Weiwei Li

MaskNGly treats protein language model embeddings as a readable source of coevolutionary signal, using a mask-and-probe readout to decide directly whether an N-X-S/T sequon will be N-glycosylated, without training on annotated data. On the N-GlyDE test set it reaches an AUC of 0.940 and an MCC of 0.808, comparable to published supervised neural-network predictors; in independent glycoproteomic data, 106 of 111 previously unreported sites scored above the 0.5 threshold.

MaskNGly treats protein language model embeddings as a readable source of coevolutionary signal, using a mask-and-probe readout to decide directly whether an N-X-S/T sequon will be N-glycosylated, without training on annotated data. On the N-GlyDE test set it reaches an AUC of 0.940 and an MCC of 0.808, comparable to published supervised neural-network predictors; in independent glycoproteomic data, 106 of 111 previously unreported sites scored above the 0.5 threshold.