Lucia Beatriz Chemes

The paper introduces a classification framework for disordered flexible linkers and creates a comprehensive dataset of 1640 domain linkers, annotated with structural and sequence features.

The paper investigates the energy landscape of coupled folding and binding in intrinsically disordered proteins, finding that electrostatic interactions compensate for energetic frustration.

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

LINKER-Pred provides deep learning tools to predict disordered flexible linkers in proteins using sequence data, achieving state-of-the-art performance on benchmarks.

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.