RoBERTa Base HumAb VH Model
This is a RoBERTa model trained from scratch for antibody humanization of Variable Heavy (VH) chain sequences using Masked Language Modeling (MLM).
Model Description
This model was trained on approximately 141 million human antibody variable heavy chain (VH) sequences for humanization tasks. It achieves an accuracy of 91.46% and can be applied to antibody sequence analysis, humanization, and the study of VH chain patterns.
Usage
from transformers import RobertaTokenizer, RobertaForMaskedLM
# Load tokenizer and model from Hugging Face
tokenizer = RobertaTokenizer.from_pretrained("hemantn/roberta-base-humAb-vh")
model = RobertaForMaskedLM.from_pretrained("hemantn/roberta-base-humAb-vh")
Using AnthroAb Python Package
For easier antibody humanization, you can use the AnthroAb Python package which provides a high-level interface for antibody humanization tasks. This package is available on PyPI and includes both VH and VL chain models.
Installation
conda create -n anthroab python=3.10
conda activate anthroab
pip install anthroab
Quick Usage
import anthroab
# Humanize a heavy chain sequence (VH)
vh_sequence= "**QLV*SGVEVKKPGASVKVSCKASGYTFTNYYMYWVRQAPGQGLEWMGGINPSNGGTNFNEKFKNRVTLTTDSSTTTAYMELKSLQFDDTAVYYCARRDYRFDMGFDYWGQGTTVTVSS"
humanized_vh = anthroab.predict_masked(vh_sequence, 'H')
print(f"Humanized VH: {humanized_vh}")
# Humanize a light chain sequence (VL)
vl_sequence = "DIQMTQSPSSLSASV*DRVTITCRASQSISSYLNWYQQKPGKAPKLLIYSASTLASGVPSRFSGSGSGTDF*LTISSLQPEDFATYYCQQSYSTPRTFGQGTKVEIK"
humanized_vl = anthroab.predict_masked(vl_sequence, 'L')
print(f"Humanized VL: {humanized_vl}")
Features
- Easy Installation: Install directly from PyPI with
pip install anthroab - High-Level API: Simple functions for antibody humanization
- Dual Chain Support: Separate models for VH and VL chains
- Sequence Infilling: Fill masked positions with human-like residues
- Mutation Suggestions: Get humanizing mutations for frameworks and CDRs
- Embedding Generation: Create vector representations of antibody sequences
The AnthroAb package uses this RoBERTa model (hemantn/roberta-base-humAb-vh) for VH chain humanization along with a companion VL model for light chain processing.
Model Details
Architecture
- Model: RoBERTa (trained from scratch)
- Architecture: RobertaForMaskedLM
- Model Type: Masked Language Model for antibody sequences
Specifications
- Hidden Size: 768
- Number of Layers: 12
- Number of Attention Heads: 12
- Intermediate Size: 3072
- Max Position Embeddings: 192
- Vocabulary Size: 25 tokens
- Model Size: ~164 MB