ProtBert
| Property | Value |
|---|---|
| Training Data | Uniref100 (217M protein sequences) |
| Architecture | BERT-based Transformer |
| Paper | ProtTrans Paper |
| Training Infrastructure | TPU Pod V3-512 |
What is prot_bert?
ProtBert is a sophisticated protein language model based on the BERT architecture, specifically designed for understanding and analyzing protein sequences. Developed by Rostlab, it was trained on an extensive dataset of 217 million protein sequences from Uniref100, making it one of the most comprehensive protein language models available.
Implementation Details
The model employs a masked language modeling (MLM) approach, trained on uppercase amino acids sequences. Unlike traditional BERT, it treats each protein sequence as a complete document, eliminating the need for next sentence prediction. The model was trained for 400k steps using a combination of sequence lengths (512 and 2048) on a TPU Pod V3-512.
- Masking Strategy: 15% of amino acids are masked during training
- Vocabulary: 21 amino acids (rare amino acids U,Z,O,B mapped to X)
- Training Configuration: Lamb optimizer with 0.002 learning rate
Core Capabilities
- Protein Feature Extraction
- Masked Language Modeling for Proteins
- Secondary Structure Prediction (3-states: 75-83% accuracy)
- Localization Prediction (79% accuracy)
- Membrane Protein Prediction (91% accuracy)
Frequently Asked Questions
Q: What makes this model unique?
ProtBert's uniqueness lies in its ability to capture biophysical properties from protein sequences through self-supervised learning, effectively learning the "grammar" of protein sequences without human labeling.
Q: What are the recommended use cases?
The model excels in protein feature extraction and can be fine-tuned for various downstream tasks such as secondary structure prediction, localization prediction, and membrane protein identification. It's particularly effective when fine-tuned rather than used solely as a feature extractor.
