ProtBert-BFD
| Property | Value |
|---|---|
| Developer | Rostlab |
| Training Data | BFD Dataset (2.1B protein sequences) |
| Paper | ProtTrans Paper |
| Training Infrastructure | TPU Pod V3-1024 |
What is prot_bert_bfd?
ProtBert-BFD is a sophisticated protein language model based on the BERT architecture, specifically designed for understanding protein sequences. It was trained on the massive BFD dataset containing 2.1 billion protein sequences, making it one of the most comprehensive protein language models available. The model operates on uppercase amino acids and employs masked language modeling (MLM) for protein sequence analysis.
Implementation Details
The model was trained using a combination of sequence lengths (512 and 2048) over one million steps, utilizing advanced hardware infrastructure. It implements a modified BERT architecture that treats each protein sequence as a separate document, eliminating the need for next sentence prediction.
- Training utilized 936 nodes with 5616 GPUs
- Implements masked language modeling with 15% masking rate
- Uses Lamb optimizer with 0.002 learning rate
- Trained on sequence lengths of 512 (800K steps) and 2048 (200K steps)
Core Capabilities
- Secondary structure prediction (3-states: Q3=76-84%, 8-states: Q8=65-73%)
- Subcellular localization prediction (Q10=74%)
- Membrane protein prediction (Q2=89%)
- Feature extraction for protein sequences
- Masked amino acid prediction
Frequently Asked Questions
Q: What makes this model unique?
The model's training on 2.1 billion protein sequences (equivalent to 112 times the size of Wikipedia) and its ability to capture biophysical properties from unlabeled data make it unique. It can understand the "grammar" of protein sequences without supervised training.
Q: What are the recommended use cases?
The model is ideal for protein feature extraction, secondary structure prediction, and can be fine-tuned for various downstream tasks in protein analysis. It's particularly effective when fine-tuned rather than used purely as a feature extractor.
