MSAGPT

MSAGPT 📖 Paper: MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative Pre-Training MSAGPT is a powerful protein language model (PLM). MSAGPT has 3 billion parameters with three versions of the model, MSAGPT, MSAGPT-Sft, and MSAGPT-Dpo, supporting zero-shot and few-shot MSA generation. MSAGPT achieves state-of-the-art structural prediction performance on natural MSA-scarce scenarios.

Overall Framework

Visualized Cases

Visualization of improved structure prediction compared with nature MSA. Yellow: Ground truth; Purple: Predictions based on MSA generated by MSAGPT; Cyan: Predictions from MSA generated by natural MSA.

Get Started:

Option 1：Deploy MSAGPT by yourself

We support GUI for model inference.

First, we need to install the dependencies.

# CUDA >= 11.8
pip install -r requirements.txt

Model List

You can choose to manually download the necessary weights. Then UNZIP it and put it into the checkpoints folder.

Model	Type	Seq Length	Download
MSAGPT	Base	16K	🤗 Huggingface 🔨 SwissArmyTransformer
MSAGPT-SFT	Sft	16K	🤗 Huggingface 🔨 SwissArmyTransformer
MSAGPT-DPO	Rlhf	16K	🤗 Huggingface 🔨 SwissArmyTransformer

Situation 1.1 CLI (SAT version)

Run CLI demo via:

# Online Chat
bash scripts/cli_sat.sh --from_pretrained ./checkpoints/MSAGPT-DPO --input-source chat --stream_chat --max-gen-length 1024

The program will automatically interact in the command line. You can generate replies entering the protein sequence you need to generate virtual MSAs (or add a few MSAs as a prompt, connected by "<M>"), for example: "PEGKQGDPGIPGEPGPPGPPGPQGARGPPG<M>VTVEFVNSCLIGDMGVDGPPGQQGQPGPPG", where "PEGKQGDPGIPGEPGPPGPPGPQGARGPPG" is the main sequence, and "VTVEFVNSCLIGDMGVDGPPGQQGQPGPPG" are MSA prompts, and pressing enter. Enter stop to stop the program. The chat CLI looks like:

You can also enable the offline generation by set the --input-source <your input file> and --output-path <your output path>. We set an input file example: msa_input.

# Offline Generation
bash scripts/cli_sat.sh --from_pretrained ./checkpoints/MSAGPT-DPO --input-source <your input file> --output-path <your output path> --max-gen-length 1024

Situation 1.2 CLI (Huggingface version)

(TODO)

Situation 1.3 Web Demo

(TODO)

Option 2：Finetuning MSAGPT

(TODO)

Hardware requirement

• Model Inference: For BF16: 1 * A100(80G)

• Finetuning:

  For BF16: 4 * A100(80G) [Recommend].

Natural MSA-scarce benchmark

Please find the 199 cases along with their retrieved MSAs in the natural-msa-scarce-cases.txt file. Each line is structured as follows:

<PDB-id> <Primary Sequence> <M> <MSA1> <M> ... <MSAn>

Explanation of the Data Structure

• <PDB-id>: The unique identifier for the protein structure.
• <Primary Sequence>: The amino acid sequence of the protein.
• <M>: A delimiter separating different sections.
• <MSA1> ... <MSAn>: The multiple sequence alignments retrieved for each case.

License

The code in this repository is open source under the Apache-2.0 license.

If you find our work helpful, please consider citing the our paper

@article{chen2024msagpt,
  title={MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative Pre-Training},
  author={Chen, Bo and Bei, Zhilei and Cheng, Xingyi and Li, Pan and Tang, Jie and Song, Le},
  journal={arXiv preprint arXiv:2406.05347},
  year={2024}
}