Multiplex Identification of Post‐Translational Modifications at Point‐of‐Care by Deep Learning‐Assisted Hydrogel Sensors

Abstract Multiplex detection of protein post‐translational modifications (PTMs), especially at point‐of‐care, is of great significance in cancer diagnosis. Herein, we report a machine learning‐assisted photonic crystal hydrogel (PCH) sensor for multiplex detection of PTMs. With closely‐related PCH sensors microfabricated on a single chip, our design achieved not only rapid screening of PTMs at specific protein sites by using only naked eyes/cellphone, but also the feasibility of real‐time monitoring of phosphorylation reactions. By taking advantage of multiplex sensor chips and a neural network algorithm, accurate prediction of PTMs by both their types and concentrations was enabled. This approach was ultimately used to detect and differentiate up/down regulation of different phosphorylation sites within the same protein in live mammalian cells. Our developed method thus holds potential for POC identification of various PTMs in early‐stage diagnosis of protein‐related diseases.