| --- |
| language: |
| - en |
| library_name: pyod |
| tags: |
| - protein-structure |
| - anomaly-detection |
| - one-class |
| - autoencoder |
| - protease-inhibitor |
| - structural-filtering |
| - biology |
| - bioinformatics |
| - unsupervised-learning |
| datasets: |
| - MEROPS |
| - UniProt |
| - AlphaFold |
| model_name: Structural_module-protease_inhibitor |
| metrics: |
| - reconstruction_error |
| --- |
| |
| # Structural_module-protease_inhibitor |
|
|
| This model is an unsupervised, one-class deep learning autoencoder designed to filter protein structures. It identifies candidates that are structurally inconsistent with curated protease inhibitor (PI) features learned from known PI databases. |
|
|
| Use colab userinterface for making prediction from protein structure files in .cif format. |
| [](https://colab.research.google.com/drive/1JLhLpvXG4plzPtIliG_CJnYui6P8Pu1J?usp=sharing) |
|
|
| ## Model Description |
| An autoencoder model trained on the structural embedding of known protease inhibitors and assigns a **reconstruction error** to inputs. High reconstruction errors indicate potential non-PIs based on structural features learned from the training distribution, allowing for the filtering of non-PI-like structures. |
|
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|
|
| * **Model type:** Fully connected Autoencoder (via PyOD) |
| * **embeddings calculated using:** [RCSB embedding model](https://github.com/rcsb/rcsb-embedding-model) |
|
|
| ## Intended Uses & Limitations |
|
|
| ### Intended Use |
| * Structural filtering and pre-selection of protease inhibitor–like protein structures in large-scale datasets. |
| * Quality control for generated or predicted protein structures in the context of PIs. |
|
|
| ### Limitations |
| * **Novel Folds:** Novel PI folds absent from the MEROPS training data may be incorrectly rejected (false positives for anomalies). |
| * **Not for Clinical Use:** This model is not intended for functional annotation or clinical decision-making. |
|
|
| ## Training Data |
| The model was trained on **17,889 curated protease inhibitor structures**: |
| 1. **Source:** MEROPS database. |
| 2. **Mapping:** Sequences were mapped via similarity search against taxonomy-restricted UniProt datasets (fungi, plants, bacteria). |
| 3. **Structures:** Corresponding 3D structures were obtained from the **AlphaFold Protein Structure Database using uniprot Ids**. |
|
|
| ## Technical Specifications |
|
|
| ### Input Format |
| The model accepts **fixed-length continuous protein structure embeddings** derived from the RCSB embedding model. |
| > **Important:** Embeddings must be standardized using the provided `scaler.pkl` before inference. |
|
|
| ### Architecture |
| Implemented in PyTorch via the PyOD library: |
| * **Encoder:** Geometrically decreasing layers. |
| * **Bottleneck:** Latent representation layer. |
| * **Decoder:** Symmetric reconstruction layers. |
| * **Regularization:** Batch normalization and dropout. |
|
|
| ### Training Procedure |
| * **Optimizer:** Adam with weight decay. |
| * **Hyperparameter Tuning:** Optimized via Bayesian optimization (Optuna, TPE sampler) on a 10% validation split (~1,789 structures). |
| * **Objective:** Minimize Mean Squared Error (MSE) reconstruction loss on validation set. |
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