Elizaveta Mukhaleva

The paper develops an interpretable machine learning model to identify allosteric modulators of the AT1R receptor and discovers a negative allosteric modulator.

DRUMBEAT is a machine learning model that identifies time-resolved cooperative events in protein dynamics, revealing folding pathways and critical residues.

The paper uses machine learning and simulations to show that distant residue interactions in the G protein core influence GPCR selectivity and can be engineered to change signaling preferences.

This paper develops an interpretable machine learning model to identify residue communities involved in GPCR conformational transitions, with applications to dopamine receptor subtype-specific drug design.

The paper identifies frustrated residues in GPCR:G protein interfaces and engineered degrader complexes, showing higher frustration in engineered interfaces.

BaNDyT uses Bayesian network modeling to analyze molecular dynamics trajectories, providing data-driven insights into protein function and interactions.

This study uses protein frustration analysis to identify key residues distinguishing strong from weak BRD4-Cereblon degraders.

This study shows that lipids like PIP2 and GM3 modulate the conformation of ICL3 in beta-2AR, affecting G protein coupling through membrane tilting and allosteric effects.

The paper determines the cryo-EM structure of mTAAR7f bound to DMCH, revealing its unique binding site and activation mechanism distinct from ORs.