> We introduce Mutational Effect Transfer Learning (METL), a specialized protein language model that bridges the gap between traditional biophysics-based and machine learning approaches by incorporating synthetic data from molecular simulations. We pretrain a transformer on millions of molecular simulations to capture the relationship between protein sequence, structure, energetics, and stability. We then finetune the neural network to harness these fundamental biophysical signals and apply them when predicting protein functional scores from experimental assays. METL excels in protein engineering tasks like generalizing from small training sets and extrapolating to new sequence positions. We demonstrate METL's ability to design functional green fluorescent protein variants when trained on only 64 experimental examples.
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