Abstract
Protein phosphorylation plays a crucial role in regulating a wide range of
biological processes, and its dysregulation is strongly linked to various
diseases. While many phosphorylation sites have been identified so far, their
functionality and regulatory effects are largely unknown. Here, a deep learning
model MMFuncPhos, based on a multi-modal deep learning framework, is
developed to predict functional phosphorylation sites. MMFuncPhos
outperforms existing functional phosphorylation site prediction approaches.
EFuncType is further developed based on transfer learning to predict whether
phosphorylation of a residue upregulates or downregulates enzyme activity
for the first time. The functional phosphorylation sites predicted by
MMFuncPhos and the regulatory types predicted by EFuncType align with
experimental findings from several newly reported protein phosphorylation
studies. The study contributes to the understanding of the functional
regulatory mechanism of phosphorylation and provides valuable tools for
precision medicine, enzyme engineering, and drug discovery. For user
convenience, these two prediction models are integrated into a web server
which can be accessed at http://pkumdl.cn:8000/mmfuncphos.

Presenter: Youbi Noh (CSB Lab. Offline Internship Program)
Date: 2025.03.27 (THU) 19:00 ~ 22:00

실험실 인턴십 프로그램에 참가 중인, 노유비 학생이 주도하여 딥러닝 (ESM-2 + GCN)을 활용하여 Phosphorylation sites를 예측하는 모델에 대한 저널클럽을 진행하였습니다.