Gül H. Zerze

The paper shows that full-length Staphylococcal protein A fails to fold stably despite its individual domains being stable, challenging assumptions about modular protein folding and AI prediction accuracy.

This paper evaluates blind predictions of dynamic water and ion ensembles around RNA using cryo-EM densities from CASP16, finding that molecular dynamics approaches perform best.

The paper classifies human transcription factors based on effector domain sequences using unsupervised learning and identifies features correlating with transcriptional activity and co-evolution with DNA-binding domains.

This paper introduces a Monte Carlo framework to simulate biomolecular condensate nucleation in the grand canonical ensemble, revealing classical and nonclassical nucleation mechanisms in FUS-LC and NDDX4 proteins.

The paper develops a dynamical density functional theory framework to model non-equilibrium phase dynamics in biomolecular condensates, integrating experimental data with molecular descriptors to capture long-timescale processes.

This study optimizes the MM-OPES method to sample the free energy surface of an H-type RNA pseudoknot, identifying effective CVs and temperature ranges for accurate FES prediction.

This paper adapts a deep learning-driven sampling technique for RNA stem-loop folding, showing it can efficiently explore phase space with lower computational cost than traditional methods.