Zsuzsanna Dosztányi

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.

This study identifies POLE2 as a general NPF-motif receptor, revealing its role in recruiting proteins involved in replication, DNA repair, and transcription through SLiM-mediated interactions.

The paper identifies 57 high-confidence LC8 partners in the ciliary-centrosomal system, revealing motif evolution and expansion of the LC8 interactome.

The paper develops a pipeline to transfer annotations between disordered proteins using orthologs and multiple sequence alignments, expanding the availability of annotations beyond globular domains.

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 investigates the evolutionary origin of intrinsically disordered regions linked to cancer, finding many have ancient origins despite their functional importance in tumor formation.

The paper identifies that mutations in intrinsically disordered regions (IDRs) are a distinct class of cancer drivers with unique functional mechanisms and therapeutic implications.

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

The paper identifies three scenarios of folding-binding interplay in IDPs, showing how binding partner properties influence protein sequence, structure, function, and regulation as much as inherent disorder.