Gábor Erdős

The paper introduces DEEP-Boltz, a framework that extracts energetic potentials from protein structure predictors, outperforming existing methods in predicting mutation-induced stability changes.

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.

The paper uses deep-learning models to identify functional motifs in intrinsically disordered proteins by analyzing pathogenic variants from ClinVar.

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.

The paper identifies redox-sensitive conditionally disordered protein regions across proteomes and links their prevalence to biological processes and disease.