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| license: apache-2.0 |
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| # Struct2Seq-GNN |
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| ## Model Description |
| Struct2Seq-GNN is a lightweight, 6-layer Graph Neural Network designed for inverse protein folding (structure-to-sequence prediction). By mapping the 3D spatial coordinates of protein backbones to their corresponding amino acid sequences, this model serves as a foundational tool for computational protein engineering and structural bioinformatics workflows. |
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| ## Intended Uses & Limitations |
| * **Primary Use:** Computational protein design, generating plausible sequences for novel or heavily modified protein backbones. |
| * **Limitations:** This is a lightweight architecture built as an independent research project. While it achieves high native sequence recovery, it is not intended for out-of-the-box production of clinical therapeutics without further validation and optimization. |
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| ## Training Data & Procedure |
| * **Dataset:** Trained on biological protein assemblies from the PDB, clustered at a 30% sequence identity cutoff to prevent data leakage. |
| * **Data Augmentation:** During training, 0.1 Å standard deviation Gaussian noise was applied to all input atomic coordinates. This critical augmentation prevents the model from "reading out" the native sequence from over-refined crystal artifacts, forcing it to learn the underlying biophysics of the fold. |
| * **Hardware:** Trained efficiently over ~65 epochs on a 4-GPU HPC cluster. |
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| ## Evaluation Metrics |
| The model demonstrates strong generalization and robust learning of physical constraints: |
| * **Global Sequence Recovery:** ~33% validation accuracy across all residues. (Achieving >30% sequence identity strongly suggests the generated sequences will reliably adopt the target 3D fold). |
| * **Convergence:** Validation loss plateaued smoothly at ~2.236. |
| * *(Optional: Add your 5.0 Å binding pocket recovery metric here once you calculate it!)* |
