Giulio Tesei

The paper uses single-particle light scattering to reveal structural heterogeneity in biomolecular condensates formed by Ddx4N1, showing distinct populations with different physical properties.

The paper introduces MEM-CALVADOS, a residue-level model for flexible proteins at membrane interfaces, validated against experimental data and applied to signaling protein assemblies.

The paper develops coarse-grained models for simulating double-stranded RNA and DNA in the CALVADOS framework to study their behavior in protein-nucleic acid condensates.

This study shows multi-component biomolecular condensates suppress amyloid formation by sequestering proteins, limiting their availability for fibril formation.

The paper develops a stickiness scale for disordered proteins based on biophysical experiments and compares it to existing hydropathy scales.

This study uses double mutant cycles to analyze molecular interactions in biomolecular condensates, finding additive and non-additive contributions to phase separation propensity.

This paper develops a coarse-grained model for PEG to study crowding effects on disordered proteins, showing how PEG titrations can quantify phase separation propensities.

This study develops a coarse-grained model to simulate phosphorylated disordered proteins, showing that phosphorylation's effect on global dimensions is mainly due to additional charge.

The paper systematically identifies degrons in human cytosolic proteins and links them to disease mechanisms through machine learning and structural analysis.

The paper develops a machine learning model to predict phase separation propensities of disordered proteins from sequence, identifying 5% of human IDRs likely to undergo homotypic phase separation.

This paper introduces a coarse-grained RNA model to simulate protein-RNA condensates and shows it captures key interaction aspects.

The paper introduces a coarse-grained model for multi-domain proteins that accurately simulates conformational properties and phase separation behavior.