Juliana Glavina

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.

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