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 # Leveraging a large language model to predict protein phase transition: a physical, multiscale and interpretable approach
 We apply a unified modeling framework to predict protein phase transition (PPT). In classification task A proteins exhibiting experimental evidence of undergoing a phase transition, forming either droplets or amyloids, are consolidated into a single dataset (+Droplet drivers and +Amyloids). Phase transition propensity is predicted versus the preference to maintain the native soluble state (-PT). In classification task B the unified dataset is utilized to predict the propensity to form droplets versus amyloid aggregates. To accomplish this, we fine-tune the ESM-2 model to predict PPT and compare its performance to biophysical knowledge-based models (e.g., random forest).
 
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 This repository provides code for predicting protein phase transition (PPT) propensity, including two examples of AD-related proteins, their associated genes, and transcription factors.
 
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