Wayland Yeung

The paper classifies bacterial STK sequences into families and identifies distinguishing features, providing insights for drug discovery.

The paper extends the Protein Kinase Ontology (ProKinO) to uncover new insights about dark kinases, identifying PAK5 as a frequently mutated kinase in cancers and highlighting its potential in drug discovery.

The paper evaluates all possible CXXX sequences as farnesyltransferase targets in yeast using a reporter system, revealing expanded specificity beyond the canonical CaaX motif.

This paper introduces an alignment-free protein classification framework using sequence embeddings to infer evolutionary relationships and functional motifs across diverse protein superfamilies.

This paper introduces a deep learning model to predict glycosyltransferase folds using secondary structure features, identifying novel fold families and improving fold classification.

The paper introduces QSMART, a model that predicts protein kinase inhibitor response by combining statistical and neural network methods, achieving high accuracy and feature explainability.

The paper uses evolutionary analysis of GT-A fold sequences to identify core components and hypervariable loops contributing to catalytic diversity, and develops a machine learning classifier for donor specificity prediction.

The paper identifies EGFR/HER2 inhibitors that destabilize and degrade TRIB2 pseudokinase in cancer cells, suggesting potential for TRIB2-targeted therapies.