Yinghao Wu

This paper develops a multiscale kinetic Monte-Carlo framework to simulate protein-small molecule association and evaluates its accuracy against experimental data.

MISSTE simulates multiscale tissue ecosystems to improve CAR-T therapy by identifying spatial access as a key bottleneck for immune persistence and tumor control.

This paper develops a deep learning framework to predict protein flexibility from static structural data, achieving moderate accuracy and identifying key structural features associated with flexibility.

IDBSpred predicts IDP-binding sites on structured proteins using ESM-2 embeddings and a multilayer perceptron, achieving an ROC AUC of 0.87.

This paper develops an AI framework integrating stochastic and deterministic models to simulate multiscale biological systems, enabling adaptive treatment strategies through reinforcement learning.

The paper develops a multiscale computational framework to model dynamic signaling at cell-cell interfaces, showing how structural and spatial constraints regulate receptor organization and signaling.

This paper introduces a physics-informed diffusion model to efficiently sample conformational ensembles of multidomain fusion proteins, outperforming traditional methods in capturing large-scale dynamics.

This study develops a DAT proximity labeling system to characterize cadherin cis interactions in live cells, revealing both specific and non-specific dimerization modes and their effects on junctional stability.

The paper introduces a protein language model-based method to predict conformational B-cell epitopes on antigen structures, outperforming existing methods on benchmark datasets.

This study uses kinetic Monte-Carlo simulations to model protein-protein interactions in PROTAC-induced ternary complex formation, showing that cooperativity affects association into native-like configurations.

SuperWater predicts water molecule positions on protein structures using a generative AI framework, achieving high accuracy in crystal water coverage and prediction precision.

This study identifies small molecules that inhibit the interaction between insulin receptor and inceptor using computational methods, finding fucose derivatives and specific mutations affect binding energy.

This paper develops a deep learning model to analyze antibody-antigen interactions, achieving high accuracy in distinguishing antibody-antigen complexes from other protein complexes and identifying antigens from epitope information.

This paper uses artificial intelligence to encode and generate protein-protein binding interface fragment pairs, demonstrating the degeneracy of their conformational space and the utility of machine learning for predicting unknown interactions.

This paper develops a machine learning framework to predict interactions between cell surface proteins, achieving over 70% accuracy in human proteins and validating results against experimental data.

The paper identifies a presynaptic assembly mechanism driven by SYD-1-lipid interactions before neurexin recruitment, showing PIP2-binding residues are critical for active zone formation.

The paper develops a hybrid simulation method to study spatial organization in NF-κB signaling pathways, showing that TRAF clustering regulates oscillatory NF-κB responses.