Mingzhe Shen

This study determines the activation pathway of the human voltage-gated proton channel hHV1 using computational methods, revealing voltage-driven structural changes and lipid effects validated by experiments.

This paper introduces AiPP, an AI-based platform for predicting ligandable sites in the human proteome, and demonstrates its application in discovering covalent inhibitors for PTPN6.

This paper introduces KaML-ESM2 and KaML-ESMC models to predict protein pKa values, outperforming existing methods but facing challenges with engineered residues.

This paper develops KaML models using decision trees and graph attention networks to predict protein pKa values and ionization states, outperforming existing methods and introducing a new pKa database.

The paper develops ML models to predict covalent ligandable cysteines using a curated database, achieving high accuracy and providing a web tool for drug discovery.