A structural machine learning approach for rapid prediction of thermodynamically destabilizing tyrosine phosphorylations.

Abstract excerpt

Tyrosine phosphorylations are a prominent characteristic of numerous diseases, yet it is challenging to identify potentially (dys)functional phosphorylations among thousands of phospho-proteins. Here, we propose a machine learning method to predict the thermodynamic stability change resulting from tyrosine phosphorylation. Our approach, based on the prediction of phosphomimetic stability (ΔΔG) from structural...

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