Area of research
Molecular Biology · Materials Chemistry
Research interest
Research interests include Protein Structure and Dynamics, Enzyme Structure and Function, Monoclonal and Polyclonal Antibodies Research, and RNA and protein synthesis mechanisms.
CSN5i-3 is an orthosteric molecular glue inhibitor of COP9 signalosome.
CliPepPI: Scalable prediction of domain-peptide specificity using contrastive learning
One thousand SARS-CoV-2 antibody structures reveal convergent binding and near-universal immune escape
Cysteine-enabled cleavability to advance cross-linking mass spectrometry for global analysis of endogenous protein-protein interactions
DockFormer: Affinity Prediction and Flexible Docking with Pair Transformer
Deciphering the mechanistic basis for the pathological effect of the Gα
<sub>o</sub>
E246K mutation in neurodevelopmental disorder
Orthosteric Molecular Glue Inhibits COP9 Signalosome with Substrate-Dependent Potency
CombFold: predicting structures of large protein assemblies using a combinatorial assembly algorithm and AlphaFold2
CombFold: predicting structures of large protein assemblies using a combinatorial assembly algorithm and AlphaFold2.
Structural basis for Mis18 complex assembly and its implications for centromere maintenance
Dual neutralization of influenza virus hemagglutinin and neuraminidase by a bispecific antibody leads to improved antiviral activity
RhoMax: Computational Prediction of Rhodopsin Absorption Maxima Using Geometric Deep Learning
Predicting RNA structure and dynamics with deep learning and solution scattering
EspH utilizes phosphoinositide and Rab binding domains to interact with plasma membrane infection sites and Rab GTPases*
Integrative modeling meets deep learning: Recent advances in modeling protein assemblies
Integrative modeling meets deep learning: Recent advances in modeling protein assemblies
Discovering predisposing genes for hereditary breast cancer using deep learning
RhoMax: Computational Prediction of Rhodopsin Absorption Maxima Using Geometric Deep Learning.
Discovering predisposing genes for hereditary breast cancer using deep learning.
EspH utilizes phosphoinositide and Rab binding domains to interact with plasma membrane infection sites and Rab GTPases.
DockFormer: Affinity Prediction and Flexible Docking with Pair Transformer
Predicting RNA Structure and Dynamics with Deep Learning and Solution Scattering
Impact of <scp>AlphaFold</scp> on structure prediction of protein complexes: The <scp>CASP15‐CAPRI</scp> experiment
Impact of AlphaFold on Structure Prediction of Protein Complexes: The CASP15-CAPRI Experiment
A deep learning model for predicting optimal distance range in crosslinking mass spectrometry data
A deep learning model for predicting optimal distance range in crosslinking mass spectrometry data.
The Cdc48 N-terminal domain has a molecular switch that mediates the Npl4-Ufd1-Cdc48 complex formation
The Interactome of DUX4 Reveals Multiple Activation Pathways
ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction
ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction.