Lukasz Kurgan

The paper identifies a bias in IDR-binding site prediction benchmarks due to differences in disorder propensity between datasets, showing predictors trained on one type of data perform poorly on others.

This paper reports on the fourth CAFA challenge, showing improvements in Gene Ontology term prediction but highlighting challenges in new evaluation settings.

PROBind predicts, analyzes, and visualizes protein-protein and protein-nucleic acid binding residues using sequence and structure data, integrating multiple predictors and providing interactive tools.

OmniCleave models protease-substrate specificity using a geometric graph framework, outperforming existing methods and identifying novel substrates.

KcatNet predicts enzyme catalytic efficiency using geometric deep learning, outperforming existing methods and providing insights into enzyme-substrate interactions.

SCREEN is a graph neural network that predicts catalytic residues and assesses mutation tolerance in enzymes by integrating structural and functional information.

The paper evaluates 43 methods for predicting intrinsically disordered regions and binding regions in proteins, finding that deep learning methods outperform older physicochemical approaches but binding prediction remains challenging.