- TL;DR: Energy-based models trained on crystallized protein structures predict native side chain configuration and automatically discover molecular energy features.
- Abstract: We propose an energy-based model (EBM) of protein conformations that operates at atomic scale. The model is trained solely on crystallized protein data. By contrast, existing approaches for scoring conformations use energy functions that incorporate knowledge of physical principles and features that are the complex product of several decades of research and tuning. To evaluate our model, we benchmark on the rotamer recovery task, a restricted problem setting used to evaluate energy functions for protein design. Our model achieves comparable performance to the Rosetta energy function, a state-of-the-art method widely used in protein structure prediction and design. An investigation of the model’s outputs and hidden representations find that it captures physicochemical properties relevant to protein energy.
- Keywords: energy-based model, transformer, protein conformation
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