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---
title: Transeption IGEM BASISCHINA 2025
emoji: 🧬
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 5.34.2
app_file: app.py
pinned: false
license: mit
suggested_hardware: zero-a10g
models:
- PascalNotin/Tranception_Small
- PascalNotin/Tranception_Medium
- PascalNotin/Tranception_Large
---
# Tranception Protein Fitness Prediction - BASIS-China iGEM 2025
Welcome to BASIS-China iGEM Team's deployment of Tranception on Hugging Face Spaces!
## About This Project
This is an implementation of the Tranception model for protein fitness prediction, deployed by the BASIS-China iGEM Team 2025. Our goal is to make advanced protein engineering tools accessible to the synthetic biology community.
### Features
- **In silico directed evolution**: Iteratively improve protein fitness through single amino acid substitutions
- **Comprehensive fitness analysis**: Generate heatmaps showing fitness scores for all possible mutations
- **Zero GPU support**: Leverages Hugging Face's dynamic GPU allocation for efficient inference
- **Multiple model sizes**: Choose between Small, Medium, and Large models based on your needs
### Technical Implementation
This deployment utilizes Hugging Face's Zero GPU infrastructure, which:
- Dynamically allocates H200 GPU resources when available
- Seamlessly falls back to CPU processing when GPUs are unavailable
- Ensures efficient resource management for all users
## About BASIS-China iGEM Team
We are a high school synthetic biology team participating in the International Genetically Engineered Machine (iGEM) competition. Our 2025 project focuses on protein engineering and computational biology applications.
## Credits
This implementation is based on:
**Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval**
by Pascal Notin, Mafalda Dias, Jonathan Frazer, Javier Marchena-Hurtado, Aidan N. Gomez, Debora S. Marks, and Yarin Gal.
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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