Mátyás Pajkos

This study evaluates five computational methods for modeling conformational ensembles of flexible multidomain proteins using SAXS data, highlighting discrepancies and biases in their predictions.

This paper presents a generative framework for designing intrinsically disordered proteins using protein language models, showing that large datasets are essential for accurate conformational control.

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.