README.md

metadata

widget:
  - output:
      url: images/stableprot_abstract_graphic_nature.png
    text: '-'
base_model: westlake-repl/SaProt_650M_AF2
instance_prompt: null
license: cc-by-nc-nd-4.0
license_name: strictrestrictedlicense
license_link: LICENSE
tags:
  - biology
  - proteins
  - protein-language-models
  - thermal-stability
  - structure-aware
  - saprot

StableProt

Prompt

-

Model Overview

StableProt is a structure-aware deep learning framework for predicting protein melting temperature ($T_m$) and host organism optimal growth temperature (OGT).

By coupling SaProt 650M Foldseek 3Di structural tokens with parameter-isolated dual neural projection heads and observation-level uncertainty estimation, StableProt delivers continuous point estimates alongside calibrated 95% predictive intervals.

• GitHub Repository: https://github.com/Bibhuprasadbehera/StableProt
• Base Backbone: westlake-repl/SaProt_650M_AF2

---

Repository Contents

This repository hosts the weights for the 5-seed production ensemble:

• seed1/ to seed5/: PyTorch state dicts (model_tm.pt and model_ogt.pt).
• config.json: Architecture specifications and projection head hyperparameters.
• normalization_stats.json: Ground-truth z-score normalization parameters (tm_mean, tm_std, ogt_mean, ogt_std).

---

Loading Model Weights

import torch
import json

# 1. Load normalization parameters
with open("normalization_stats.json", "r") as f:
    norm_stats = json.load(f)

# 2. Load trained weights for a specific seed (e.g. seed 1)
tm_state = torch.load("seed1/model_tm.pt", map_location="cpu", weights_only=False)
ogt_state = torch.load("seed1/model_ogt.pt", map_location="cpu", weights_only=False)

print("StableProt model weights loaded successfully!")

## Download model


[Download](/bibhu111/StableProt/tree/main) them in the Files & versions tab.