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This study shows that larger protein language models can overestimate sequence likelihood, leading to poor fitness prediction, and recommends moderate model sizes for better performance.

The paper introduces three geometric learning models for predicting protein stability changes upon mutations, achieving improved performance over existing methods.

SPIRED-Fitness predicts protein structure and fitness from single sequences using an end-to-end framework combining SPIRED with downstream neural networks.