics sequence databases

• BFD,
• MGnify,
• UniRef90,
• NR database for BLAST,
• Genomics and metagenomics sequence databases for DeepMSA2,
• ColabFold dataset for MMseqs2,
• Small BFD sequence database
• Uniprot sequence database

Structure databases

• PDB70,
• PDB (MMCIF format)

Data structure

├── model_params (models and parameters for AlphaFold2, RoseTTAFold2, ect.)
├── bfd
├── blast_dbs
├── JGIclust
├── metaclust
├── mgnify
├── pdb70
├── pdb_mmcif
├── small_bfd
├── uniclust30
├── uniref30
└── uniref90

MSA-based structure prediction

• AlphaFold2
• RoseTTAFold2

Single sequence-based structure prediction

• OmegaFold
• ESMFold
• RaptorX-Single

Multiple sequence alignment generation

• HH-suite3,
• jackhmmer
• BLAST
• DeepMSA2
• MMseq2

Multiple sequence alignment selection

• PLMSearch

Protein model quality assessment

• EnQA
• QATEN

```bash
python run_mode.py --input_path example.fasta --mode msa
```

```bash
python run_mode.py --input_path example.fasta --mode feature
```

```bash
python run_mode.py --input_path example.fasta --mode disgram
```

```bash
python run_mode.py --input_path example.fasta --mode pipline
```