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genbio-ai/
GB.Protein-RAG-16B

Name: GB.Protein-RAG-16B
Availability: InStock

pytorch

fm4bio

genbio-ai/GB.Protein-16B

finetune

other

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GB.Protein-RAG-16B

GB.Protein-RAG-16B is a multimodal protein language model that integrates Multiple Sequence Alignment (MSA) and structural data, building upon the GB.Protein-16B foundation. The training process comprises three main stages:

2D RoPE encoding fine-tuning
Initial training on 100 billion tokens from UniRef50/UniClust30 MSA data
Subsequent training on 80 billion tokens from AlphaFold Database MSA and structural data

Model Architecture Details

GB.Protein-RAG-16B employs a transformer encoder-only architecture featuring sparse Mixture-of-Experts (MoE) layers that replace dense MLP layers in each transformer block. Utilizing single amino acid tokenization and optimized through masked language modeling (MLM), the model activates 2 experts per token via top-2 routing mechanisms.

More architecture details are shown below:

Model Arch Component	Value
Num Attention Head	36
Num Hidden Layer	36
Hidden Size	2304
FFN Hidden Size	7680
Num MoE Layer per Block	8
Num MoE Layer per Token	2
Vocab Size	44
Context Length	2048

Pre-training of GB.Protein-RAG-16B

Here we briefly introduce the details of pre-training of GB.Protein-RAG-16B. Mainly divided into three stages: (1) 1D -> 2D RoPE encoding finetuning; (2) UniRef50/Uniclust30 MSA finetuning; (3) AlphaFold Database MSA & Structure tokens finetuning.

Data

UniRef50/Uniclust30 MSA dataset: We utilized sequences from UniRef50 as queries to search for homologous sequences in UniClust30, subsequently constructing multiple sequence alignments (MSAs). UniRef50 comprises a total of 53.6 million sequences. Using HHblits, we searched all sequences, identifying over 25 homologous sequences for 23.7 million of them. This dataset was directly used as the training set, referred to as HHblits_MSA. The remaining 29.9 million sequences were input into MSA Retriever, resulting in 7.7 million sequences with more than 25 homologous sequences. This dataset was designated as Retriever_MSA. During training, RAGPLM randomly sampled from the two datasets with probabilities of 0.75 and 0.25. Refer to GB.Protein-RAG-3B paper (link) for more information.

AlphaFold Database MSA & Structure dataset: We downloaded all structural data from the AlphaFold Database and kept only those where more than 40% of amino acids had a pLDDT score > 70. The remaining sequences were clustered using mmseqs (seq id=0.5), and one representative per cluster was retained, resulting in 46.9 million sequence/structure pairs. For each structure, we used genbio-ai/GB.StructureTokenizer to obtain structure tokens and embeddings. MSA Retriever was used to obtain the corresponding MSA.

Training Details

Model training is divided into three stages:

(1) 1D -> 2D RoPE Encoding Fine-tuning

Same training data as GB.Protein-16B, but with 2D rotary position embedding for token encoding.

(2) UniRef50/UniClust30 MSA Fine-tuning

The model from Stage 1 is further fine-tuned on the UniRef50/Uniclust30 MSA dataset. See the GB.Protein-RAG-3B paper for more.

(3) AlphaFold Database MSA & Structure Fine-tuning

We fine-tuned the model with concatenated query and homologous sequences. Structure embeddings (dim = 384) are linearly mapped to 2304 and added to the query token embeddings.

Sequence Masking

Randomly sample 0.05 × L span positions from a query of length L. Span lengths follow a geometric distribution (p=0.2), capped at length 10. On average, ~15% of query tokens are masked.
When a residue is selected, its aligned residues across all sequences (MSA column) are also masked.
For masked MSA columns: 80% are replaced with <MASK>, 10% with random amino acids, and 10% left unchanged.

Structure Masking

In 20% of cases, structure embeddings are replaced with 0.
In 80% of cases, a number of amino acids is sampled using the BetaLinear30 distribution and corresponding embeddings are zeroed. (BetaLinear30 = 20% Uniform(0,1) + 80% Beta(3,9)).

Positional Embedding

We use 2D rotary position embedding to help the model distinguish token chain identities and residue indices. See GB.Protein-RAG-3B paper (link) for more information.

Loss Function

Total loss is a weighted sum of sequence loss (weight 1.0) and structure loss (weight 0.01).

Sequence loss: CrossEntropy loss for masked token prediction.
Structure loss: CrossEntropy loss for masked structure token prediction.

Hyper-params	(1) 1D -> 2D finetuning	(2) UniRef50/Uniclust30 MSA finetuning	(3) AFDB MSA & Structure tokens finetuning
Initialized parameters	GB.Protein-16B	Stage (1)	Stage (2)
Data	ColabFoldDB, UniRef	HHblits_MSA, Retriever_MSA	AFDB MSA & Structure tokens
Global Batch Size	512	256	256
Sequence length	2048	12800	12800
Per Device Micro Batch Size	1	1	1
Precision	Mixed FP32-FP16	Mixed FP32-FP16	Mixed FP32-FP16
LR	[5e-6,5e-5]	[1e-6, 1e-5]	1e-5
Num Tokens	10 billion	100 billion	80 billion

Tokenization

We encode protein sequence with single amino acid resolution with 44 vocabularies, where 24 tokens represent amino acid types and 20 are special tokens. Sequences were also suffixed with a [SEP] token as hooks for downstream tasks.

Results

Supervised Downstream Tasks

Supervised DMS Fitness Score Prediction (25 Samples)

How to Use

Build Downstream Models Using ModelGenerator

For more information, visit: Model Generator

bash

mgen fit --model SequenceClassification --model.backbone aido_protein_rag_16b --data SequenceClassificationDataModule --data.path <hf_or_local_path_to_your_dataset>
mgen test --model SequenceClassification --model.backbone aido_protein_rag_16b --data SequenceClassificationDataModule --data.path <hf_or_local_path_to_your_dataset>

Use Directly in Python

Embedding

python

import torch
from modelgenerator.tasks import Embed
model = Embed.from_config({"model.backbone": "aido_protein_rag_16b"}).eval()
model.backbone.max_length = 12800
restypes = 'ARNDCQEGHILKMFPSTWYV'
data = {
    'sequences': [''.join(random.choice(restypes) for _ in range(50))],
    'msa': [ [ ''.join(random.choice(restypes+'-') for _ in range(50)) for _ in range(25) ] ],
    'str_emb': np.random.normal(size=(1, 50, 384))
}
transformed_batch = model.transform(data)
with torch.no_grad():
    embedding = model(transformed_batch)

print(embedding.shape)

Sequence Level Classification

python

import torch
from modelgenerator.tasks import SequenceClassification
model = SequenceClassification.from_config({"model.backbone": "aido_protein_rag_16b", "model.n_classes": 2}).eval()
model.backbone.max_length = 12800
restypes = 'ARNDCQEGHILKMFPSTWYV'
data = {
    'sequences': [''.join(random.choice(restypes) for _ in range(50))],
    'msa': [ [ ''.join(random.choice(restypes+'-') for _ in range(50)) for _ in range(25) ] ],
    'str_emb': np.random.normal(size=(1, 50, 384))
}
transformed_batch = model.transform(data)
with torch.no_grad():
    logits = model(transformed_batch)

print(logits)
print(torch.argmax(logits, dim=-1))

Token Level Classification

python

import torch
from modelgenerator.tasks import TokenClassification
model = TokenClassification.from_config({"model.backbone": "aido_protein_rag_16b", "model.n_classes": 3}).eval()
model.backbone.max_length = 12800
restypes = 'ARNDCQEGHILKMFPSTWYV'
data = {
    'sequences': [''.join(random.choice(restypes) for _ in range(50))],
    'msa': [ [ ''.join(random.choice(restypes+'-') for _ in range(50)) for _ in range(25) ] ],
    'str_emb': np.random.normal(size=(1, 50, 384))
}
transformed_batch = model.transform(data)
with torch.no_grad():
    logits = model(transformed_batch)

print(logits)
print(torch.argmax(logits, dim=-1))

Regression

python

from modelgenerator.tasks import SequenceRegression
model = SequenceRegression.from_config({"model.backbone": "aido_protein_rag_16b"}).eval()
model.backbone.max_length = 12800
restypes = 'ARNDCQEGHILKMFPSTWYV'
data = {
    'sequences': [''.join(random.choice(restypes) for _ in range(50))],
    'msa': [ [ ''.join(random.choice(restypes+'-') for _ in range(50)) for _ in range(25) ] ],
    'str_emb': np.random.normal(size=(1, 50, 384))
}
transformed_batch = model.transform(data)
with torch.no_grad():
    logits = model(transformed_batch)

print(logits.shape)

Citation

Please cite GB.Protein-RAG-16B using the following BibTex code:

@inproceedings{sun_mixture_2024,
    title = {Mixture of Experts Enable Efficient and Effective Protein Understanding and Design},
    url = {https://www.biorxiv.org/content/10.1101/2024.11.29.625425v1},
    doi = {10.1101/2024.11.29.625425},
    publisher = {bioRxiv},
    author = {Sun, Ning and Zou, Shuxian and Tao, Tianhua and Mahbub, Sazan and Li, Dian and Zhuang, Yonghao and Wang, Hongyi and Cheng, Xingyi and Song, Le and Xing, Eric P.},
    year = {2024},
    booktitle={NeurIPS 2024 Workshop on AI for New Drug Modalities},
}

@article {Li2024.12.02.626519,
    author = {Li, Pan and Cheng, Xingyi and Song, Le and Xing, Eric},
    title = {Retrieval Augmented Protein Language Models for Protein Structure Prediction},
    url = {https://www.biorxiv.org/content/10.1101/2024.12.02.626519v1},
    year = {2024},
    doi = {10.1101/2024.12.02.626519},
    publisher = {bioRxiv},
    booktitle={NeurIPS 2024 Workshop on Machine Learning in Structural Biology},
}

genbio-ai/GB.Protein-RAG-16B

GB.Protein-RAG-16B

Model Architecture Details

Pre-training of GB.Protein-RAG-16B

Data

Training Details

(1) 1D -> 2D RoPE Encoding Fine-tuning

(2) UniRef50/UniClust30 MSA Fine-tuning

(3) AlphaFold Database MSA & Structure Fine-tuning

Sequence Masking

Structure Masking

Positional Embedding

Loss Function

Tokenization

Results

Supervised Downstream Tasks

Supervised DMS Fitness Score Prediction (25 Samples)

How to Use

Build Downstream Models Using ModelGenerator

Use Directly in Python

Embedding

Sequence Level Classification

Token Level Classification

Regression

Citation

genbio-ai/
GB.Protein-RAG-16B